ICT Systems and Sustainability: Proceedings of ICT4SD 2020, Volume 1 [1st ed.] 9789811582882, 9789811582899

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Advances in Intelligent Systems and Computing 1270

Milan Tuba Shyam Akashe Amit Joshi   Editors

ICT Systems and Sustainability Proceedings of ICT4SD 2020, Volume 1

Advances in Intelligent Systems and Computing Volume 1270

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Indexed by SCOPUS, DBLP, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/11156

Milan Tuba Shyam Akashe Amit Joshi •

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Editors

ICT Systems and Sustainability Proceedings of ICT4SD 2020, Volume 1

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Editors Milan Tuba Singidunum University Belgrade, Serbia

Shyam Akashe ITM University Gwalior, Madhya Pradesh, India

Amit Joshi Global Knowledge Research Foundation Ahmedabad, Gujarat, India

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-15-8288-2 ISBN 978-981-15-8289-9 (eBook) https://doi.org/10.1007/978-981-15-8289-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The Fifth International Conference on ICT for Sustainable Development (ICT4SD 2020) targets theory, development, applications, experiences and evaluation of interaction sciences with fellow students, researchers and practitioners. The conference is devoted to increasing the understanding role of technology issues, how engineering has day by day evolved to prepare human-friendly technology. The conference provided a platform for bringing forth significant research and literature across the field of ICT for Sustainable Development and provided an overview of the technologies awaiting unveiling. This interaction will be the focal point for leading experts to share their insights, provide guidance and address participant’s questions and concerns. The conference was to be held during July 23–24, 2020, at Hotel Vivanta by Taj, Panaji, Goa, India, but due to the pandemic, this year it was organized through digital mode. The conference was organized by Global Knowledge Research Foundation, Supporting Partner InterYIT, International Federation for Information Processing, State Chamber Partner Goa Chamber of Commerce & Industry, National Chamber Partner as Knowledge Chamber of Commerce & Industry. Research submissions in various advanced technology areas were received after a rigorous peer review process with the help of program committee members and 187 external reviewers for 1000+ papers from 19 different countries including Algeria, USA, United Arab Emirates, Serbia, Qatar, Mauritius, Egypt, Saudi Arabia, Ethiopia, Oman out of which 160 were accepted with an acceptance ratio of 0.15. Technology is the driving force of progress in this era of globalization. Information and communication technology (ICT) has become a functional requirement for the socioeconomic growth and sustained development of any country. The influence of information communications technology (ICT) in shaping the process of globalization, particularly in productivity, commercial and financial spheres, is widely recognized. The ICT sector is undergoing a revolution that has momentous implications for the current and future social and economic situation of all the countries in the world. ICT plays a pivotal role in empowering people for self-efficacy and how it can facilitate this mission to reach out to grassroots level. Finally, it is concluded that ICT is a significant contributor to the success of the ongoing initiative of Startup India. In order to recognize and reward v

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the extraordinary performance and achievements by ICT and allied sectors and promote universities, researchers and students through their research work adapting new scientific technologies and innovations. The two-day conference had presentations from the researchers, scientists, academia and students on the research work carried out by them in different sectors. ICT4SD Summit is a flagship event of the G R Foundation. This is the fourth edition. The summit was addressed by eminent dignitaries including Shri Manguirsh Pai Raikar, Chairperson, ASSOCHAM MSME National Council; Shri. Prajyot Mainkar, Chairman, IT Committee of Goa Chamber of Commerce and Industry; Mike Hinchey, President, IFIP and Chair IEEE, UK and Ireland; Milan Tuba, Vice-Rector for International Relations, Singidunum University, Serbia; Prof. Lance Fung, Australia; Prof. Jagdish Bansal, India; Mr. Aninda Bose, Springer; Dr. Amit Joshi, Director, G R Foundation. The overall conference had one inaugural session, one keynote session and 18 technical sessions during two days. Belgrade, Serbia Gwalior, India Ahmedabad, India

Milan Tuba Shyam Akashe Amit Joshi

Contents

Challenges in Adoption of Secure Digitization of Graduate Assessments: Scope for Blockchain Technologies . . . . . . . . . . . . . . . . . . S. K. Shankar, Rajeswari Mukesh, and M. K. Badrinarayanan Non-performing Asset Analysis Using Machine Learning . . . . . . . . . . . Rohan Ninan Jacob

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Volatile Memory Disk Forensics: Investigate the Criminal Activity of RAMDisk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nilay Mistry, Annan Christian, and Bansari Bhavsar

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Modem Functionality Diagnostic Tool with Graphical User Interface for Cellular Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arghya Biswas, Sudakar Singh Chauhan, and Antara Borwankar

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Regression Model to Estimate Effort for Software-Oriented Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Vignaraj Ananth, S. Aditya, S. Ragul Kesavan, and B. Keerthiga

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Innovative Technology-Based Social Enterprises for Inclusive Sustainable Healthcare in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pradnya Vishwas Chitrao, Pravin Kumar Bhoyar, and Rajiv Divekar

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Classification of Leaves Using Convolutional Neural Network and Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sapan Naik and Hinal Shah

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A Low-Cost IoT-Enabled Device for Real-Time Air Quality Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Surya Kiran Pokala, V. Panchami, Kelavath Jaisingh, and Sandeep Kumar Yedla Fuzzy-Based Predictive Analytics for Early Detection of Disease—A Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . V. Kakulapati, R. Sai Sandeep, V. Kranthi kumar, and R. Ramanjinailu

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A Framework for Remote Health Monitoring . . . . . . . . . . . . . . . . . . . . 101 K. Viswavardhan Reddy and Navin Kumar GenNext—An Extractive Graph-Based Text Summarization Model for User Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Komal Kothari, Aagam Shah, Satvik Khara, and Himanshu Prajapati An Adaptive Control for Surrogate Assisted Multi-objective Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Duc Dinh Nguyen and Long Nguyen Study and Analysis of NOMA Downlink Transmission Model in 5G Wireless System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Sanjeev Kumar Srivastava Network Structural Analysis Based on Graphical Measures and Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Atul Kumar Verma, Mahipal Jadeja, and Rahul Saxena Lung Tumor Classification Using CNN- and GLCM-Based Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Amrita Naik and Damodar Reddy Edla A Distributed Implementation of Signature Matching Algorithms for IoT Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Menachem Domb Graph-Based Multi-document Text Summarization Using NLP . . . . . . . 177 Abhilasha More and Vipul Dalal Fault Exploratory Data Analysis of Real-Time Marine Diesel Engine Data Using R Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Cheng Siong Chin and Nursyafiqah Binte Nazli A Hybrid Key Management System Based on ECC and Data Classification to Secure Data in the Cloud . . . . . . . . . . . . . . . . . . . . . . . 199 Hinddeep Purohit and Ravirajsinh Vaghela Life Cycle Management for Indian Naval Warship Equipment . . . . . . . 209 Alok Bhagwat and Pradnya Vishwas Chitrao Framework for Securing IoT Ecosystem Using Blockchain: Use Cases Suggesting Theoretical Architecture . . . . . . . . . . . . . . . . . . . 223 Anshul Jain, Tanya Singh, and Nitesh Jain Feature Selection for Handwritten Signature Recognition Using Neighborhood Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Kamlesh Kumari and Sanjeev Rana Blockchain-Based E-Voting Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Shreya Shailendra Chafe, Divya Ashok Bangad, and Harsha Sonune

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SOLID: A Web System to Restore the Control of Users’ Personal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Manishkumar R. Solanki Enhanced Three-Dimensional DV-Hop Algorithm . . . . . . . . . . . . . . . . . 269 Abhinesh Kaushik and D. K. Lobiyal Synthesis of VLSI Structural Cell Partitioning Using Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 P. Rajeswari, Theodore S. Chandra, and Amith Kiran Kumar Identification and Classification of Foreign Bodies from Rice Grains Using Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Rajlakshmi Ghatkamble Improving Collaboration Between Government and Citizens for Environmental Issues: Lessons Learned from a Case in Sri Lanka . . . . 297 Mohamed Sapraz and Shengnan Han A Study on Blockchain Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Manjula K. Pawar, Prakashgoud Patil, and P. S. Hiremath What Influences Consumer Behavior Toward Information and Communication Technology Applications: A Systematic Literature Review of UTAUT2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Jick Castanha, Subhash Kizhakanveatil Bhaskaran Pillai, and Indrawati Secure and Energy-Efficient Remote Monitoring Technique (SERMT) for Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Sohini Roy and Arunabha Sen Balanced Accuracy of Collaborative Recommender System . . . . . . . . . . 341 Akanksha Bansal Chopra and Veer Sain Dixit Using Transfer Learning for Detecting Drug Mentions in Tweets . . . . . 357 Laiba Mehnaz and Rajni Jindal A Comprehensive and Critical Analysis of Cross-Domain Federated Identity Management Deployments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Tejaswini Apte and Jatinderkumar R. Saini Concentric Morphology Model in Detection of Masses from Mammogram: A Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Madhavi Pingili, M. Shankar Lingam, and E. G. Rajan A Survey on DevOps Techniques Used in Cloud-Based IOT Mashups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 M. Ganeshan and P. Vigneshwaran Digital Assistant with Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . 395 Vijay Verma, Aditya Sharma, Ketan Kumar Jain, and Sushant Adlakha

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An Experimental Approach Toward Type 2 Diabetes Diagnosis Using Cultural Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Ratna Patil, Sharvari Tamane, and Kanishk Patil Predicting Students Performance Through Behavior and Computational Thinking in Programming . . . . . . . . . . . . . . . . . . . . . . . 417 Vinayak Hegde, H. N. Meghana, R. Spandana, and M. S. Pallavi Deployment of Ambulance with Maximum Area Coverage by Using Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 431 Amandeep Kaur Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Datla Anurag Varma, Ravi Ashish, V. Venkata Sai Sandeep, B. Venkatesh, and R. Kannadasan Revisiting Algorithms for Resource Discovery in Unstructured Peer to Peer Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Abhijith Jayan, Subi George Samuel, and P. Jayakumar 802.11 Frame-level Network IDS for Public Wireless Networks . . . . . . . 453 Anish Sujanani and Shashidhar Pai Machine Learning for Malicious URL Detection . . . . . . . . . . . . . . . . . . 463 Gold Wejinya and Sajal Bhatia Implementation of OSPFv3 in IPv4 and IPv6 for Establishing a Wide Area Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Fatema Zaman Sinthia, Afsana Nasir, Bijan Paul, Mohammad Rifat Ahmmad Rashid, and Md Nasim Adnan Obstacle Avoiding and Fire Extinguishing Robot for Everyday Fire Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Moumita Kabir, Fatima Noor Popy, Bijan Paul, Mohammad Rifat Ahmmad Rashid, and Khan Raqib Mahmud A Survey on Internet of Things (IoT): Communication Model, Open Challenges, Security and Privacy Issues . . . . . . . . . . . . . . . . . . . . 493 R. Venkadeshan and M. Jegatha Violence Detection Through Surveillance System . . . . . . . . . . . . . . . . . . 503 Abhishek Deshmukh, Kshitij Warang, Yash Pente, and Nilesh Marathe Influence of Big Data Capabilities in Knowledge Management—MSMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Ravi Shankar Jha and Priti Ranjan Sahoo Logistically Supervised Aspect Category Detection Using Data Co-occurrence for Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Anice John and Reshma Sheik

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Automated Dietary Monitoring System: A Novel Framework . . . . . . . . 535 Samiul Mamud, Punyasha Chatterjee, Saubhik Bandyopadhyay, and Suchandra Bhandari Intruder Insinuation and Smart Surveillance Using Face Detection and Mask Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 M. Rajesh Khanna, G. Prakash Raj, S. Prem Kumar, and N. S. Vignesh Raaj Misinformation Analysis During Covid-19 Pandemic . . . . . . . . . . . . . . . 553 Shubhangi Rastogi and Divya Bansal An Agent-Based Cloud Service Negotiation in Hybrid Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 Saurabh Deochake and Debajyoti Mukhopadhyay IoT-Based Smart Farming Application for Sustainable Agriculture . . . . 573 Sanjeevakumar M. Hatture and Pallavi V. Yankati Follow Me: A Human Following Robot Using Wi-Fi Received Signal Strength Indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 V. Geetha, Sanket Salvi, Gurdeep Saini, Naveen Yadav, and Rudra Pratap Singh Tomar Implementation of Arithmetic Unit for RNS Using 2n+ 3 as Base . . . . . 595 Nagaraj R. Aiholli, Uday V. Wali, and Rashmi Rachh Hybridization of Artificial Bee Colony Algorithm with Estimation of Distribution Algorithm for Minimum Weight Dominating Set Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Sima Shetgaonkar and Alok Singh Privacy Preservation for Data Sharing with Multi-party Access Control in Cloud Computing: A Review Paper . . . . . . . . . . . . . . . . . . . 621 Shubhangi Tanaji Khot, Amrita A. Manjrekar, and Rashmi J. Deshmukh Pixel-Based Attack on ODENet Classifiers . . . . . . . . . . . . . . . . . . . . . . . 629 Nitheesh Chandra Yaratapalli, Reethesh Venkataraman, Abhishek Dinesan, Ashni Manish Bhagvandas, and Padmamala Sriram Survey on Advanced Spectrum Sharing Using Cognitive Radio Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Mohamed Hassan, Manwinder Singh, and Khaild Hamid An AI-Based Pedagogical Tool for Creating Sketched Representation of Emotive Product Forms in the Conceptual Design Stages . . . . . . . . . 649 Sunny Prakash Prajapati, Rahul Bhaumik, Tarun Kumar, and Unais Sait

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Social Interaction-Enabled Industrial Internet of Things for Predictive Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 M. S. Roopa, B. Pallavi, Rajkumar Buyya, K. R. Venugopal, S. S. Iyengar, and L. M. Patnaik Emerging Trends in the Marketing of Financially Engineered Insurance Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Sunitha Ratnakaram, Venkamaraju Chakravaram, Nitin Simha Vihari, and G. Vidyasagar Rao Attitude of the International Students Towards Integrating ICT in Foreign Language Learning: A Case Study . . . . . . . . . . . . . . . . 685 Khushboo Kuddus and Nafis Mahmud Khan Limiting Check Algorithm: Primality Test . . . . . . . . . . . . . . . . . . . . . . . 697 Gunjan Sethi and Harsh Factors Affecting the Local Governance . . . . . . . . . . . . . . . . . . . . . . . . . 707 Ngo Sy Trung, Do Huu Hai, Pham Van Tuan, and Nguyen Tien Long A Multi-factorial Code Coverage Based Test Case Selection and Prioritization for Object Oriented Programs . . . . . . . . . . . . . . . . . . 721 Prashant Vats, Zunaid Aalam, Satnam Kaur, Amritpal Kaur, and Saravjit Kaur New Horizons for the Application of Microalgae in the National Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 L. N. Medvedeva and O. Roiss Context of the Ghanaian Government: Social Media and Access to Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 Adasa Nkrumah Kofi Frimpong, Ping Li, Samuel Adu-Gyamfi, and Sandra Chukwudumebi Obiora Convolutional Neural Network and Data Augmentation for Behavioral-Based Biometric User Identification . . . . . . . . . . . . . . . . . . . 753 Sakorn Mekruksavanich and Anuchit Jitpattanakul Cybersecurity Attacks: Analysis of “WannaCry” Attack and Proposing Methods for Reducing or Preventing Such Attacks in Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763 Sumaiah Algarni Enhancing Trust and Immutability in Cloud Forensics . . . . . . . . . . . . . 771 Pranay Chauhan and Pratosh Bansal A Fuzzy Time Series Prediction Model of the COVID-19 Epidemic . . . . 779 Mohammad Minhazul Alam, S. M. Shahadat Hossain, Md. Romman Riyadh Shishir, Sadman Hasan, Eumna Huda, Sabrina Yeasmin, Abdul Motaleb, and Rashedur M. Rahman

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Modelling of Generation and Utilization Control for Isolated Hybrid Microgrid for Rural Electrification . . . . . . . . . . . . . . . . . . . . . . 791 Kuldip Singh, Satyasis Mishra, Davinder Singh Rathee, Demissie Jobir Gemecha, and R. C. Mohanty Toyama de Walking: Practice of Location-Based Data Collection Using Augmented Reality Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Yuya Ieiri, Hung-Ya Tsai, and Reiko Hishiyama Graphene-Based Wireless Power Transmission: Charge as You Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 P. C. Nissimagoudar, H. M. Gireesha, R. M. Shet, Nalini C. Iyer, Ajit Bijapur, and H. R. Aishwarya Vision-Based Driver Authentication and Alertness Detection Using HOG Feature Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825 P. C. Nissimagoudar, A. V. Nandi, H. M. Gireesha, R. M. Shet, and Nalini C. Iyer ICT4D in Channelling Welfare Services in India: Case Study of Haqdarshak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835 Komal Ramdey and Hasnain Bokhari Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845

About the Editors

Milan Tuba is the Vice Rector for International Relations, Singidunum University, Belgrade, Serbia and was the Head of the Department for Mathematical Sciences at State University of Novi Pazar and the Dean of the Graduate School of Computer Science at John Naisbitt University. He received B.S. in Mathematics, M.S. in Mathematics, M.S. in Computer Science, M.Ph. in Computer Science, Ph.D. in Computer Science from University of Belgrade and New York University. From 1983 to 1994 he was in the U.S.A. first at Vanderbilt University in Nashville and Courant Institute of Mathematical Sciences, New York University and later as Assistant Professor of Electrical Engineering at Cooper Union School of Engineering, New York. During that time he was the founder and director of Microprocessor Lab and VLSI Lab, leader of scientific projects and theses supervisor. From 1994 he was Assistant Professor of Computer Science and Director of Computer Center at University of Belgrade, from 2001 Associate Professor, Faculty of Mathematics, University of Belgrade, from 2004 also a Professor of Computer Science and Dean of the College of Computer Science, Megatrend University Belgrade. He was teaching more than 20 graduate and undergraduate courses, from VLSI Design and Computer Architecture to Computer Networks, Operating Systems, Image Processing, Calculus and Queuing Theory. His research interest includes nature-inspired optimizations applied to computer networks, image processing and combinatorial problems. Prof. Tuba is the author or coauthor of more than 200 scientific papers and co editor or member of the editorial board or scientific committee of a number of scientific journals and conferences. He was invited and delivered around 60 keynote and plenary lectures at international conferences. Member of the ACM, IEEE, AMS, SIAM, IFNA. Shyam Akashe is a Professor at ITM University, Gwalior, Madhya Pradesh, India. He completed his Ph.D. at Thapar University, Punjab, and his M.Tech. in Electronics and Communication Engineering at the Institute of Technology & Management, Gwalior. He has authored 190 publications, including more than 50 papers in SCI-indexed journals. His main research focus is low-power system on chip (SoC) applications in which static random access memories (SRAMs) are xv

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omnipresent. He has authored two books entitled Moore’s Law Alive: Gate-All-Around (GAA) Next Generation Transistor published by LAMBERT Academic Publishing, Germany, and Low Power High Speed CMOS Multiplexer Design published by Nova Science Publishers, Inc., New York, USA. He has also published over 120 papers in leading national and international refereed journals of repute. Dr. Akashe has participated in numerous national and international conferences and presented over 100 papers. Amit Joshi is currently the Director of Global Knowledge Research Foundation, also an Entrepreneur & Researcher who has completed his Masters and research in the areas of cloud computing and cryptography in medical imaging. Dr. Joshi has an experience of around 10 years in academic and industry in prestigious organizations. Dr. Joshi is an active member of ACM, IEEE, CSI, AMIE, IACSIT, Singapore, IDES, ACEEE, NPA, and many other professional societies. Currently, Dr. Joshi is the International Chair of InterYIT at International Federation of Information Processing (IFIP, Austria). He has presented and published more than 50 papers in national and international journals/conferences of IEEE and ACM. Dr. Joshi has also edited more than 40 books which are published by Springer, ACM, and other reputed publishers. Dr. Joshi has also organized more than 50 national and international conferences and programs in association with ACM, Springer, and IEEE to name a few across different countries including India, UK, Europe, USA, Canada, Thailand, Egypt, and many more.

Challenges in Adoption of Secure Digitization of Graduate Assessments: Scope for Blockchain Technologies S. K. Shankar, Rajeswari Mukesh, and M. K. Badrinarayanan

Abstract Blockchain technology finds variety of applications in everyday life. It is also capable of disrupting the implications on activities which were founded on time-stamped record keeping of titles of ownership. In education, blockchain technology shall disrupt activities such as the qualifications, licensing and accreditation, management of student records, IPR management and payments. Hence, blockchain technology-based products or services are significantly different from previous Internet-based commercial developments. Also blockchain is catching up interest in the education sector. However, the stakeholders within education are largely unaware of the social advantages and potential of blockchain technology. Within the educational sphere, the adoption of blockchain technology in the replacement of paper-based assessments has both advantages and challenges. This paper attempts to explore the challenges faced by faculty and graduate students in the adoption of digitized assessments and technical issues in achieving secured system. By identifying the challenges in implementation, it will be easy to achieve successful adoption of digitized assessments in a large scale. Keywords Blockchain · Assessments · Accreditation

S. K. Shankar (B) · R. Mukesh · M. K. Badrinarayanan Hindustan Institute of Technology & Science, Padur, Chennai, India e-mail: [email protected] R. Mukesh e-mail: [email protected] M. K. Badrinarayanan e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_1

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1 Introduction 1.1 Blockchain Technology The blockchain technology has provided fresh opportunities to deliver new business models in the consolidated markets. The implementation of blockchain technology in the education sector is in its nascent phase, and more tangible results can be achieved in the future [1–5]. With the widespread usage of cryptocurrencies such as Bitcoins, Ethereum or Swarm which offer a stable public blockchain that can be used for secondary purposes such as document verification, it can be used for verification of university degrees and can provide a timely and solid solution. The blockchain technology has proven itself to be easy, trustworthy and cost-effective; hence, it can be adopted with ease [1]. Blockchain [1] is a new technology which is based on new forms of distributed software architectures, which withers the dependence on centralized integration points, decentralizes transactional data sharing across large networks of untrusted participants where components find agreements on shared states. Blockcerts, an open-source platform, are mainly used for issuing and verifying the certificates which have been issued digitally using blockchain. In this paper, we analyse the challenges faced by faculty and graduate students in adoption of digitized assessments and technical issues in achieving a secured system. By identifying the challenges in implementation, it will be easy to achieve successful adoption of digitized assessments in a large scale.

1.2 Digitized Assessments With the changing industry requirements, evolving learner aspirations and increasing expectations of the society, it becomes essential to infuse new models of curriculum with components of technology adoption by the institutions of higher education. The changes in existing higher education scenario are essential in order to improve the quality of education, make it suitable to the young learners and increase its ability to trigger innovation, build research interests and contribute actively to the development of industry and the society. Innovations in higher education should be reflected in terms of freedom to develop new curriculum, designing industry-relevant courses, introduction of new assessment methods and greater flexibility for the students to have plethora of course choices matching their interests, needs and long-term career goals [1, 2]. Digitization of assessments shall be an effective starting point for bringing about changes in higher education. However, the most challenging aspects in adoption of digitization of assessments are access, quality, values, relevance and ethics. This study focuses on challenges in digitization of assessments from the perspective of faculty members who are the implementers of this change in the front end.

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2 Study Methodology The study is descriptive. It was conducted with selected faculty members who have developed and implemented digitized assessments through platforms like Google Classrooms, Moodle, etc. A list of benefits and challenges in digitization of assessments were collected from literature, which were reorganized as suitable to the present study under the guidance of expert faculty members in the domain of blockchain technologies. The sampling method was purposive sampling, where that faculties who were enthusiastic about digitization of assessment and have implemented the same for the courses they handle were specifically selected for the study. The data from the filled in questionnaire were processed using Google analytical tools to get descriptive statistics, and the results were interpreted.

3 Analysis In the new era of higher education, the focus has shifted from teacher-centric to learner-centric education. The emphasis is on learning which has placed the learner as the focal point of all academic transactions. Teachers being the important stakeholder, it is essential to understand their perspective and opinion about the digitized assessment systems. The present study draws upon the perspectives of teachers problems faced and recommended suggestions for better implementation of secure digitization of graduate assessments. The data for the study were collected by administering a four-part questionnaire with a five-point grading scale. The digitization of assessments comes with both benefits and challenges. The perception of faculty members employing digitized assessment procedures is important for repeated use and successful implementation of digitized assessments. The perceived benefits and challenges of digitized assessments are given below.

3.1 Perceived Benefits They are low cost, high security, enhancing student’s assessments, better control of data access, enhancing accountability, identity authentication, improving the management of student’s records and enhancing learner’s interactivity.

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3.2 Perceived Challenges They are preparatory issues, scalability, immutability, cost, question settings, fitness to all domains (creative subjects), typing ability of students, privacy and security, trust building and familiarity with the interface. From the analysis presented above, it could be clearly observed that a majority of the faculty members feel that digitized assessments are useful. But we cannot ignore the fact that a sizeable number of faculty members, a little less than half of them, are still not convinced that digitized assessments are useful. While taking a closer look at the perceived benefits of digitized assessments Fig. 1), better control of data access, enhanced students assessments and improved management of student records emerge to be the top three in the perceived benefits. There are concerns about the learner’s interactivity, security and cost dimensions, which can be attributed to the limited platforms which are being used by the respondents. Among the perceived challenges, Fig. 2, the top three issues mentioned by the respondents are typing ability of the students, structural limitations in question

Fig. 1 Perceived benefits of digitized assessments

Fig. 2 Perceived challenges in digitized assessments

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Fig. 3 Overall usefulness of digitized assessments

setting and familiarity with the interface. While typing ability and familiarity can be built through training of students over a period of time, the platform support needs to be worked out for supporting diverse formats of questions sometimes formula-based/pictorial to overcome the challenge (Fig. 3).

4 Multiple Regression Analysis Multiple regression analysis is a powerful technique used to predict the value of a variable based on the values of one or more variables of others. To understand the influence of the perceived challenges on the perception of overall usefulness of digitized assessments, a multiple regression analysis was performed, and the results are presented (Tables 1 and 2). It can be observed from the results, Table 3, that only two variables, viz. cost and scalability have significant p-values and can be inferred to be having a stronger impact on the perception of overall usefulness of the digitized assessments (Tables 4 and 5). Among the perceived benefits, Table 6, enhancing learners’ interactivity, enhancing accountability and transparency and better control of data access emerge Table 1 Regression statistics—challenges

Regression statistics Multiple R

0.900101097

R square

0.810181985

Adjusted R square

0.710277767

Standard error

0.631098104

Observations

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Table 2 Multiple regression analysis—significance statistics df

SS

MS

F

Significance F

Regression

10.00

32.30

3.23

8.11

0.00

Residual

19.00

7.57

0.40

Total

29.00

39.87

Table 3 Multiple regression analysis—challenges Coefficients

Standard error

t stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.21

0.58

0.37

0.72

−1.01

1.44

−1.01

1.44

Familiarity with the interface

0.24

0.19

1.23

0.23

−0.16

0.64

−0.16

0.64

Trust building

0.07

0.17

0.43

0.67

−0.28

0.43

−0.28

0.43

−0.10

0.16

−0.58

0.57

−0.44

0.25

−0.44

0.25

Cost

0.35

0.14

2.53

0.02

0.06

0.64

0.06

0.64

Immutability

0.10

0.18

0.54

0.60

−0.29

0.48

−0.29

0.48

Scalability

0.24

0.14

1.75

0.10

−0.05

0.54

−0.05

0.54

Preparatory issues

−0.17

0.19

0.89

0.39

−0.56

0.23

−0.56

0.23

Structural limitations in question settings

−0.10

0.20

0.52

0.61

−0.52

0.32

−0.52

0.32

Fitness to all domains (creative subjects)

0.17

0.13

1.31

0.21

−0.10

0.43

−0.10

0.43

Typing ability of students

0.18

0.20

0.91

0.38

−0.24

0.60

−0.24

0.60

Privacy and security

Table 4 Regression statistics—benefits

Regression statistics Multiple R

0.90

R square

0.80

Adjusted R square

0.73

Standard error

0.61

Observations

30.00

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Table 5 Multiple regression analysis—significance benefits statistics df Regression

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SS

MS

F

Significance F

31.94

3.99

10.57

0

0.38

Residual

21

7.93

Total

29

39.87

Table 6 Multiple regression analysis—benefits Coefficients

Standard error

t stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

1.14

0.63

1.81

0.08

−0.17

2.46

−0.17

2.46

Enhancing learners interactivity

0.63

0.16

3.91

0.00

0.29

0.96

0.29

0.96

Improving the management of students records

−0.32

0.23

−1.36

0.19

−0.80

0.17

−0.80

0.17

Identity authentication

−0.09

0.18

−0.47

0.64

−0.46

0.29

−0.46

0.29

Enhancing accountability and transparency

0.48

0.17

2.74

0.01

0.11

0.84

0.11

0.84

Better control of data access

−0.42

0.23

−1.86

0.08

−0.89

0.05

−0.89

0.05

0.35

0.22

1.61

0.12

−0.10

0.79

−0.10

0.79

−0.05

0.21

−0.25

0.81

−0.49

0.39

−0.49

0.39

0.15

0.16

0.98

0.34

−0.17

0.48

−0.17

0.48

Enhancing students assessments High security Low cost

as the significant variables in influencing the overall perceived usefulness of the digitized assessments. It is important to indicate at this point that it is essential to integrate the digital assessment into the entire learning process from the course design. It is also for essential assess what they learn and not just what they have learnt. Considering the fact that learning is an ongoing process that assessment should also be done continuously, and instructional design can be helpful for planning and implementing the same, and we have to select the right methodologies (i.e. assessment), strategies and learning technology to enhance it. One more strategy that should be considered is to use different assessment methods for measuring different processes and outcomes. Apart from the popular ones like

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multiple choice questions, online role-plays, scenario-based activities, judged mathematical expression and online discussions, one can also utilize techniques like ePortfolios assessment, concept maps or personal response systems. Usage of ePortfolios, scenario-based activities, role-paying and simulations can make the process authentic since they provide evidence of learning. These also help in assessing the skills like critical thinking, decision-making, team work, etc. For several generations, the idea of feedback has been associated with the last mile wrap up activity. It is essential to acknowledge that for effective learning to take place, learners need substantial, regular and meaningful ongoing feedback. Digitized assessments offer an appropriate format for providing such regular feedback. The suggestions can be personalized and be given by different stakeholders including self, peers, teacher and professionals. Such a feedback shall be used to improve learning in the process and not kept aside as a final activity. Providing continuous feedback will encourage learners to make course corrections and enhance their learning experience. In order to scale up digitized assessments, self-regulated learning should be promoted.

5 Conclusion Digitization of assessments is the order of the day, and the higher education system has to adopt the digital way at a rapid pace. The digital learning platforms are attracting users across the globe and redefining the assessment and learning experience altogether. Though the penetration into the market like India is currently low, it is not a far future that these complimentors shall replace the brick and mortar higher education system. In this scenario, to implement digitized assessments in a mass scale, it is essential to understand the perspectives of faculty who are the opinion leaders and implementers at the ground level. This study is an attempt in that direction and has found that there are few operational challenges, but the perceived benefits outweigh the challenges. With proper training to both faculty and students, building secure platforms with blockchain technology will help to overcome the challenges identified, and the digitization of assessments will be soon a reality in higher education sector.

References 1. S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System (2008). Available online https:// bitcoin.org/bitcoin.pdf. Accessed on 12 June 2019 2. A. Grech, A.F. Camilleri, Blockchain in Education (Publications Once of the European Union, Luxembourg, 2017) 3. R. Collins, Blockchain: a new architecture for digital content. E-Content 39, 22–23 (2016)

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4. M. Memon, U.A. Bajwa, A. Ikhlas, Y. Memon, S. Memon, M. Malani, Blockchain beyond Bitcoin: block maturity level consensus protocol, in Proceedings of the 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Bangkok, Thailand, 22–23 November 2018, pp. 1–5 5. V. Gatteschi, F. Lamberti, C. Demartini, C. Pranteda, V. Santamaría, Blockchain and smart contracts for insurance: is the technology mature enough? Future Internet 10, 20

Non-performing Asset Analysis Using Machine Learning Rohan Ninan Jacob

Abstract Loans are given by the banks to the customer, according to their needs, and are being monitored by various entities within the organization, so that adequate amount is received to the customers and assuring that they would not default or certain financial advisories can help them in letting know whether a non-performing assets (NPA) case can be triggered or not. Though there are many softwares in market which can do this job quite well, but for small financial institutions and banks, it becomes a big hurdle to deploy such solution. Unfortunately, there has not been a cost-effective method of identifying and predicting these cases and has also led to some failure in monitoring of NPA activities, in organizations. Therefore, an effective approach is to use a machine learning algorithm, with the use of open-source softwares, that could help in predicting the NPAs by using the essential parameters and thereby giving a cost-effective solution to such an issue, which many of the banking and financial organizations are facing. Keywords NPA · Parameters · XGBoost · Gradient boosting · Feature importance

1 Introduction The international monetary fund devises the NPA standards for the financial institutions, based on which the loan is standardized to be NPA [1] if the criteria are met. Of which some of them are as follows: • Payments that are related to interest as well as principal that can be up to 90 days or more • A minimum 90 days of payments comprising of interest which is capitalized, delayed or refinanced because of agreement clause by the organization

R. N. Jacob (B) Department of Electronics and Telecommunications Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_2

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• Payments which are less than overdue for 90 days, but there are other reasons of doubt whether those payments would be made in full or not. Conventional collection and recovery process of the financial institution have always been manual-driven and not data-driven. These are involved by a large number of people who adopt various techniques (mostly manual) for the collection process and hence lack professional approach. This generic process of collection and recovery which is lacking focused approach has been resulting in accumulated non-performing assets, the ever-increasing cost of collection, and dissatisfied customers. With the advent of credit risk management solutions or other NPA detection softwares, it is possible to track fraud cases resulting in NPA and makes tracking of these quite easy. However, such software costs a lot and hence an efficient cost-effective method could help the organization to monitor and track such activities, thereby relying on the power of open-source softwares. Due to increase in the development and a great community support of open source, it was possible to implement an algorithm that helped in resolving such cases. The paper presents an analysis which can be done by various financial institutions and organizations, in making sure that NPAs can be in control implying to which will give them better decisions to monitor such incidents and help them in saving a ton of money. The main algorithm that is used here is the XGBoost algorithm that would help in the execution speed and the model performance. The essential part of the algorithm is the scalability that helps in the fast learning by parallel and distributed [2] computing, offering adequate memory usage. It is one of the gradient boosting methods which falls in a supervised learning technique and aims in predicting target variable. XGBoost is an ensemble-based learning which is composed of several base learners.

2 Algorithm and Program Flow The gradient boosting, when used in regression, the regression tree, helps in mapping the input data point to one of the leaves which comprises a continuous score. Here, the XGBoost algorithm helps in minimizing a regularized objective function which results in the combining of a convex loss function (based on the difference between the predicted and target outputs) as well as a penalty term for model complexity (in other words, the regression tree functions). The training proceeds iteratively, adding new trees and predicting the residuals or errors of prior trees that are then combined with previous trees to make the final prediction. The fact that requires adding new tress is to aggregate the number of trees or predictors which are individually weak classifiers, so aggregation is required in order that prediction can improve. It is called gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models.

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While gradient boosting has an approach of negative gradients, in order to optimize loss function, this makes use of Taylor series which is to calculate value of this loss function by means of distinguished base learners [3]. XGBoost need not explore all possible tree structures but helps in the building up of a tree greedily, whereby the regularization term is helping to reduce the building up of complex tree by means of several leaf nodes [4]. Apart from that, XGBoost consists of other techniques which include column subsampling, shrinkage, splitting criteria, etc. Details of the work flow are as follows: • Input data was provided by Hacker Earth, which was later on then converted to test and train data to check and validate the accuracy as well as the precision and the parameters which had to be monitored along with its description was also provided by the same. • With the large data which was received, there were many changes which needed to be done in data, like in fields: ‘income’ and ‘undreamt’ were missing and hence needed to be filled in with the mean values. Such values had to be filled by taking upon mean values and then filling up of the result at those blank values or ‘Nan’, which is the process of imputing missing values. Some parameters need not need to be a part of this analysis and hence were dropped out of the consideration. Apart from that, for better understanding of the terms, renaming was done. • Splitting of both training and test set was performed in which 70% of data was used in training and 30% used in the test set. It is important to make sure that the splitting ratio should never be the same, which will alter the efficiency and reliability of the model. It even helps in gaining deeper insights into reliable decisions. • For increasing the efficiency of the model, we performed cross-validation. It is used to help in enhancing the predictive as well as accuracy performance of the developed models, helping in seeing how they work outside the sample [5] to a new data set, i.e., test data. The reasons come in for a cross-validation technique are when we fit a model, we are fitting it to a training data set. Without crossvalidation, we only have information which gives us insights into how will the model perform with respect to the sample data. Ideally, we would like to see how the model performs when we have a new data in terms of accuracy of its predictions [6]. In science, theories are judged by its predictive performance. There two types of cross-validation you can perform: leave one out and k fold. • After training the model, the best iteration is selected, due to which, we came to know the accuracy of the model, giving us the prediction. Also, we came to know the ROC-AUC. Accuracy is one of the essential factors for evaluating classification models. It is nothing but the fraction of predictions which represents the correctness of the model, which is given by [7] (Fig. 1).

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Fig. 1 Accuracy equation

Fig. 2 AUC equation

The reason why predictions are important in machine learning is because machine learning model predictions help the businesses to make highly accurate guesses in order that they are likely outcomes of a question based on historical data [8]. The area under a curve (AUC) for a classifier on a given data set can be represented as the probability of P(f (X+) > f (X−)), where f (X+)corresponds to a random variable which pertains to distribution of the outputs (scores) for the classifier, belonging to the positive observations and f (X−) the one corresponding to the negative observations. Mathematically, represented ⎧ by [9] (Fig. 2). ⎨ 0 if x < 0, where g() is defined as 0.5 if x = 0, ⎩ 1 if x > 0. The ROC-AUC curve gives us an indication on how well the model created is performing with the data. This gives also an insight whether the model which is deployed has underfit or overfit the training and test set. A graphical workflow of program is illustrated as follows (Fig. 3).

3 Results The test set results were compared with respect to the training set, for making sure that the model worked well and has a good efficiency in its performance. We even determined the accuracy in proving it so. The feature importance was evaluated, which gives us an insight into every feature in the data and more its score, the more it signifies to decision making. Some of the important features during the analysis of this problem was total interest fees, home ownership, employee length, loan amount, batch enrolled, etc. The training process was initiated, where the best iterations were determined. Below figure represents the start of the iteration (Fig. 4). While the training set of iterations were ongoing and later on completed, the best iteration was selected, thus evaluated the model accuracy and ROC-AUC (Fig. 5). The feature importance of all features was evaluated, which are represented in the plot as shown below, which includes all the features used during our analysis. The y-axis represents the feature importance values, and the x-axis shows the features used in the model building (Fig. 6).

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Fig. 3 Program flow

Input Data

Data Cleaning

Imputing Missing Values

Splitting and Training

Cross Validation

Prediction

4 Summary With the use of XGBoost algorithm, we were able to train the existing data and get insights into the parameters that help us in the governing as well as determination of NPA. These parameters are taken into consideration that the bank is responsible in the legit keeping of this data. With respect to this bank, they were not performing well for the last 3 quarters and usually depend on their group of investors to see whether the loan can be given to particular customers, based on their entire record and wants to make sure that whether they will default or not. Feature importance is one such important criteria enabling us to see the necessary parameters enabling the

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Fig. 4 Training using XGBoost classifier

Fig. 5 Results obtained after selecting the best iteration

Fig. 6 Feature importance of variables, found after training and the best iteration selected during the analysis

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organization to monitor, and each of their customers and along with XGBoost gives them an indication of the NPA case going to get triggered. Taking the highest scores, which were obtained during feature importance, we were able to come to derive our results. Also, the model that was developed had an accuracy of 94.11%, and ROC-AUC was 92.48%.

5 Future Scope • With organization seeking help in need of open-source solutions, the program when integrated to live databases would be able to track such cases which will help real-time risk management. • A business intelligence tool, with support of Python scripts, can help in creation of dashboards that will help the particular user/organization to keep a record and monitoring of NPA’s with ease.

6 Conclusion This paper has taken into account to focus upon the NPA and other similar solutions that exist in the present world, where cost has always been a major factor, for financial organizations, especially small and medium-sized enterprises (SMEs) to monitor their NPAs, but along with that the prediction and accuracy were always a doubtful case when thinking of open-source platforms. This era has a vital role in the development of variety of many developed or developing Python libraries, enabling the use of modern and optimized solutions to exist in open sources and thereby with use of appropriate machine/deep learning algorithms would help in obtaining more accuracy as well as gaining more insightful analysis, which was not the case 10–20 years back due to reasons like lack of data, software availability and other technical constraints, etc. With the advent of such high powered and optimize algorithm has made it possible to strengthen decision making as well as opening variety of capabilities in different domain.

References 1. Non-performing loan, https://en.wikipedia.org/wiki/Non-performing_loan. Last accessed on 4 May 2020 2. An End-to-End Guide to understand the Math behind XGBoost, https://www.analyticsvidhya. com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/understandthe-math-behind-xgboost/. Last accessed on 1 Apr 2020

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3. XGBoost Algorithm: Long May She Reign, https://www.kdnuggets.com/2019/05/xgboost-alg orithm.html,last. Accessed on 21 Mar 2020 4. Unveiling Mathematics Behind XGBoost, https://www.kdnuggets.com/2018/08/unveiling-mat hematics-behind-xgboost.html. Last accessed on 8 Apr 2020 5. What is the purpose of performing cross-validation? https://www.researchgate.net/post/ What_is_the_purpose_of_performing_cross-validation. Last accessed on 21 Mar 2020 6. Purpose of Cross validation, https://towardsdatascience.com/5-reasons-why-you-should-usecross-validation-in-your-data-science-project-8163311a1e79. Last accessed on 4 May 2020 7. Classification:Accuracy, https://developers.google.com/machine-learning/crash-course/classi fication/accuracy. Last accessed on 14 Jan 2020 8. This is how computers predict the future, https://qz.com/1261817/predictive-algorithms-arenot-all-that-complicated/. Last accessed on 20 Mar 2020 9. Cristiano Leite Castro, Antonio Padua Braga: Optimization of the Area Under the ROC Curve, in 10th Brazilian Symposium on Neural Networks. IEEE computer society, Washington DC (2008), pp. 142

Volatile Memory Disk Forensics: Investigate the Criminal Activity of RAMDisk Nilay Mistry, Annan Christian, and Bansari Bhavsar

Abstract In this ever-expanding cyber space, computer is a very valuable asset, but if used with malicious intentions it can create an environment of chaos. Computer forensics which is a part of digital forensics aims at collecting, analyzing, and documenting digital evidences such that they are admissible in court of law. But integrity of digital evidences comes under scrutiny due to anti-forensics tools and techniques that are exercised by cybercriminals to evade detection. RAMDisk is one of such technique used by cybercriminals to avoid detection and perform clean up after the crime that has been committed. Cybercriminals use this technique to store sensitive information and data in RAMDisk and discard it once the purpose has been served. This paper discusses the working of three popular RAMDisk softwares namely AMD Radeon RAMDisk, ImDisk Toolkit. and Miray RAM Drive. We have also identified proprietary drivers and unique signatures for each software and what approach can be adopted by a forensics investigator to investigate them. Keywords RAMDisk · Random access memory · RAM · Anti-forensics · Counter-forensics · Cybercrime · Volatile memory

1 Introduction Locard’s principle for digital forensics can be defined as, any perpetrator of crime leaves a trace, and it can be either physical or virtual. In digital crimes, there is no physical contact between the perpetrator and the crime scene, but the perpetrator N. Mistry (B) · A. Christian · B. Bhavsar Institute of Forensic Science, Gujarat Forensic Sciences University, Sector 9, Gandhinagar, Gujarat 382007, India e-mail: [email protected] URL: https://www.gfsu.edu.in/ A. Christian e-mail: [email protected] B. Bhavsar e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_3

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would leave a digital trace behind. As the chance of leaving a trace increases, there is always a concern about the integrity of the evidences because of the anti-forensics exercised by perpetrator to manipulate or delete the evidence which would misguide the forensic analyst [1]. Anti-forensics is a set of tools, techniques, methods, and procedures that hinder the scientific analysis of evidences by affecting its quality, quantity, and integrity. Marcus Roger categorized anti-forensics into four categories: data hiding, artifact wiping, trail obfuscation, and attack against computer forensic tools [3]. In data hiding category, data is hidden into unallocated or slack space of a hard disk. Metadata of files, Master Boot Record (MBR), and hidden partitions on hard drive can also be used to hide secret data. Another category is artifact wiping, and in this category different tools are used to destroy the data by overwriting it multiple times, making the data irretrievable. Tool like Eraser is very popular, but it also leaves indicators of anti-forensics in the form of registry entries [2]. The worst are prophylactic tools, they not only delete the artifacts but also wipe out any indicators of anti-forensics or its remnants. The method of trail obfuscation has been prevalent since the era of 70s [3]. It tries to misguide or divert the forensic investigator in a completely different direction. Lastly, attack against computer forensics tools is a relatively new technique as compared to the other three. In this categories, the flaws and zero-days in the forensics tools are exploited to manipulate and forge fake digital evidences, thus questioning the integrity and reliability of the evidences collected using those tools. RAMDisk (also called as RAM drive) is a block of random-access memory (primary storage or volatile memory) that a computer’s software treats as if the memory were a disk drive (secondary storage). RAMDisk is a program that takes a portion of the system memory and uses it as a disk drive. RAMDisk is used for both benign and malicious purposes. RAMDisk is extensively used among gamers because of its fast input/output (I/O) throughput, but it is equally popular among cybercriminals to avoid data recovery. RAMDisk cannot be categorized into any one of the anti-forensics categories defined by Rogers. It is a combo of both data hiding and artifacts wiping category. RAMDisk can be used to hide data in partition created from RAM, and once the motive is accomplished, it can be again merged into the system memory. RAM being a highly volatile memory, the data stored in the RAM either gets lost or overwritten by other data, and hence, artifact wiping is achieved. In this paper, the research work is concentrated on anti-forensic that is achieved with the use of RAMDisk and also identified different freeware software available in the market that are used to create RAMDisk and the drivers that they use. Also identified the signatures that they leave behind on their usage. In addition to that, how an investigator can identify whether RAMDisk was employed as an anti-forensic technique and what approach an investigator can take to perform digital forensic if such is the case.

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2 Different Software Available for RAMDisk As discussed, a plethora of softwares are available for creating a RAMDisk. However, here, we will discuss working of three popular software available for creating a RAMDisk.

2.1 AMD Radeon RAMDisk AMD Radeon RAMDisk provides a freeware version of the software which can be used to create RAMDisk. An important feature of AMD Radeon RAMDisk from security perspective is that RAMDisk has been designed to permanently delete any information that has not been backed up or auto-saved making the information completely unrecoverable. When the RAMDisk is successfully started, a kernel level driver named RAMDiskVE.sys is loaded into Windows\System32\drivers folder as shown in Fig. 1. This driver is available to Windows whenever RAMDisk is started. Once the RAMDisk has been stopped, the driver is removed from the folder.

2.2 ImDisk ImDisk is a free and open-source software. Few key features of ImDisk is that it gives an option to allocate memory dynamically which makes the data carving more difficult. ImDisk utilizes service named ImDisk Virtual Disk Driver Helper which starts in the background when the computer boots, so user can create a RAM based file

Fig. 1 AMD Radeon RAMDisk driver

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Fig. 2 ImDisk driver

Fig. 3 Miray RAM Drive driver

system which is automatically erased as soon as the computer is powered off. ImDisk driver named imdisk.sys can be found in the Windows\System32\drivers folder as shown in Fig. 2.

2.3 Miray RAM Drive Miray RAM Drive is another tool for creating RAMDisk. Key feature of Miray RAM Drive include that it provides persistence storage which facilitates saving the image of RAMDisk on a hard disk. Miray Ram Drive uses service named MRDService. The service then runs Windows Kernel mode device driver mrdo.sys which initializes the RAM drive. The driver is located at C:\Program Files (×86)\Miray RAM Drive\driver\×64 (Fig. 3).

3 Signature Creation and Identification When a software is installed, it leaves behind some kind of traces. These traces can be either in form of unique files or folder that are created, starting up a specific service or modification made to the Windows registry. These traces can be used to create

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signatures that can uniquely identify a software. In this work, we have used registry snapshot analysis, and updates made to the Windows registry are used to create signatures. Signatures were identified by comparing the snapshots of registry taken before installing and after uninstalling the software using utility named Regshot.

3.1 Signature Creation For identification of signature, following steps where performed chronologically (Table 1). 1. Create test system • Windows 10 machine • i7 64 bit processor with 8 GB RAM 2. Take and save a snapshot of the registry (using Regshot) 3. Execute one of the following desired activity: • Install RAMDisk creation software • Uninstall RAMDisk creation software 4. Take and save another snapshot after the desired activity has been performed (using Regshot) 5. Compare the snapshots 6. Identify and extract unique signatures.

3.2 Identification of Signature Regshot allows comparison of registry snapshots giving a detailed analysis of any keys or subkeys created, deleted, or modified in the Windows registry. Unique signatures were identified by manually filtering the report generated by Regshot. The unique signature identified for installation and un-installation of each vendors are listed in the tables.

Table 1 Driver location Vendor name AMD Radeon RAMDisk ImDisk Miray RAM Drive

Driver name

Driver location

RAMDiskVE.sys imdisk.sys mrdo.sys

Windows\system32\drivers Windows\system32\drivers C:\Program files (×86)\Miray RAM Drive\driver\×64

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Table 2 AMD Radeon RAMDisk signatures Action Signature Install Uninstall

NA HKLM\SYSTEM\CurrentControlSet\Services\Ramdisk\

Table 3 ImDisk signatures Action Signature Install Uninstall

HKLM\SOFTWARE\Classes\*\shell\ImDiskMountFile HKLM\SYSTEM\ControlSet001\Services\bam\UserSettings\S-1-5-21209205877-1768948508-1567415345-1001\\Device\HarddiskVolume3\Program Files\ImDisk\

Table 4 Miray RAM Drive signatures Action Signature Install Uninstall

HKLM\SOFTWARE\Miray\Miray RAM Drive 1.1.0-1033-604626AB-787D-49C4-9C55-280C5AFA0ED0 HKLM\SYSTEM\CurrentControlSet\Services\MirayRAMDrive

Along with installation signatures, registry key denoting the date of installation for few vendors are identified. These dates can be useful for creating the time-line based analysis of the crime. The location of registry key for AMD Radeon RAMDisk is HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall\BBC956B0DD9-4A48-ACAC-DC6AC0FE10D5\InstallDate (Table 2). The value of key denotes the date when AMD Radeon RAMDisk software was installed. Similar registry key has been identified for Miray RAM Drive, and the key location is HKLM\SYSTEM\ControlSet001\Control\Class\4d36e97b-e32511ce-bfc1-08002be10318\0001\DriverDate with value suggesting the date when the driver was installed (Tables 3 and 4).

4 RAMDisk Data Carving and Artifacts Analysis Methodology 4.1 Test RAMDisk Creation Methodology To analyze RAMDisk for data carving and related artifacts, a test RAMdisk was created of 200 MB in size using AMD Radeon RAMDisk software in Windows 10 machine. Few files were saved in the RAMDisk later to be recovered if they

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were deleted or the RAMDisk was unmounted. In total seven files were saved in the RAMDisk, out of which five files were pictures, and two files were text documents.

4.2 Volatile Memory Capturing Methodology RAM being a volatile memory, an extra care must be taken while acquiring the memory dump. If right tools and techniques are not used, then important artifacts might get contaminated or sometimes even get lost. In this work, we have used FTK Imager which is a free tool and widely prevalent in the industry for capturing volatile memory. FTK Imager creates a bit-by-bit image of the memory including the unallocated and slack space. We have captured the RAM for both the scenarios, i.e., while the RAMDisk is still mounted and while it is unmounted.

4.3 Artifact that a Device Was Mounted One of the key artifact for analyzing a RAMDisk is to find unusual entries in mounted devices registry key. The list of mounted devices can be find out by analyzing HKLM\SYSTEM\MountedDevices registry. As shown in Fig. 4, an unknown mounted device which is a DOSDevice type has been highlighted. Such unknown and unusual entry depicts that some sort of storage device was mounted as disk. Hence, it could be concluded that a RAMDisk was used as an anti-forensics technique, and further analysis of RAM dump is needed to find more potential artifacts.

4.4 Analyzing Memory Dump for Artifacts The memory dumps obtained from FTK Imager are in raw form. To analyze this dumps, we have to use a hex editor. Here, we have use wxHexEditor to analyze the RAM dumps. wxHexEditor provides a user-friendly interface which is divided into two parts. In the left part, the bit-by-bit representation of the RAM dump is shown in a hexadecimal form, and the right side denotes the values in string of hexadecimal values.

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Fig. 4 Unusual mounted device

4.5 Analyzing Image of RAM While the RAMDisk Is Still Mounted 4.5.1

Recovering Data from the RAMDisk

Analyzing the RAM dump taken while the RAMDisk was still mounted, it was found that all the data can be recovered from the RAM dump. Even if files were deleted on RAMDisk, they can be recovered from the dump. All the files saved on the RAMDisk can be carved from the RAM image using any data carving tool. All the files which were not deleted can be found in their original form. One of such JPEG file is shown in Fig. 5.

4.5.2

Analyzing Image of RAM While the RAMDisk Is Unmounted

When the RAMDisk is unmounted, the RAMDiskVE.sys driver is removed, and the RAM blocks allocated to the RAMDisk are again merged into the RAM. Partial files can be found by analyzing RAM image. When the RAM blocks are dis-allocated and again merged into the RAM, few blocks get overwritten by other processes, and only few partial files can be recovered from the image of the RAM. Partial parts of the

Fig. 5 File in RAM image

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Fig. 6 Partial text found in the RAM image Table 5 Default location of .img file Vendor AMD Radeon RAMDisk ImDisk Miray RAM Drive

Default location C:\RAMDisk.img User Defined(window opens in Documents) C:\Users\Miray\pers.img

text file can also be found at random locations of the RAM, hence proving that the blocks of RAM allocated to the RAMDisk were not consecutive (Fig. 6). Merging those parts of the file a large proportion of the original text file can be recovered. All the data in their original form can be recovered from the .img file that the RAMDisk software creates to restore the RAMDisk if it gets corrupted or it has to be reinstated in case of power off. This .img file can be mounted as an external media drive and all the data saved in it can be viewed. Different vendor has a different default location where this .img file is stored. Default location of each vendor is given in Table 5.

5 Conclusion This work has concentrated on RAMDisk used as an anti-forensics technique and identified signatures for three popular softwares that are prevalent in the market. This signatures can be used to identify which software was used to create a RAMDisk, and based on the software identified, the investigator can plan out the approach to perform forensic investigation on the system. Installation and running of a driver identified for each vendor confirms that RAMDisk was used, and so forensic analyst should look for files and forensic artifacts in the RAM image. From the above analysis, it was concluded that deleted files can be recovered from the RAM image while the RAMDisk is mounted. But once the RAMDisk is unmounted, the blocks of RAM get overwritten by other processes, and recovering the file in its original form gets difficult. So image of RAM should be taken as soon as possible to avoid overwriting of the blocks allocated to RAMDisk. While in the

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scenario when RAMDisk is unmounted long before and data cannot be recovered from RAM dump, in such situation, forensic analyst should look for .img file stored at the default location to recover all the data in its original form.

References 1. R. Harris, Arriving at an anti-forensics consensus: Examining how to define and control the anti-forensics problem, Digit. Invest. (2006). https://www.sciencedirect.com/science/article/ pii/S1742287616300378 2. K.J. Park, J.M. Park, E. Kim, C.G. Cheon, J. James, Anti-forensic trace detection in digital forensic triage investigations. J. Digit. Forensics Secur. Law (2017). https://commons.erau. edu/jdfsl/vol12/iss1/8/ 3. M. Rogers, Anti-Forensics (Lockheed Martin, San Diego, 2005). http://www.cyberforensics. purdue.edu/docs/Lockheed.ppt 4. K. Conlon, I. Baggili, F. Breitinger Anti-forensics: furthering digital forensic science through a new extended, granular taxonomy. Digit. Invest. (2016). https://www.sciencedirect.com/ science/article/pii/S174228761630037 5. J. Zheng, Y.A. Tan, X. Zhang, C. Liang, C. Zhang, An anti-forensics method against memory acquiring for android devices, in Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC) (2017). https://ieeexplore.ieee.org/abstract/document/ 8005797/ 6. T. Rochmadi, I. Riadi, Y. Prayudi, Live forensics for anti-forensics analysis on private portable web browser. Int. J. Comput. Appl. (2017). https://pdfs.semanticscholar.org/9974/ 3a16bd4c294cc6410fb1c15ba2f3c8229c05.pdf 7. K. Lee, H. Hwang, K. Kim, B. Noh, Robust bootstrapping memory analysis against anti-forensics. Digit. Invest. (2016). https://www.sciencedirect.com/science/article/pii/ S1742287616300408 8. N. Zhang, R. Zhang, K. Sun, W. Lou, Y.T. Hou, S. Jajodia, Memory forensic challenges under misused architectural features. IEEE Trans. Inf. Forensics Secur. (2018). https://ieeexplore. ieee.org/abstract/document/8323390/ 9. H.K. Mann, G.S. Chhabra, Volatile memory forensics: a legal perspective. Int. J. Comput. Appl. (2016). https://pdfs.semanticscholar.org/f140/f3bdb657f4dcfbfd4bf0183524bfa925a872.pdf 10. P. Feng et al., Private data acquisition method based on system-level data migration and volatile memory forensics for android applications. IEEE Access 7, 16695–16703 (2019). https:// ieeexplore.ieee.org/iel7/6287639/6514899/08625420.pdf 11. A. Chetry, U. Sharma, Memory forensics analysis for investigation of online crime-a review, in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (IEEE, 2019) 12. A. Kazim et al., Memory forensics: recovering chat messages and encryption master key, in 2019 10th International Conference on Information and Communication Systems (ICICS) (IEEE, 2019) 13. R.A. Awad et al., Volatile Memory Extraction-Based Approach for Level 0–1 CPS Forensics (Oak Ridge National Lab (ORNL), Oak Ridge, TN (United States), 2019). https://www.osti. gov/servlets/purl/1606814

Modem Functionality Diagnostic Tool with Graphical User Interface for Cellular Network Arghya Biswas, Sudakar Singh Chauhan, and Antara Borwankar

Abstract In both Internets of things and conventional cellular networks, the modem is a very important component. So it should be highly stable and trustable. Any kind of error or misbehavior must be diagnosed and need to resolve properly. In this paper, we are proposing a very useful tool that can diagnose the workflow of a cellular modem. The tool can observe the modem very closely and diagnose each step from the basic power on a feature to the registration on the network with the establishment of the data connection. If the modem faced any kind of error in between this, then the tool is capable of detecting the error, and it will solve the problem or recommend the possible solution to get rid of the error. We are also proposing a remote modem diagnosis feature with this tool. If the modem is deployed in some other device on the same network, then our tool is also able to diagnose the modem. With this diagnosis tool, users can save a lot of time to diagnose and fix the modem error. Keywords Cellular modem · Diagnose tool · AT command · Internet of things · GSM and LTE network · Yocto

1 Introduction Cellular technology is one of the largest technologies in our daily life. This technology was introduced only for voice communication. In the latter days, the same technology was started to use for the data connection. Nowadays, cellular technology is being used in various fields. One of the most important fields is the Internet of things A. Biswas (B) School of VLSI & Embedded System Design, NIT Kurukshetra, Kurukshetra 136119, India e-mail: [email protected] S. S. Chauhan ECE Department, NIT Kurukshetra, Kurukshetra 136119, India e-mail: [email protected] A. Borwankar Intel Technology India Private Limited, Bangalore 560103, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_4

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(IoT). The most important part of the cellular networks is the modem (modulation– demodulation) [1]. A proper fault diagnostic tool is required to implement, and bug solves the modem-related issues. In this paper, we are proposing a functionality diagnostic tool of a modem for the cellular network with a graphical user interface.

2 Literature Survey In [2], the authors proposed an on-board diagnostic (OBD) system for euro standard vehicles’ engine. The OBD takes the data related to the state of the engine, the exact timing of when a spark happens, etc., and sends the data to a remote server. They used the GSM technology with the AT command to communicate with the server. A graphical user interface (GUI) on the server side shows the coming data and suggests some required action if any error occurs. The authors proposed in [3] a multi-node mesh network to analyze the data from DTE. They grab the data from one node and send it to the other nodes, and then the system dials up to initiate the cellular connection. In other nodes, the modem must send the ASCII or string response according to the command that fired. They chose WSN topology for communication and some shell to troubleshoot the issues. In [4], authors used to send a 2.5 GHz signal after 15 min gap over a corn and soybean field for 7 months. After analyzing the RSSI, rainfall, temperature, etc., they concluded that the RSSI is affected by the preset and absent of the crops. Authors proposed to share the lower layer ID IMSI for several IoT devices in [5] to optimize the state information. It will be recognized by the upper layer devices. They also proposed to use time division concept in machine-type communication to upload the data in different time to enhance the speed of the system. Authors proposed in [6], machine to machine services to implement smart resemble metering in the application for the vehicle. They also measure the quality of the signal overhead and analyze the data. With the proposed system, they mainly focused on reducing the signaling load on the cellular network sever. In [7], authors proposed a tool to collect the signal information and control plane message to analyze the performance degradation due to the misconfiguration of cellular network. In these analyzing steps like time threshold, control flow sequence and signaling failure, authors used more than 3.1 million control plane messages from 13 main cellular operators to analyze the circuit-switched fallback technology in 3G and LTE system. In this paper [8], authors summarized the main issue in the 5G technology evolution for fault management. To overcome those issues, the authors proposed an application from an operational perspective based on machine learning. They describe the details workflow of the application. They use the deep learning process to survey the issues and based on that they also build the block diagram for the typical fault management system. According to the authors [9], their proposed system is able to diagnose the fault in the cellular network for the IoT devices. They proposed a data-driven indoor

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diagnostic framework for this application. This framework uses the machine learning technique and trained some prediction models. Those models are able to construct a global view of the radio environment which is well known by its name radio environment map (REM). Later, this REM will be used for diagnosing and managing the faults in the cellular network at the location. In [10], authors proposed to combine the self-organizing networks (SON) and traces. According to them, it will help to detect the issued related to the radio interface and easy to solve the issues. As the call traces functions have the user information so it will be easier to detect and resolve the issue automatically with this proposed model. This model also helps to assess the RF condition based on the location data.

3 Motivation All the projects that are based on the cellular network have a common component, called. This modem is an essential part of GSM comm. In the IoT applications, the modem keeps also a big contribution. So, we should concentrate on the functionality of the modem. And we should make sure that the modem should work perfectly under all circumstances. But if it faces any error, then we should also be prepared to fix the issues. So, for this point of view, we are motivated to make a proper functionality diagnostic tool with a graphical user interface for modem cum cellular networks.

4 Research Gaps Conventionally, a cellular modem is diagnosed by manual method. The test modem needs to be connected with a computer via a carrier board. Then AT commands are serially sent to the modem from the computer one by one. The person responsible for the diagnose observer the response from the modem and decide the reason of error. So this procedure is too much time consuming, and sometime the person needs to remember all AT command and the command sending sequence. So, we feel a need for an automated tool with a graphical user interface that can send all those AT commands and grab the response from the modem and sometime the tool can try to solve the error by its own. Another big gap that we found is remote access to the modem. If the modem is deployed as part of a product in a remote location, then it is a bit difficult to access and diagnose the modem if any issue occurs. So, in this project, we are also trying to focus this remote access part also.

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5 System Overview 5.1 System Architecture The modem functionality diagnostic tool is designed for the UNIX-based operating system only. The GUI part will be installed in the local Linux system. But for the hardware, there are two possible options. If the modem is in the local location, then it can be connected via a carrier board through the USB port. But if the modem is deployed in the remote location, then one client and one host script must execute to connect and communicate over the server. In our setup, we were using the Intel XMM7560 modem device. One USB type carrier board was used to communicate with this modem. If the modem is in the remote location, then the system must connect over the server to communicate with the modem. For this setup, we used the Intel IA board. And the modem is connected via the PCIe interface with the Intel IA board. The board itself is connected with the server. In our case, we test it over the LAN server. For the WAN, we need some network side changes.

5.2 System Installation The entire modem diagnosis tool package contains the diagnostic tool binary itself and the dependency libraries and an auto-installation script. The package has different binaries for the different mode of modem. For the remote mode, the target must be set up before installing the tool. Firstly, the UNIX system and the Intel IA board must be connected on the same local server and able to ping each other. In the installation process, the installer will ask for the mode selection. According to the mode, it will install the correct binary, the dependency scripts and libraries. Figure 1 shows the mode selection process. Fig. 1 Installation mode selection

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Fig. 2 Log for host socket and remote socket binary

For the remote or PCIe mode, we must go through some extra steps after installation. After successful ping in both host and platform, a couple of binary needs to run in both the system like Fig. 2 to initiate the socket [11] connection. The next step is common for the both mode of operation. In this step, the tool binary is being executed and opens the GUI. For the PCIe mode, the tool checks some extra things like device’s presence, device drivers, network interface, AT ports and modem state before sending the first AT command. If the tool found any problem here, then it will report the error. Now the tool will send AT command, grab the response for the command and show in the GUI. First, few commands will grab the information about the modem, SIM, PIN, network, etc. Then, it registers the SIM with the live network and reads the network type, signal strength, operator name, SPN, context-related data and location info. Figure 3 is the AT command flow. If all the response is okay and does not have any issue, then the tool down the network interface for the modem and put the IP address, subnet mask and default gateway on that network interface and finally up the same. Users can verify the network interface by ping any global IP. After every step, the tool grabs the response. If any error came in the response, then it sends the error response to the error checking block and tries to resolve the error. If the error is resolved, then the tool came back to the next AT command else, and it will suggest the possible step to resolve the error.

6 Implementation Details The hardware implementation is different for both USB and PCIe mode. In the USB mode, a carrier board is needed. So, for this configuration UNIX is PC able to communicate with the modem directly over the AT ports which automatically come up when the board is connected to the PC.

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Fig. 3 Diagram for the full system

For the PCIe mode, the Intel IA board must be connected with the UNIX PC via LAN with the successful socket communication. As the modem is connected in the PCIe bus so the PCIe drivers are also needed. Figure 4 is a screenshot of the tool, contains basic information like device port, IMEI, IMSI, roaming status, operator name, registration status, registration mode and network signal strength 3GPP type location, MCC, MNC, LAC, TAC and CID.

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Fig. 4 Info tab of the tool

Some elements may change the value WRT time and location. So, these values are continuously updated in the GUI. Figure 5 is the SIM tab that contains the SIM details like SIM presence, IMSI, service provider name, MCC, MNC, subscriber number, PIN required or not, PIN entry retries SIM card identifier, card slot count and the current card slot number. This tab also has the features to unlock the PIN. The diagnose tab in Fig. 6 contains the PCIe device name, drivers needed for the modem, network interfaces, AT ports and modem status. This tab also contains the

Fig. 5 SIM tab of the tool

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Fig. 6 Diagnose tab of the tool

context for the modem and two buttons to refresh the data and resolve the current issue. Figure 7 is also showing the diagnose tab with some error messages. Somehow the modem has been timed out, and the AT ports are also not available in it. In this case, the issue may solve by clicking the resolve button. Most of the cases, the tool automatically resolves the problem by clicking the resolve button. But if it is beyond the scope of the tool, then it suggests the steps and hopes for the user interface.

Fig. 7 Diagnose tab for the tool with error message

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7 Conclusion and Future Work Scope Modem functionality diagnostic tools can be used to diagnose the modem-related issue in different types of hardware platforms. This will reduce the time to manually diagnose the modem by sending AT command one by one. This tool is designed in such a way that it can work with other plugins also. So, this plugin feature gives the versatility of the software. Till the date, this tool supports three main plugins like modem manager, Ofono and AT manager. The diagnose feature is available only in the AT manager plugin. This tool is totally free and open source. So, anyone can download and modify it. This tool still has some scope of upgradation or future work. For the PCIe type modem, we need an extra Linux-based computer. The tool needs to run in the host Linux PC. User needs to create a network socket connection in the both Linux PC and the Intel IA system. If the socket connection is successfully built, then the tool can communicate with the modem through the socket connection over the IP address. But in the future, it is also possible to embed the tool itself on the Yocto [12] recipe with the dependencies and build the OS for the Intel IA platform. Then the extra socket connection is not further needed. In this diagnose tool, some modem manager feature is discarded and only focuses on the data session diagnose. So, it is also possible to add the modem manager features like messages, contacts, radio frequency, etc. And it is also possible to add the diagnose feature for these features also. Then, it will be a full feature modem diagnosis tool.

References 1. H. Kwon, K. Kim, C. Lee, The unified UE baseband modem hardware platform architecture for 3GPP specifications. J. Commun. Netw. 13, 70–76 (2011). https://doi.org/10.1109/jcn.2011. 6157254 2. M.N. Iqbal, L.Y. Xin, W.U. Rehman et al., Diagnostic tool and remote online diagnostic system for Euro standard vehicles, in 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC) (2017). https://doi.org/10.1109/itoec.2017.8122328 3. A. Alyazidi, M.B.H. Frej, N. Gupta, Multi-node partial-mesh network communication via AT commands, in 2018 IEEE Long Island Systems, Applications and Technology Conference (LISAT) (2018). https://doi.org/10.1109/lisat.2018.8378013 4. K.P. Hunt, J.J. Niemeier, L.K.D. Cunha, A. Kruger, Using cellular network signal strength to monitor vegetation characteristics. IEEE Geosci. Remote Sens. Lett. 8, 346–349 (2011). https://doi.org/10.1109/lgrs.2010.2073677 5. M. Ito, N. Nishinaga, Y. Kitatsuji, M. Murata, Reducing state information by sharing IMSI for cellular IoT devices. IEEE Int. Things J. 3, 1297–1309 (2016). https://doi.org/10.1109/jiot. 2016.2587823 6. N. Kouzayha, M. Jaber, Z. Dawy, Measurement-based signaling management strategies for Cellular IoT. IEEE Int. Things J. 4, 1434–1444 (2017). https://doi.org/10.1109/jiot.2017.273 6528

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7. B. Hong, S. Park, H. Kim et al., Peeking over the cellular walled gardens—a method for closed network diagnosis. IEEE Trans. Mob. Comput. 17, 2366–2380 (2018). https://doi.org/10.1109/ tmc.2018.2804913 8. S. Basangar, B. Tripathi, Literature review on fault detection of equipment using machine learning techniques, in 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (2020). https://doi.org/10.1109/iccakm46823.2020.905 1543 9. S.-F. Chou, H.-W. Yen, A.-C. Pang, A REM-enabled diagnostic framework in cellular-based IoT networks. IEEE Internet Things J. 6, 5273–5284 (2019). https://doi.org/10.1109/jiot.2019. 2900093 10. A. Gomez-Andrades, R. Barco, P. Munoz, I. Serrano, Data analytics for diagnosing the RF condition in self-organizing networks. IEEE Trans. Mob. Comput. 16, 1587–1600 (2017). https://doi.org/10.1109/tmc.2016.2601919 11. M. Xue, C. Zhu, The socket programming and software design for communication based on client/server, in 2009 Pacific-Asia Conference on Circuits, Communications and Systems (2009). https://doi.org/10.1109/paccs.2009.89 12. A.P. Navik, D. Muthuswamy, Dual band WLAN gateway solutions in Yocto Linux for IoT platforms, in 2017 International Conference on Internet of Things for the Global Community (IoTGC) (2017). https://doi.org/10.1109/iotgc.2017.8008968

Regression Model to Estimate Effort for Software-Oriented Projects V. Vignaraj Ananth, S. Aditya, S. Ragul Kesavan, and B. Keerthiga

Abstract The assessment of the product development initiative remains a complicated issue requiring additional analysis. The efficiency of the development in software depends on an appropriate estimation of the effort needed for software development and enables project managers to design software life cycle operations correctly with the efficient software cost estimating techniques. The major experimental work conducted throughout this paper is to accurately forecast the problems in developing program-related projects. The parallelization of the behavior factors is achieved using the M5P approach to maintain statistical precision. Besides, performance differences with random forest are offered with the models acquired using the M5P Method to demonstrate the efficiency attained with each method. Keywords Machine learning · M5P technique · Random forest technique

1 Introduction Presently used techniques of effort calculation such as feature point analysis and COCOMO failed to effectively measure the value and energy needed for software development. These approaches are not sufficient for calculating cost and effort This work was supported in part by the Department of Computer Science and Engineering of Thiagarajar College of Engineering. V. V. Ananth (B) · S. Aditya · S. R. Kesavan · B. Keerthiga Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India e-mail: [email protected] S. Aditya e-mail: [email protected] S. R. Kesavan e-mail: [email protected] B. Keerthiga e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_5

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since they are equipped to create procedurally based applications. The computational and object-oriented approach contrasts as the previous distinguishes records and procedure. The use case point (UCP) is based on the use case diagram for calculating the effort of a provided software product. It aims to provide precise measurement of effort from the testing process. Case and actor are graded as ordinary, mediocre, and advanced into one of the three levels by use. The number of operations per use case helps complexity to be calculated in terms of value. Significantly over the past periods, the UCP model has been regularly established. This means that the software’s size and performance are purely corresponding with one another. Regardless of this factor, UCP’s computing contribution equation is not widely recognized by software enterprises. The M5P technique is used in this analysis to tackle the constraints of the UCP model and to improve the predictive efficiency of estimating program effort. The results obtained using the M5P-based effort evaluation method are then computed by using random forest (RF) to determine its efficiency. Section 1 describes the introduction to effort estimation. Section 2 provides the literature survey. Section 3 describes the algorithm involved in this paper. Section 4 shows the experimental design. Section 5 describes the experimental results, and Section 6 provides the conclusion and future.

2 Literature Review (A) Early-stage software effort estimates based on case point use using random forest technology: Software effort estimation was done by the techniques random forest, log-linear regression, and stochastic gradient boosting. Out of which random forest yielded more accurate results as it generated trees in parallel and averaged the outcome of the results [1]. (B) A summary and evaluation for the description of machine learning strategies of automated soil mapping: This research compared ten machine learning techniques designed to map broad classes of soil and soil orders. With the greatest precision, support vector machine and KNN achieved performance. (C) Models for the urban air quality control system: Based on comprehensive studies, M5P outperforms other algorithms due to efficiency of the tree structure and efficient generalization capabilities. On the other hand, the artificial neural network achieved the worst results because of its poor generalization ability when working on a small dataset with many attributes that leads to a complex

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network that overfits the data, while having support vector machine better than artificial neural network in our case due to its adaptability with high-dimensional data [2].

3 Basic Concepts (A) Overfitting: Instead of the inherent relation, a mathematical formalism of overfitting represents the random error. A overfit happens because the configuration becomes too complicated, as in the number of sightings and many parameters. An overfitted model has low predictive efficiency because it responds with small variations within the training results. (B) Tree pruning: Pruning is a method which automatically resizes the decision trees through eliminating portions of the tree that really have no potential for particular grouping. Pruning minimizes the difficulty of overall classifiers, thereby increasing statistical precision while decreasing overfitting [3]. (C) Tree smoothing: It helps to address the strong divergences at the leaves induced all through the pruning among neighboring static models. (D) Weka Tool: Data processing applications for clustering or regression analysis of datasets: This is open-source software. Methodologies mentioned here are performed using this device [4].

4 Algorithm Description (A) M5P Algorithm: The M5 algorithm generates a regression tree by testing repetitively dividing the dataset into a single variable [5, 6, 7], which decreases ambiguity about the dependent (target) variable (Fig. 1).

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Fig. 1 M5P flowchart

(B) Random Forest Algorithm: The algorithm of Breiman is commonly used to apply the RF technique [8]. The measures discussed below are assessed in the construction of an RF method for effort [9–11]. These phases help to construct any tree with the random forest method. (1)

S denotes total the number of trees and dataset of R points (h1, g1) (h2, g2). (hR, gR) is considered. (2) Repeat the steps from 3 to create a T number of trees. (3) T represents the number of training cases, and V refers to the number of variables in the classifier. (4) Split the dataset as training data and testing set. Training data is represented as the number of variables in the classifier. (5) To the loaded data and RF tree, Zq is built by repeatedly rehabilitating the corresponding phases for each terminal nodes of the tree until the minimum node size n minimum. The identifiable dataset is generated for each tree. (6) To evaluate the preference at a tree node, the no. of input variables v is chosen. (7) For every tree, randomly choose the number of input variables. (8) Within the training, the set is determined as the best division based on these v variables. The value of m should be kept constant all over the forest’s growth. Almost every tree must be expanded to the maximum and not pruned. 9) Therefore, the outcomes are obtained from a group of trees J1, J2, …, Jq. (10) For every one of the trees in the forest, the input factor must be set aside.

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5 Experiments and Results (A) Dataset Collection Barada Prasanna Acharya [12] using the proposed solution obtained datasets containing 149 UCP-based results. The datasets have been taken from different separate Web sites, fifty datasets taken from the ISBSG [13], and thirty-four datasets have been taken from medium-sized organization and remaining taken from Western University. In the dataset, the initial column reveals the software dimension. The remaining columns represent the rate of productivity, project’s complexity, and actual effort, respectively [14, 15]. (B) Results See Figs. 2, 3, 4, and 5. (C) Performance Analysis     prediction accuracy PRED = 1 − (ei − f i ) s ∗ 100% where ei actual effort f i predicted effort s number of projects in the dataset PRED for random forest = 97.52% and PRED for M5P = 98.5%

Fig. 2 M5P output in Weka tool

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Fig. 3 Random forest output in Weka tool

Fig. 4 M5P actual effort versus predicted effort chart

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Fig. 5 Random forest actual effort versus predicted effort chart

6 Conclusion In this paper, among different estimation techniques, we choose M5P and random forest methods. M5P technique performed better than the RF method and also eliminates overfitting. Hence, it improves predictive accuracy by the reduction in overfitting. M5P models a single tree and predicts the output; whereas, random forest creates many parallel trees and averages its output. In the future, M5P can be contrasted with other approximate effort techniques and to analyze a generalization of results and apply M5P to software project datasets from various domains.

References 1. B. Heung, H.C. Ho, J. Zhang, A. Knudby, C.E. Bulmer, M.G. Schmidt, An overview and comparison of the machine- learning techniques for classification purposes in digital soil mapping. Geoderma 265, 62–77 (2016) 2. C. Lopez-Martina, A. Abran, Neural networks for predicting the duration of new software projects. J. Syst. Softw. 101, 127–135 (2015) 3. K. B. Shaban, A. Kadri, E. Rezk, Urban air pollution monitoring system with forecasting models. IEEE Sens. 6, 101–115 (2016) 4. A. Panda, S.M. Satapathy, S.K. Rath, Empirical validation of neural network models for agile software effort estimation based on story points. Procedia Comput. Sci. 57, 772–781 (2015)

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5. F. Sarro, A. Petrozziello, M. Harman, Multi-objective software effort estimation, in 2016 IEEE/ACM38th International Conference on Software Engineering (ICSE) (IEEE, 2016), pp. 619–630 6. P. Rijwani, S. Jain, Enhanced software effort estimation using multi layered feed forward artificial neural network technique. Procedia Comput. Sci. 89, 307–312 (2016) 7. A. Idri, M. Hosni, A. Abran, Improved estimation of software development effort using classical and fuzzy analogy ensembles. Appl. Soft Comput. 49, 990–1019 (2016) 8. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001) 9. P. Pospieszny, B. Czarnacka-Chrobo, A. Kobylinski, An effective approach for software project effort and duration estimation with machine learning algorithms. J. Syst. Softw. 137, 184–196 (2018) 10. P. Phannachitta, K. Matsumoto, Model-based software effort estimation—a robust comparison of 14 algorithms widely used in the data science community. Int. J. Innov. Comput. Inf. Control 15, 569–589 (2019) 11. H. Mustapha, N. Abdelwahed, Investigating the use of random forest in software effort estimation. Procedia Comput. Sci. 148, 343–352 (2019) 12. S.M. Satapathy, B.P. Acharya, S.K. Rath, Early-stage software effort estimation using random forest technique based on use case points. Research Article. Instrum. Eng. Technol. J. 18, 63–70 (2017) 13. O. Hidmi, B.E. Sakar, Software development effort estimation using ensemble machine learning. Int. J. Comput. Commun. Instrum. Eng. 4(1), 143–147 (2017) 14. L.L. Minku, X. Yao, Ensembles and locality: Insight on improving software effort estimation. Inf. Softw. Technol. 55, 1512–1528 (2013) 15. ISBSG: The international software benchmarking standards group, http://www.isbsg.org (2011)

Dr. V. Vignaraj Ananth is Assistant Professor, Computer Science and Engineering in Thiagarajar College of Engineering, Madurai, Tamil Nadu, India. His research interests in Software Effort Estimation, Internet of Things, and Data Analytics. He has published Research Article in 15 International Journals and 11 International Conferences.

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S. Aditya is an undergraduate student, Computer Science and Engineering in Thiagarajar College of Engineering, Madurai, Tamil Nadu, India. His research area includes Machine Learning and Internet of Things. He has published Articles in 04 International Journals and 02 International Conferences.

Innovative Technology-Based Social Enterprises for Inclusive Sustainable Healthcare in India Pradnya Vishwas Chitrao, Pravin Kumar Bhoyar, and Rajiv Divekar

Abstract India is witnessing rapid progress in the twenty-first century. The progress unfortunately is very uneven and non-inclusive. Many people are below the poverty line and often have to do without sufficient food. Many children do get the benefit of primary education. Girls and women do not get adequate nutrition or equal opportunities of education and employment. In healthcare, in particular, we see a major disparity in the facilities available to different sections of society in India. A privileged few get access to sophisticated, advanced healthcare facilities while the poor in urban slum areas and especially in rural India either have no access to good quality basic healthcare or cannot afford them. A lot of social enterprises in India are using smart innovative technology to solve this problem of making available quality healthcare to the urban poor and the needy rural in India at affordable rates. Objective The paper studies two social enterprises, the first being AAarogyaVedh Healthcare InfoTech Pvt. Ltd. which provides cardiopulmonary resuscitation (CPR) and the second being a family owned social enterprise that offers a juggad ambulance cum medical unit called AmbuPod that uses innovative technology to make available quality and economical healthcare in a timely manner. Research Methodology The researchers have conducted personal interviews by way of primary research. They have also availed of secondary resources. Significance of the Study The study will encourage enterprises to use technology in order to provide for high grade healthcare available to everyone in India. It will inspire decision makers, NGOs, and business entities to help such social enterprises. The study also brings out the fact that these social enterprises not only provide crucial services like cost-effective measures for creating physical, mental, or emotional well-being (health care), but also create job opportunities.

P. V. Chitrao (B) Symbiosis Institute of Management Studies (SIMS), Constituent of Symbiosis International University, Pune, India e-mail: [email protected] P. K. Bhoyar · R. Divekar SIMS, Pune, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_6

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Keywords Social enterprises · Innovative technology · Cost-effective healthcare · Job opportunities

1 Introduction India has the distinction of having the second largest population in the world. It is also has the fifth largest GDP. But around three fifths of Indian population is rural [4]. India faces a lot of issues in terms of development which are related to major areas like healthcare, employment, water, sanitation, clean environment, and means of livelihood. The economic growth being witnessed in India does not include many people who nonetheless pay for this growth [1]. On the one hand, life expectancy in India has increased by around ten years. But, on the other hand, infant mortality in India in 2015 was 47.7 per 1000 live births [14]. In June 2019, the infant mortality rate has gone down to 36.7 per thousand births, but India’s rank in the world is still 52nd worldwide in this respect [7]. Hospitals in India, especially government ones, are overcrowded; consequently, people do not get proper treatment. Good quality and cost-effective healthcare facilities in semi-urban and rural regions are rare, and if available are very costly as a result of which healthy life is enjoyed by very few. Often, cultural and social rules restrict usually women from receiving quality health care administered by male doctors. Social entrepreneurship pinpoints a major societal concern and then utilizes entrepreneurial parameters to begin and operate a business for social benefit that will bring about a positive difference in society. Many social entrepreneurs in India thought of novel ideas to resolve social issues in a sustainable manner and thus proved to be great value creators for society. They used disruptive technological innovations to bring about quality healthcare that can be afforded by all. Technology today helps doctors give consultations through video sessions to persons facing health issues in rural areas that face chronic low network and makes available cost-effective lifesaving gases for treatments and techniques performed in hospitals in an emergency in order to support life after the failure of one or more vital organs. Social entrepreneurship is the buzz word in India in the twenty-first century as the country is attempting to attain an evenly spaced out economic growth across all sections of in hospitals society as against a mere growing GDP statistic. It attempts to tackle various issues like education, quality health care for all, climate changes, and energy efficiency. Social enterprises’ work in the field of healthcare related services is especially important. There are no hospitals in many rural parts of India or for that matter even ambulances. The urban poor also often cannot pay for these facilities. Precious time is spent in an attempt to reach the person requiring immediate therapeutic intervention to the medical center. Some exurban areas even lack good road connectivity. These sections of society consequently do not get proper treatment for what are seen as standard cough and cold, and resort to grandmother’s cures. In remote backward areas, pregnant women, who are about to deliver, often are dependent on a midwife who may occasionally be caught up in an emergency at

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some other place and therefore may not be in a position to reach the woman who has gone into labor on time. As a matter of fact, reaching medical services to India’s remote areas is a major problem [12]. Women who migrate to metro cities are often afraid of approaching a doctor on account of cultural up bringing or sheer lack of knowledge of from where to get the required information. In the case of cardiopulmonary resuscitation (CPR), many Indians do not even know about it. Often, CPR is used as an excuse or a prank to have mouth-to-mouth breathing, and thus introduce humor or romance in films. While mouth-to-mouth breathing is often mistaken to be CPR, it is actually not as effective as putting hard pressure on the chest. CPR is a scientifically accepted medical intervention and can be conducted with ease and efficacy through imparting of basic training, until medical help arrives. The physical differences between a man and woman deter men from administering CPR on women.

2 Literature Review India is going through a very uneven growth phase and is facing many developmental issues like poor healthcare, unemployment and poverty on account of very fat population growth and the consequent environmental and social needs created by limited resources. Despite the immense growth of industries and service sectors postIndependence, India is still basically an agriculture-based economy [10]. Poverty forces people to live in surroundings which do not have decent shelter, potable water or proper sanitation [13] and which attract illnesses. Rapid unsystematic and noncatered for development and rigid adherence to unconventional business processes to generate business has caused the deterioration of the current physical fitness care frame work in rural and semi-pastoral areas besides major cities [9]. Women in India have a tendency of ignoring their ailments because either they have no time to spend a lot of time waiting in clinics or because do not like being examined and exposed to male doctors and attendants in clinics or because they are worried that their illness will become known to someone who in turn may gossip about it in their social circles. They therefore unnecessarily put up with a lot of discomfort and run the danger of their illness getting aggravated.

2.1 Public Healthcare Facilities The government-run health services in India are bad and not quality driven. Government basically has very inadequate funding per person which in turn leads to hiring of substandard healthcare experts and infrastructure. The poor have to rely on such government facilities especially if they have no medical insurance. At this stage, India cannot cater for the citizens’ social insurance that will facilitate access to

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quality public as well as private healthcare facilities to the poor. The law guarantees every individual access to quality healthcare. But approximately, 60% of the population in India cannot use this right on account of various reasons like absence of good facilities, too less number of qualified healthcare personnel, and unequal access to primary drugs and medical amenities. Government is carrying out many schemes which are not useful as they are not implemented properly.

2.2 Private Healthcare On account of government healthcare systems being extremely substandard in terms of quality, many Indians take recourse to private medical attention. In India, around 92% of the time, people opt for private medical services. In India, about 70% of the population is from the country side. But, personal medical treatment is costly and is often not checked in terms of adherence to certain accepted national or international standards; its quality also varies. Uneducated and poor rural folks cannot afford it nor rely on it. Good quality health care is often not availed by women who migrate to cities on account of lack of reliable sources of information. Cardiopulmonary resuscitation (CPR) is a technique used to save lives and is used in emergencies where a person stops breathing in situations where the patient is almost drowning or suffers a heart attack. CPR combines chest compressions and mouth-to-mouth rescue breathing. Both these actions facilitate the flow of oxygenated blood to the brain and other vital organs until the patient gets professional medical treatment. But in India, many a times no trained person or a paramedic is nearby to administer this life-saving technique. Precious moments are lost by the time a person can be reached to a hospital. Some times in rural areas, either there is no hospital nearby or no ambulance is either available or cannot reach the patient through the narrow pathways. Many a times, the family cannot even afford an ambulance. Also, even if a trained person is available on the spot, if the patient is a female, social norms prevent this life-saving first aid being administered.

2.3 Social Enterprises and Technology for Better Healthcare “Social entrepreneurship” is the twenty-first-century global phenomenon that shows hope of bridging the ever widening difference between the pressure for fulfilling social and habitat requirements and the correspondent reservoir of means. Entrepreneurship that results in economic growth is today attracting academic attention. However, it is only lately that social enterprises are being considered as a stepping stone to social progress [11]. Social entrepreneurship is viewed as the solution to the market and government’s inability to fulfill the requirements of a given community [15]. Social entrepreneurs in the twenty-first century are considered today as instruments of transformation in society; these persons use systematic solutions to

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issues in society, and thereby try to change things in society in a useful manner [6]. Many of them employ innovative technology to make their enterprise a workable business model. Technology can bring about such differences. A lot of enterprises and nongovernment organizations are utilizing mobile technology for aiding government lessen the load on the government health services. India has more than 900 million people who use mobile phones. Today at a global level, the mobile cellular subscriptions have reached the 6.8 billion mark (Union IT 2013), which has helped technology in covering up the communication loopholes and increasing globally growth in economy and progress [2]. Cell phones in India are popularly used because they charge less fee rates. Studies conducted only a short period earlier indicate that the expenditure on mobile phone bills in Indian rural families is around Rs. 5 per month whereas in the city for poor families is around Rs. 37 on a monthly basis (Department of Health and Family Welfare, Govt. of India MCTS, 2013). Research has indicated that m-Health mediations and prescriptions are in vogue both in southern India (Shet et al. 2010) as also in the country areas in northern parts of India [8]. Telemedicine and mobile health clinics thus are the new innovative and adaptable solutions that can answer India’s rural healthcare issues. They are not mere “helplines.” It is a fact that telemedicine provides the basic technology infrastructure that will bring rural areas the benefits of medical consultancy, diagnostic services and treatment guidelines through Internet and telephone connections. These centers employ teleconferencing technology and remote diagnostic devices for understanding a patient’s basic parameters and conveying it to a doctor in an urban center for diagnosis. The doctor then prescribes treatment that is supervised by the local primary care worker in the rural region. The technology is an effective answer to the healthcare issue of a lack of trained doctors in India’s rural areas. Most trained practitioners are usually trained in the cities and therefore do not wish to relocate to rural areas on account of an absence of well-paid jobs and well-equipped hospitals in rural areas In the case of emergency first aid, wherein patient needs artificial resuscitation, the first 5 to 6 min are critical for the patient’s recovery. Globally, only 30% people are saved, and in India, the number is less than 1%. This is because even in metropolitan areas, the response time of an ambulance is forty minutes. As a result, people suffering from cardiac arrest do not get timely medical aid. Cardiac arrest is basically “electrical disturbance which causes the heart to stop beating.” The patient collapses, becomes unresponsive, and usually stops breathing. This deprives vital organs of blood flow and hence of oxygen. This can cause severe and irreversible damage to brain in nine minutes. As a result, there is death in 90% of cases in out-of-hospital settings. In fact, cardiac arrest is the most important cause of sudden cardiac death. The answer to this is giving mouth-to-mouth resuscitation (or cardiopulmonary resuscitation, i.e., CPR). CPR is “a procedure that is performed in an emergency when the heart ceases to beat” [5]. It consists of 100–120 chest compressions/min of 5–6 cm depth each. The chest compressions: breaths ratio is 30:2. Chances of

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survival improve by 2–3 times when CPR is provided within 4–6 min after cardiac arrest. There is low level of public awareness of CPR because of scarcity of trained healthcare workers (nursing/paramedic staff), and poor doctor-to-patient ratio. At present, it is 1:1700. Hence, more than 99% patients do not receive appropriate CPR especially in rural areas. This is because of underdeveloped emergency response system. Medical assistance arrives extremely late, and so mortality increases by 10% with each passing minute. Consequently, there is poor survival rate. There is also no mechanical CPR-providing device currently available in India which is designed specifically to fulfill the requirements in Indian setup. There is therefore a need to have cost efficient life device that is easy to use and which is available in all prominent public places. Ultimately, technological innovations are helping promote quality healthcare at affordable rates. Steven Steinhubl, Director (Digital Medicine) of Scripps Translational Science Institute, has accurately predicted that healthcare delivery in future is going to undergo a massive transformation on account of the convergence of genomics, digital devices and precision medicine [3]. Most villagers in India, who suffer from heart disease, do not have access to consistent and quality medical facilities. The George Institute for Global Health has initiated in India low cost-effective strategies that will provide good medical care for people. It uses sophisticated wireless technologies including mobile phones for giving medical care that facilitate the healthcare provider in giving case specific computer-based medical decision support for generating the Systematic Medical Appraisal Referral and Treatment (SMART) of a patient in that area. Every rural area in India is given an Accredited Social Health Activist or ASHA, who is usually an experienced female appointed to operate in her own milieu. She is thus not an unknown person and is in fact someone whom the villagers trust; she is usually expected to have cleared junior college. The group has started this system in over fifty villages in Andhra Pradesh, and given training to ASHAs to find out persons with heart ailments, and to provide the required medical care. A good healthcare system allows sufficient universal access that will not overload the system. This is because the monetary costs for making available correct healthcare and consistent improvement is periodically provided. Its prominent features include competence training, accountability, good quality medical facilities, and profitable, efficient utilization of the latest pertinent investigation. It serves vulnerable sections of society like kids, women, disabled and the elderly.

3 Objective The paper tries to understand how a social enterprise can use innovative technology to make feasible reasonably priced good healthcare to poor and to villagers as also provide emergency CPR in small towns, rural areas, as well as cities. It also tries to find out how such an organization can train and give good jobs to people.

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4 Research Methodology The researchers interviewed two business owners. The first enterprise is AAarogyaVedh Healthcare InfoTech Pvt. Ltd. Healthcare Info Tech Pvt. Ltd. The second was the co-owner and co-founder of Lynkambupod Pvt. Ltd, namely Ms. Yamini Lavanian. They also availed of secondary sources of information.

4.1 Genesis of AarogyaVedh Healthcare InfoTech Pvt. Ltd. AarogyaVedh Healthcare InfoTech Pvt. Ltd. Healthcare Info Tech Pvt. Ltd. Was started in April 2019 by Mr. Shreekar Mangeshkar and Dr. Akshara Jogi. Mr. Mangeshkar with qualifications in postgraduation certificate in physics and nuclear technology and Dr. Akshara Jogi with a medical degree and an M.Sc. in molecular neuroscience found that unlike in UK, hardly any attention is paid to healthcare subcenters, and more attention is paid to metropolitan city centers. The two first focused on cardiac arrest cases. They found that while globally only 30% people are saved after a cardiac arrest, in India, the figure is less than 1%. This is because no one helps the patient especially if it is a case of road accident. In metropolitan cities, the average response time of any ambulance service is around forty minutes. But in cardiac arrest, first aid has to be given in the first nine minutes. Also, in India, CPR is given manually and requires patience and strength. So they first have developed an automatic cardiopulmonary resuscitation (CPR) integrated with automated external defibrillator (AED).

4.2 Designing the CPR Machine The founders saw that in rural areas, people are not aware of medical procedures. They think that the doctor, who is giving CPR manually, is killing the patient, and they often beat up the person. But their perception toward machines is more positive. So they developed an automated CPR. To check the response time of the ambulance to the cardiac arrest, they developed the emergency procedure which will intimate the nearest police station, fire brigade, and emergency medical hospital capable of handling CPR. They plan to install these CPR machines in medical boxes at any public place in malls, retail shops, petrol pumps, and even cars (they are in discussion with automobile manufacturers). Here, only a push of a button will inform all emergency facilities nearby. One just has to put the CPR machine on the patient, and the machine will work on its own. So any school going child or even a minimally literate person can operate it without any special knowledge. In CPR, one has to put pressure on chest surface area corresponding to lower half of the sternum (breast bone). So the founders of the

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enterprise designed the machine in such a way that it will fit on a person of any age, gender, weight, and will work in all weather conditions. The process of international patenting takes around five to six years. For Indian patent, one first has to file a provisional patent wherein one checks the patentability of one’s idea within a period of one month. If this yields positive results, then one gets a year’s time frame in which one can develop one’s product or making changes in it. Then one can file for a long-term patent after giving the feasibility as well as financial feasibility reports. After this, one can apply for international patent wherein the International Committee checks and validates the idea and then gives the recognition certificate.

4.3 Business Model and Differentiating Features From January 2019, the two owners have so far invested around Rs. 2–3 lakhs. For background research, they took around four years. There are thirty other similar devices which have existed in markets globally as of now out of which there are only four in production and all are only sold in USA. These machines are calibrated for heart problems over there. The owners have tie-ups with different doctors and so, on the basis of the feedback from them, they prepared a device that caters to Indian requirements. They have priced their device at Rs. 30,000/– whereas all other devices cost between Rs. 3 lakhs to Rs. 7 lakhs. Also, the other devices require moving the patient around as also the presence of three para medics to fit the machine on to the patient. This therefore takes around 2–3 min of the crucial time frame of first aid to a cardiac arrest patient. Also, the two entrepreneurs have come up with different kinds of pneumatic pumps and lithium batteries integrated with portable solar cells for powering their own CPR device. The portable cells are also available in the market and in IISER, Pune, Mumbai, Delhi, as also in IISC, Bangalore. So these batteries are also developed in markets in India and cost between Rs. 4000/– to Rs. 10,000/– per battery. But if mass production of these batteries happens, the cost will go down to around Rs. 3000/– per battery. The product right now is in the cardio care category but will soon be under basic life support, i.e., holistic emergency response system. A lay person with minimal training will be in a position to use the CPR device. They are also planning to tie up with a rural institute under the government scheme of earn and learn and offer training with certification and employment with respect to assembling of the medical instruments. The owners plan to focus in future on heart casualties and neurocasualties and different scanning devices like MRI, CT scan, and PED Scan. A CT scan machine costs around Rs. 70 lakhs and so many people find it too costly. So they will focus on the gray areas where people do not get proper treatment. The machine should ideally be available in sub centers where first aid is expected to be given.

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4.4 Strengths/Merits of the CPR Device • Easy to use with very basic training imparted to even lay persons. • Cheaper than all available devices. • Time taken to fit it on to the patient is Threshold(S1 ) 7.1. THEN GO TO 10 7.2. Else GO TO 7

8.

IF V2 > Threshold(S2 ) 8.1. THEN GO TO 10 8.2. Else GO TO 7

9.

IF V3 > Threshold(S3 ) 9.1. THEN GO TO 10 9.2. Else GO TO 7

10. SEND EMAIL ALERT TO USERS 11. IF power is ON 11.1. THEN GO TO 2. else 12 12. Device is in OFF State 13. Stop

5 Implementation The sensors such as MQ135, MQ7, and MQ8 are connected with the Arduino Uno kit. The sensors sense the concentration of the harmful gases. The output of the sensors is an analog voltage in volts. The Arduino Uno will convert the analog voltage into

A Low-Cost IoT-Enabled Device for Real-Time Air Quality … Fig. 3 Flowchart for IoT-enabled air quality monitoring system

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Fig. 4 Hardware implementation of the air quality monitoring system

digital data. The ESP8266 is a Wi-Fi module which is attached to the Arduino Uno kit to transmit the sensor data to the cloud. The Twilio [18] cloud communication platform acts as a service for sending SMS alerts to the users. Figure 4 shows the hardware implementation for monitoring the quality of air.

6 Results The data from the Wi-Fi channel is stored in the Cloud. Users should connect to the cloud to retrieve the data from the cloud. The sensor data is analyzed as a result, and a datasheet is being generated. Figures 5 and 6 show the graphical representation, and Fig. 7 shows the datasheet obtained from the ThingSpeak. The sensor data receiving from the sensors is represented in graphical form by using the ThingSpeak. If the values from all 3 sensors are optimal, then the quality of air in the atmosphere is good.

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Fig. 5 Real-time data analysis of the MQ-135 and MQ-8 sensors

Fig. 6 Real-time data analysis of the MQ-7 sensor

7 Conclusion The proposed system, IoT-enabled air quality monitoring system is successfully implemented and checked in various areas. The integration of the system with the cloud service ThingSpeak makes the system smart for doing the alert process fast, cost-effective, real time, and flexible. ThingSspeak is used to evaluate the observed stream data from the sensors. AT commands of ESP8266 which is used to send data to ThingSpeak are used for client and server communication, to set up the TCP/UDP connection with the host and port, to find the status of the connection, for setting the length of the data, an API key for sending the data to ThingSpeak channel, to set the Wi-Fi mode, to enable multiple connection, and to close the TCP/UDP connection. This implemented system sends email message alerts to the users depending on the threshold level. It also stores large amounts of data in the cloud platform. The device can serve many users who are living near industrial areas and in high traffic areas. The proposed system will monitor, measure, and alert the amount of harmful gases in the air, thereby saving mankind and the next generation. The proposed system is easily portable and scalable to any kind of monitoring system with small modifications.

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Fig. 7 ThingSpeak datasheet for the 3 sensors connected to system

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In the future, it is better to add artificial intelligence for predicting the air quality using optimal machine learning algorithms or neural networks or rule-based expert systems. Thus, we are able to diagnose, forecast, plan, and control the air pollution especially in metropolitan cities

References 1. J. Kang, K.I. Hwang, A comprehensive real-time indoor air-quality level indicator. Sustainability 8(9), 881 (2016) 2. S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode, Nath, B. (2013, August). Real-time air quality monitoring through mobile sensing in metropolitan areas, in Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing (pp. 1–8) 3. V. Laijawala, M., Masurkar, R. Khandekar, Air Quality Monitoring System (2019). Available at SSRN 3454389 4. C.V. Saikumar, M. Reji, P.C. Kishoreraja, IOT based air quality monitoring system. Int. J. Pure Appl. Math. 117(9), 53–55 (2017) 5. S. Kumar, A. Jasuja, Air quality monitoring system based on IoT using Raspberry Pi, in 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE (2017) 6. G.B. Fioccola, R. Sommese, I. Tufano, R. Canonico, G. Ventre, Polluino: an efficient cloudbased management of IoT devices for air quality monitoring, in 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). IEEE (2016, September), (pp. 1–6) 7. M.F.M. Firdhous, B.H. Sudantha, P.M. Karunaratne, IoT enabled proactive indoor air quality monitoring systems for sustainable health management, in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). IEEE (2017, February), (pp. 216– 221) 8. T.H. Nasution, M.A. Muchtar, A. Simon, Designing an IoT-based air quality monitoring system, in IOP Conference Series: Materials Science and Engineering (IOP Publishing, vol. 648, No. 1, 2019, October), p. 012037 9. K. Zheng, S. Zhao, Z. Yang, X. Xiong, W. Xiang, Design and implementation of LPWA-based air quality monitoring system. IEEEAccess, 4, 3238–3245 (2016) 10. https://www.olimex.com/Products/Components/Sensors/Gas/SNS-MQ135/resources/SNSMQ135.pdf 11. https://www.sparkfun.com/datasheets/Sensors/Biometric/MQ-7.pdf 12. https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MQ-8%20Ver1.3%20-%20Manual. pdf 13. https://www.mathworks.com/help/thingspeak/ 14. M. Benammar, A. Abdaoui, S.H. Ahmad, F. Touati, A. Kadri, A modular IoT platform for real-time indoor air quality monitoring. Sensors 18(2), 581 (2018) 15. H. Kumar, S. Karthikeyan, G.M. Manjunath, M.S. Rakshith, Solar power environmental air pollution & water quality monitoring system based on IoT. Pers. Commun. Embed. Syst. Sign. Process. PiCES 2(8), 197–199 (2018) 16. K.S.E. Phala, A. Kumar, G.P. Hancke, Air quality monitoring system based on ISO/IEC/IEEE 21451 standards. IEEE Sens. J. 16(12), 5037–5045 (2016) 17. C. Xiaojun, L. Xianpeng, X. Peng, IOT-based air pollution monitoring and forecasting system, in 2015 International Conference on Computer and Computational Sciences (ICCCS), Noida, (2015), pp. 257–260

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18. H.F. Hawari, A.A. Zainal, M.R. Ahmad, Development of real time internet of things (IoT) based air quality monitoring system. Indonesian J. Electr. Eng. Comput. Sci. (IJEECS) 13(3), 1039–1047 (2019)

Fuzzy-Based Predictive Analytics for Early Detection of Disease—A Machine Learning Approach V. Kakulapati , R. Sai Sandeep, V. Kranthi kumar, and R. Ramanjinailu

Abstract Influenza is significant promising and re-emerging contagious disease, causing high morbidity and mortality throughout the world. Influenza also called swine flu, hoard flu, pig flu. The primary task of healthcare providers is providing diagnostic product services at low costs and to diagnose patients accurately. The use of data analytics and machine learning is a breakthrough technology that can have a significant impact on the healthcare field. Machine learning methods can be used for disease identification because they mainly apply to data themselves and give priority to outcomes of specific tasks. In this work, a framework of classification and regression trees (CART) algorithm is to create the fuzzy rules to employ for improved disease prediction. Validate the proposed method by choosing public medical datasets. Results on swine flu data demonstrate that our method distinguished the enhancement of the disease prediction accuracy. The implementation results exhibit that the aggregation of two methods such as fuzzy rule-based CART algorithm with irrelevant data elimination methods that could be useful in predicting disease. The proposed system can be helpful for healthcare providers as a healthcare analytical technique. Keywords CART · Fuzzy · Prediction · Accuracy · Technique · Rules

V. Kakulapati (B) · R. Sai Sandeep · V. Kranthi kumar · R. Ramanjinailu Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India 501301 e-mail: [email protected] R. Sai Sandeep e-mail: [email protected] V. Kranthi kumar e-mail: [email protected] R. Ramanjinailu e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_9

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1 Introduction Influenza, also known as swine flu, is a communicable virus that affects pigs. This virus identified as a pandemic in 1919, and still, it is a seasonal virus though this virus cannot affect humans and become a worldwide outbreak in 2009. This flu is first started in pigs and caused by virus strain called as H1N1, and this virus is a grouping of swine, bird flu, and human inheritable factors that assortment collected in greedy pigs then transmit to the humans [1]. This H1N1 was identified as a usual type of seasonal flu. In the year 2009, nearly 62 million humans affected by swine flu and above 12.5 k people [2] died due to this pandemic—report by Centers for Disease Control and Prevention (CDC) and nearly 5.8 lakh people died due to this virus throughout the world [3]. Influenza also caused by the H1N1 virus, which also called Spanish flu and genetic factors, illustrates that this virus advanced from swine flu or bird flu. The number of people died due to the H1N1 pandemic throughout the world, and the fatality rate is higher in adults [4]. Analysis of electronic health records contains patient privacy information, which requires to be accurate for prediction, identification and diagnosis of the diseases. For accurate prediction, identification and diagnosis in an efficient manner, to investigate and practice suitable ML cataloguing algorithms that appropriately detect and diagnose disease. Nowadays, massive resolution in the area of ML methods due to the vast usage of computational techniques in the analysis of electronic health records precisely and efficiently. For better diagnosis methods, which help physicians appropriately diagnose the disease, they need to investigate reliable models to address the clinical trials. Many healthcare datasets require pre-processing before building a model as these contain noisy, duplicate and inappropriate data, which may reduce the accuracy of classification. The accuracy of disease prediction is based on the data and the classification models. One of the significant tasks in machine learning is classification [5–7] that retrieves facts from practical problems and develop a model, which appropriately predicts the target class. Fuzzy logic is a processing technique that compromises with inexactness and incorrect information. It is a procedure of the crisp rules wherein the certainty variables lie between 0 and 1. The logic applies to perception based on the degree of truth (1.0) or degree of false (0.0), and this logic utilized for completely imprecise perceptions. These fuzzy rules are used in healthcare applications for better accurate and efficient diagnosis. For identification of hidden and useful rules of observations by using a decision tree, fuzzy logic is used in healthcare application, which handles the diagnosis ambiguity and imprecision of symptoms [8]. In this work, propose a system for the early detection of swine flu, which uses CART and fuzzy logic for classification. CART uses an implicit feature section using different techniques, and fuzzy logic implemented for better classification and improved accuracy.

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2 Related Work Many researchers investigated the performance analysis between individual classifies and ensemble classifiers [9]. Automated decision-making systems [10] are useful to cooperating clinicians rapidly and diagnose enduring correctly within a short time. This automated system utilizes healthcare data to attain practical consequences. The health analysis method illustrates that how the cataloguing, regression tree, and random Forest models assimilated to form a hybrid intellectual classification is called as the fuzzy min-max network [11]. Intelligent swine flu prediction software [12] developed by utilizing naïve Bayes algorithm for categorizing the swine flu enduring into three types; those are the minimum possible, likely or maximum possible, and attained 63% accuracy. The combination of decision tree and naïve Bayes algorithms was employed on the identical inputs to discover the genuine suspicious cases and predict the credible investigation of a swine flu from the suspicious zone. Appropriate suspicious cases, naïve Bayes correctly classified when compared with decision trees [13]. In [14], naïve Bayes classifiers were detecting swine flu ailment from its warning signs and exploit likely consequences. Heart disease [15] prediction using fuzzy rules and blurring was acquainted with data to remove uncertainties, and a membership function has been adding. Classification and regression tree are a parametric classifier and more efficiently classified than other parametric methods and consequently, removing the uncertainty in the data by applying a fuzzy membership function. In [16], an expert system based on fuzzy rule is developing for asthma-related ailment treatment. In another method, a proficient method is with a combination of different classification techniques such as neural network (nn), C5.0 DT (decision tree) and linear discriminate analysis (LDA) for the cataloguing of dermatology illness [17].

3 Framework See Fig. 1.

3.1 Fuzzy Logic It provides an induction morphology that empowers human intelligence abilities to apply to decision support systems and is based on mathematical calculation for better recognition of the uncertainties related to human intelligence and perceptive.

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Fig. 1 Proposed framework of fuzzy-based classification and regression prediction of swine flu

Membership function (MF) is a curvature which defines in what manner every argument in the contribution recorded to a degree of membership between the values 0 and 1 [18].

3.2 Fuzzification The lexical variables of the uncertainty intended in the procedure of uncertain sets and where these variables are described as membership functions. The estimation of this method of the degree of fit in fuzzy crisp rules is known as fuzzification. The participation capacities might be triangular, trapezoidal, Gaussian, or ringer moulded. As data about the level of the participation utilized for further development, a significant amount of data may get failure in the process of fuzzification. This is happened due to the method can perceive as a nonlinear revolution of the data. The interpretation smears the aggregation regulations, utilizing the knowledge base and thus membership functions to the terms of yield factors are determined, lastly, after defuzzification, the yield result is acquired.

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3.3 CART It is one of the analytical systems used in machine learning and describes how a destination variable can predict from other values. Consequently, the decision tree where every node is a split in an interpreter variable and every node at the termination has an estimation for the destination variable. In this instance, the swine flu test dataset is classified appropriately.

3.4 Rule Induction A set of IF-THEN rules represented as a learned model is called as rule induction, which rule antecedent—which is the If part, and rule consequent—which is the else part. The former contains attribute tests, and the consequent contains a class prediction.

3.5 Membership Function The fuzzy membership function is an interpretation of the pointer function in typical sets. On account of triangular or trapezoidal membership, data lost in the provinces of membership where the zero slope and the membership functions are not differentiable at these points. Consequently, systems having triangular or trapezoidal membership functions can come across difficulties of knowledge from information. To overcome these problems, smoother membership functions such as Gaussian or bell function may use.

3.6 Inference The fuzzy input is changed into the fuzzy output with help of If-Then rules.

4 Implementation For implementation purposes, the swine flu dataset was chosen from the UCI machinery database and then, pre-processing the dataset for the removal of unnecessary data and irrelevant data and then splitting this dataset into training and test datasets. Generated fuzzy crisp rules intend with the decision tree from which the regulations can comprehend. Selecting random samples from the given dataset then

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applying the CART algorithm to generate decision tree for each sample, which can produce predicting results. In each step of algorithm, voting is accomplishment for each projected consequence, and the maximum voted projected consequences as the concluding prediction outcome. Generate the frbs framework by training the model, and the attributes prepared with the editor of membership function in the fuzzy editor. For easy to understand and widely used in the different application membership function is Triangular. The CART rules are taken in the regulation editor to methods the fuzzy rule base, and these utilized for exhibiting the consequences. The regulation observer makes the defuzzied yield, which can easily understand whether the persistent is affected with swine flu or not. Statistics and Confusion Matrix Prediction 1 2 1 133 2 2 1 80 Accurateness: 0.9861 95% CI: (0.9599, 0.9971) No Information Rate: 0.6204 Kappa: 0.9704 Mcnemar’s Test P-Value: 1 Sensitivity: 0.9925 Specificity: 0.9756 Pos PredValue: 0.9852 Neg PredValue: 0.9877 Prevalence: 0.6204 Recognition Rate: 0.6157 Recognition Incidence: 0.6250 Stability Accurateness: 0.9841 “Positive” Class: 1 (Table 1, Fig. 2). The lexical terms on the contribution variables: [1] “small” “medium” “large” “small” “medium” “large” “small” [8] “medium” “large” “small” “medium” “large”. Membership function parameter values on the contribution variable: Small medium large small medium large small medium large small medium large [1,]

2.0

4.00

3.0

2.0

4.00

3.0

2.0

4.00

3.0

2.0

4.00

3.0

[2,]

0.0

0.23

0.6

0.0

0.23

0.6

0.0

0.23

0.6

0.0

0.23

0.6

[3,]

0.2

0.43

0.8

0.2

0.43

0.8

0.2

0.43

0.8

0.2

0.43

0.8

[4,]

0.4

0.53

1.0

0.4

0.53

1.0

0.4

0.53

1.0

0.4

0.53

1.0

[5,]

NA

0.73

NA

NA

0.73

NA

NA

0.73

NA

NA

0.73

NA

40 16 8 15

Runny nose ≥ 1.5

Headache < 3.5

Headache ≥ 3.5

Headache ≥ 3.5

Headache < 3.5

3

4

5

6

7

* denotes prolong i.e., continuation

24

Runny nose < 1.5

2

25

64

Root

1

n

Split

S. No.

Table 1 Probabilities of swine flu symptoms

0

7

0

0

8

8

24

Loss

2

1

2

1

2

1

2

yval

yprob

(0.0000000 1.0000000)*

(0.5333333 0.4666667)*

(0.0000000 1.0000000)*

(1.0000000 0.0000000)*

(0.2000000 0.8000000)

(0.6666667 0.3333333)

(0.3750000 0.6250000)

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Fig. 2 Classification tree of swine flu symptoms

The number of linguistic terms on each variable Chill Runny1se Headache Fever Swine flu [1,]

3

3

3

3

2

The fuzzy IF-THEN rules: V1

V2 V3

V4 V5 V6 V7

V8V9

V10 V11

V12 V13

V14 V15

V16

1 IF Chill is small and Runny1se is large and Headache is medium and Fever is large 2 IF Chill is large and Runny1se is large and Headache is small and Fever is small 3 IF Chill is large and Runny1se is large and Headache is medium and Fever is large 4 IF Chill is large and Runny1se is small and Headache is medium and Fever is large 5 IF Chill is small and Runny1se is large and Headache is large and Fever is small 6 IF Chill is large and Runny1se is small and Headache is large and Fever is large 7 IF Chill is small and Runny1se is small and Headache is small and Fever is small 8 IF Chill is small and Runny1se is large and Headache is large and Fever is large V17

1 THEN Swineflu is 2 THEN Swineflu is 3 THEN Swineflu is 4 THEN Swineflu is

V18 V19 V20

2 2 2 1

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Fig. 3 Symptoms of swine flu disease

5 THEN Swineflu is 6 THEN Swineflu is 7 THEN Swineflu is 8 THEN Swineflu is

1 2 1 2

The certainty factor: [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,]

1.25 1.25 1.25 0.75 0.75 1.25 0.75 1.25

Estimating the percentage of error percentage to distinguish the accurateness of the model. R > err < −100 * sum (pred! = real. swinflu/length(pred) R > err [1] 0 (accuracy = 100) (Figs. 3 and Fig. 4).

5 Performance Analysis The classifier performance is subject to features data to be analysed. The performance exhibits in the form of accuracy and the error rate. Several types of validation can implement for the evaluation of classification performance. In the previous study, implemented a random forest algorithm and attained 97% accuracy. In this work,

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Fig. 4 Plot of membership functions

utilize fuzzy rule-based classification and regression tree with 98% accuracy with 0 classification error rate. The proposed system is exhibited efficiently predicting the disease.

6 Conclusion In this work, swine flu ailment is cataloguing wherein the contribution features fuzzified utilizing a lexical membership function to hold imprecise and uncertainty data. The process of fuzzification makes the prototypical more complicated due to its fuzzy growth that receipts training the model in more consuming time which increased values of fuzzified are permitted to the model to retrieve the appropriate features that make a substantial input. The proposed model demonstrates the classification of swine flu disease prediction with 99% accuracy and 0 classification error rate, which concludes that fuzzy logic and CART together give an efficient disease prediction.

7 Future Work In the future, automation of swine flu prediction by using a large dataset and also develops an intelligent framework based on similarity measures of swine flu symptoms. Depending on the similarity measures of patients and location-wise categorize them by utilizing the clustering techniques. Predicting these swine flu patients and provide recommendations to avoid the disease using deep learning and AI techniques. In the future, develop new prediction techniques to enhance classification accuracy and efficiency of swine flu patient privacy and provide a personalized recommendation to the patient for better diagnosis by applying nature-inspired optimization techniques.

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References 1. T. Jilani et al., H1N1 influenza (swine flu). Stat Pearls. Updated December 14, 2019 2. Centers for Disease Control and Prevention. CDC Estimates of 2009 H1N1 Influenza Cases, Hospitalizations and Deaths in the United States 3. F. Dawood et al., Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. Lancet. Infect. Dis 12(9), 687–695 (2012). https://doi.org/10.1016/s1473-3099(12)70121-4 4. D. Jordan, The deadliest flu: the complete story of the discovery and reconstruction of the 1918 pandemic virus. Centers for Disease Control and Prevention. Updated December 17, 2019 5. H. Das, A.K. et al., A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification computational intelligence in data mining (vol. 2, Springer, New Delhi, 2015), pp. 461–471 6. R. Sahani et al., Classification of intrusion detection using data mining techniques Progress in computing, analytics and networking (Springer, Singapore, 2018), pp. 753–764 7. H. Das et al., Classification of diabetes mellitus disease (DMD): a data mining (DM) approach Progress in computing, analytics and networking (Springer, Singapore, 2018), pp. 539–549 8. V. Khatibi, et al., A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Expert Syst. Appl. 37, 8536–8542 (2010) 9. C.-H. Weng et al., Disease prediction with different types of neural network classifiers. Telematics Inform. 33(2), 277–292 (2016) 10. J. Soni et al., Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011) 11. C.L. Manjeevan Seera, A hybrid intelligent system for medical data classification. Expert Syst. Appl. 41(5), 2239–2249 (2014) 12. A. Ankita et al., Naïve Bayes classifier for prediction of swine flu disease. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(4), 120–123 (2015) 13. M.J. Mangesh et al., Comparative study of decision tree algorithm and Naive Bayes classifier for swine flu prediction. Int. J. Res. Eng. Technol. 4(6), 45–50 (2015) 14. B.A. Thakkar et al., Health care decision support system for swine flu prediction using naïve bayes classifier, in 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 101–105 15. S. Suganya et al., A proficient heart disease prediction method using Fuzzy-Cart algorithm. Int. J. Sci. Eng. Appl. Sci. (IJSEAS) 2(1) (2016). ISSN: 2395-3470 16. Z.H.M. Fazel et al., A fuzzy rule based expert system for diagnosing asthma. Trans. E Ind. Eng. 17(2), 129–142 (2010) 17. B.V. Ramana et al., A critical study of selected classification algorithms for liver disease diagnosis. Int. J. Database Manag. Syst. (IJDMS) 3(2) (2011) 18. Y.H. Joo et al., Fuzzy systems modelling: an introduction. IGI Glob. Distrib. (2009)

A Framework for Remote Health Monitoring K. Viswavardhan Reddy and Navin Kumar

Abstract Technologies like sensor and cloud networks are evolving mainly due to their applications and importance. The main mechanism in such networks is the deployment of sensor nodes, collection of data, sending the data to cloud for storage purpose, processing, analyzing, and presenting the results. Nowadays, continuous monitoring of fluctuating vital health signals of a patient or person especially from remote has become essential. It is more important in the pandemic like COVID19 situation. Hence, there is a need to design, develop, and implement a device to perform such effective and efficient monitoring system. In this paper, we designed and developed a framework for smart health monitoring system (SHMS). In this system, hardware device embedded with various modules like electrocardiogram (ECG), electromyography (EMG), galvanic skin response (GSR), pulse rate, body position, and snore detection sensors are integrated. All these sensed vital signals are read by particle photon (PP) ARM cortex M3 microcontroller and uploaded to PP cloud. From the cloud, the data is exported into .csv file and imported into the developed e-Health web and mobile application. Keywords Sensors · Body area networks · Heart rate · Vital signals · Internet of things · Cloud computing · E-Health monitoring · Medical diagnosis

1 Introduction Nowadays, healthcare applications have become an important area among the researcher’s in the field of wireless body area networks (WBANs). Also, with the rise in aging population and chronic diseases, wearable sensors have become very K. Viswavardhan Reddy (B) Department of ECE, Jain University, Bengaluru, India e-mail: [email protected] N. Kumar Department of ECE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_10

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popular among medical, sports, military, and many other commercial fields [1]. These sensors are intensively helpful in collecting systematic and decisive data of people’s daily activities. The following are some of the parameters that have been identified as most common vital signals found in patients [2]: detecting the motion of a patient, heart rate, blood pressure, blood glucose, pulse rate, oxygen saturation, muscle conditions, brain activities, body balance, body weight, and body fat. These are collected by the sensors such as electrocardiogram (ECG), electroencephalogram (EEG), EMG, physiological sensor, motion sensors, glucose sensors, and these are remotely monitored by authorized doctors/physicians. With the advancements in body sensors, cloud computing, wireless, Internet of things (IoT), machine learning (ML), and decision-making technologies, it is made possible by the physician to examine the patient health conditions before the patients/person visits the clinic. However, such system has not become very common and still requires practical implementation, use cases, and effective end-to-end operation. The usage of care alarm in the context of caretaking services for senior citizens of the Växjö City has been presented in [3]. For this, a simulation model was developed using arena software and the results are validated. Similarly, novel advances in wearable sensors and systems that can monitor movement, physiology, and environment have been discussed in [4] with some special use cases on Parkinson’s disease, stroke, and severe injuries in head/neck. The appeal for wearable sensors technology has lately advanced due to the concept of smart textiles. Authors in [5] discussed the usage of carbon nanotubes in the domain of medical field as they are admirable in picking up weak vital signals. Moreover, it is decided to use nanotubes for design and development of ECG sensors: possessing weightless and easy to wear. Antonio Celesti et al. [6] designed, developed, and implemented remote patient monitoring system in collaboration with universities and companies in Italy and South Africa. Big health application system (BHAS) [7] using IoT and bigdata technologies has been discussed by the authors and a strategy for optimizing the usage of sensor devices and various computing resources in delivering advanced applications to the users. To oversee the human biomedical signals, sensing and monitoring framework using IoT is suggested in [8]. Furthermore, a case study was carried to validate the proposal: monitoring the heart condition of each player during a football match [8]. However, these studies revealed the constrained approach to patient-related information while making the decisions and communicating with care team is not effective [9]. In this work, a small, lightweight, low cost, powerful, and integrated SHMS has been developed and presented. The general architecture consists of sensor network, a cloud network, and associated links (web interface and application) and terminals. The sensors are deployed with human body and in the close proximity of patients to collect necessary data. This data is uploaded to the cloud via Wi-Fi or subscriber identity module (SIM) module. Moreover, a web and mobile application have been developed. With this, patients can view their health information and can take prior appointments. If the patient is found in serious condition, doctors get connected to the server by proper authentication details to access data and provide the necessary treatment support.

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The content of rest of the paper is as follows. Section 2 discusses smart health monitoring system architecture with sensing components. Section 3 details the design and development of e-Health web and mobile application along with steps involved in deploying the application in amazon web service (AWS). Section 4 presents a conclusion and some ideas regarding the future work that can be done based on this work.

2 Smart Health Monitoring System Architecture 2.1 System Architecture System as shown in Fig. 1 illustrates the proposed remote healthcare monitoring architecture. At the patient side, we have various sensors discussed below for capturing the vital signals, placed over the body as a part of necessary accessories. These analog signals are then sent to processor for monitoring their symptoms. The processor further processes the collected data and is stored in the cloud. In the cloud, a web and mobile application are developed and deployed. This web application is a major interaction system between the patients and hospitals or doctors. Later, this data is retrieved by the doctors or therapists through the mobile application when patient book for appointments, understanding that patient has sensor elements, that is, BSN. In BSN, sensors are the major components of acquiring the vital signal information from the human body through various modules. Some of the major sensor components used in the entire system are worth describing here. ECG Sensor (AD8232). This sensor measures electrical activity of the heart. ECG is used to determine heart rate (HR), heart rhythm and helps in diagnosing heart arrhythmias, heart attacks, and heart failure. In this work, we have used three electrode

Fig. 1 Proposed SHMS architecture

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Table 1 Cable colors and their placement

Cable color

Signal

Black

RA (right arm)

Blue

LA (left arm)

Red

RL (right leg)

Fig. 2 Normal ECG signal from the module

pad leads and are placed closer to the heart for better measurements. The three leads are properly placed with the help of color coding based on Einthoven’s triangle as shown in Table 1. Once the leads are placed on the body, we will get a single-color graph in our serial monitor as shown in Fig. 2. The device helps in acquiring the signal in the presence of noisy conditions like motion artifacts and remote electrode placement by two-pole high-pass filter. To compute heartbeat, we detect R–R peak intervals from Fig. 2. In order to have dynamic HR estimation, fast Fourier transform (FFT), peak detection, and smoothing have to be done for every 0.5 s in a moving window. Then for smoothening the signal, we have used K-points moving average filter (4 points). The change in the mathematical statement for a L-point discrete time moving average filter with respect to input ‘x’ and output ‘y’ is given in (1): y[n] =

L−1 1 x[n − k] L k=0

(1)

And, a 4-point moving average filter takes present and preceding three pieces of input and computes the average [10] using (2): y[n] = 0.2(x[n] + x[n − 1] + x[n − 2] + x[n − 3]

(2)

Later we calculate the difference of smoothed infrared (IR) signal and then apply to bandpass hamming window filter of 3 dB for filtering particular frequencies given in (3).

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 w(n) = 0.54 − 0.46 cos

 2π n 0≤n ≤ M −1 M −1

(3)

where M represents number of points in window. After the above process, it is time for locating the peaks of the signal. Peak location is actually index of the sharpest location of raw signals since we flipped the signal. Then, we find the distance between the two peaks and we calculate the HR using: HR = (ss /(nextpeak - previouspeak)) ∗ 60 Body position sensor Accelerometer Sensor (ADXL335). This sensor helps in knowing the body position. It is extensively being used in low power, gesture, and angle-sensing systems like mobile phones, gaming systems, sports and fitness devices. The ADXL335 is a three-axle accelerometer with low noise and 320uA less power utilization. The bandwidth of this sensor depends on capacitors values and can be selected to suit for certain applications ranging from 0.5 to 1600 Hz for the X- and Y-axes, and a range of 0.5–550 Hz for the Z-axis. Figure 3 shows the accelerometer output on serial monitor of different body positions. EMG Sensor (EMGSEN). It measures small electrical signals generated by the movement in the muscles like lifting arm up, clenching fist, or even the simplest of movements like moving a finger as shown in Fig. 4. With the help of this signal, we can detect muscle fatigue, defined as a gradual deterioration of the muscle performance during long period of time. Pulse sensor (SEN 11574). It helps to read heartbeat during exercises by placing it to earlobe or fingertip and output is seen over serial monitor. This sensor consists of bright LED (red in color 660 nm) and a light detector. This light is absorbed by the tissues and peripheral blood. A photodetector collects the light reflected signals using I2C protocol which acts as a communication interface. Body Temp (LM35). It can measure temperature from −55 to +150 °C. From the module, we first receive adc values and then convert into equivalent voltage using temp_val = (temp_adc_val * 0.8). This voltage output of LM35 increases 10 mV per

Fig. 3 Accelerometer attached to PP and its output from the console

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Fig. 4 a Overall raw EMG signal. b The graphical view of the first muscle contraction (0–10,800 samples) [11]

degree celsius rise in temperature using temp_val = (temp_val/10), and finally, we convert °C to °F with temp_val = (temp_val * 1.8) + 32 as shown in Fig. 5. All the sensed information is sent to microcontroller (120 MHz Particle Photon ARM cortex M3) which converts analog waves into digital values. Moreover, the device has come with inbuilt tiny Wi-Fi device (Broadcom BCM43362 Wi-Fi chip) for connecting projects and products for the IoT. All the components are chosen in such a manner to reduce the size of the device as much as possible. The board is programmed with compatible version of Arduino programming environment, allowing for testing on Arduinos. Furthermore, a framework for retrieving data over

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Fig. 5 Output from pulse sensor showing BPM as 82

the Internet is provided. Typical energy consumption of the board with WiFi enabled is approximately 80 mA. The whole device was programmed in basic embedded C, programming of microcontroller is a part of embedded development. The program in controller helps to read and get data from sensors and publish it over the cloud using the command particle. Publish. The prototype is tested by implementing as shown in Fig. 6.

3 Development of E-Health Web and Mobile App E-Health is a web and mobile application developed using Python, Django 1.10 Framework, and Postgresql Database running on all major operating systems. The applications are deployed on Amazon Elastic Compute Cloud (EC2) vm instance so that the developed applications can be easily accessible from anywhere and to all people. Below are some of the steps involved in the development of application on amazon web services (AWS).

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Fig. 6 Prototype of the product

3.1 Steps in Deploying Application in AWS Sign into the AWS and then click EC2 console to launch instance for creating and configuring the virtual machine (VM). Later, click on run instance, then it will show the instance id, running status, and public ip address allocated. Inside the Ubuntu VM, we install packages like python3-pip, pip3 install django ==1.11, and Postgres. Later, we create database and we move the developed application project into the server. Inside the server, we install various software such as gunicorn which is a python web server gateway interface HTTP server; nginx (a web server, reverse proxy, load balancer, mail proxy and HTTP cache), supervisor (a client/server paradigm that allows to control a number of processes). The whole application is running in AWS with public IP address of http://18.223.149.193:8000/. After logging into ADMIN page, navigate to patient page → Add patient by filling all the mandatory fields. Similarly, navigate to doctor’s page and then add doctors by filling his details as shown in Fig. 7. Mobile application. Currently, the e-Health mobile application has been developed for android phones only. Both doctors and patients can login with registered mobile phone number. Figure 8 displays one of the patient’s ECG 24 BPM, and this leads to bradycardia symptoms. If the patient wants to take the appointment from the list of suggested doctors, he/she clicks view icon on list, then select date and time and click on fix appointment as shown in Fig. 9. The doctors of e-Health application are registered by the admin staff of the hospital in the system. Once the appointment has been fixed, doctors can login into their app

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Fig. 7 Admin page displaying add patient, add doctors, and list of patients added so far

Fig. 8 Displays patient having high fever

and can view the patient’s appointment history and their medical records as shown in Fig. 10. Moreover, if doctor is not available at that particular date and time, he/she can update their OPD timings as shown in Fig. 11. Since government of India banned giving medication through online, giving prescriptions over online is not shown.

4 Conclusion and Future Work An SHMS framework for remote health monitoring of patients with the integration of Amazon cloud has been presented in this paper. The designed device is developed to operate at a very low power, so that it helps for long term monitoring. A controller board attached with sensor modules is discussed in Sect. 2 measures body temperature, ECG, EMG, body position, etc., then process these vital signals and transmits wirelessly to the cloud. Moreover, we also designed and demonstrated a secured web and mobile application for general public, where patients can view their medical records from anywhere, and at any time. The system also consisting a provision to add doctors, and also, it displays the list of doctors and their specialties, who can give the service to the public. Hence, this framework is more efficient and cost-effective way for providing quality health services to public. In future, we would like to develop an algorithm for suggesting doctors, predicting diseases, and evaluating the performance of algorithm through accuracy. Moreover,

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Fig. 9 Doctor appointment

we would like to update and upgrade the system with incorporating the functionality of directly uploading the images of medical reports and storing them in database. Furthermore, we test the device with different ages and gender and fabricating the device for better and more accurate results.

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Fig. 10 Doctors application: displaying list of patients who have taken appointment and their medical records Fig. 11 OPD timings of doctor

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References 1. G. Aroganam, N. Manivannan, D. Harrison, Review on wearable technology sensors used in consumer sport applications. J. Sens. 19, 1983 (2019) 2. D. Dias, J. Paulo Silva Cunha, Wearable health devices-vital sign monitoring, systems and technologies. Sensors 18(8), 2414 (2018, July 25) 3. D. Perez, S. Memeti, S. Pllana, A simulation study of a smart living IoT solution for remote elderly care, in Proceedings of Third International Conference on Fog and Mobile Edge Computing (FMEC), Barcelona, (2018), pp. 227–232 4. M.M. Rodgers, V.M. Pai, R.S. Conroy, Recent advances in wearable sensors for health monitoring. IEEE Sens. J. 15(6), 3119–3126 (2015) 5. M. Bansal, B. Gandhi, CNT based wearable ecg sensors, in Proceedings of 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, (2019), pp. 208–213 6. A. Celesti, M. Fazio, F. Galán Márquez, A. Glikson, H. Mauwa, A. Bagula, F. Celesti, M. Villari, How to develop IoT cloud e-Health systems based on FIWARE: a lesson learnt. J. Sens. Actuator Netw. 8, 7 (2019) 7. Y. Ma, Y. Wang, J. Yang, Y. Miao, W. Li, Big health application system based on health Internet of Things and big data. IEEE Access 5, 7885–7897 (2017) 8. H. Mora et al., An IoT-based computational framework for healthcare monitoring in mobile environments. J. Sens. 17, 2302 (2017, October) 9. D. Tantrigoda, S. Boralugoda, S. Perera, An approach for visualizing error and obtaining a measure of central tendency regarding a set of time series using discrete haar wavelet. J. Wavelet Theor. Appl. 10(1), 1–18 (2016) 10. Moving Average Filter in Python and Matlab, Gaussian Waves: Signal Processing simplified. Online available at https://www.gaussianwaves.com/2010/11/moving-average-filter-mafilter-2/ 11. E.F. Shair, A.R. Abdullah, T. Zawawi, T.N. Shuhada, S. Ahmad, S. Salleh, Auto-segmentation analysis of emg signal for lifting muscle contraction activities. J. Telecommun. Electr. Comput. Eng. (JTEC) 8, 17–22 (2016)

GenNext—An Extractive Graph-Based Text Summarization Model for User Reviews Komal Kothari, Aagam Shah, Satvik Khara, and Himanshu Prajapati

Abstract User opinions are considered as the core of any business as it helps in reviewing the product or the service. It is the voice of the customer and every business strives to get full benefit from it. Relatively, this research work is based on analysis of the user reviews to provide a better approach to effectively understand the core of the numerous customer reviews present over the Web. It is almost inhumane job to search and read all these user reviews manually, and hence, the concepts of text summarization are applied with the sole aim to derive the meaningful abstract of the entire review for quicker understanding of its content. This research article peculiarly focuses on preparing a genre independent review summary model that can be used on any existing systems. GenNext is a unique model which provides extractive text summary for multi-genre data and even for user review of as less as one sentence is developed with graphical method of extractive text summarization. This model has been proven significantly accurate in providing effective summaries as compared to the existing summarization models in the market, with the help of sentiment analysis and polarity classification through natural language processing techniques. The summaries are also assigned related emoticons based on their polarity scores for animated representation. Keywords User review · Review analysis · Genre independent · Text summarization · Extractive summarization · Sentiment polarity identification · Sentimental analysis · Natural language processing (NLP) K. Kothari (B) · S. Khara · H. Prajapati Silver Oak Group of Institute, Gujarat Technological University, Ahmedabad, India e-mail: [email protected] S. Khara e-mail: [email protected] H. Prajapati e-mail: [email protected] A. Shah Indira Gandhi National Open University, Ahmedabad, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_11

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1 Introduction With the easy availability of Internet on fingertips, it has become extremely necessary for the companies to reach in the sleeves of the individual to understand and examine the pros and cons of the commodity from their valued consumers. Many Web portals provide a platform for the consumers to post their feedback and opinion after evaluating products or services they used, with the aim to understand the consumer’s needs. Internet has become a huge warehouse where users over the world put in their opinions and share their views. Reportedly, there has been an effective hike in the volume of these customer reviews over the Web. These customer opinions are extremely necessary for the business heads, stakeholders as well as peer customers since it represents the voice of the customer and their experiences with the product or service. These reviews can essentially help a business to discover market requirement and to develop new plans accordingly for the growth of the business. Web portals, like Amazon, Yelp, Flipkart, and many more, facilitate the customers with enriched user reviews for each product. But any human is unlikely to read and understand all the reviews and hence the concept of text summarization is applied on these user reviews to analyze and understand the long textual reviews in much lesser time. In this research work, we have developed a unique model to constructively summarize the long textual reviews and provide an abstract with similar meaning intended in the review. Related Work: There are various approaches and algorithms developed in the field of text summarization. A survey article [1] presented in this field, expressively examines all the related works conducted in text summarization and highlights the common problems such as the over dependency and limitation of the summary model on the genre of the user reviews provided in the dataset. Also, most of the work conducted is limited to summarize a group of sentences in larger user reviews and cannot provide summary of the user review containing as less as one sentence. While a study by Hu et al. [2] was based on portrayal of effectiveness of extractive summarization and insists to pursue it by recognizing the top-most derivative sentences acquired from the review. And a research work conducted by Mallick et al. [3] highlight the usage of graphical method of test summarization where textual sentences are treated as nodes and similarity among two different sentences as the weight of the edge between them to build a graph-based summary. Through GenNext model, we provide conceptual evaluation of user reviews from multiple genres using text summarization and natural language processing methods to understand the summarized meaning and compare it with user review. GenNext is a unique model that summarizes the user reviews independent of their genre and review length. Graph-based extractive summarization method provides a meaningful short summary extracted from the top-most important sentences from the elaborated user reviews. We have enhanced this method to provide summary for shorter user reviews. After the model provides the summary, natural language processing techniques are used to evaluate and compare the meaning of the obtained summary with the user review.

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We started with gathering datasets from different genres, viz., restaurants, hotels, airlines, products, and wines from different Web portals like Yelp and Amazon, which were openly accessible for scholastic objectives. The different datasets were then merged into one to generate a random multi-genre review dataset. This dataset was then cleaned by data cleaning techniques including removal of noisy data and unwanted rows. Figure 1 is the flow diagram of the applied methodologies for the project.

Fig. 1 Flow diagram

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2 Research Design and Used Methodologies to Develop GenNext (Proposed Approach) 2.1 Data Gathering and Cleaning Data gathering is the process of collecting data from various sources for further usage. Various algorithms are then performed on the collected dataset to obtain desired outcome. Here, we have gathered datasets from different open-source datasets of Yelp and Amazon which are available for academic purposes. Datasets from different genres such as restaurant reviews, airline reviews, product reviews, and hotel reviews are gathered to prepare a huge multi-genre dataset. Data cleaning is done on this dataset to remove noisy and unwanted data. A multi-genre dataset is formed as per the required rows such as unique reference number, product, user review, star rating, and more, while other rows are eliminated to maintain standard format throughout the merged dataset.

2.2 Text Summarization Using GenNext Algorithm After merging the dataset, we now have a rich source of raw data containing important assets necessary for further process. Text summarization method is to be applied on user reviews of different genre and indefinite length. Before moving forward, let us understand the concept text summarization by GenNext algorithm. Text summarization implies the strategy for shortening long briefs of textual content. The aim is to make a sound and familiar summary having just the major components laid out in the document. Applying text summarization lessens understanding time, quickens the way toward analysis of data, and expands the volume of data that can accommodate in an area. The latest research in the field represents an approach to gather priority sentences from the content by text summarization feature such as word and phrase frequency in a graph-based extraction method. This approach prioritizes the sentences of a textual review as a component of high occurrence words by avoiding common stop words [4]. Let us understand text summarization in detail. Basically, there are two main types to summarize text: Extraction-based summarization: The method that includes extracting important phrases from the textual document and merging them to make a meaningful abstract is called extraction-based summarization. The extraction is made dependent on the pre-characterized condition without altering anything in the indigenous text document. Thus, the significant task which is of extreme significance in following this methodology is to pick and concentrate the correct sentences for summarization [1]. Input document → sentences similarity → weight sentences → select sentences with higher rank.

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Abstraction-based summarization: This methodology utilizes propelled ML algorithms and NLP strategies to acquire a new abstract from the heap of text documents. In this methodology, it is likewise conceivable that a few parts of the summary may not by any means present in the original document, however, they are added utilizing NLP strategies to give shorter and significant synopsis. This strategy incorporates summarizing and when applied with deep learning standards, this technique can conquer the linguistic errors from the extractive summarization. The algorithms required to perform abstraction summarization are hard to create; and that is on the grounds that the utilization of extraction methods is highly popular. [1] Input document → understand context → semantics → create own summary. Since extraction methods are more popular and widely used, and after effective survey of existing text summarization models in the market, we have developed GenNext on graphical and extractive method of text summarization to overcome the problems in existing models. Let us understand GenNext in detail. GenNext Algorithm for Text Summarization: GenNext is a unique model developed with the combination of graphical representation and keyword extraction. GenNext is the new age general purpose, graph-based ranking algorithm that works within the concepts of NLP to preserve the meaning of the original text. The foundation of GenNext lays within deciding the value of a vertex in a graph, obtained from a global data recursively derived from the entire graph. • Sentences/phrases are extracted from the content by discovering series of words and assigning them as a vertex. • A directed graph is made to recognize the reoccurrence the words which are related to each other. • A score is calculated for each expression that is the sum of individual score from the graph. • Top N expressions are then extracted. GenNext algorithm weighs the score of each vertex through voting and recommendation system. When a vertex corresponds to other vertex, it casts a vote in the backend for that vertex. The vertex that gains highest votes is considered highly important and is extracted to form a summary of that review. With the help of variety of datasets feed in the GenNext algorithm, we can say that the model is genre independent and can summarize user reviews of any genre and of any length. GenNext is capable of summarizing user review with length as small as one sentence, which was earlier the limitation in Gensim and other ranking algorithms. GenNext provides 20% of the total length of the user review in the summary for quick and better understanding of the summaries as well as easy accessibility while reciprocating through huge number of reviews.

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2.3 Sentiment Analysis for NLP Transition Between GenNext Summaries and User Reviews Sentiment analysis otherwise called as opinion mining is a field inside natural language processing (NLP) that assembles entities that attempt to recognize and separate opinion inside a text. As a rule, other than distinguishing the sentiment, these frameworks extract characteristics of the expression, e.g.: polarity, subject, opinion holder [5]. Sentiment analysis is a method that extricates feelings from the crude writings. The intention of the client after the content written in the review is comprehended through sentiment analysis. It gives positive negative qualities of the content. This can be extremely helpful in understanding the client surveys. The negative, positive, or neutral aspects obtained from the sentiment of the user review are termed as polarity. Sentiment polarity score is considered as the evaluation result of the textual data based on the amount of positive or negative content written in the text [6]. Polarity_Scores = Positive/Negative In this research, the sentiment analysis is performed on the GenNext summaries and the corresponding user review to find the polarity score, check the efficiency of the NLP transition between summary and review, and verify from the polarity scores whether the original meaning of the review is retained in the GenNext summary or not. The polarity scores help in identifying the overall meaning conveyed in the user review and the obtained summary and is also used in comparing the efficiency of GenNext with other models in the market. In addition, word and frequency count method was applied on the GenNext summaries to gain an overall perspective of whether the summary is missing out on important words from the review or not. The difference of the frequently used words in the review and GenNext summary is almost negligible. We can say that the most common words used to describe the review are also the most common words used in its corresponding summary (Fig. 2).

2.4 Emoticon Assignment to GenNext Summaries Emoticons are considerably the best way to show emotions. Emoticon assignment is done for better visualization and interpretation of the summary. We have assigned emoticons to the corresponding GenNext summary as per polarity scores as shown in Fig. 3. Following are the tasks followed by GenNext model 1. Construction of vectors for intermediate representation of the input text 2. Scoring the vectors based on the importance irrespective of the length

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Fig. 2 a Word cloud of review, b word cloud of GenNext summary

Fig. 3 Emoticon assignment for polarity scores ranging from (-1) to (1)

3. Selection of a summary comprising of highly important vectors 4. Maintaining the length ratio between summary and user review for any length or genre 5. Sentiment analysis and polarity score is calculated of GenNext summaries and user reviews 6. Emoticons are assigned to corresponding GenNext summaries for better representation (Fig. 4).

Fig. 4 GenNext model

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Fig. 5 a Average sentiment polarity of GenNext is almost equivalent with the user reviews and b Bleu score comparison between GenNext and other algorithms

3 Result and Discussion Through this research, we have presented a unique methodology that summarizes the user reviews independent of their genre and contextual length through ranking and extractive algorithms in text summarization. We have tried to overcome the existing problems in extractive methods through GenNext, and an approach to compare sentiment polarity scores as well as visual representation with emoticon assignment. In this research, GenNext is proven far more accurate than the existing systems with much wider implication scopes in the future (Fig. 5).

4 Conclusion It is clearly evident that GenNext is significantly accurate with the highest score of 0.686322, through Bleu method of finding accuracy, as compared to other algorithms. GenNext is the most efficient extractive summarizer model with highest Bleu score that can provide summary for multi-genre user reviews. Nevertheless, there is always a chance of improvement. Huge and varied datasets can be used to train the machine and advanced sentiment analysis methods can be used to provide more accurate and genre independent results over the growing time.

References 1. K. Kothari, A. Shah, S. Khara, H. Prajapati, A novel approach in user reviews analysis using text summarization and sentiment analysis: survey. Int. J. Creat. Res. Thoughts (IJCRT) 8(3), 3067–3072 (2020). ISSN:2320-2882. Available at: http://www.ijcrt.org/papers/IJCRT2003417. pdf 2. Y. Hu, Y. Chen, H. Chou, Y.-H. Hu, Y.-L. Chen, H.-L. Chou, Opinion mining from online hotel reviews—a text summarization approach. Inf. Process. Manag. 53(2), 436–449 (2017). https:// doi.org/10.1016/j.ipm.2016.12.002

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3. C. Mallick, A. Das, M. Dutta, A. Das, A. Sarkar, Graph-based text summarization using modified TextRank, Soft Computing in Data Analytics (Springer, Berlin, 2019), pp. 137–146. https://doi. org/10.1007/978-981-13-0514-6_14 4. Medium, A Quick Introduction to Text Summarization in Machine Learning (2020) [online]. Available at: https://towardsdatascience.com/a-quick-introduction-to-text-summarization-inmachine-learning-3d27ccf18a9f 5. H2oai.github.io, Natural Language Processing Tutorial—Sentiment Analysis (2020) [online]. Available at: https://h2oai.github.io/tutorials/natural-language-processing-tutorial-sentimentanalysis/#0 6. A. Shah, K. Kothari, U. Thakkar, S. Khara, User review classification and star rating prediction by sentimental analysis and machine learning classifiers, Information and Communication Technology for Sustainable Development (Springer, Singapore, 2019), pp. 279–288. https://doi. org/10.1007/978-981-13-7166-0_27

An Adaptive Control for Surrogate Assisted Multi-objective Evolutionary Algorithms Duc Dinh Nguyen and Long Nguyen

Abstract Multi-objective problems (MOPs), a class of optimization problems in the real-world, have multiple conflicting objectives. Multi-objective evolutionary algorithms (MOEAs) are known as great potential algorithms to solve difficult MOPs. With MOEAs, based on the principle of population, we have a set of optimal solutions (feasible solution set) after the search. We often use the concept of dominance relationship in population, and it is not difficult to find out set of Pareto optimal solutions during generations. However, with expensive optimization problems in the real world, it has to use a lot of fitness function evaluations during the search. To avoid expensive physical experiments, we can use computer simulations methods to solve the difficult MOPs. In fact, this way often costs expensive in computation and times for the simulation. In these cases, researchers discussed on the usage of surrogate models for evolutionary algorithms, especially for MOEAs to minimize the number of fitness callings. There are a series of proposals which were introduced with the usage of RBF, PRS, Kriging, SVM models. With the concept of machine learning, these MOEAs can solve expensive MOPS effectively. However, with our analysis, we found that using a fixed ratio of fitness functions and surrogate functions may make the unbalance of exploitation and exploration of the evolutionary process. In this paper, we suggest to use an adaptive control to determine the effective ratio during the search. The proposal is confirmed though an experiment with standard measurements on well-known benchmark sets. Keywords K-RVEA · Surrogate · Kriging · Adaptive control

D. D. Nguyen (B) Military Information Technology Institute, Academy of Military Science and Technology, Hanoi, Viet Nam e-mail: [email protected] L. Nguyen (B) Department of Information Technology, National Defense Academy, Hanoi, Viet Nam e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_12

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1 Surrogate Assisted Multi-objective Evolutionary Algorithms 1.1 Surrogate Models A multi-objective optimization problem involves at least two conflicting objectives, and it has a set of Pareto optimal solutions. MOEAs use a population of solutions to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research attention during the past decade. They are still one of the hottest research areas in the field of Computational Intelligence, especially to solve difficult MOPs in the real world. In multi-objective optimization, a problem is defined as a expensive problem when it has fitness functions which require time-consuming experiments or computer simulations. Surrogate models are used to approximate in simulation way to reduce the computational cost for expensive problems. The models are described as below: → → x ) is a If we call f (− x ) is an origin fitness function of a MOP, then we have f  (− meta function, which is indicated as below: → → → x ) = f (− x ) + e(− x) f  (−

(1)

→ → function e(− x ) is the approximated error. In this case, the fitness function f (− x ) is not to be known, and the values (input or output) are cared. Based on the responses of the simulator from a chosen dataset, a surrogate is constructed, and then the model generates easy representations that describe the relations between preference information of input and output variables. There are some approaches for the surrogate models, which are divided into some kinds such as the radial basis function (RBF), the polynomial response surface (PRS), the support vector machine (SVM), and the Kriging (KRG). The common concepts of using surrogate models are: Instead of using known fitness functions always, to reduce the number of calculations, the evaluations are suggested to use surrogate functions, which are simpler. Therefore, without loss of generality, this paper investigates the use of the original and surrogate functions with Kriging model in the solving process of the MOEAs for expensive problems. In [8], the authors proposed a method named “Kriging,” which is a response surface method bases on spatial prediction techniques. This method minimizes the mean-squared error to build the spatial and temporal correlation among the values of an attribute. In [9], the authors developed a parametric regression model which design and analysis of computer experiments, called DACE. The model is an extension of Kriging approach for at least three-dimensional problems. The model is a combination of a known function f (x) and a Gaussian random process f  (x) that is assumed to have mean zero and covariance. In [2], the authors build a procedure which uses Kriging approximations and NSGA-II algorithm to optimize the aircraft design. The Kriging-based genetic algorithm procedure gives shapes which are optimized for both the initial peak and perceived noised level of the signature along with

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minimum drag. In [6], by providing a design of experiments (DOE) capability into the framework, the authors hybridized the desirable characteristics of EAs and surrogate models such as RSM to obtain an efficient optimization system. In [7], in the problems with two objective functions, thermal resistance and pumping power have been selected to assess the performance of the micro-channel heat sink. In [10], the authors deeply discussed on surrogated MOEAs and indicated that: Kriging appears to perform well in most situations; however, it is much more computationally expensive than the rest. In [5], the KRG is used, and the term Kriging points directly to the origin of these prediction methods dating back to the sixties, when the mining engineer Krige used Gaussian random field models (GRFMs) to predict the concentration of ore in gold and uranium mines. In [11], authors proposed such a method, MOEA/D-EGO, for dealing with expensive MOPs. The algorithm decomposes an MOP into a number of single-objective optimization subproblems. At each iteration, a predictive distribution model is built for each individual objective in the MOP by using fuzzy clustering and Gaussian stochastic process modeling.

1.2 K-RVEA To build up our research, we analyzed the usage surrogate parameters on KRVEA [3], and the algorithm is short described as below: Based on RVEA [1], a reference vector based MOEA named K-RVEA was proposed for solving many objective problems. In the algorithm, search process is guided by a set of predefined reference vectors inspired from the direction vectors with the idea is to partition the objective space into a number small subspaces using a set of reference vectors. An elitism selection strategy is employed inside each subspace and using an angle-penalized distance (APD) to measure the distance of the solutions to the ideal point and the closeness of the solutions to the reference vectors. For more detail about the algorithm, see [1].

1.3 Issues of Using Fixed Ratio In our work, we analyzed the evolutionary processes’ behavior during the search with surrogated assisted multi-objective evolutionary algorithms. For MOEA in general, according to the searching process, the quality of the achieved solution population varies according to the general rule; that is, in the first generations, the convergence quality of the algorithm is poor, the calculation quite diverse due to the fact that random generated solutions have not really evolved over generations. After that, the solution quality gradually got better, the diversity also gradually decreased and towards the Pareto optimal front spread. The last generations of a process with a specified number of generations, the quality of the population converged was good because of the evolution process over many generations. An algorithm is assessed

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well if the diversity of the population is maintained when the solutions spread evenly over the Pareto optimization front. In many analyzes of the evolutionary trend of evolution, in order to ensure a quality balance between convergence and diversity, it is necessary to guide the algorithm to keep a balance between exploration and exploitation cascade of evolution. Therefore, in the early stages, the ability to converge and the end-stage need to enhance the diversity of the population. In K-RVEA, the authors use parameters to determine whether the model update is fixed, which can lack the algorithm’s adaptability, possibly losing the balance between exploration and exploitation of the algorithm. We will test the K-RVEA algorithm on the class of expensive problems, with different parameters to evaluate the above comments. We used wmax, the number of generations before updating the Kriging model with values: 10, 20, 30, 50, and 70 with DTLZs problems [4] (DTLZs from 1 to 9) in 20,000 calculations. We reported the results on GD and IGD, the popular metrics to measure the performance of the algorithm. The results are shown in Tables 1 and 2. Through the test results on DTLZs with GD, IGD measurements, we find that: At each time, depending on the time in evolution, the quality of the population using the values the parameter wmax, the number of generations before the model update is carried out, has a strong influence on the ability to explore and exploit, in other words, the convergence and diversity of the solution population. Specifically, in the early generations, the quick update of the model to use a surrogate function seems to produce better results (when the wmax value is small, corresponding to DTLZ1, DTLZ2, DTLZ4, DTLZ5 problems). Of course, it also depends on the number of non-dominated solutions in the population that each problem is achieved according to evolution (DTLZ3, DTLZ7, DTLZ9). When in the next generation, there is a change in the adapt for increasing generation, ignoring the update of Kriging model (increasing wmax value, corresponding to DTLZ1, DTLZ2, DTLZ6, DTLZ7) and sustainable as the case of DTLZ9 problem. Applying a predetermined number of generations, which then updates the model, can be algorithmically inefficient, reducing convergence and diversity of the algorithm. The application of numerical value generation to apply the original objective functions before using surrogate function according to Kriging model depends on the quality of solution of the current generation. Specifically, when the number of non-dominated solutions in the population is small, far from the Pareto optimal class, it would be appropriate if this parameter is applied at a relative level (in the case of experiments about 30, 50). If the ratio is high, the number of assessments depends largely on the original objective functions, the effect of using surrogate function does not make much sense. If a low rate is applied, when the number of individuals is not small, updating the model will not have many changes in quality. When the number of non-dominated solutions in the population is already in the majority, but not close to the Pareto optimization front, it is possible to increase the number of evaluations by the surrogate function and the frequency of updating the Kriging model (depending on the quality, number of non-dominated solutions in the population, the value could be 20, 30). When the number of non-dominated solutions in the obtained population is approaching the Pareto optimal front, then the surrogate function rate and Kriging model update frequency may be higher, respectively, 10.

An Adaptive Control for Surrogate Assisted Multi-objective …

127

Table 1 GD values on DTLZs benchmark set DTLZ1 10

50.7441

7.4836

5.6692

4.1103

3.2282

2.7263

2.5096

2.4711

2.4463

20

41.9546

10.0818

10.9039

3.7888

3.7041

3.7041

4.0672

4.0672

4.0097

4.0097

30

39.0192

9.4198

7.2260

5.4170

3.9909

3.9062

3.9777

4.0149

3.9320

3.7725

50

42.5498

10.4695

6.7609

5.9252

5.5349

4.6894

4.7492

4.3930

4.1622

4.0916

70

43.5096

12.6410

8.0107

6.6715

7.1815

6.2105

5.7701

5.2643

5.0035

4.7697

4.7267

3.3725

3.3725

3.3725

3.3725

3.3725

3.3725

3.3725

3.3725

ADC 46.6079

2.3877

DTLZ2 10

0.1032

0.0023

0.0014

0.0011

0.0009

0.0008

0.0008

0.0007

0.0007

0.0006

20

0.1151

0.0014

0.0009

0.0007

0.0006

0.0005

0.0005

0.0005

0.0004

0.0004

30

0.0999

0.0015

0.0010

0.0008

0.0006

0.0005

0.0005

0.0004

0.0004

0.0004

50

0.1076

0.0015

0.0009

0.0006

0.0005

0.0005

0.0004

0.0004

0.0004

0.0004

70

0.1165

0.0014

0.0010

0.0007

0.0006

0.0005

0.0005

0.0004

0.0004

0.0004

ADC 0.1227

0.0019

0.0011

0.0008

0.0007

0.0006

0.0006

0.0005

0.0005

0.0004

DTLZ3 10

183.1624 43.1944

36.1737

33.9130

30.8676

29.8677

28.4940

30.4472

29.3890

30.4415

20

171.6720 65.1569

57.1465

47.8764

47.3865

42.2447

40.1520

38.8519

36.7741

36.5496

30

220.4797 74.7820

56.8731

52.7536

52.7041

48.8366

44.5964

42.4959

41.5517

40.6848

50

194.0912 85.5257

83.2413

70.2972

59.2193

50.7384

50.3928

49.8387

48.0394

44.5380

70

151.5813 77.2795

68.4175

59.9071

51.6176

47.8923

49.5671

51.3997

48.6738

48.7996

ADC 183.1624 69.5647

54.4809

48.0276

47.8869

43.2640

42.0306

40.3582

39.2478

42.3035

DTLZ4 10

0.1739

0.0148

0.0093

0.0063

0.0051

0.0043

0.0038

0.0034

0.0031

0.0029

20

0.1613

0.0113

0.0072

0.0054

0.0044

0.0039

0.0035

0.0033

0.0031

0.0029

30

0.1634

0.0108

0.0077

0.0060

0.0051

0.0044

0.0038

0.0035

0.0033

0.0031

50

0.1841

0.0115

0.0074

0.0057

0.0048

0.0042

0.0038

0.0034

0.0031

0.0028

70

0.1564

0.0129

0.0085

0.0073

0.0063

0.0055

0.0050

0.0047

0.0045

0.0042

ADC 0.1564

0.0129

0.0085

0.0073

0.0063

0.0055

0.0050

0.0047

0.0045

0.0042

DTLZ5 10

0.1407

0.0068

0.0057

0.0054

0.0050

0.0046

0.0042

0.0042

0.0042

0.0041

20

0.1571

0.0096

0.0069

0.0063

0.0056

0.0053

0.0052

0.0050

0.0049

0.0048

30

0.1207

0.0111

0.0082

0.0068

0.0063

0.0058

0.0056

0.0054

0.0051

0.0051

50

0.1584

0.0052

0.0046

0.0042

0.0041

0.0041

0.0040

0.0040

0.0039

0.0038

70

0.1368

0.0101

0.0072

0.0065

0.0062

0.0056

0.0055

0.0052

0.0050

0.0049

ADC 0.1286

0.0071

0.0055

0.0048

0.0045

0.0042

0.0039

0.0039

0.0037

0.0037

DTLZ6 10

0.9746

0.4591

0.4409

0.3822

0.3919

0.3913

0.3306

0.3261

0.3428

0.3140

20

0.9513

0.4647

0.4897

0.4926

0.4734

0.4403

0.4151

0.3610

0.3625

0.3456

30

0.9965

0.4503

0.3943

0.4227

0.3993

0.3845

0.3798

0.3657

0.3867

0.3529

50

0.9480

0.5927

0.5124

0.4898

0.4478

0.4311

0.4444

0.4308

0.4108

0.3957

70

0.9599

0.5421

0.4830

0.4446

0.4565

0.4716

0.4370

0.4339

0.4248

0.4184

ADC 1.0474

0.4951

0.5234

0.5070

0.4695

0.4490

0.4279

0.4099

0.3823

0.3597

(continued)

128

D. N. Duc and L. Nguyen

Table 1 (continued) DTLZ7 10

2.0241

0.0026

0.0012

0.0008

0.0006

0.0005

0.0005

0.0004

0.0004

0.0003

20

2.6543

0.0012

0.0007

0.0005

0.0004

0.0003

0.0003

0.0003

0.0002

0.0002

30

2.6896

0.0009

0.0005

0.0004

0.0003

0.0003

0.0003

0.0002

0.0002

0.0002

50

2.3290

0.0010

0.0007

0.0005

0.0004

0.0004

0.0003

0.0003

0.0002

2.0384

70

2.0943

0.0007

0.0005

0.0005

0.0004

0.0003

0.0003

0.0003

0.0002

0.0002

ADC 2.0241

0.0009

0.0007

0.0004

0.0004

0.0003

0.0003

0.0003

0.0002

0.0002

DTLZ8 10

0.0548

0.0322

0.0322

0.0322

0.0322

0.0322

0.0322

0.0322

0.0322

0.0322

20

0.0587

0.0327

0.0327

0.0327

0.0327

0.0327

0.0327

0.0327

0.0327

0.0327

30

0.0706

0.0358

0.0358

0.0358

0.0358

0.0358

0.0358

0.0358

0.0358

0.0358

50

0.0728

0.0303

0.0303

0.0303

0.0303

0.0303

0.0303

0.0303

0.0303

0.0303

70

0.0635

0.0357

0.0357

0.0357

0.0357

0.0357

0.0357

0.0357

0.0357

0.0357

ADC 0.0635

0.0310

0.0310

0.0310

0.0310

0.0310

0.0310

0.0310

0.0310

0.0310

DTLZ9 10

3.4231

2.2998

1.5890

1.3484

1.4657

2.2714

1.4092

1.2228

1.1880

1.1553

20

6.3959

2.5242

2.3244

2.1598

1.9384

1.9384

2.3200

1.5506

1.6016

2.1090

30

4.2762

2.1689

1.9599

1.7168

1.6040

1.4022

1.5312

1.4316

1.3738

1.2709

50

3.9907

1.5387

1.1807

0.9758

2.3940

2.1892

1.8939

1.8939

1.8430

1.7425

70

4.5920

2.5457

1.9841

2.2016

2.0792

2.0511

1.9588

1.8728

1.9472

1.7881

ADC 3.7733

2.5289

2.2851

2.1059

2.0260

2.0260

1.8907

1.8280

1.2761

1.1746

In general, from the above assumptions, it is assumed that increasing or decreasing the number of generations (or increasing/decreasing the value of wmax) is responsive depending on key factors such as time of evolution, quality convergence of the population, and the computational complexity of each problem.

2 Using Adaptive Control for Surrogate Assisted MOEAs In this section, based on the analysis from the above experimental results, the assumptions about appropriate parameter adjustment during evolution create an effective balance between convergence quality and diversity of the population. Specifically, adjusting the parameter on the number of generations before updating the model according to the assessed factors and assuming that the direct influence in algorithms using a representative function is: the process time of the process evolution, the quality of the current population and the computational characteristics of the problem. Building the rate of using surrogate function and frequency of updating Kriging model flexibly, depending on the quality of the solution population in the current generation, to determine the quality of the solution, we use two defined values at a time t, with the test problem j as follows:

An Adaptive Control for Surrogate Assisted Multi-objective …

129

Table 2 IGD values on DTLZs benchmark set DTLZ1 10

37.5017

15.9551

15.9551

15.9551

8.6306

8.6306

8.6306

6.4437

6.4437

6.4437

20

69.2926

12.2018

9.0805

6.0770

6.0770

6.0770

6.0770

6.0770

6.0770

6.0770

30

73.5400

18.0276

18.0276

18.0276

18.0276

18.0276

18.0276

18.0276

18.0276

18.0276

50

75.3314

21.8503

21.8503

21.8503

18.8011

18.8011

18.8011

18.8011

18.8011

18.8011

70

56.6293

17.9351

17.9351

17.9351

15.7866

15.7866

11.9698

11.9698

11.9698

11.9698

9.9032

9.9032

9.9032

9.9032

9.9032

9.9032

9.9032

9.9032

9.9032

ADC 30.8156 DTLZ2 10

0.4863

0.0523

0.0417

0.0372

0.0343

0.0326

0.0314

0.0302

0.0293

0.0284

20

0.4862

0.0461

0.0368

0.0325

0.0297

0.0287

0.0281

0.0273

0.0265

0.0259

30

0.4720

0.0467

0.0382

0.0326

0.0293

0.0283

0.0268

0.0260

0.0249

0.0244

50

0.5233

0.0476

0.0387

0.0340

0.0306

0.0295

0.0285

0.0276

0.0272

0.0267

70

0.4848

0.0502

0.0427

0.0372

0.0338

0.0324

0.0302

0.0292

0.0283

0.0272

ADC 0.5617

0.0528

0.0398

0.0354

0.0335

0.0320

0.0305

0.0295

0.0287

0.0278

DTLZ3 10

500.9795 154.0806 154.0806 154.0806 154.0806 154.0806 154.0806 154.0806 154.0806 154.0806

20

608.2876 195.9133 185.9003 185.9003 185.9003 185.9003 185.9003 185.9003 177.8553 177.8553

30

421.7119 282.9219 229.2645 229.2645 229.2645 222.9678 132.3652 132.3652 132.3652 132.3652

50

533.7593 229.4352 225.6308 225.6308 196.0629 196.0629 196.0629 196.0629 196.0629 195.8602

70

672.9475 281.1927 274.5212 246.6838 246.6838 246.6838 183.5229 183.5229 183.5229 183.5229

ADC 500.9795 235.2239 204.4000 204.4000 204.4000 182.6566 182.6566 182.6566 182.6566 182.6566 DTLZ4 10

0.9201

0.1337

0.0794

0.0595

0.0549

0.0495

0.0477

0.0458

0.0444

0.0434

20

0.9733

0.0753

0.0606

0.0543

0.0503

0.0465

0.0447

0.0418

0.0408

0.0396

30

0.8311

0.0745

0.0626

0.0568

0.0520

0.0466

0.0441

0.0421

0.0408

0.0399

50

0.8441

0.0786

0.0637

0.0577

0.0541

0.0512

0.0474

0.0442

0.0420

0.0413

70

0.8792

0.0833

0.0662

0.0629

0.0582

0.0552

0.0542

0.0524

0.0512

0.0504

ADC 0.8792

0.0833

0.0662

0.0629

0.0582

0.0552

0.0542

0.0524

0.0512

0.0504

DTLZ5 10

0.3699

0.0395

0.0397

0.0384

0.0343

0.0330

0.0329

0.0327

0.0301

0.0301

20

0.3739

0.0583

0.0525

0.0391

0.0371

0.0357

0.0355

0.0347

0.0345

0.0344

30

0.4081

0.0722

0.0597

0.0496

0.0492

0.0478

0.0457

0.0449

0.0421

0.0421

50

0.3483

0.0381

0.0337

0.0317

0.0307

0.0367

0.0364

0.0364

0.0364

0.0363

70

0.3860

0.0494

0.0426

0.0412

0.0411

0.0394

0.0393

0.0388

0.0384

0.0376

ADC 0.4247

0.0421

0.0371

0.0380

0.0373

0.0345

0.0338

0.0336

0.0333

0.0333

DTLZ6 10

8.2380

1.8545

0.8540

0.8110

0.8110

0.8110

0.8110

0.8110

0.7906

0.7724

20

8.4386

2.2229

1.3579

1.3532

1.3532

1.2986

1.2986

1.2557

1.2557

1.2557

30

8.2757

2.4312

2.4304

1.8914

1.7946

1.7637

1.6556

1.6556

1.6556

1.6437

50

8.2283

2.7022

2.5015

2.4640

2.4640

2.4640

2.2781

2.2781

2.0575

2.0575

70

8.4906

2.4451

2.4451

2.4451

2.1171

2.0384

2.0384

2.0384

2.0384

2.0384

ADC 8.4051

2.9249

1.8047

1.6488

1.6488

1.6488

1.6488

1.6334

1.5731

1.5731

(continued)

130

D. N. Duc and L. Nguyen

Table 2 (continued) DTLZ7 10

8.1067

0.0611

0.0397

0.0332

0.0295

0.0276

0.0256

0.0243

0.0231

0.0222

20

7.4668

0.0492

0.0406

0.0338

0.0317

0.0296

0.0280

0.0268

0.0256

0.0246

30

8.6059

0.0514

0.0424

0.0380

0.0358

0.0341

0.0312

0.0303

0.0293

0.0282

50

8.8847

0.0707

0.0599

0.0543

0.0469

0.0440

0.0420

0.0407

0.0398

0.0477

70

8.2937

0.0725

0.0598

0.0559

0.0506

0.0487

0.0459

0.0442

0.0433

0.0424

ADC 8.1067

0.0618

0.0546

0.0456

0.0433

0.0411

0.0359

0.0343

0.0320

0.0312

DTLZ8 10

0.2309

0.2050

0.2050

0.2050

0.2050

0.2050

0.2050

0.2050

0.2050

0.2050

20

0.2590

0.1599

0.1599

0.1599

0.1599

0.1599

0.1599

0.1599

0.1599

0.1599

30

0.2190

0.1827

0.1827

0.1827

0.1827

0.1827

0.1827

0.1827

0.1827

0.1827

50

0.2516

0.2357

0.2357

0.2357

0.2357

0.2357

0.2357

0.2357

0.2357

0.2357

70

0.2397

0.1935

0.1935

0.1935

0.1935

0.1935

0.1935

0.1935

0.1935

0.1935

ADC 0.2397

0.1790

0.1790

0.1790

0.1790

0.1790

0.1790

0.1790

0.1790

0.1790

DTLZ9 10

11.3847

6.3491

6.1299

6.1299

5.6413

5.3711

4.9843

4.9843

4.9843

4.9843

20

11.1618

6.3076

6.2910

6.2910

6.2910

6.2910

5.9713

5.9713

5.9713

5.9066

30

11.1510

7.7752

7.7752

7.7563

7.7378

7.7212

7.7103

7.7103

7.7103

7.7099

50

11.2579

7.9947

7.9363

7.9294

7.5453

7.5453

7.5453

7.5453

7.5453

7.5453

70

11.1964

6.6274

6.3928

6.3837

6.3837

6.3837

6.3837

6.3837

6.3837

6.3830

ADC 11.2529

6.5146

6.4943

6.4943

6.4943

6.4943

6.4943

6.4943

6.4697

6.4697

Qt =

n ND ∗ n gen Cj qsize tsize

(2)

In which, n ND is the number of non-dominated solutions in the main population, qsize is the size of the population, current generation n gen , tsize is the number of generations. C j is the complexity of the problem; this is a parameter dependent on the complexity of the test function; it depends on the size of decision spaces, number of objectives, and number of calculations for objective functions; we suggest to choose practical values C j ∈ [0.8, 1], and the default value is 1. The constant 60 is used, which is the upper range of wmax; it helps the control to get the maximized value, 50. Based on the Q t value, we will calculate the value of wmax ∈ R (the rate of using surrogate function and the frequency of updating the Kriging model) in the range [10, 50], and a normalize function is defined to adjust valid wmax values. wmax = normalize(Q t ∗ 60)

(3)

In the experiments, we used the origin K-RVEA and the modified version M-K-RVEA with the usage of the above adaptive wmax. We also used the DTLZs benchmark set with GD, IGD metrics. The results are reported in rows ADC on Tables 1 and 2. We also reported and compared the behaviors of K-RVEA (with the default wmax is 30) and M-K-RVEA.

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Analyzing and comparing the results, we can found that M-K-RVEA with adaptive parameter control can get more balance of convergence and diversity for obtained population. In details, in the early stages, it gets better in 6 problems on GD and 6 problems on IGD. During the generations (the later stages), M-K-RVEA gets equal or better on 5 problems, one is the same. It is also better in 6 problems on IGD. In the last stages, M-K-RVEA is getting better in 7 problems on GD and 5 problems in IGD. In other problems, K-RVEA gets the same results with M-K-RVEA or a bit better than M-K-RVEA. In general, through the experimental results, it is proposed to use adaptive parameter control that has an effective effect to implement the optimal target optimization algorithm using general surrogate functions, algorithms based on Kriging model in particular. The obvious meaning of using the response parameter is to assess the quality of the population at the time of determining whether to update the Kriging model or not? The results using adaptive parameter control demonstrate the hypothesis of the impact of the time process, the current convergence quality of the population, and the characteristics of the problem on maintaining the equilibrium of quality of convergence and diversity of the population, through ensuring a balance between the exploration and exploitation process of evolution. The effectiveness of parameter selection in the current stages also depends on the quality and effectiveness of the previous stage, proving that a timely adjustment to maintain a balance between exploration and exploitation is needed to set. However, from the results of some problems, K-RVEA has a better quality, although not large, it also gives us issues that need to be adequately posed such as stability, stainability and random factors course. These factors influence the choice of parameters appropriately or guide evolution to better efficiency.

3 Conclusion In this paper, we have analyzed the effects of time factor, algorithm quality, and problem characteristics on the process of maintaining the convergence and diversity of the population over generations. The implications for maintaining the exploration and exploitation capabilities of the evolutionary algorithms of maximally targeted targets using surrogate functions. The paper evaluated the properties of the temporal process through the number of calculations, the quality of the population according to the number of non-dominated solutions in the population and the characteristics of spatial problems, the number of calculations of the problem. Those values are basis for calculating and determining the parameters that determine the time when updating machine learning model, in our experiment is the Kriging model. The results have proved the hypothesis and suggested the evaluation of factors affecting the evolution process to determine the parameters in a flexible way, contributing to the guidance of well-balanced algorithms, more about the quality of convergence and diversity through the search process. The results also suggest further research

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issues for the evaluation and parameter selection in the evolution process of the multi-objective optimal evolution algorithms, especially algorithms using surrogate functions in solving expensive problems in the field of multi-objective optimization.

References 1. R. Cheng, Y. Jin, M. Olhofer, B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016) 2. S. Choi, J.J. Alonso, H.S. Chung, Design of a low-boom supersonic business jet using evolutionary algorithms and an adaptive unstructured mesh method. AIAA Paper 1758, 2004 (2004) 3. T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, K. Sindhya, K-RVEA: a kriging-assisted evolutionary algorithm for many-objective optimization. Sci. Comput. B 2 (2016) 4. K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable test problems for evolutionary multiobjective optimization. TIK-Report no. 112. Technical report, TIK, ETH, Zurich (2001) 5. M.T.M. Emmerich, K.C. Giannakoglou, B. Naujoks, Single- and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Trans. 10(4), 421–439 (2006) 6. L. Gonzalez, J. Periaux, K. Srinivas, E. Whitney, A generic framework for the design optimisation of multidisciplinary UAV intelligent systems using evolutionary computing, in 44th AIAA (2006), p. 1475 7. A. Husain, K.-Y. Kim, Enhanced multi-objective optimization of a microchannel heat sink through evolutionary algorithm coupled with multiple surrogate models. Appl. Therm. Eng. 30(13), 1683–1691 (2010) 8. G. Matheron, Principles of geostatistics. Econ. Geol. 58(8), 1246–1266 (1963) 9. J. Sacks, W.J. Welch, T.J. Mitchell, H.P. Wynn, Design and analysis of computer experiments. Stat. Sci. 4, 409–423 (1989) 10. I. Voutchkov, A.J. Keane, Multiobjective optimization using surrogates (2006) 11. Q. Zhang, W. Liu, E. Tsang, B. Virginas, Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans. 14(3), 456–474 (2010)

Study and Analysis of NOMA Downlink Transmission Model in 5G Wireless System Sanjeev Kumar Srivastava

Abstract This paper examines NOMA downlink transmission model in 5G technology wireless system. In this system, a mathematical model for unordered probability outage, ordered probability outage and optimum allocation is developed. The outcome of the model is simulated for NOMA downlink and the analysis of the same is discussed. When channel gain is increasing, capacity of optimum power allocation and capacity of random power allocation in NOMA improve the overall system performance. But in large context, capacity of optimum allocation in downlink transmission will produce better result as compared to that of random allocation due to fairness in order to distribute available resources equally on high priority basis to get high net throughput. Keywords NOMA · OMA · OFDMA · MIMO · MUD · LDM

1 Introduction Non Orthogonal Multiple Access (NOMA) is one of the main principles of designing radio access techniques for the 5G technology wireless network system. It is based on the fundamental concept of wireless technology [1]. Therefore, the motivation in this technology serves more than one users in each orthogonal resource block that is, time slot, subcarrier, spreading code etc. In contrast, a conventional Orthogonal Multiple Access (OMA) technique includes scarce bandwidth resource which is solely occupied by the user despite its poor channel conditions. This impacts negatively the spectrum efficiency and in turns the degradation of the throughput of the overall system. Hence, the novelty of this model is that NOMA ensures that the users with better channel conditions can simultaneously utilize the same bandwidth resources. As a result, fairness has to be guaranteed. Therefore, the net throughput of NOMA can be significantly larger than that of OMA. In addition to its spectrum efficiency S. K. Srivastava (B) PCE, Mumbai, Maharashtra, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_13

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gains, NOMA can enable massive connectivity. This is key element of supporting the Internet of Things (IoT) infrastructure in 5G technology [2]. The extension of NOMA to cellular networks is a recent development. However, the central principles of NOMA have been studied as successive interference cancellation in the context of Multi User Detection (MUD) and Multi Input and Multi Output (MIMO) Vertical (V)—BLAST receivers which is one of the main components of NOMA. NOMA has been shown to be compatible with existing wireless systems and can be integrated with other communication technologies to further improve their efficiency. It can be blended with existing OMA techniques such as TDMA and OFDMA [3]. NOMA has also been recommended for the 3GPP LTE. Multi User Superposition Transmission (MUST) enables two serving users simultaneously on the same OFDMA subcarrier. NOMA is also being proposed for the Fifth Generation New Radio (5G NR) standard which is termed as Resource Spread Multiple access (RSMA) which is ideally suited for asynchronous and grant-free access. It can be used for IoT and mission critical applications. Thus, NOMA substantially enhances the spectral efficiency of TV broadcasting through superposition of multiple data streams. Hence, NOMA has immense potential towards enhancing the spectral efficiency in 5G networks and supporting massive connectivity. The contribution in this system model is that mathematical analysis of NOMA probability of unordered outage, NOMA with channel ordering in downlink transmission and NOMA optimal power allocation in downlink transmission have been derived and then its simulated results have been discussed in detail.

2 NOMA Model The downlink NOMA model is easier and simple as compared to that of uplink. In downlink, base station decides to which user allocates what power on basis of channel gain whereas, in uplink, it is not clear who decides the allocation of power. Therefore, NOMA downlink model is preferred [4] (Fig. 1). In this model, (it has been) two users are considered (two users), namely user 1 as weakest channel and user 2 as strongest channel (respectively). (Now, in this model), User 1 decodes signal through base station so that it can limit the effect of noise in user 2. Hence, in NOMA downlink, base station creates a superposed signal containing data of both users [5], xs =

√ √ a1 ρs x1 + a2 ρs x2

(1)

√ √ where x 1 and x 2 denote symbols of user 1 and user 2 and a1 ρs and a2 ρs denote fractions allocated user 1 and user 2 respectively. The quantity ρ s shows the total transmitting Signal to Noise Ratio (SNR),

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Fig. 1 NOMA Downlink transmission

ρs =

Ps σ2

(2)

where Ps represents total transmit power. The received symbol at each user is given by, yi = h i xs + n i = h i

√  √ a1 ρs x1 + a2 ρs x2 + n i

(3)

Total power constraint is satisfied with, a1 + a2 = 1

(4)

Now, NOMA downlink with user 1, y1 = h 1 xs + n 1 = h 1

√  √ a1 ρs x1 + a2 ρs x2 + n 1

(5)

Hence, corresponding SNR for decoding user 1, γux11 =

a1 ρs |h 1 |2 a1 ρs β1 = 2 a2 ρs β1 + 1 a2 ρs |h 1 | + 1

where β1 = |h 1 |2 and n 1 = 1. The achievable rate for user 1,

(6)

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   log2 1 + γux11 = log2 1 +

a1 ρs β1 a2 ρs β1 + 1

 (7)

Here, the desired rate must be less than the maximum rate, otherwise probability of outage will be faulty and subsequently, no power will be generated. In the case of NOMA downlink with user 2, y2 = h 2 xs + n 2 = h 2

√  √ a1 ρs x1 + a2 ρs x2 + n 2

(8)

Hence, corresponding SNR for decoding user 1 at user 2, γux22 =

a1 ρs |h 2 |2 a1 ρs β2 = 2 a2 ρs β2 + 1 a2 ρs |h 2 | + 1

(9)

where β2 = |h 2 |2 and n 2 = 1. After decoding 1st user and successive cancelling interference, y2 = h 2

√  a2 ρs x2 + n 2

(10)

SNR for decoding user 2 at source 2 is, ysx22 = a2 ρs |h 2 |2 = a2 ρs β2

(11)

Hence, for given maximum rates of user 1 and user 2, it is therefore necessary that, (12) (13) and

are the minimum guaranteed QoS rates for users 1 and 2 respectively.

3 Analysis of NOMA Model In this section, mathematical analysis of NOMA probability of unordered outage, NOMA with channel ordering in downlink transmission and NOMA optimal power allocation in downlink transmission is discussed in detail.

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3.1 NOMA Probability of Unordered Outage Probability of unordered Outage at user 1 in NOMA, For given QoS constraints,   log2 1 + γux11 < R˜ 1 ˜ a1 ρs β1 ⇒ γux11 = 1+a < 2 R1 − 1 = R 1 2 ρs β2 ⇒ (a1 − a2 R1 )ρs β1 < R1

(14)

For Probability of unordered Outage < 1, a1 − a2 R 1 > 0 ⇒ R 1
β˜1  = 1 − Pr B1 > β˜1 , B2 > β˜2   = 1 − Pr B1 > β˜1 Pr B2 > β˜1

(31)

Probability of Outage Ordered NOMA at user 1,      1 β 1 β1 ˜ exp − 2 dβ1 = exp − 2 Pr B1 > β1 = δ12 δ1 δ1

(32)

   β 1 Pr B1 > β˜1 = exp − 2 δ1

(33)



Similarly,

Since,       β 1 β 1

F × exp − 2 B1 β1 = 1 − exp − 2 δ1 δ2      1 1 1 1 ˜1 β exp − + + = δ12 δ22 δ12 δ22   β 1 1 = 2 exp − 2 δ3 δ3

(34)

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 Pout = Pr C u 1 < R˜ 1   a1 ρs β˜1  < 2 R1 − 1 = R 1 = Pr 1 + a2 ρs β˜1   R1 = Pr β˜1 < ρs (a1 − a2 R1 ) Pout

Pout

  1 β˜1 ∫ = exp − 2 dβ˜1 δ32 δ3 0   R1 ρs (a1 −a2 R1 ) 1 β˜1 ∫ = exp − 2 dβ˜1 δ32 δ3 0   R1 = 1 − exp − ρs (a1 − a2 R1 )

(35)

R1 ρs (a1 −a2 R1 )

(36)

where, user 1 is weaker signal and probability of Outage is increasing at user 1 and δ32 is smaller. Channel gain at user 2, 2   β˜2 = h˜ 2 = max |h 1 |2 , |h 2 |2

(37)

CDF of ordered user 2,     FB˜ 2 β 2 = Pr B˜ 1 ≤ β˜2 Pr B˜ 2 ≤ β˜2      β˜2 β˜2 = 1 − exp − 2 1 − exp − 2 δ1 δ2       β˜2 β˜2 β˜2 = 1 − exp − 2 − exp − 2 + exp − 2 δ1 δ2 δ3

Pout

1 1 1 = 2+ 2 2 δ3 δ1 δ2 ⎛ ⎞ 2 2 a1 ρs h˜ 2 ⎜ ⎟ = 1 − Pr ⎝ 2 ≥ R1 , a2 ρs h˜ 2 ≥ R1 ⎠ 1 + a2 ρs h˜ 2       φ φ φ = Pr β˜2 < = FB˜ 2 = 1 − Pr β˜2 ≥ ρs ρs ρs

 R2 R2 φ= , (a1 − a2 R1 ) a2

(38) (39)

(40) (41)

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Since, probability of outage at user 2 is decreasing,  Pout = FB˜ 2

φ ρs



      ∅ ∅ ∅ 1 − exp − 2 − exp − 2 + exp − 2 ρs δ1 ρs δ2 ρs δ3

(42)

3.3 NOMA Optimal Power Allocation with Downlink The sum capacity of Downlink NOMA is given as, CUX11 + cUX22  = log2 1 +

a1 ρs β1 1 + a2 ρs β1

 + log2 (1 + a2 ρs β2 )

(43)

Since, a1 + a2 = 1  = log2

1 + ρs β1 1 + a2 ρs β1

 + log2 (1 + a2 ρs β2 )

   1 + a2 ρs β2 = log2 (1 + ρs β1 ) + log2 1 + a2 ρs β1

(44)

where (1 + ρs β1 ) is constant.  2 ρs β2 Hence, for optimal power allocation, one has to maximize, 1+a 1+a2 ρs β1 where (β2 > β1 )

(45)

The above quantity has been observing monotonically to allocate the power by increasing the value of a2 with constrained QoS. 

1 + a2 ρs β2 1 + a2 ρs β1



1 + a2 ρs (β1 + β2 − β1 ) 1 + a2 ρs β1 a2 ρs (β2 − β1 ) (β2 − β1 ) =1+ =1+ 1 + a2 ρs β1 ρs β1 + a12 =

Hence, max sum-rate occurs for maximum value of a2 . However, this has to be in the feasible region (i.e., QoS constraints) [6],

(46)

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  log2 1 + γux11 ≥ R˜ 1 a1 ρs β1 ˜ ⇒ γux11 = ≥ 2 R1 − 1 = R 1 1 + a2 ρs β1 ⇒ (1 − a2 )ρs β1 ≥ (1 + a2 ρs β1 )R1 ⇒ ρs β1 − R1 ≥ a2 ρs β1 (1 + R1 ) ρs β1 − R1 ⇒ a2 ≤ ρs β1 (1 + R1 ) ρs β1 − R1 ⇒ a2 ≤ ρs β1 (1 + R1 )

(47)

Further, we must have,   log2 1 + γsx22 = log2 (1 + a2 ρs β2 ) ≥ R˜ 2 ˜

⇒ a2 ≥

2 R2 − 1 R2 = ρs β2 ρs β2

(48)

Thus, a2 lies in the interval, R2 ρs β1 − R1 ≤ a2 ≤ ρs β2 ρs β1 (1 + R1 )

(49)

Further, for above condition to hold, we must have [7], R2 ρs β1 − R1 ≤ ρs β2 ρs β1 (1 + R1 ) R2 R1 ρs ⇒ − ≤ β2 (1 + R1 ) β1 (1 + R1 ) R1 R2 R1 R2 ⇒ ρs ≥ + + β1 β2 β2

(50)

4 Result of the System Model By referring Eq. (19) for probability of unordered outage at user 1 and Eq. (26) for probability of unordered outage at user 2 respectively, the simulation result in Fig. 2 shows the fixed value of NOMA. channel gain and channel fading coefficient parameters are considered. When channel gain increases, probabilities of user 1 and 2 will limit the outage of serving a single user in each orthogonal resource block. By referring Eq. (34) for probability of ordered outage at user 1 and Eq. (42) for probability of ordered outage at user 2 respectively, the simulation result in Fig. 3

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Fig. 2 Probability of unordered outage for NOMA downlink transmission

Fig. 3 Probability of ordered outage for NOMA downlink transmission

shows the ordered value of NOMA. Again, channel gain and channel fading coefficient parameters are considered. When channel gain increases, probability of user 1 will limit the outage probability of user 2 due to noise and successive interference cancellation with reference to multiuser detection and vertical blast of MIMO system. By referring Eq. (50) for optimum power allocation of downlink transmission, the simulation result in Fig. 4 compares the capacity of optimum power allocation with capacity of random power allocation in NOMA. It has again considered channel gain and channel fading coefficient parameters. When channel gain is increasing, capacity of optimum power allocation and capacity of random power allocation in NOMA improve the overall system performance.

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Fig. 4 NOMA downlink optimum allocation

5 Conclusion Hence, capacity of optimum allocation in downlink transmission will produce better result as compared to that of random allocation due to fairness in order to distribute available resources equally on high priority basis to get high net throughput.

References 1. J. Ding, J. Cai, C. Yi, An improved coalition game approach for MIMO-NOMA clustering integrating beamforming and power allocation. IEEE Trans. Veh. Technol. 68(2), 1672–1687 (2020) 2. S.M.R. Islam, N. Avazov, O.A. Dobre, K.S. Kwak, Power domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges. IEEE Commun. Surv. Tutor. 19, 721–742 (2016) 3. M.F. Kader, M.B. Shahab, S.Y. Shin, Non-orthogonal multiple access for a full-duplex cooperative network with virtually paired users. Comput. Commun. 20, 1–9 (2019) 4. Z. Wei, J. Yuan, M. Elkashlan, Z. Ding, A survey of downlink non-orthogonal multiple access for 5G wireless communication networks. ZTE Commun. 14, 17–21 (2017) 5. L. Yu, P. Fan, X. Lei, T. Mathiopoulos, BER analysis of SCMA systems with codebooks based on star-QAM signaling constellations. IEEE Commun. Lett. (2019) 6. L. Lei, D. Yuan, C.K. Ho, S. Sun, Power and channel allocation for non-orthogonal multiple access in 5G systems, tractability and computation. IEEE Trans. Wirel. Commun. 15, 8580– 8594 (2018) 7. L. Anxin, L. Yang, C. Xiaohang, J. Huiling, Non-orthogonal multiple access (NOMA) for future downlink radio access of 5G. China Commun. 12, 28–37 (2015) 8. Y. Sun, Z. Ding, X. Dai, O.A. Dobre, On the performance of network NOMA in uplink CoMP systems. A stochastic geometry approach. IEEE Trans. Commun. (2020)

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9. K. Higuchi, A. Benjebbour, Non-orthogonal multiple access (NOMA) with successive interference cancellation. IEICE Trans. Commun. E98-B, 403–414 (2015) 10. Z. Ding, Z. Yang, P. Fan, H.V. Poor, On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users. IEEE Signal Process. Lett. 21, 1501–1505 (2014)

Network Structural Analysis Based on Graphical Measures and Metrics Atul Kumar Verma , Mahipal Jadeja , and Rahul Saxena

Abstract In the current era of rapid information exchange within a communication network like social media, computer networks, etc., identifying the communication pattern and densely interconnected region and analyzing it can yield noteworthy information. This information gathering may help in identification of suspicious group activities, identifying any anomaly in the group behavior, etc. In this paper, use of basic graph theory ideas and concepts has been made to identify densely interconnected region and patterns of communication within and between different communities. Further, this information will be utilized to identify any deviation from the general trend which may give us scope to further investigate the region of network. Such studies may be employed upon social media networks to identify suspicious groups. Based on general graph metrics analysis like motifs, community detection, average degree distribution, etc., various scenarios will be depicted to see how the evaluated metrics can be used to look for the solutions of the problem. The graphical models will be mimicked using SNAP (Simulation and Networks Analytics tool) and machine learning algorithm to evaluate the results depending upon various scenarios. Finally, the paper will conclude with the analysis of the results obtained on the aspect that how different situations can be handled based upon the network metrics. Keywords Communication network · Social media · Densely interconnected region · SNAP · Community detection · Social networks · Network metrics

A. K. Verma (B) · M. Jadeja Malviya National Institute of Technology, Jaipur, India e-mail: [email protected] M. Jadeja e-mail: [email protected] R. Saxena Manipal University Jaipur, Jaipur, India e-mail: [email protected]; [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_14

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1 Introduction Social network means social interconnection and personal relationship through network. Social networking occurs when one person interacts with other person online. A graph is plotted by studying and analyzing the data and interconnection of social network. This graph is known as social network graph where the node represents people and the line between the nodes, called edges. Many networks like Internet, email networks, social network and citation network of documents show community structure which includes set of nodes that are interconnected [1]. Community detection displays the concealed yet significant structure in network such as groups in social network [2]. Communities can be defined by two ways. The first one is clustered approach in which nodes are densely interconnected as compared to the other nodes of the graph. Second is the denotation approach in which set of nodes have same interests. So that, the denotation clusters can be identified by both approaches of the nodes. Social networks are classic example of graphs with dense interconnection. Communities are set of nodes which probably share common equity within the graph. A lot of researches is being focused on network structural analysis for noteworthy information. The objective of this paper is the development of a network analysis-based tool for identifying the communication pattern and densely interconnected region and analyzing them. Graph and network theory have various interesting metrics and results which are handy in finding the solutions to the realistic problems. This paper mainly focuses on depicting the applicability of basic graph metrics of the network. Support vector machine (SVM) classifies data in linear as well as nonlinear manner. In order to categorize in different classes, SVM determines the linear separations in the search space. It is used to resolve the problems of classification, clustering, etc [3, 4]. In this paper, consider two scenarios. The first scenario deals with the identification of communities. Look for the community which has dense network, i.e., larger degree nodes than average degree of graph. Identify the most common patterns throughout the graph using motifs. Compare the frequency of these patterns with this community. All rare patterns in the community may be suspected one. In the second scenario, we ensure that all rare patterns in community are suspected or not. So, we read all chat log file of the rare patterns and apply support vector machine (SVM) classifier to ensure whether all rare patterns are suspicious/anomaly or not. Thus, following the ideology discussed above, the flow of the paper will be as follows: Section 1 deals with the basic introduction and what will be the focus areas of the manuscript. Section 2 deals with the state-of-the-art methods dealing with the computational solutions provided to the problems of interest using various theories and ideologies. Section 3 covers the scenarios, discussion, mapping the problems to graphical model and evaluating the graph measures. Section 4 deals with proposed framework that works on detection of suspicious/anomaly node or person. Finally, based on the methods proposed and justifications made with respect to the situations,

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conclusions are drawn and future directions will be discussed over the area and approach.

2 Related Work There have been various investigations on network structural analysis using graph theory ideologies and using lemmas and results interesting views have been presented. Chen et al. [5] introduced detection of community algorithms that improve the cluster quality measurements by divide and conquer of the given densely interconnected structure. This algorithm can be used to optimize any densely interconnected graph quality metric. Newman [6] shows the network, modularity matrix or modularity of particular matrix can be expressed in terms of eigenvectors. This expression tends to generate spectral algorithm for detection of a community. Yang et al. [7] proposed communities from vertex attributes an accurate and scalable algorithm for detecting overlapping clusters in networks with vertex attributes. Tang et al. [8] discussed the relationship between detecting financial crimes and the social web, and used select case studies to illustrate the potential for applying social network analysis techniques. Kumar and Singh [9] proposed a system for detection of the sentiments in online messages and comments exchanged over social networking and blogging sites, which monitors communication between users and identifies the messages of individuals who exhibits anomaly behavior over time. The proposed system is also able to identify group of people who are discussing about same topics which may be about a suspicious activity. Based upon the discussion so far, the current state of the art suggests that though a lot of works has been done especially in the area of community detection and anomaly identification in social network, there are very few that have come up with a generalized framework. The approaches suggested are more inclined to the text data mining based on the user data available on web. Further, the suspicious information identification has a lot of insights available on approaches for identification of ‘outlier nodes’ but the area still lacks to have a framework that can be applied on various scenarios to produce some vital information. In this paper, an attempt has been made to propose a framework to accomplish these tasks and using the network structure analysis and metrics as per the relevance of applicability, the paper will try to suggest solutions to the problems in various network scenarios.

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3 Social Network-Based Measures and Solutions 3.1 Community Detection for Analysis Community structure exploration is deemed to be the basic analysis technique which mainly centers on network’s internal structure. Sample network with illustration of cluster is shown in Fig. 1, where the communities indicate densely interconnected set of nodes. The capability to identify such network is important in various reallife applications like networks of friends on social sites, computer communication networks, etc.; finding strongly interlinked set of nodes in network would give useful information for its evaluation. Measure of modularity computes the true cluster in a network given by Eqs. 1 and 2. M∝

 |(no. of edges within groups) − (expected no. of edges within groups)| s∈P

(1) • M is the modularity; • P is the partition of graph; • S ∈ P is the partitioning of the graph into groups;

M(G, P) =

  Kx K y 1  Ax y − 2n s∈P s∈P s∈P 2n

(2)

• Axy is the edge weight between nodes x and y; • K x and K y are the sum of the weight of the edges attached to nodes x and y, respectively; • 2n is the sum of all the edge weight of the graph. Fig. 1 Community detection

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If the no. of edges in the groups increases from the expected number, then modularity will be positive. Hence, one should identify those clustered in network where exists large positive modularity. Apart from community detection using modularity, there are several more methods engaging different techniques like hierarchical clustering, Girvan–Newman method, etc.

3.2 Detecting Suspicious/Anomalies in Network/Graph Data An object in a data set is usually called suspicious/anomaly activity if it contradicts from the normal/known behavior of the data, or it is not connected/similar to any other object in terms of its characteristics. All real-life application data is interconnected to each other through networks like social networks of friends, computer communication networks, the citation networks of documents, etc. From practical outlook, it is important to explore the rare connections and emerging possibilities. Such exploration results in deliberating anomalous activities by communities in network. Pattern discovery activity is one of the important approaches to find out anomalies in graph. It is interesting to note how anomalous behavior of communities represented by graph pattern and specific method is needed for detection of such patterns. For this purpose, it will be fruitful to develop tools and techniques to detect suspicious network activities in multi-dimensional vector data. In Fig. 2, it is possible to have more than one suspicious in a data set as depicted. There are three isolated objects that are suspected in this figure. Fig. 2 Various suspicious in a data set

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3.3 Graphical Analysis Using Stanford Network Analysis Project (SNAP) In this section, general graph metrics will be analyzed using SNAP tool. Understanding these graph measures and its importance will help to propose the model for various network structures. Considering the graphical data set obtained from Wikipedia [10], having edge list between various nodes of the network will be considered as a reference network with number of nodes equal to 7115 and 103,689 edges between them. The graphical distribution of the average out-degree (no. of edges directed from source node to any other node in the network) comes out to be as shown below. By analysis of Fig. 3 we get, the out-degree distribution is not a well-distributed graph (more axial), so a log–log scale distribution curve has been considered to understand the distribution properly in Fig. 4. These results have been obtained over Fig. 3 Out-degree distribution curve

Fig. 4 Log–log out-degree distribution curve

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the Wikipedia edge list network using SNAP library functions. The curve seems to follow the power law distribution [11] and heavy-tailed distribution. The average degree comes out to be an approximately four, i.e., four out connections for majority of nodes in the graph. Similarly, other metric represents path length (distance or no. of hop counts to reach from one node to other node). The expression for average path length is given as: h¯ =



1 2E max

hxy



x,y=x

where hxy refers to the distance from node x to node y, and E max refers to max. number of edges [n(n − 1)/2], where n is the total no. of nodes. This measure gives an idea of the spread of the graph, while the average degree distribution gives the detailing about how many neighboring nodes can be reached.

4 Proposed Framework In the proposed framework, system will undergo the following steps to detect the accurate anomaly, Fig. 5. Let us take one example for better understanding of this framework with the help of Fig. 6. (i)

First, consider a social network with lots of nodes that are interconnected to each other. (ii) On analyzing this interconnection in networks, we found communities, i.e., dense connected subgraph using NMF method [12]. (iii) Now system will randomly select motifs with same pattern [13]. (iv) These motifs may or may not contain suspicious node. (v) Reading of chat log file present in selected motifs. (vi) Preprocess data by NLTK [14]. (vii) Extract features based on specified suspicious keywords. If found to be suspicious ‘1’, else ‘0’. All feature value will be stored in matrix. (viii) Apply SVM to classify suspicious (class 0) and non-suspicious (class 1) category [4]. (ix) Based on category, the system will ensure detection and declaration of accurate anomaly.

5 Conclusions and Future Scope The paper proposes a framework for the detection of suspicious node in social network. The state-of-the-art method provides an insight for enhanced and accurate detection of anomalies. The methods and techniques discussed are focused on

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Fig. 5 Framework to detect anomaly in network

identifying dense interconnection, thereby analyzing and detecting the suspicious node in the social network. This analysis provides a technique that enables system to find out randomly rare patterns present in communities. This will help to analyze only the rare patterns of subgraphs in the network. However, this approach works on randomized algorithm for the selection of rare patterns in the communities. So, the probability of non-suspicious pattern selection may be there. Moreover, this analysis could be done using multiple methods for the detection of anomaly. Further, there exists a scope on working out different methods to enhance the detection of anomaly in the network. The strategies suggested are more focused on identifying the correct suspicious node, but rare pattern selection is not up to the mark. In the paper, an attempt has been made to propose a generalized framework building upon the idea of analysis of the social network on the basis of selection of

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Fig. 6 Example of proposed framework

rare patterns or motifs which may contain suspicious node. The idea for identifying rare nodes of the network explicit have following steps: (i) Community detection; (ii) random selection of motifs; (iii) feature extraction; (iv) SVM classifier. In the future, this proposed framework needs to implement using different methods like SNAP to analyze computational aspects and challenges for detection of anomaly in the social network.

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References 1. M.E. Newman, M. Girvan, Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004) 2. S. Fortunato, Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010) 3. S. Tong, D. Koller, Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001) 4. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 5. M. Chen, K. Kuzmin, B.K. Szymanski, Community detection via maximization of modularity and its variants. IEEE Trans. Comput. Soc. Syst. 1(1), 46–65 (2014) 6. M.E. Newman, Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006) 7. J. Yang, J. McAuley, J. Leskovec, Community detection in networks with node attributes, in 2013 IEEE 13th International Conference on Data Mining (IEEE, 2013, December), pp. 1151– 1156 8. L. Tang, G. Barbier, H. Liu, J. Zhang, A social network analysis approach to detecting suspicious online financial activities, in International Conference on Social Computing, Behavioral Modeling, and Prediction (Springer, Berlin, Heidelberg, 2010), pp. 390–397 9. A.S. Kumar, S. Singh, Detection of user cluster with suspicious activity in online social networking sites, in 2013 2nd International Conference on Advanced Computing, Networking and Security (IEEE, 2013), pp. 220–225 10. V. Indu, S.M. Thampi, A nature-inspired approach based on Forest Fire model for modeling rumor propagation in social networks. J. Netw. Comput. Appl. 125, 28–41 (2019) 11. A. Clauset, Inference, models and simulation for complex systems. Lecture Notes CSCI, 70001003 (2011) 12. L.Y. Tang, S.N. Li, J.H. Lin, Q. Guo, J.G. Liu, Community structure detection based on the neighbor node degree information. Int. J. Mod. Phys. C 27(04), 1650046 (2016) 13. H. Yin, A.R. Benson, J. Leskovec, D.F. Gleich, Local higher-order graph clustering, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017), pp. 555–564 14. J. Yao, Automated sentiment analysis of text data with NLTK. J. Phys.: Conf. Ser. 1187(5), 052020 (2019)

Lung Tumor Classification Using CNNand GLCM-Based Features Amrita Naik and Damodar Reddy Edla

Abstract Identification of malignant tumors in lung CT in early stage is very important for early intervention and favorable outcomes. So far, researchers have used several machine learning architectures to identify the malignant tumors in low-dose CT scans. In this paper, we use a deep learning architecture which combines CNN with GLCM-based texture features to classify the lung nodule into benign and malignant. Most of the deep learning model uses softmax activation function, whereas we use SVM classifier to predict the malignancy in the nodule so as to avoid the risk of over-fitting. Our model was validated on LUNA dataset on which it achieved an accuracy of 93.1%, sensitivity of 95.97%, specificity of 86.8% and positive predictive value of 94.25%. Keywords Deep learning · CNN · Gray-level co-occurrence matrix · Haralick · SVM

1 Introduction Lung cancer has caused majority of the death worldwide [1]. The survival rate of the patient decreases as there is delay in diagnosis of malignant lung tumor [2]. Most of the lung cancers are detected at a later stage of disease. Timely screening of lung tumors can reduce the risk of mortality due to lung cancer. Hence, lowdose CT scans can be used as a screening tool to diagnose lung cancer at an early stage. The radiologist has to interpret each of these scans and identify the malignant tumors. There could be a possibility of observation error by the radiologist. Hence, a computer-aided diagnostic tool could be used to automate the detection of lung lesion so as to assist the radiologist in better decision making. Several machine learningbased decision-making tools like neural network are being used to accomplish this task. Currently, deep learning models like convolutional neural network are showing better results in classification of medical images. A. Naik · D. R. Edla (B) National Institute of Technology, Goa, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_15

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A neural network requires hand-crafted features as input, whereas CNN extracts features on its own. CNN is composed of three layers: convolution, pooling and fully connected layer. The convolution and pooling layer extracts the CNN feature from the image. However, CNN cannot gather spatial information between the features. Texture is another important feature which can be used for effectively classifying the medical images. Gray-level co-occurrence matrix is a statistical method of analyzing texture and extracts spatial relation of pixel. The accuracy of machine learning algorithm depends largely on features selected for classification. And hence, we plan to combine both CNN feature and statistical feature like gray-level co-occurrence matrix to provide better accuracy in detection of malignant tumors in CT. Although different approaches are proposed for lung nodule classification, a novel approach is needed using deep learning strategy.

2 Preliminaries 2.1 Convolutional Neural Network Convolutional neural network (CNN) [3] uses multiple hidden layers to learn features from image in each layer. Our CNN architecture uses three convolution layers, three pooling layers, one fully connected layer and a softmax layer. Each convolution layer is followed by pooling layer and uses a ReLU activation function. The CNN architecture is explained in brief in Fig. 1.

Fig. 1 Extraction of CNN features

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The first convolution layer takes a nodule of size 50 × 50 as input. The size of the kernel in this layer is 3 × 3 with a stride of 1. This layer produces 32 features of size 48 × 48 which is fed to the ReLU layer so that it can only retain the positive values. The output of the ReLU layer is sent to the max-pooling layer of size 2 × 2 to get 32 features of size 24 × 24. This output is sent to second convolution layer which uses a kernel of size 3 × 3 and generates 64 features of size 22 × 22. The output of convolution layer is sent to ReLU layer which is then followed by the max-pooling layer so as to produce 64 features of size 11 × 11. The output of max-pooling layer is fed to the third convolution layer with a kernel of size 3 × 3. The output of the third convolution layer is 128 features of size 9 × 9 and is fed to the ReLU layer. The output is finally fed to the max-pooling layer to get 128 features of size 4 × 4. The output of final layers is flattened to get 2048 CNN features. These features are then fed to the fully connected layer and finally to the softmax layer to predict the presence of malignancy in the nodule.

2.2 Gray-Level Co-occurrence Matrix Texture is a very important feature for analyzing images. Texture-based features are used by most of the researcher and have also provided promising results [4– 6]. Initially, a co-occurrence matrix is created in different directions of θ and with a specific distance ‘d’ to determine relative position of a pair of pixels in the respective direction. We then extract statistical feature of texture using gray-level co-occurrence matrix (GLCM) obtained from the lung nodule in CT scans. In this paper, we considered the value of d = 1, 2 and θ = 0°, 45°, 90° and 135° and hence creating eight different gray-level co-occurrence matrices. Haralick and Shanmugam [7] used these co-occurrence matrices to define 14 statistical features. We extract six texture features such as contrast, dissimilarity, homogeneity, angular second moment (ASM), energy and correlation from four different co-occurrence matrices. The formula for each of these features is given in Table 1. where M and N are dimensions of the image μx =

N −1 M−1 

P[i, j] ∗ i

i=0 j=0

μy =

M−1 N −1 

P[i, j] ∗ j

i=0 j=0

σx =

M−1 N −1  i=0 j=0

(i − μx )2 P[i, j]

160 Table 1 Haralick features

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Formula

Contrast

−1 M−1  N

(i − j)2 P[i, j]

i=0 j=0

Dissimilarity

M−1 −1  N

P[i, j]|i − j|

i=0 j=0

Homogeneity

−1 M−1  N i=0 j=0

Angular second moment

−1 M−1  N

P[i, j] 1+|i− j|

P 2 [i, j]

i=0 j=0

Energy



−1 M−1  N

P 2 [i, j]

i=0 j=0

Correlation

−1 M−1  N i=0 j=0

(i−μx )( j−μ y ) P[i, j] σx σ y

Since there are eight co-occurrence matrices and six features defined for each co-occurrence matrix, 48 features are gathered from each image.

3 Proposed Work In this paper, we propose to design and implement a model which in turn combines the features extracted from CNN with Haralick texture features. Apart from extracting dominant features from CNN, the model also gathers advantage from statistical features extracted from gray-level co-occurrence matrix to get spatial information between pixels. A brief explanation of the proposed architecture is demonstrated in Fig. 2. The input to the model is a segmented lung nodule. This lung nodule is then fed to the CNN model to extract CNN features. The image is passed through 3 layers

Fig. 2 Proposed architecture

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of CNN model, wherein each layer consists of convolution and pooling layer. The output of feature extraction phase of CNN model is a feature map of size 2048. Before the feature is extracted from the image, we also train the CNN model with training datasets so as to update the weights. The lung nodule is also converted into eight GLCMs for θ = 0°, 45°, 90°, 135° and d = 1 and 2. We extract 6 statistical features such as contrast, dissimilarity, homogeneity, angular second moment, energy and correlation from each GLCM. Thus, we extract 48 Haralick texture features from the nodule. Both the 2048 CNN features and 48 Haralick features are concatenated and fed to SVM classifier to predict the malignancy of the nodule which could be either benign or malignant. SVM has proved to be a very good two-group classifier with high-dimensional feature map [8].

4 Experiments and Results 4.1 Experimental Setup The dataset was evaluated with i7 processor, NVIDIA GeForce RTX 2070 (8 GB onboard memory). The implementation of our networks is based on Keras 2.2.4 library with TensorFlow as back end.

4.2 Data Description We used data from publicly available dataset LUNA16 [9] which contains 888 CT scan images collected from LIDC-IRDC. The dataset uses 551,065 annotations, out of which, 1351 were labeled as malignant and the rest are benign. The scans of each person contain 200 slices, each of size 512 × 512 pixels. A team of four radiologists have evaluated these nodules, and if the average score of the four radiologist is more than 3, the nodule is considered to be malignant.

4.3 Simulation LUNA dataset also maintains an annotation file which keeps record of the x, y, z coordinates of the nodule. Nodules were then cropped based on the coordinates mentioned in the annotation. The database consists of 6131 training sets, 1903 test sets and 1534 validation datasets. Initially, we train the CNN with 70 epochs. The learning rate and batch size were set to 0.02 and 100, respectively. The loss function used for training was categorical cross-entropy. In this experiment, Adam optimizers have provided better training performance and converge faster. After every epoch, the

162 Table 2 Accuracy of the proposed work with different classifiers

A. Naik and D. R. Edla Classifier

Accuracy

Precision

SVM

93.10

86.88

Decision tree

91.32

83.27

Linear regression

91.69

82.82

Naive Bayes

90.22

80.35

K-nearest neighbor

90.90

88.69

AdaBoost

91.09

81.51

weights were updated. These updated weights were then used to generate the CNN feature map for training and testing. During training process, we use the trained CNN model to generate the CNN features for the nodule. We have used 80% of the dataset for training and 20% of the data for testing. Table 2 gives a summary of accuracy of the proposed model with different classifiers. It is evident from Table 1 that SVM has proved to provide better results when compared to other classifiers. SVM classifier provides an accuracy of 93.1%, sensitivity of 95.97% and specificity of 86.8% with the proposed architecture. Figure 3 gives a plot of training score and testing score of SVM classifier for different values of gamma parameter. The training and validation score increases as the value of gamma increases indicating that there is no over-fitting issue.

Fig. 3 Validation curve with SVM classifier

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5 Conclusion In this paper, a computer-aided detection system to classify lung nodules using CNN and GLCM features and support vector machines was proposed. The proposed system has provided accuracy of 93.1% and positive predictive value of 94.25%. We also observed that SVM classifier has provided better classification accuracy when compared to other classification algorithm.

References 1. F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68, 394–424 (2018) 2. S.C. Byrne, B. Barrett, R. Bhatia, The impact of diagnostic imaging wait times on the prognosis of lung cancer. Can. Assoc. Radiol. J. 66(1), 53–57 (2015) 3. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105 4. A. Kumar, S. Mukhopadhyay, N. Khandelwal, 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images, in Proceedings of the SPIE Medical Imaging Conference, Lake Buena Vista, Florida, USA, 28 Feb 2013 (SPIE, Bellingham, WA, 2013), pp. 1–4 5. J. Wang, H. Wu, T. Sun, X. Li, W. Wang, L. Tao, et al., Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters. Asian Pac. J. Cancer Prev. 14, 6019–6023 (2013). https://doi.org/10.7314/apjcp.2013.14.10.6019 6. H. Wu, T. Sun, J. Wang, X. Li, W. Wang, D. Huo, et al., Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography. J. Digit. Imaging 26, 797–802 (2013). https://doi.org/10.1007/s10278012-9547-6 7. R.M. Haralick, K. Shanmugam, Textural features for image classification. IEEE Trans. Syst. Man Cyber. 6, 610–621 (1973). https://doi.org/10.1109/tsmc.1973.4309314 8. C. Cortes, V.N. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 9. A.A.A. Setio, A. Traverso, T. De Bel, M.S. Berens, C. van den Bogaard, P. Cerello, H. Chen, Q. Dou, M.E. Fantacci, B. Geurts et al., Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017)

A Distributed Implementation of Signature Matching Algorithms for IoT Intrusion Detection Menachem Domb

Abstract Common software systems manage large databases and huge incoming and outgoing data. A major security risk is the intrusion of viruses via devices connected to the system network. Each virus has a unique signature. Early detection of these signatures is the core of most anti-virus software packages. Each anti-virus uses a multi-string-matching algorithm to detect the existence of a virus signature in the incoming data or a given data file. String matching uses pre-defined signature lists, which are growing fast and may contain a few million entries. Studies show that anti-viruses may have an impact on the data transmission performance and propose ways to cope with it either by enhancing the processing power, improvements to the matching algorithms and apply selective detection with low-risk probability. The introduction of the Internet of things (IoT) exposes the Internet to major security risks as numerous devices are expected to be connected to the Internet. IoT devices have poor processing resources, and it is not able to run himself proper anti-virus software and so, allowing malicious virus attacks without any way to prevent it. Some solutions propose incorporating in the IoT environment a powerful security gateway processor to handle the anti-virus aspect. This solution is against the concept of IoT as an independent entity able to cope with such challenges. This paper presents a hierarchical distributed and parallel processing framework that accelerates multi-string matching for IoT devices by utilizing the accumulated excess processing power of the local IoT devices network, eliminating the need for high capacity robust computers. Keywords Anti-virus · Multi-string-matching algorithms · Signature matching · NIDS · Intrusion detection · IoT · Parallel and distributed processing · Data transmission security

M. Domb (B) Ashkelon Academy College, Ashkelon, Israel e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_16

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1 Introduction The recent increase in the number of devices, the traffic volume, introduction of IoT, cloud computing, and other Internet expansions has made our dependency on data networks absolute and irreversible. As a side effect, it encourages intentional attempts to penetrate the network and utilize the transmitted data for other purposes or simply to cause malicious damage. Basic protection from such malicious attacks is to discover the penetrator’s unique pattern character-string or signature and block it. Assuming such a signature is discovered, it is added to a list of universal signatures and distributed to the public via anti-virus systems. Each local network connected to the Internet is expected to scan every incoming and outgoing data packet to ensure it does not contain any of the signatures listed in the corresponding table. Once such a signature is discovered, the file transmission is blocked, and the entire incoming file is removed. The comparison is done by a matching algorithm. Some of the popular string-matching algorithms include Aho–Corasick, Knuth–Morris–Pratt, Rabin–Karp, Boyer–Moore, and Wu–Manber. The Aho–Corasick algorithm uses a finite state machine that provides the best worst-case performance but requires a significant amount of memory for the state machine. Boyer–Moore provides the best performance when searching for a single signature but scales poorly. The Wu–Manber algorithm is similar to Boyer–Moore but consumes less memory than Aho–Corasick and provides a better average-case performance [1]. Practical implementations of the above string-matching (SM) algorithms show that it may not be sufficient for current high-speed and capacity networks and new modalities such as cloud computing and IoT. IoT devices have limited capacity in computer power, memory size, and storage space and thus demonstrate poor performance when processing heavy applications. Compromising on string-matching algorithms (SMA) for IoT puts the entire Internet data transmission at risk, especially due to the forecast of numerous additions of IoT devices to the Internet. This work proposes a framework implementation of stringmatching technology adjusted to the IoT environment. It is based on distributed and parallel processing of SMA by a network of IoT devices. It is done by splitting the matching process into micro-processes where each processes a short piece of the input string and scans a limited number of signatures. In the next section, we present the problem statement. The rest of this paper outlines the related work, the proposed solution, an example, feasibility experiment and conclusions, and future work.

2 The Problem Statement Common implementations of existing string-matching algorithms (SMA) are costly concerning the memory space and cache memory it consumes. Seemingly, there is no proper string-matching mechanism applicable for IoT devices. We could solve this

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by deploying a dedicated security-server that will handle all security aspects for the IoT network including the execution of SMA. However, such solutions complicate the incorporation and integration of IoT on the Internet and eliminate the entire IoT concept. Hence, there is a real need for an appropriate solution, which we propose in this article.

3 Related Work Data string-matching algorithms are used to detect malicious and malware files by monitoring incoming and outgoing data through the network. The main function matches data patterns at hand with pre-defined lists of malicious data patterns. Some commonly used algorithms are Boyer–Moore, Aho–Corasick, Naïve String search, Rabin–Karp String Search, and Knuth–Morris–Pratt. These algorithms are described in [2, 3] and were compared to determine which algorithm is most efficient for intrusion detection. Obeidat and Alzuby [4] propose a new matching approach that uses an array for each character and stores its index and then attempts to match it using the character index search within the indexes array created. Several works use graphics processor units (GPU) to accelerate expression pattern matching. GPUs have the bandwidth required to process large data volumes and heavy transmission of data streams. A traditional deterministic finite automaton (DFA) approach and a new state machine for extended finite automaton (XFA) are proposed to accelerate regular expression matching on GPU. In [5], a hierarchical parallel GPU machine is proposed to accelerate regular expression matching and resolve the problem of its state explosion. The results show that it demonstrates significant improvement on performance compared to other GPU implementations using bloom filtering. An advanced proposal of parallel processing of the AC algorithm is described in [6]. It proposes a revised version of AC which works on an integrated CPU-GPU configuration. Experiments show that its performance is better than when CPUs or GPUs are used exclusively. A comprehensive review of the distributed implementation of matching algorithms is outlined in [1], from which we have adopted some basic ideas for our proposal. These proposals focus on running SMAs concurrently using threading, which requires powerful CPUs and GPUs. This approach prevents SMAs from being executed in an IoT constraint environment. The approach we present is based on condensing SMA algorithms so that they consume much fewer computing resources and dividing their input into small pieces so they can be processed by microprocessers such as IoT devices.

4 The Proposed Solution Due to the poor processing resources with which IoT devices are equipped, they are unable to efficiently apply basic security measures such as detecting malicious

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incoming data transmissions. The common detection approach discovers the signature in the malicious data packets, which is done by string-matching algorithms given a list of known signatures. All the existing efficient string-matching algorithms consume processing resources close to the maximum capacity of most IoT devices, which may slow down the overall data transmission. We propose a comprehensive implementation system that operates properly in the IoT environment. It uses reference tables to manage the entire detection process, starting with dividing the detection process into many micro-processes, which are distributed to a network of IoT devices and simultaneously launch the SMAs to analyze the input data packets. The implementation is divided into two main stages, i.e., the setup and preparation stage and the execution stage. In the setup and preparation stage, several reference tables are defined and loaded to each IoT device connected to the local network. When an IoT device is about to accept data packages, it launches the control and monitoring system, which analyzes the reference tables and distributes each data packet to the devices that execute the corresponding SMA. Once the SMA process is done, the results are sent to the initiated IoT device to integrate the results received and react accordingly. Figure 1 details the steps in the two stages of the proposed control and monitoring system. Figure 1 depicts the five consecutive steps of the framework system. The first stage is setting-up the parameters the system will follow when executed. The first reference table is the IoT devices and its attributes, such as device ID, processing power, memory size, storage space, availability, overall strength rank, SMA preferences, maximum input string, and parallel type. The second table is the input file attributes, such as packet size. In the first step, the system analyzes the reference tables to determine the strategy of how to distribute the SMA tasks to the IoT devices. This includes which devices will participate in the detection process for the incoming file, which SMA will be executed in each device, the input string size, the list of malicious signatures, signature portion, and parallelism type. In step 2, The SMA processes are launched with the above parameters. In step 3, the incoming data is manipulated and continuously sent in step 4 to the participating IoT devices for signature detection. If the signature has been found, the system takes the proper action in step 5. The following sections elaborate on the use of IoT devices to run SMAs and the improvements in addition to the above basic decryption.

4.1 Alignment of SMA to Properly Operate on IoT Devices Based on [5, 1], it is apparent that several string-matching algorithms require moderate processing power and resources when applying the detection process towards a reasonable input packet size and an average signature length. Even the Aho–Corasick matching algorithm, which normally consumes a large memory size, has been optimized by compression of the state representation and more [5]. Now, it

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SMA Monitoring & Control System Reference Tables a. IoT devices attributes b. Input file attributes

1

Analyze reference tables

2

Set-up and initiate the IoT devices

3

Distribute data packets to devices End of

4 5

IoT1

IoT2

IoT3

IoTn

Analyze input and distribute final result

Fig. 1 Framework main components and processes

seems to be able to properly operate in high-end IoT devices. Therefore, it is recommended to perform a comprehensive performance test of the most popular SMAs for all types of IoT devices used in the specific implementation and associate the best SMA suitable to each IoT device. This framework proposes a systematic way of optimizing the deployment of SMAs for a given implementation by reducing its space consumption and minimizing its execution time. The descriptions provided in the following subsections are depicted in Fig. 2 to clearly illustrate how the framework operates and how the improvements are implemented.

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SMA Distributor Initiate Devices

Divide Packets & Distribute IoT

IoT

IoT

IoT

IoT 1

Divide Signature & Distribute

5

IoT

IoT

IoT

IoT

IoT

IoT

IoT

IoT

IoT

IoT

If Sig. Found

Integrate Results & Update

2

3

4

Fig. 2 Splitting input packets and dividing the signature file and length

4.2 Splitting Input Packets into Shorter Strings The processing time and the associated consumed resources depend on the algorithm’s underlying concept and its actual implementation, the length of the input string, the length of the corresponding signature, and the number of signatures searched for. The standard execution of an SMA assumes a data packet as input, which may be too big to handle in one cycle. However, when we divide each packet into smaller strings, it may be processed by the IoT device. The size of each packet portion will be adjusted to the specification of the IoT device expected to process it. The input can be handled sequentially or parallelly. In the sequential mode, all portions of the same packet are processed sequentially by the same IoT device, and in the parallel mode, all portions of the same packet are processed at the same time, each portion by a different device. In the first mode, we ensure that the entire packet

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is processed by the same device to avoid missing any signature, assuming it keeps the sequence and continuity of the pieces of the same packet and between packets. When a signature is discovered, the process is completed, the file is blocked, and all accepted packets of this file are purged, and notification is broadcasted to the IoT network. In the parallel mode, when a signature is discovered by one device, it notifies the other devices working on the same packet to stop working on the piece at hand and wait for the next file, as the current file is infected. In this mode, a signature may go undetected when one piece has just the beginning part of the signature, and the remaining part of the signature is processed by another device. To handle such cases, devices should be synchronized by using an internal messaging system that allows an instant message to be sent to the other devices processing portions of the same packet. To ensure a match of the two edges of the signature, an overlapping of a bit between any two pieces of the same packet can be added. If needed, the Harris stitching algorithm [7] can be employed. Arrow number 1 in Fig. 2 depicts the possibility of a hierarchical division of the input data file into subfiles in several levels enabling parallel processing of signature detection in its packets. This also enables the possibility of dividing long packets into shorter strings and forwarding them to a preferred IoT device for processing. Arrow 5 in Fig. 2 depicts the process where an IoT device discovers a complete or a partial [at the string end/start] signature, notifies the results of the integration step, which analyzes the message, concludes the results, and forwards it to the main process, which broadcasts it and stops all activities related to the defected file.

4.3 Dividing the Signature Aldwairi et al. [8] conducted a performance experiment of running the Wu–Manber matching algorithm using FPGAs hardware. The achieved performance has improved as expected. However, for long input strings, the performance decreased. Therefore, if the signature is significantly long, it can be divided into smaller parts. The search can be done serially or parallelly. In a serial mode, the same device sequentially checks the pieces of the same signature. If a signature piece does not appear in the packet, the process will skip the current signature and move to the next signature. In the parallel mode, if one of the devices realizes that the signature piece does not appear in the packet, it will notify the other devices to skip to the next signature and thereby expedite the process. Arrow 3 in Fig. 2 depicts this process. The input string is repeatedly checked against all the pieces of the same signature.

4.4 Dividing the Signatures Table At the same experiment described in [8], it was observed that for a large signatures database, the performance decreases, which means that smaller signature tables are

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recommended. To shorten the processing time, we divide the signatures table into pieces such that it fits the capacity of each device that is expected to process it. Similar to the previous section, this can be executed serially or parallelly, where each approach has its pros and cons. Arrow 2 in Fig. 2 depicts this option by repeatedly checking the same input string against the entire list of signatures, even when it is divided into several sub-tables. Arrow 3 in Fig. 2 provides an additional option of checking the same input string by a series of IoT devices, where each device has a different sub-table with a unique list of signatures. Figure 2 depicts the entire process including its main loops. It starts with selecting the IoT devices that will be involved in the malicious signature’s detection process. The selection is done by scanning the devices’ attributes table. Then, it initiates the selected devices to ensure its readiness to execute the process. Once this step is done, the process divides the load among the IoT devices.

5 Example To better understand the proposed system, we provide a simple example that follows the steps illustrated in Fig. 1. Below are the tables with the setup parameters in the example. In this example scenario, a file of 30k bytes packet length is transmitted; the system scans each packet to discover any malicious signature. A signature of 54 bytes appears in the 56th packet in the last 23 bytes of the second piece, and the remaining 31 bytes appear at the beginning of the third piece of the packet. IoT1 found the first part, and IoT2 found the rest of the signature. The control system is expected to connect the two matching partial signatures into one signature and react accordingly.

5.1 Analysis of the Reference Table Before a stream of data is scheduled to appear, the system sets the optimized configuration parameters to ensure the best detection process performance. To do so, it collects the input attributes from Table 2 and the configuration attributes from Table 1. In this example, the system evenly distributes the input file among the three IoT devices in a round-robin sequence.

5.2 Set up and Initiate the IoT Devices The system triggers IoT1, IoT2, and IoT3 devices and launches the packet formatting process and the associated SMA in each device.

A Distributed Implementation of Signature Matching Algorithms … Table 1 IoT devices attributes

Table 2 Datastream attributes

Attribute

173

IoT1

IoT2

IoT3

Device ID

D10

D20

D30

Processor type (mips)

200

300

400

Memory size (Mb)

50

100

150

Storage space (Mb)

300

500

700

SMA name

SM1

SM2

SM3

Max string size (bytes)

3k

9k

15k

String parallelism (V/H)

V

H

V

Signature tables ID

ST1

ST2

ST3

Max signature length (b)

500

900

1300

Signature parallelism

H

V

H

Availability

A1

L2

L3

Attribute

Input 1

Input 2

Input 3

Type

File

Stream

File

Packet length (k)

30

NA

40

Total size (Mb)

500

NA

1000

5.3 Distribute Data Packets to the Devices The input file fits the definition entered in Input 1 in Table 2. The system accepts data packets [30k] and splits each into pieces according to the lengths defined in Table 1 of the corresponding device that is expected to process it. In our example, the packet is divided into 5k, 10k, and 15k bytes and is forwarded to IoT1, IoT2, and IoT3, respectively. No signature is detected in 55 packets that pass through the system and the SMAs. However, when the 56th packet arrives, it is divided into three pieces and forwarded to the three devices, respectively. SM1 detects a partial signature match of 23 bytes at the end of the analyzed piece. It then sends a message to the system. At the beginning of the piece, SM2 also detects a partial signature of 21 bytes complying to the end of the signature and sends a message to the system. The system integrates the two partial matches discovered and realizes that the file is affected. It notifies the source device that owns the file transmission and reacts according to the organization’s policy.

6 Feasibility Check and Experiment The target in this experiment was to ensure the feasibility and applicability of the proposed framework and simulate the entire process. We developed two software

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modules, (1) a pseudo-SMA similar to the compressed version of the Aho–Corasick algorithm as described in [5]. The similarity focuses in terms of processing complexity and resource consumption; (2) a control and monitoring system that manages the input file detection process. It starts with accepting packets, dividing it into smaller pieces, and distributes the pieces to the IoT devices connected to the same local network. Each IoT device applies the pseudo-SM algorithm to detect if it contains a malicious signature. In case of a positive match, the system reacts as defined. We implemented the framework using four Arduino IoT devices. All devices were loaded with the pseudo-SM algorithm along with the control and monitoring system. The data packets were taken from a billing system of a cellular phone company. The signatures have been taken from the Broadcom Web site following the instructions in [9]. For testing purposes, we randomly incorporated signatures within the data packets. The execution of the example went well and proved that the framework can deliver the service it is expected to provide. Although the experiment’s purpose was just to prove the framework feasibility, we ran an initial performance test, but the results were not distinct, and hence, this is left to be conducted in the future.

7 Conclusions and Future Work The recent growing trend of IoT integration in the Internet broadens its security risk. This is due to its poor capacity and resources which limits it from implementing proper security measures, especially the detection of malicious strings mixed in incoming or outgoing data. This paper proposes a comprehensive framework for enabling a variety of string-matching algorithms to properly operate in the IoT environment and thereby close the security gap created by the IoT. The framework is based on a combination of condensed string-matching algorithms and a variety of techniques to better control the size of the input to be processed at a time. Future work will focus on implementing the framework by developing an open, robust, and efficient framework that easily can be integrated into heterogeneous configurations. Run a comprehensive implementation to evaluate the framework performance impact.

References 1. C.V. Kopek, E.W. Fulp, P.S. Wheeler, Distributed data parallel techniques for content-matching intrusion detection systems, in MILCOM 2007—IEEE Military Communications Conference, Orlando, FL, USA, 2007, pp. 1–7 2. V. Dagar, V. Prakash, T. Bhatia, Analysis of pattern matching algorithms in network intrusion detection systems, in 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Bareilly, 2016, pp. 1–5

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3. N. Tran, M. Lee, S. Hong, J. Bae, Performance optimization of Aho-Corasick algorithm on a GPU, in 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, VIC, 2013, pp. 1143–1152 4. I. Obeidat, M. Alzuby, Developing a faster pattern matching algorithms for intrusion detection system. Int. J. Comput. (S.l.), 278–284 (2020). ISSN 2312-5381. Available at: computingonl ine.net/computing/article/view/1520 5. C.H. Lin, et al., A novel hierarchical parallelism for accelerating NIDS using GPUs, in Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI) (IEEE, 2018), pp. 578–581 6. V. Sanz, A. Pousa, M. Naiouf, A. De Giusti, Efficient pattern matching on CPU-GPU heterogeneous systems, in Algorithms and Architectures for Parallel Processing. ICA3PP 2019, ed. by S. Wen, A. Zomaya, L. Yang. Lecture Notes in Computer Science, vol. 11944 (Springer, Cham, 2020) 7. S. Mistry, A. Patel, Image stitching using Harris feature detection. Int. Res. J. Eng. Technol. (IRJET) 03 (2016). www.irjet.net. p-ISSN: 2395-0072, e-ISSN: 2395-0056 8. M. Aldwairi, Y. Flaifel, K. Mhaidat, Efficient Wu-Manber pattern matching hardware for intrusion and malware detection, in International Conference on Electrical, Electronics, Computers, Communication, Mechanical and Computing (EECCMC), Tamil Nadu, India, 28–29 Jan 2018 9. R. Bryan, Manually add virus signatures to new installation package. Broadcom. https:// community.broadcom.com/symantecenterprise/communities/community-home/digestviewer/ viewthread?MessageKey=df36eff8-2acb-4982-86ce-f95929489213&CommunityKey=1ec f5f55-9545-44d6-b0f4-4e4a7f5f5e68&tab=digestviewer#bmdf36eff8-2acb-4982-86ce-f95 929489213 10. C. Hung, C. Lin, P. Wu, An efficient GPU-based multiple pattern matching algorithm for packet filtering. J. Signal Process. Syst. 86, 347–358 (2017). https://doi.org/10.1007/s11265016-1139-0

Graph-Based Multi-document Text Summarization Using NLP Abhilasha More and Vipul Dalal

Abstract Text summarization is the process of generating the condensed view of the text by selecting useful and relevant information from the original source documents. It is a sub-topic of natural language Processing. Text summarization is a technique for understanding the aim of any document, to visualize large text document within short duration. Summarization provides flexibility and convenience. Business leaders, analyst, and researchers must undergo huge number of documents on a day-to-day basis to stay ahead, and an oversized portion of their time is spent just deciding what document has relevancy and what is not. By finding out important and relevant sentences and creating summaries, it is possible to quickly check whether or not a document is worth reading. In this paper, we propose to design automatic text summarizer to summarize the multiple text documents. The input to the system is the multiple sources of news articles. Important sentences from the source document are selected and arranged in the destination documents or the summarized documents. This is called as the extraction technique in automatic text summarization. Here, one document visited is being processed, and its summary is generated using extraction technique through which a weighted graph is constructed using language processing. Keywords Text summarization · Natural language processing · Weighted graph · Text rank algorithm · Preprocessing · Extraction

1 Introduction Voluminous information is available to us over the Internet due to the widespread of the World Wide Web. There is information available through various sources pertaining information in the form of news legal documents, journals related to A. More (B) · V. Dalal Vidyalankar Institute of Technology, Mumbai, India e-mail: [email protected] V. Dalal e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_17

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various genres, e-magazines, e-books, etc. Daily e-new sources provide us with news containing information on various topics like politics, sports, classifieds, etc. Update of some particular topics can re-appear frequently for few days with some variations. Usually people just want a gist of what the news is about due to lack of time to read all the sources of news thoroughly. Here, a text summarization tool is needed that can save a lot of time. Automatic text summarization is condensing or summing up the original text to a summary without being meaningless. The condensed text or summary can be extracted from a single source document or from multiple sources of documents, which can be further classified as single document summarizer or multi-document summarizer, respectively. To read this, available immeasurable information form online resources can be very tedious and time consuming for the users linked with different sectors of interest. A text summarizer can be used by wide variety of people to read a concise document. For example, it can be used by a person to catch up on news on the go, or for a lawyer to get a last minute summary, a student to understand a concept, etc. Therefore, a summarizer can be used as a tool for any person to have a condensed view of any article within less time. Text summarization is the sub-topic of natural language processing which helps to encapsulate the enormous data available online. The text summarizing tool takes as input a document that can be one independent document or a stack of documents, which the user can select from wide variety of sources like the World Wide Web, user’s personal documents, or e-books. Then, it performs preprocessing of the input, removes stop words, and then tokenizes. A sentence analysis is then performed which includes sentence scoring and sentence ranking method. After ranking the sentences, the summary of these documents is generated that can be used as a quick access for the user. Text summarization can be broadly classified as extractive summarization and abstractive summarization. Extractive summarization: A few selected words is taken from the text and merged together to form a summary. It can be thought of as a highlighter that selects the main information from a source text [1]. In machine learning, extractive summarization usually involves weighing the essential sections of sentences and using the results to get summaries. Different types of algorithms and methods can be used to gage the weights of the sentences and then rank them according to their relevance and similarity with one another—and further joining them to generate a summary. Abstractive summarization: It uses semantic understanding to select words, even those words that do not appear in the source documents. They understand the text using advanced natural language techniques and generate a concise text that is completely a new text. This text aims at conveying the most critical information from the original text.

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2 Literature Review Dutta et al. [2] devised a methodology to summarize the tweets. Tweets are taken as input, and a subset, i.e., the condensed view of these tweets, is generated. The challenge in summarizing the tweets is that they very small to be provided as input, and also the language used in tweets is informal. Here, the similarity between the tweets is generated by the semantic similarity between the tweets. Similar tweets are clustered using community detection techniques through which a representative tweet is selected. Natesh et al. [3] propose a methodology of graph-based text summarization. NLTK is used for tokenizing, POS tagging, pronoun resolution, and sentence extraction. A graph-based ranking algorithm is used to generate weighted graph, and then, a shortest path algorithm is applied to create the summary of the input documents. Similarity between the sentences is found using the method of parts of speech and relation among the sentences. Ranking of these sentences is done using a ranking algorithm, and a graph is created depending on the closeness of sentences. Thakkar et al. [1] proposed the methodology of unsupervised learning on input text to extract sentences using graph-based ranking algorithms and shortest path algorithms for the users to view the summary on small screens like a mobile phone within a short duration of time. Extractive summarization method is used where parts of speech and sentences are selected and then a shortest path algorithm is applied to it. Gulati and Sawarkar [4] have proposed a method of summarizing multiple documents in Hindi language. They have used the summarization concept of preprocessing and extraction of feature terms. The extracted summary of these documents will then generate a consolidated single summary using fuzzy logic for ranking the sentence based on weights of the sentences. Hindi as a language is chosen as majority of people in India use this as a language to communicate that is a national language of the country. Dave and Jaswal [5] propose a method of multi-document text summarization, wherein five input documents have been considered for summarizing that can include around 10,000 sentences. The documents can be in the form of PDF, DOC, or TXT. Comparison of existing tools is shown in the paper. The paper emphasizes on multi-document text summarization methods using human-based evaluation to make the summary readable. Naik and Gaonkar [6] propose a single document text where a rule-based summarizer is used. 15 news articles are provided as an input to the system from DUC 2002 dataset. The output of this summarizer is compared to GSM summarizer to get better results than the existing summarizer.

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3 Problem Statement Thousands of pages can be referred to find information of any type. Millions of news sources providing thousands of different genres of news are available every day. To remain updated with the current events in the society or the environment, one has to read through all these news articles; however, reading through all these sources is a tedious task, especially during any special event like a cricket or a football match, natural disaster, a movie release, elections, etc. News related to such events can be too much to read. We aim to design a text summarizer for news articles for a user who wants to keep oneself updated about more than one genre of news. The user can select the various documents through which the news is to be read and get the summarized content that is relevant, meaningful, and precise.

3.1 Objectives Below is the list of objectives for the text summarization tool: • • • •

Designing a multi-document news articles summarizer. Design a user-friendly summarizer. Reducing redundancy in the generated summary. Generate a weighted graph to show dependency of sentences.

4 Proposed System In this project, we aim to design automatic text summarizer to summarize the multiple text documents. The input to the system will be multiple sources of news articles. Firstly, preprocessing on the input text document will be performed. Then, an extractive summary will be generated by selecting words of importance from the input text and arrange them in the output. The last step would consist of ranking the sentence based on feature weights and thus generating the output, i.e., the summarized content. The following steps will be included in preprocessing the input [2, 3]: i.

Tokenizing: Tokenizing can be performed on a paragraph or on individual sentences. Tokenizing a paragraph means to split the paragraph into independent sentences, and tokenizing a sentence means to split every word in a sentence. Every word that is split is an independent token. ii. Tagging parts of speech: Each generated token is then identified as a relevant part of speech depending on the tags of neighboring words. Semantic analysis of the sentence is performed in this step which identifies to be the most important part of processing the

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text as the context or the meaning of the token should also be considered while identifying the part of speech. iii. Resolving a pronoun: Objects or a person is considered to be a noun in the sentence. Appropriate mapping of pronoun to the corresponding nouns is done in this step. iv. Building a graph: After relevant nouns and pronouns are extracted, a weight or a rank is assigned to them. The nouns represent vertices, and the relevant edges are created depending on the weights of the tokens or the sentences. v. Sentence extraction: A consolidated weight of a sentence is calculated with the nouns present in that sentence. Highest scoring sentences are then taken to create a summary of the input text. These are the basic steps of extractive text summarization. Using a lexical dictionary, tokenizing and parts of speech tagging can be made easy. Further, for sentence extraction and graph building, we have used a text rank algorithm.

4.1 Flow of the System The flow of the proposed system is shown in Fig. 1. Input will be provided in the form of documents. Extractive summary includes removal of phrases and sentences, from the input text and combine them together to create a summary. After processing a single document and generating summary of individual documents, the summary will be further combined to generate a common condensed summary of multiple documents depending on the ranking of the sentences generated through individual summaries. Extracting and arranging meaningful and relevant sentences to create a summary is a challenge and is vital in extractive method of text summarization. A text rank algorithm will be used for extractive and unsupervised technique. The steps in the algorithm will be as follows: 1. 2. 3. 4.

Concatenate all the text contained in the articles. Split the text into individual sentences Find vector representation for each and every sentence. A matrix then stores the similarities between sentence vectors calculated in the previous step. 5. A graph is created with the help of similarity matrix with sentences as vertices and similarity scores as edges, for sentence rank calculation. 6. A final summary is formed by few top-ranked sentences. 7. This algorithm will be an iterative process till a single summary of all documents is generated (Fig. 2).

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Fig. 1 Flow of system

Fig. 2 Text rank algorithm

5 Future Scope i.

In the future scope of this system, the articles provided as input can be in the form of online sources or the Web sites providing news. ii. The user can bookmark the Web site that he/she uses regularly for reading the news. The bookmarked sources can be used for summarization.

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iii. Rather than generating summary of individual pages and then merging it, a consolidated summary can be generated by processing all the news sources together. iv. A different algorithm or a technique like fuzzy logic or regression models can be used to generate the summary of multiple documents.

6 Conclusion In this paper, we propose a methodology of automatic text summarization of more than one document, i.e., multi-document text summarizer using a graph-based approach. The set of multiple documents is given as an input to the summarizer. Here, we consider multiple e-news sources available online to be given as an input to the proposed system. The sources should be in a form of a word document. Every sentence in the document is first processed, and every word is independently determined as a token. The tokens are then identified with correct part of speech tag. Text rank algorithm is used for ranking the text through which, finally, the words in the sentences are connected by a link and a graph is built for the entire text. Depending on the factors such as highest rank of a noun and then highest rank of a sentence, a weight is calculated. After the calculating, a graph is drawn with nouns as vertices and sentences as links between them. Thus, we have devised a method of multi-document text summarizer which uses concept of natural language processing and an algorithm that makes it convenient to draw a graph of relevant nouns and sentences. Acknowledgements It is a genuine pleasure to express my deep sense of thanks and gratitude to my guide Dr. Vipul Dalal, for giving me all the support and guidance for the project. His dedication and keen interest had been solely and mainly responsible for carrying out the project work. His timely and scholarly advice helped me a lot to accomplish this task. I would like to extend my sincere esteems to the staff of Vidyalankar Institute of Technology and my colleagues for their timely support.

References 1. K. Thakkar, R. Dharaskar, M. Chandak, Graph-based algorithms for text summarization, in International Conference on Emerging Trends in Engineering & Technology (2010), pp. 516– 519. https://doi.org/10.1109/icetet.2010.104 2. S. Dutta, S. Ghatak, M. Roy, A.K. Das, A graph based clustering technique for tweet summarization, in 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (IEEE, 2015) 3. A.A. Natesh, S.T. Balekuttira, A.P. Patil, Graph based approach for automatic text summarization. Int. J. Adv. Res. Comput. Commun. Eng. 5(2) (2016) 4. A.N. Gulati, S.D. Sawarkar, A novel technique for multi document Hindi text summarization, in 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, 2017, pp. 1–6

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5. H. Dave, S. Jaswal, Multiple text document summarization system using hybrid summarization technique, in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2015, pp. 804–808 6. S.S. Naik, M.N. Gaonkar, Extractive text summarization by feature-based sentence extraction using rule-based concept, in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2017, pp. 1364– 1368

Fault Exploratory Data Analysis of Real-Time Marine Diesel Engine Data Using R Programming Cheng Siong Chin and Nursyafiqah Binte Nazli

Abstract The uncertainties in diesel engine parameters often result in an inaccurate model that give rise to poor fault analysis. A typical marine diesel engine model consists of few subsystems such as freshwater cooling system, lubrication oil cooling system, air cooler system and exhaust gas system. Instead of modeling and validating the simulation results with the real marine diesel engine data to determine the possible faults occurrence, this paper describes an exploratory data analysis on the actual data to identify the faults. R programming is used to carry out the analysis such as kmean clustering by generating the graphical representation of the data from each subsystem. Exploratory data analysis is used together with the fault tree analysis to analyze the results for possible faults. Based on the data analysis, it is found that there are several faults in the fuel oil system, air cooler system and scavenge air system that requires fault corrective actions that may lead to increase shipping cost. Keywords Marine diesel engine · Exploratory data analysis · Fault analysis · R programming

1 Introduction Many diesels engine modeling has been studied and analyzed for past several years and presented various results comparing with real running engines. Diesel engine design has a few important criteria which focus on efficiency, pollutant emissions, and fuel flexibility. Diesel engines have been modelled in different ways like filling and emptying methods [1], relying on basic equations like ideal gas law and conservation of mass to simulate air path dynamics of the combustion chamber [2]. The modeling and control of the fuel injection and the turbocharger [3, 4] were carried out. With increased computer processing power, the use of computational fluid dynamic (CFD) to model the geometry of engines can reach to millions of cell producing super high C. S. Chin (B) · N. B. Nazli Faculty of Science, Agriculture and Engineering, Newcastle University Singapore, Singapore 599493, Singapore e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_18

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accuracies with a price of relatively slow calculations due to a significant amount of finite elements used [5, 6]. It is often used to investigate and identify fault such as a possible spot of high thermal stress concentration in the engine. There are many toolboxes available in MATLAB for different systems design. For example, controlling the engine timing model, the model of a piston engine and internal combustion engine with the throttle and rotational inertia can be found in Simscape toolboxes such as Simscape Driveline and Simscape Multibody. A twostroke large-bore natural gas lean burn engine with turbocharger was also proposed [7]. The model consists of few different sub-parts with parameters such as air-fuel ratio to final output torque. Another software named Lotus Engineering Software is capable of modeling the combustion and gas flow processes using air-fuel ratio and valve timing events. The solver calculates the torque and volumetric efficiencies by considering the influence of heat transfer and friction phenomena [8]. This program was used for vehicle simulation. Models can be generated quickly using their inbuilt functions. The software can estimate the fuel consumption, top speed, acceleration and power output. Lotus engine simulation can be utilized for two or four strokes engine with great user interface. There are many different models available from its library but designing a new model for fault analysis is not entirely possible in the software. Wave Ricardo [9] is another software for validating designs using available input data. Many engine manufacturers build multiple variations of the engine. It covers from motor cars to marine engine and power generators. Wave offers detailed design and technology selection from a huge database consisting of combustion models and excellent turbine physics with the clear result and reports after the simulation. However, it can be quite complex to design from scratch before any fault analysis. GT-Power [10] is an alternate simulation software that uses advanced computing to simulate various on-going processes in an internal combustion engine. Logical interface and components are used along with icons and connectors. Two engine operating modes are available either for design or analysis purposes. However, the GT-Power engine simulation requires a mathematical tool that is not easy to configure for general engine simulation and fault analysis. OpenModelica [11] is another package for modeling mechanical systems for car manufacturers including engine modeling using open source codes. OpenModelica implementation can be added in a modular manner. It has modules for equation sorting, optimization, graphical visualization, and code generation. However, the state machine structure identification from the basic model that requires equations and variables that often leads to frequent error messages during simulation that leads to more uncertainties and does not have a statistical module for data analysis. As observed from the above software, one of the significant problems of engine modeling includes the accuracy of the results when compared with real engine data (even with the availability of actual engine data). As seen in the software mentioned above, the non-graphical-based modeling of an engine is quite complex to model even before any fault analysis. Hence, to circumvent the problem, the R programming on a real marine diesel engine is proposed to identify the possible faults exist in various subsystems of the marine diesel engine. It reduces the uncertainty due to

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modeling inaccuracies (such as fitting the curves of the actual engine data, unknown parameters, and other modeling assumptions). Also, the commercial software could be quite expensive and complex to use. The use of open source software environment for statistical computing and graphics such as R for fault data analysis gives engineer more transparency and flexibility to apply statistic analysis on the data to identify faults on an engine (or other systems of interest). Second, the codes written in R can be reused to allow ship owner and engineer to review their engine performance quicker for possible faults before implementing the corrective actions. The data analytics enable the ship operators to determine the possible faults in the marine diesel engine. The present study can be used for ship hull performance and design that would impact the operating cost of the ship further. The paper is organized as follows. The real input data from B&W 10s90MEC9 engine on a container ship are shown in Sect. 2. The descriptions of each subsystem in the engine such as exhaust system, freshwater system, fuel oil system and lubricating oil system are shown in Sect. 3. In Sect. 4, the engine fault analysis on the real engine is conducted using the R programming. Lastly, Sect. 5 concludes the paper.

2 Actual Data of Marine Diesel Engine The data from a marine diesel engine on board of APL Boston containership has dimensions of 328 m × 46 m × 9.7 m (see Fig. 1). The ship uses a compact electronically controlled ten cylinders two-stroke diesel engine, MAN B&W (10S90MEC9). The parameters of the engine are attained and collated in an excel sheet from local monitoring and control system through sensors placed in the engine. The data used in this research were collected from a Bluetracker onboard server (see Fig. 2) installed on the container ship. The server is connected to the shipboard network systems. It transfers the data to on-shore at a regular basis (e.g. 15 min interval) for subsequent

Fig. 1 APL Boston containership (containership-info, 2016)

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Fig. 2 Bluetracker system architecture

data analytics such as predictive modeling. The navigation and electro-mechanical systems data are collected through the use of several sensors pre-installed on various machinery in the engine room and propeller shaft. As shown in Table 1, the real-time engine data consists of several variables (24 inputs and five outputs) from a marine diesel engine. In this study, a section of the marine engine data was extracted from the Bluetracker on-shore server for fault data analysis. The data consist of around 732 samples, measured at an interval of 4 h for the period 1 July 2014 (1200 h) to 30 October 2014 (0800 h). Note that not all the input and output variables are used for the analysis in this paper. Table 1 Input and output variables for marine diesel engine (https://ieee-dataport.org/documents/ container-vessel-data) Variable Inputs

Fuel oil inlet pressure (bar), fuel oil inlet temp (°C) Fuel oil inlet viscosity (cst), main lubricating oil inlet pressure (bar) Main lubricating oil inlet temp (°C), cylinder lubricating oil inlet temp (°C) Air cooler 1 CW inlet temp (°C), air cooler 2 CW inlet temp (°C), air cooler 3, CW inlet temp (°C) Air cooler CW inlet pressure (bar), starting air inlet pressure (bar) Jacket cooling fresh water inlet pressure indicator (bar), jacket cooling fresh water inlet temp (°C), Piston cooling oil inlet pressure (bar), intermediate bearing 1 temp (°C) Intermediate bearing 2 temp (°C), intermediate bearing 3 temp (°C) Thrust pad temp (°C), fuel oil mass flow (kg/h), turbocharger exhaust inlet 1 temp (°C), turbocharger exhaust inlet 2 temp (°C) Turbocharger exhaust inlet 3 temp (°C)

Outputs Shaft power (kW), shaft RPM (rpm), shaft torque (kNm) Engine power (% maximum continuous rating), Turbocharger RPM indicator (rpm)

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3 Main Diesel Engine Subsystems A few subsystems within the engine involving heat input from fuel to exhaust gas and the cooling subsystem is described. The five main subsystems such as the fuel oil system, lubrication oil system, freshwater system, charge air cooling and exhaust system can be seen below. The fuel oil system for a diesel engine can be split into two parts, the fuel supply, and the fuel injection systems. For heavy fuel oil, it is stored in the tanks at the double bottom from that is pumped to the settling tank to get heated. The oil goes through fuel oil purifier and transfers to the daily service tank. After the mixing tank, there is a booster pump to increase the pressure into the combustion chamber by a fuel injector. Proper lubrication decreases the power to overcome friction and reduces wear and tear. Lubricating oil is used as a coolant and sealant. Lubricating oil for an engine is kept at the bottom of the sump, or in a drain tank placed at the lower part of the engine. The oil is absorbed from the reservoir through a strainer before passing through a set of pump and fine filters. Subsequently, it goes through a cooler before going to the engines before circulating to several branches of the conduit. The lubricating oil system provides a constant flow of filtered oil to all the engine bearings and other surfaces. The use of seawater causes corrosion which the salt deposited on the cooling surfaces reduces the heat flow. Fresh water is used to cool the cylinder jacket covers, piston, exhaust valve, and turbochargers working under high temperature and heat. Fresh water is cooled by using seawater. The freshwater removes the heat from the cylinder liners, cylinder heads, exhaust valves and the turbochargers before it returns to the engine. The temperature of the cooling water is controlled via a three-way control valve. A fresh water generator (FWG) which is used to produce fresh water from sea water is also incorporated. In a most marine diesel engine, turbocharger and supercharger are used to increase the density of the air to enable more fuel to be burnt. It, therefore, increases the power output of an engine. However, this charged air causes an increase in the temperature of the air due to adiabatic compression in the turbocharger. The charged air could not be fed directly to the engine as it is out of the operating limit of the engine. The compressed air from turbocharger goes through the charge cooler system made of fins and tubes to cool the charge air before going back to the engine. It increases the density of air and reduces the engine thermal loading.

4 Fault Analysis Using R Programming Data analysis is the process of examining data using logical interpretation and transforming the raw data into useful information. There are mainly four steps in data analysis namely: problem definition and planning, data preparation, analysis, and deployment. As shown in Fig. 3, the first step is problem definition and planning. The problem for this case is whether there is any fault in the engine. The plan is to use

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Fig. 3 Fault tree analysis for input (left) and output (right) variables

exploratory data analysis, fault tree analysis and analytical methods to analyze the data. The second step is data preparation. The raw data parameters are obtained from the engine and collated in an excel worksheet. The raw data is characterized, explored and cleaned for further processing. The third step is analysis. R-Studio is used to generate the summary and the graphical representation of the data. Exploratory data analysis and fault tree analysis is used to analyze the graphs generated. The last step is deployment. The results and analysis from the previous step are deployed to achieve the project objectives before documentation or report writing. The exploratory data analysis plays a crucial role in data analysis as it assists in the selection of appropriate analysis method to apply on the data, detection of a fault, and evaluation of the rationality of the assumptions. It is mainly involved in summarizing and visualizing data before carrying out formal modeling for visual representation. By generating visual representations of data exploration, characteristics of the data can be interpreted, qualities and features of the data can be examined rather than a huge data set, and new patterns or correlations can be discovered. Making sense of the data is easier by understanding the relationships between the variable, which can be identified and interpreted by generating summary tables and data visualizations in R Studio. A simple summary table helps in understanding the relationship between variables and provide information of the data which includes meaning, median, mode, minimum value, maximum value, and standard deviation. Visual representation aids in finding relationships between variables. The box plot is the visual representation of the summary table. It identifies outliers which gone beyond the data range. Scatter plot allows the trend of the variables and the type of relationship that could be present between the two variables. Typically, a positive linear trend should be observed in the scatter plots when the variables are plotted against each other. Any plot inconsistencies or outliers that occur in the scatter plot could be an indication of an error or fault occurrence. The k-means clustering is a common clustering technique which groups the data into clusters. One condition is to indicate how many clusters are needed. The k-means clustering technique identifies relationships which may exist between two variables. The k-means algorithm can be shown as follows.

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Step 1: Choose the initial number of clusters, k Step 2: Compute the cluster sum of squares difference between data point and the mean of the cluster Step 3: Allocate the data into the cluster where the mean is nearest to it Step 4: Re-compute the mean of the cluster from the data to minimize the withincluster sum of squares. In other words, the objective is to minimize the cost function: J = arg min S

k  

x − µi 2

(1)

i=1 x∈Si

where µi is the mean of points in S i given a set of observations (x1 , x2 , . . . , xn ) where each observation is a D-dimensional real vector. The k-means clustering aims to partition n observations into k sets S = {S 1 , S 2 , …, S k } to minimize (1). The Euclidean distance derived from the each point as ||x − µi ||2 will be the minimum value of all distance from the data set to the centroid. The data are then inspected and analyzed to determine the cause of the faults. Investigative methods such as research, brainstorming, and fault tree analysis are used to determine the faults or malfunctions are occurring in the systems and also rectify the faults. Fault corrective action improves the procedures taken to remove sources of abnormalities or other undesirable conditions. A fault tree analysis (in Fig. 3) of the marine propulsion system is used to visualize the faults that may occur by the parameters. It is a deductive method of analysis to find fault in the respective subsystems. Since the engine is made up of several subsystems, they are divided into two groups namely: input variables system and output variables system (see Fig. 3). For example, under the input variables, the faults in the fuel oil system can be due to leakage, the low mass flow rate of fuel and incomplete combustion. The raw engine parameters are collated, cleaned and prepared in an Excel worksheet before imported into R studio. The data is then separated into different months in its various systems. By breaking down the data into smaller subsets, the data will be simplified for analysis. Function slice() is used to separate the data into different months in R studio, which is presented in the environment tab. The data is ready to be analyzed by exploratory data analysis. Example of the R programming codes written in version 3.2.4 can be downloaded from https://docs.google.com/document/ d/12FTWVUjCRvzV33pgKVBE5JCXvbWdDU-fdwltl74NEM4/pub.

4.1 Data Analysis on Fuel Oil System A few irregularities are found in the specific fuel consumption and fuel oil mass flow rate. The specific fuel consumption against the days for each month is plotted. The scatterplot examines the trend and the specific fuel consumption. From Fig. 4, it is

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Fig. 4 Scatter plot of specific fuel oil consumption for each month

observed that the specific fuel oil consumption is quite constant throughout the month except for a few days where the consumption goes down to 0 t/day. It may be due to the ship was at the port or not moving. Also, there are two spikes in September and October that reach 163 t/day on the average consumption of 120 t/day. With reference to Fig. 3, the sudden surge in consumption may be due to fault or leakage in the fuel oil tank due to unsecured seals or incomplete combustion. The higher fuel consumption may result in overcoming strong wind and wave during the voyage. The fuel oil inlet temperature is plotted against the fuel oil mass flow using scatter plot with regression to show the relationship between these two variables in Fig. 4. The fuel oil inlet temperature in July and August is higher and inconsistent as compared to September and October. It indicates that there might be a fault in the fuel oil system in the July and August. The fuel oil inlet viscosity and fuel oil inlet pressure for July to October are plotted as shown in Figs. 5 and 6, respectively. Figure 5 represents the fuel oil inlet viscosity (cst) over days in a specific month. The typical range of fuel oil viscosity lies between 10 and 12 cst for each month. One outlier is observed in September showing a zero viscosity (not possible in practice). With reference to the fault tree analysis in Fig. 3, this may be due to the low mass flow rate of the fuel causing incomplete combustion and error in the viscosity measurement. In Fig. 5, a high value of viscosity of around 17.5 cst can be seen in the month of August and October. This value corresponds to a low pressure as seen in Fig. 6. Since it appears only once without continuous trend, it is unlikely to cause by clogged fuel filter, pipeline or intermittent sensor fault. On the other hand, the range of the fuel oil inlet pressure falls between 7 and 8 bars except for July and August that show a higher pressure reaching 12 bars. The results highlight a possible fault in the fuel oil system. As a result, another parameter to study is the fuel oil inlet temperature (°C) against the fuel oil flow meter temperature (°C) for each month. The K-means cluster plot gives clustered view of the temperature data between the flow meter outlet and the inlet of the main engine. The desired plots should exhibit a linear trend in the temperature, i.e. a high temp at the flow meter outlet should accompany by high temperature in the inlet of the main engine. As seen in Fig. 7, the fuel oil inlet temperature for July and August is lower than September and October. Also, there

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Fig. 5 Fuel oil inlet viscosity for each month

Fig. 6 Fuel oil inlet pressure for each month

is a group of the cluster in July exhibits value approximately 20 °C higher than the rest. In summary, it is evident that there is a possibility of a fault in the fuel oil system, predominantly in the path between the flow meter and inlet of the main engine during

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Fig. 7 Fuel oil inlet temperature versus FO flow meter for each month (by k-means)

July and August. For example, there are some installed components along the path between the flow meter and the inlet of the main engine. For example, a fault in the heater due to the faulty control of heating elements may lead to the abnormality in the fuel oil temperature causing the pressure to increase and lower the output temperature. The pipeline may have some leakages or blockages that reduce the fuel oil temperature.

4.2 Data Analysis on Air Cooler System The air cooler CW inlet temperature is plotted to examine the air cooler CW inlet temperature for each month. It is desired to see the outlet temperature is much lower than the inlet temperature after going through the air-cooling system. In Fig. 8, the air cooler CW inlet temperature for the three months are quite consistent. Two clusters (in cyan and blue) have a lower temperature than (green and red). As observed in the scatter plot, there are two lower temperature points at the beginning of the month due to lower ambient air at the start of the voyage. As shown in Fig. 9, the air cooler CW outlet temperature exhibits the same trend across each month. The highest cluster (red) occurs near the beginning of each month which rises to the maximum temperature (around 40 °C) in the cooler CW outlet. Moreover, the high-temperature phenomena are observed to be quite far from the remaining data. The high-temperature may cause insufficient cooling of intake air, or foreign objects blocking the cooler intake. The data analytics and Internet technology have a great impact on the current maritime industry as manpower cost increases and greater uncertainties in the oil price and global politics related to oil and gas. The data collected on the board of

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Fig. 8 Scatter plot of air cooler CW inlet temperature in August

Fig. 9 Scatter plot of air cooler CW outlet temperature in October

the ship and analysis performed to enable the ship owners and operators to better understand the underlying faults as a result of the current equipment performance such as in this study, the marine diesel engine. The current cost reduction due to the online data collection and analysis is approximately 10% as no manual data collecting by engineers (time are free to do other important tasks) are needed. This proposed methodology leads to further analysis such as hull performance that can be further optimized in the hull design (i.e. dimensions and marine paint coating of the ship hull) to reduce the fuel consumption and subsequent environmental impact of shipping by greenhouse gas emissions during the voyage. A rough estimation by the shipping company is approximately 5–10% fuel consumption since frictional resistance constitutes up to around 90% of the total drag force experienced by the ship hull [12]. Hence, by reducing the frictional resistance generated as the hull flow through seawater would enable cost saving in the operating cost such as less fuel consumption.

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5 Conclusions The paper presents exploratory data analysis on the actual engine data to identify possible fault occurrences instead of using a marine diesel engine model that is often subjected to uncertainties and inaccuracies. The use of R-studio with the aid of different graphical presentation of the data allows data analysis with the help of the fault tree analysis to determine possible faults in the engine. Through conducting data analysis, several fault occurrences were detected in the engine data. In the fuel oil system, the abnormality in the fuel oil consumption in July and August may occur. The results from the fuel oil inlet pressure, temperature and viscosity show a possibility of a fault in the fuel oil system, predominantly in the path between the flow meter and inlet of the main engine during July and August. The inconsistency and low temperature in July and August for fuel oil inlet temperature against mass flow rate indicate that there is a fault occurring before the fuel enters the engine. In the air cooler system, insufficient cooling resulting in a higher temperature in the air cooler CW outlet temperature. In summary, the exploratory data analysis and fault tree analysis using R studio were carried out successfully on the real engine data from a container ship. This approach can be extended to ship hull performance and design that would impact the operating cost and performance efficiency of the ship during the voyage. In future work, engine parameters could be extracted from the engine systems when it is in its optimum condition with more accurate sensors so that more analysis can be performed to refine the fault detection and to compare with the optimum condition. An early fault detection system can be developed and installed on the vessel.

References 1. Y.H. Zweiri, J.F. Whidborne, L.D. Seneviratne, Complete analytical model of a singlecylinder diesel engine for nonlinear control and estimation, Report EM/99/10, Department of Mechanical Engineering, King’s College London, Strand, London, UK, 1999 2. J. Chauvin, A. Albrecht, G. Corde, N. Petit, Modeling and control of a diesel HCCI engine, in Fifth IFAC Symposium on Advances in Automotive Control, CA, USA (2007), pp. 1–8 3. C.W. Tan, H. Selamat, A.J. Alimin, Modeling and control of an engine fuel injection system. Int. J. Simul. Syst. Sci. Technol. 11, 48–60 (2010) 4. J. Wahlström, L. Eriksson, Modeling diesel engines with a variable-geometry turbocharger and exhaust gas recirculation by optimization of model parameters for capturing non-linear system dynamics. Proc. Inst. Mech. Eng. Part D: J. Autom. Eng. 225, 960–986 (2011) 5. L. Goldsworthy, Computational fluid dynamics modelling of residual fuel oil combustion in the context of marine diesel engines. Int. J. Engine Res. 7, 181–199 (2006) 6. H. Barths, C. Hasse, N. Peters, Computational fluid dynamics modeling of non-remixed combustion in direct injection diesel engines. Int. J. Engine Res. 1, 249–267 (2000) 7. M. Turesson, Modelling, and simulation of a two-stroke engine, Master of Science Thesis, Chalmers University of Technology, Göteborg, Sweden, 2009 8. B. Duleba, Simulation of automotive engine in lotus simulation tools. Transfer inovácií 30, 48–52 (2014)

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9. D. Cordon, C. Dean, J. Steciak, S. Beyerlein, One-dimensional engine modeling and validation using Ricardo wave, Final Report KLK434-B (N07-09), National Institute for Advanced Transportation Technology, University of Idaho, 2007, pp. 1–45 10. Z. Shaohua, J. Guodong, X. Tianfeng, Design and analysis method for internal combustion engine mufflers based on GT-power software. Autom. Technol. 7, 7–10 (2003) 11. M. Martin Sjölund, P. Fritzson, An OpenModelica Java external function interface supporting MetaProgramming, in Proceedings 7th Modelica Conference, Como, Italy (2009) 12. M. Schultz, Effects of coating roughness and biofouling on ship resistance and powering. Biofouling 23(5), 331–341 (2007)

A Hybrid Key Management System Based on ECC and Data Classification to Secure Data in the Cloud Hinddeep Purohit

and Ravirajsinh Vaghela

Abstract Cloud computing is an unparalleled innovation in the field of computer science. However, the CSPs cannot be entrusted with secret data. Data needs to be encrypted before it is uploaded to the cloud. But again, the data is only as secure as the keys used to encrypt or decrypt it. This gives rise to yet another problem, i.e., key management. This paper systematically reviews the literature, identifies the issues prevalent in key management, and presents their corresponding solutions. Post the literature review, we propose a model that borrows the best from the past efforts and fixes the shortcomings of the present models by combining all the solutions presented in this paper. Keywords Cloud security · ECC · KMS

1 Introduction The dawn of the high-speed Internet era bought with it an onslaught of innovations that dramatically mutated the way people conceived and operated computer systems. Gone are the days when one was fettered by the constraints of the hardware he or she owned. In a wink of an eye, it is now possible to dynamically reconfigure the system by shelling out a pittance. The introduction of cloud computing, a paradigm shift from traditional computing, has hailed a slew of transformations that have afforded an ever more mobile and flexible work pattern to the cyber samurais of the twenty-first century. Cloud computing is a method of leveraging the Internet to provide essential services such as storage, database, specialized hardware (e.g., high-performance GPUs), operating systems, applications, and development environments remotely. The most prevalent and authoritative definition of cloud computing is outlined in H. Purohit (B) RK University, Rajkot, India e-mail: [email protected] R. Vaghela Gujarat Technological University, Ahmedabad, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_19

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a paper published by NIST [1]. High-speed and reliable Internet connection is a pre-requisite to access the resources hosted on the cloud. Instead of hooking up all the hardware/software to their system, users can log into their accounts from virtually anywhere and take advantage of the same facilities as would have been available which had the resources been connected locally. This exonerates people from having to lug around heavy equipment and ushers in an era characterized by increased mobility. Models in cloud computing: The services offered by cloud providers can be broadly classified into three main categories, namely IaaS, PaaS, and SaaS [2]. The cloud provider purchases and hosts hardware (e.g., storage, GPUs, CPUs, etc.) in the data center. A subscriber pays only for the resources that he/she needs. The key concept here is virtualization. The subscriber has complete control over the entire technology stack. PaaS subscribers get access to operating systems and preinstalled software (usually dev tools for building applications, hosting services, etc.) in addition to hardware and storage. In the case of SaaS, right from the infrastructure, middleware apps (e.g., databases) to the applications that end-users use are all hosted on the cloud. The user does not have to manage any of the resources in the stack. However, the cybersecurity threat landscape has expanded its frontiers far and wide, making inroads into the privacy and security of individuals and organizations alike, and has assumed menacing forms that were hitherto unheard of—thanks to the attack surface cloud computing has exposed! The General Data Protection Regulation of the European Union [3] names a number of abstract, negative effects that lack of appropriate technical and organizational measures to ensure data protection can have on individuals [4]. Consider the source code of a yet-to-launch application, stored on some cloud storage, falling into the hands of the contemporary. It could mean insurmountable financial and intellectual loss. To prevent such mishaps from spreading mayhem, data protection in the cloud is imperative. Since data contain sensitive information, in no case the cloud providers can be trusted. There is minimal control over the data by the customers [5]. One solution to this problem is to encrypt the data before storing it on cloud any service that will ensure that no unauthorized person gains access to the data. Encryption, the most common solution to prevent data breaches [6], is offered in several cloud platforms [7]. But encryption/decryption of data leads to the obvious question of key management. This paper briefly reviews the problem of key management and the solutions readily available in the literature.

2 Research Methodology We have performed a systematic literature review on key management issues in cloud security. We consulted papers addressing key management systems for securing the cloud as well as survey papers with an impressively broad scope that covered security misconfigurations, virtualization issues, data storage issues, Web application vulnerabilities and other legal challenges haunting the cloud. We aimed to study the open problems in key management, analyze the proposed solutions, and then come up with our own model that improves upon the previous models.

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3 Structure Section 4 reviews and identifies weaknesses in the previous work that addresses the issue of key management in securing data in the cloud. Section 5 describes how the limitations identified in Sect. 4 can be overcome. Finally, Sect. 6 proposes a hybrid model that improves upon the previously proposed models, described in Sect. 4, by putting together all the solutions presented in Sect. 5. The hybrid model is followed by concluding remarks that illustrate how the proposed model solves the challenges identified in Sect. 4.

4 Literature Review According to European Network and Information Security Agency (ENISA), importance of key management is not well addressed [5]. Ideally, it is the data owner’s (responsibility). But due to the lack of user expertise to handle the keys, they usually hand over the key management to CSP [8]. Rafael et al. maintain that the most common place to store this tenant key is in a cloud system itself (e.g., a cloud provider) where the database is accessed [4]. This lends the CSP complete access to the data which is a serious problem mission-critical data. Another point to consider is key destruction [5]. The keys used to encrypt/decrypt data must be destroyed and completely removed from the cloud when the contract between CSP and client is canceled. Failure to destroy the keys invites the possibility that the CSP or other third party may in future access data which they should not have any access to. Since data security and key destruction are highly sensitive, the CSP must be as vocal about its security policies as possible. This transparency allows the client to trust [4] the CSP. Jäger et al. proposed a sealed cloud approach [9] which ensures that nobody—not even the CSP—other than the owner of the data has access to it. In the sealed cloud approach, the keys are not stored in the cloud. The data owner must provide the key explicitly each time he/she wants to access the data. Since the key is only known to the data owner, the privacy of the data stored is guaranteed. However, the trade-off in such a setup is that the data cannot be manipulated inside the cloud. In other words, operations such as querying the data are not possible. Had the key been managed by the CSP itself or a third party key management server, query processing would have been possible. Real-world systems often require the facility of querying the data to glean insights. Moreover, key unavailability [5] poses a major issue. If the key used to encrypt data is lost, data is rendered unrecoverable. Therefore, a sealed cloud approach is not feasible in most cases. Another approach is to use a key management software (will be referred to as KMS henceforth). Key management is the set of techniques that involves the generation, distribution, storage, revoking, and verifying keys. The KMS is primarily responsible for securely distributing the keys [5], i.e., no unauthorized user should receive the key, lest the data will be compromised. Since KMS generates the key, it also needs

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to update them [5] after it has expired. If the keys are not updated, even legitimate users will have a hard time accessing data. It can be handled by the client, CSP, both client and CSP, key splitting, centralized server, or group key management [5]. KMS should be developed by professional companies that know about encryption, control the integrity checking, key generation, key distribution, periodic key changing, and key destroy. Besides, KMS has to be verified by the cloud service provider [10]. It must be noted that using a KMS involves a lot of computational overhead [10] which in turn impedes the performance of the system as accessing or altering encrypted data inside a cloud involves extra computation (decryption followed by re-encryption) that introduces a delay. Techniques that reduce this delay need to be developed. That said, using KMS has its advantages too. A user can opt to go with different KMS providers for different files depending on the confidentiality level of the file. This strikes a balance between confidentiality and performance as low confidentiality files do not have to be encrypted/decrypted with a larger key size required for high confidentiality files. On the other hand, when the security is entirely managed by the CSP what is called trust issue comes into the picture. Much effort has been done to satisfy customers’ demand, but the security status is lack of transparency. It means that the client is not clear on how service provider secures and controls their data, so they still hold a skeptical attitude toward data security [10]. Trusted third party, proposed by I. Indu et al., acts as a cryptographic server and a KMS that uses RSA to encrypt data. Here, the TTP acts as a server where the owner of the file and the users are registered. TTP has multiple responsibilities such as owner management, user management, encryption, decryption, and access control [11]. The TTP generates a set of public and private keys once the data owner is registered in the server. The file to be uploaded is encrypted with the private key generated. The encrypted file is then uploaded to the cloud storage service. The private key is securely deleted from the TTP by overwriting it with one half of the public key. The data owner provides user list/access permission list (UAPL), user-specific security questions and answers, number of file access permission to the TTP server [11]. The data owner provides the user with the link to TTP and a portion of his/her public key. The user then registers to access the file. After successfully being registered, the user logs into the TTP and provides the portion of the owner’s public key. TTP verifies if the user has permission to access the file. If the permission has been granted, the TTP reconstructs the public key from the partial key provided by the user, decrypts it using the owner’s public key, and downloads the file. All the previous attempts in securing data in the cloud have tried to target one specific problem. But, in trying to solve that one problem, they have either introduced others or overlooked multiple aspects such as level of confidentiality and performance. For instance, if the keys are managed by the CSP, the user is freed from the onus of keeping them safe, but as a side effect, the CSP gains complete control over the user’s data. The sealed cloud approach gives complete control to the data owner. However, it is a very rudimentary solution that does not allow the user to query stored data. TTP, a KMS, seems to address the shortcomings of both of CSP managed keys and sealed cloud approach. Although robust, TTP’s performance can

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be improved. In the following sections, we will prove that the performance of TTP can be upped by classifying data before encrypting it, and by using ECC in lieu of RSA.

5 Factors Affecting the Performance and Security of TTP and Data Classification Up to this point, we have described various ways to handle the keys used to encrypt/decrypt data in the cloud. However, the security and the performance of the algorithm used to encrypt/decrypt data have not been taken into account. The highest level of security, when coupled with the least amount of computational overhead, makes for the perfect cloud data security model. Researchers have been trying to reduce the computational overhead while improving or at the very least, maintaining the same level of security as provided by the legacy models. Data classification was suggested by Tawalbeh et al. [12]. The present-day cloud storage solutions use a fixed-sized key to encrypt data. They do not take into consideration the sensitivity of the data being stored. If basic, medium, and high, all three confidentiality level data are encrypted/decrypted with a fixed-sized key, then the computation overhead will increase significantly, leading to performance degradation. The data classification model is a granular encryption technique that takes into account the confidentiality level of the data to be encrypted. Data is classified into three categories, namely basic level, confidential level, and highly confidential level. The basic level uses AES 256 + TLS, confidential level uses ten rounds of AES 128, and the highly confidential level uses AES 256 + SHA-2 to provide data integrity along with confidentiality. According to the graph provided in [12], it has been proven that the data classification technique is highly effective in reducing the computational overhead. Nevertheless, the data classification technique uses symmetric key cryptography. Asymmetric encryption algorithms provide a higher level of security. The selling point of such a cryptosystem is that the key itself need not be transported with the ciphertext thereby freeing the sender from having to protect the key while in transit. The de facto standard in public-key cryptography is the RSA algorithm. RSA algorithm generates public and private keys and encrypts/decrypts data using those keys. However, when the amount of data to be transmitted is massive or when using a resource-constrained/embedded device such as a smartphone, employing RSA would be an inefficient choice. ECC is a viable option considering the performance gain over RSA as illustrated in both the tables below. Therefore, the data classification model should use ECC instead of AES as it provides better security and performance. RSA versus other asymmetric algorithms: Key generation time, memory usage, and the amount of time taken for encryption and decryption for RSA, DSA, DH, ElGamal, and Paillier [13, 14] have been compared in [11]. The graphs and tables

204 Table 1 RSA versus ECC key size

H. Purohit and R. Vaghela Security level

RSA key size (bits)

ECDSA key size (bits)

ESP-IDF curves

80

1024

160–223

secp192r1, secp192k1

112

2048

224–255

secp224r1, secp224k1

128

3072

256–383

secp256r1, secp256k1, bp256r1

192

7680

384–511

secp384r1, bp384r1

256

15,360

512+

secp521r1, bp521r1

Source [17]

Table 2 RSA versus ECC key generation time

Key length ECC

Time (s) RSA

ECC

RSA

163

1024

0.08

0.16

233

2240

0.18

7.47

283

3072

0.27

9.80

409

7680

0.64

133.90

571

15,360

1.44

679.06

Source [18]

indicate that RSA outperforms other asymmetric algorithms. However, ECC—an eminent asymmetric algorithm—was missing from the comparison done in the paper. RSA versus ECC: A key should be produced using cryptographic module with at least FIPS 140-2 [11] compliance for memory, strength, and security requirements [15]. While both RSA and ECC satisfy the requirement mentioned above, ECC provides a similar level of security with lesser key size. The key generation time for comparable key sizes for ECC is a thousand times faster than for the RSA algorithm [16]. To put the figures into perspective, refer to the following table taken from [17] (Tables 1 and 2).

6 Proposed Model While the data classification strategy is laudatory, it can be further extended to perform automatic data classification using machine learning algorithms for certain data types, such as plaint text, in place of manual classification.

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Once the raw data has been categorized into basic level, confidential level, and highly confidential level, we fix a key size depending on the confidentiality level of the data to be encrypted. Basic level data should be encrypted using a smaller key size, highly confident level data with a higher key size, and confidential level data with a key size that falls between those two extremes. When the data owner is registered with the TTP, it generates three different key pairs, using the ECC algorithm, each corresponding to a level of security. Each private key is used to encrypt the corresponding category of data. The encrypted data is then uploaded to the cloud. All the three private keys are securely deleted from the TTP by overwriting them with one half of their corresponding public key. The data owner provides user list/access permission list (UAPL), user-specific security questions and answers, and number of file access permission to the TTP server [11]. The data owner provides the user with the link to TTP and a portion of each of his/her public keys. The user then registers to access the file. After successfully being registered, the user logs into the TTP and provides the portion of each of the owner’s public keys that he/she has received. TTP verifies if the user has permission to access the file. If the permission has been granted, the TTP reconstructs the public key from the partial key provided by the user, decrypts it using the owner’s public key, and downloads the file (Fig. 1).

Fig. 1 Diagrammatic representation of the proposed model

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7 Conclusion Albeit with a few glitches, TTP (an example of KMS) is a marked improvement over previous attempts at key management. Neither TTP nor data classification alone can provide the most efficient security solution for the data stored in the cloud. After all, data classification is only a method to speed up the process of encrypting and decrypting data that form only a part of the entire KMS, and TTP has its own set of drawbacks mentioned previously. Toward that end, we have come up with a hybrid model the improvises the existing solution by using asymmetric cryptography instead of symmetric cryptography and combines data classification with the TTP model to improve the speed of encryption and decryption. Using automated data classification and ECC in the data classification model improves the ease of use (due to the automation of data classification), security and performance of key generation, encryption, and decryption. Using this new devised data classification along with the TTP model, we achieve a highly secure, robust, scalable, and performant model to secure data in the cloud. It is worth mentioning that the proposed model can also be used for securing local storage and network attached storage systems. Whether the data is stored on the cloud, on local storage device or on a network attached storage system, it makes little difference. The underlying method to achieve efficient data privacy remains the same.

References 1. P. Mell, T. Grance, The NIST Definition of Cloud Computing (2011) 2. R. Velumadhava Rao, K. Selvamani, Data Security Challenges and Its Solutions in Cloud Computing (2015) 3. European Parliament and the Council, General Data Protection Regulation (EU) 2016/679 of the European Parliament and of the Council 4. R. Dowsley, M. Gabel, G. Hübsch, G. Schiefer, A. Schwichtenberg, A Distributed Key Management Approach (2016) 5. A.R. Buchade, R Ingle, Key Management for Cloud Data Storage: Methods and Comparisons (2014) 6. B. Grobauer, T. Walloschek, E. Stocker, Understanding cloud computing vulnerabilities. IEEE Secur. Privacy 9(2), 50–57 (2011) 7. C. Esposito, A. Castiglione, B. Martini, K.-K. Raymond Choo, Cloud Manufacturing: Security, Privacy and Forensic Concerns (2016) 8. R. Latif, H. Abbas, S. Assar, Q. Ali, Cloud Computing Risk Assessment: A Systematic Literature Review (2014) 9. H.A. Jäger, A. Monitzer, R.O. Rieken, E. Ernst, A novel set of measures against insider attacks— sealed cloud, in Lecture Notes in Informatics—Open Identity Summit 2013, ed. by D. Hühnlein, H. Roßnagel (Gesellschaft für Informatik, Bonn, 2013), pp. 187–197 10. D.W.K. Tse, D. Chen, Q. Liu, F. Wang, Z. Wei, Emerging Issues in Cloud Storage Security: Encryption, Key Management, Data Redundancy, Trust Mechanism (2014) 11. I. Indu, P.M. Rubesh Anand, S.P. Shaji, Secure File Sharing Mechanism and Key Management for Mobile Cloud Computing Environment (2016) 12. L. Tawalbeh, N.S. Darwazeh, R.S. Al-Qassas, F. AlDosari, A Secure Cloud Computing Model based on Data Classification (2015)

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13. P. Paillier, Public-key cryptosystems based on composite degree residuosity classes, in Proceedings of Advances in Cryptology (Eurocrypt ‘99), Prague, Czech Republic (1999), pp. 223–238 14. Z.E. Dawahdeh, S.N. Yaakob, A.M. Sagheer, Modified ElGamal elliptic curve cryptosystem using hexadecimal representation. Indian J. Sci. Technol. 8(15), 1–8 (2015) 15. US Nat’l Inst. for Standards and Technology, Security Requirements for Cryptographic Modules, Federal Information Processing Standards Publication (2011) 16. S. Arun, N.R. Shanker, Data security in cloud storage using elliptical curve cryptography (2018) 17. M. Suárez-Albela, P. Fraga-Lamas, T.M. Fernández-Caramés, A Practical Evaluation on RSA and ECC-Based Cipher Suites for IoT High-Security Energy-Efficient Fog and Mist Computing Devices (2018) 18. N. Jansma, B. Arrendondo, Performance Comparison of Elliptic Curve and RSA Digital Signatures (2004)

Life Cycle Management for Indian Naval Warship Equipment Alok Bhagwat and Pradnya Vishwas Chitrao

Abstract Indian Navy has laid out ambitious shipbuilding plan in coming decade with large numbers of ships and submarines planned to be built in Indian shipyards with indigenous design. However, a number of critical equipment viz. electric propulsion equipment are not available from Indian defence manufacturers. These equipment are to be imported as of now causing precious foreign exchange outgo. Further, the dependence on foreign suppliers throughout equipment life cycle of 25– 30 years costs Indian Navy dearly. Indian defence manufacturers aspire to support ‘Make-in-India’ initiative by establishing capital intensive set-up for manufacturing electric propulsion equipment in the country, but a lacuna in bid evaluation process has adverse impact on this indigenous drive. The lacuna being evaluation of commercial bids of equipment procurement on acquisition costs alone. The operation & maintenance (O&M) costs, a significant element over equipment life cycle, is not considered holistically in bid evaluation. Ministry of Defence (MoD), Govt. of India (GoI) in the recently promulgated Defence Procurement Policy (DPP-2020) has mandated consideration of long-term product support during equipment procurement. But clear guidelines for estimation of life cycle costs (LCC) does not exists, thus making commercial evaluation on the basis of LCC during procurement very complex. This paper aims to bring out practices followed in US and NATO Navies in life cycle management (LCM) practices and suggest ways to implement LCM process and LCC estimation in Indian Navy, thus providing ‘level playing field’ to Indian manufacturers struggling to support country’s drive in attaining self-reliance in critical defence projects. Keywords Indigenous warship building · LCM in Indian Navy · Indigenous manufacture of Naval equipment · LCC in Naval procurement

A. Bhagwat (B) · P. V. Chitrao Symbiosis Institute of Management Studies (SIMS), Pune, India e-mail: [email protected] P. V. Chitrao e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_20

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1 Introduction Indian Navy has embarked upon an aggressive and ambitious plan of building large number of warships and submarines in the coming decade. As many as, 30 platforms are on order on Indian shipyards with over 50 platforms in the pipeline in various pretendering stages at Naval Headquarters, New Delhi. Almost all of these platforms will be built in Indian shipyards under indigenous design. While an increasing number of warship equipment are being taken up for manufacturing within country, some of the high technology equipment viz. ship propulsion and weapon systems are imported till date. Indian industry led by renowned names like Larsen & Toubro Limited, Tata Power Group, Kalyani Group, and Mahindra Group has been rapidly setting up indigenous capability for manufacturing these equipment; however, the regulatory framework is not fully supportive towards Indian industry. In fact, it is tilted favourably towards foreign suppliers as regards deciding on the lowest bidder, or L1 bidder, as per defence procurement terminology [1]. There is a need to correct this aberration. Ministry of Defence (MoD), Govt. of India (GoI), is fully seized of this situation, and corrective actions have been initiated in recent past [2]. Warships over world are moving towards electric propulsion systems for variety of reasons, not considered in the scope of this paper. Submarines have always had electric motors for propulsion. The primary reason being non-availability of air for combustion engines. However, electric propulsion is coming in surface warships also. Indian Navy has plans to install forthcoming Destroyers and Landing Dock Ships with electric propulsion equipment comprising of high-power electric motor and associated control drive along with auxiliary support systems. Therefore, Indian industry players are actively considering taking up manufacture of electric propulsion equipment for these forthcoming shipbuilding projects. Indian Navy is in dialogue with a number of Indian manufacturers towards this goal. It is well known that setting up a new facility for manufacturing such high technology equipment is an activity involving high capital costs. The cost is eventually to be recovered from the project. Therefore, the Indian supplier does not remain cost effective if the lowest bidder is decided on the acquisition cost alone. A foreign supplier with established manufacturing set-up has lower acquisition cost. But if the life cycle costs of the equipment involving the operation & maintenance (O&M) costs over the entire life cycle of the equipment (25–30 years) is also taken into consideration in deciding the lowest bidder, the tables turn drastically in favour of Indian supplier. The O&M costs of s foreign supplier is significantly higher than that of the Indian supplier. Realising this, the MoD, in the newly drafted DPP-2020 (Defence Procurement Policy), has mandated consideration of life cycle support aspects of an equipment and platform. However, the methodology for estimation of life cycle costs does not exists explicitly in the Indian Naval procurement process as of now. The paper aims to introduce the concept of life cycle management for equipment of Indian Navy and suggest way ahead for implementation of the same.

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2 Literature Review Ameer et al. [3] explores life cycle cost relationship of an induction motor. The purpose of the paper is to estimate the annual costs of operation and maintenance of the motors of Water Corporation (WC), Australia, define the conditions under which an old electric motor would benefit from an upgrade and determine whether only the heavily used large electric motors benefit from an upgrade. A parametric life cycle cost model is developed with analogues and predictive elements. The parametric equation uses the technical specifications of the motors and attached pumps to calculate the output water and electric power. Scheduled maintenance costs are subdivided according to minor and major service tasks and calculated by taking the average of all the major/minor service orders at a pump station. The acquisition costs and the rewinding costs are considered while deciding upon whether to rewind a motor or purchase a new motor by calculating the benefits that will accrue with high efficiency and reduced maintenance costs of a new motor over a rewound motor. A decision matrix is presented in graphical form for deciding whether to rewind a motor or purchase a new one. Bhagwat and Chitrao [1] brings out the flaw in the procedure of evaluation of competitive bids in equipment of high technology areas in Indian Naval warships. A majority of such technologies are being imported. Indigenous manufacturers striving to manufacture such equipment in country are at a disadvantage, as life cycle cost of the equipment is not considered in deciding the competitive bidder. The indigenous bidder has to incur high capital costs in establishing manufacturing set-up, thus pushing the acquisition cost higher than those for the foreign supplier who has an established manufacturing set-up. However, the operation and maintenance costs for the foreign supplier is much higher than those of Indian supplier. When considered for the equipment life cycle of 25–30 years, the life cycle costs for Indian supplier in much lesser than the costs for the foreign supplier. But this factor is not considered, and Indian supplier loses out the contract to the foreign supplier. The paper suggests that life cycle costs be estimated and considered for deciding the lowest bidder. Defence Contract Management Agency (DCMAO) [4], Baltimore, in this research study for US Naval Sea Systems Command (NAVSEA), aims to determine the life cycle costs of US Navy equipment. The systems are divided into three categories: low complexity, high complexity and ordnance systems. It is argued that determination of life cycle costs in the acquisition phase results in best estimate of life cycle costs of an equipment. The study focused on determining the integrated logistics support (ILS) costs. ILS costs represent a large portion of life cycle costs. The report identifies various factors that determine the ILS costs of an equipment. Draft DPP-2020 [2] (Defence Procurement Policy), released by Govt. of India, Ministry of Defence (MoD), in April 2020, is a revised version of the earlier DPP released in year 2016. DPP-2020 has addressed the issue of life cycle costing for equipment and platforms being procured for Indian Navy. In order to optimize life cycle support, DPP mandates that the equipment procurement agency should ensure provision of engineering support plan (ESP), annual maintenance contract (AMC),

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comprehensive maintenance contract (CMC), life cycle support contract (LCSC) and performance-based logistics (PBL). Therefore, it is evident that the Ministry of Defence, Govt. of India, has recognised the importance of considering life cycle costs for a Naval equipment. The DPP will soon come into force; however, there is still a need to evolve a model for estimation of life cycle costs of a complex equipment, especially where imports are involved. Fajardo et al. [5] brings out importance of life cycle management (LCM) in NATO. The paper suggests that the organizations within NATO should undergo change to align with the principles of LCM and goes on to recommend alignment of acquisition and logistics processes so as to achieve LCM. The paper explains various enablers required to be in place before commencing implementation of LCM; a key enabler being a unified business process methodology for acquisition and logistics. A life cycle working group has been created in NATO to address issues related to poor management, ill-defined roles, and responsibilities of stakeholders and difficulties of technology insertion. The objectives are to establish life cycle partnership between all stakeholders, to have a total and shared view of the project, to create seamless life cycle management processes that extend from mission analysis to product disposal and to ensure continuous technology insertion by stressing preference to commercial and non-developmental solutions to mission needs. Gualeni et al. [6] argue that the increasing complexities of ships have necessitated consideration of life cycle costs (LCC) and life cycle assessment (LCA) during ship design stage. An life cycle performance assessment (LCPA) tool combining LCC and LCA is developed as part of HOLISHIP European project. The tool allows cost/benefit assessment over the ship life cycle, considering both the design and operation. The paper aims to develop operational and maintenance costs, defining a structured and flexible tool to evaluate the maintenance costs for different ship solutions. It goes on to provide a general overview of the LCPA tool developed during HOLISHIP project. It describes the maintenance techniques commonly used and also the maintenance prediction model. Martin et al. [7] undertakes a research study to review and assess the life cycle management (LCM) of equipment instaled on US Naval ships. The study reckons that that the Navy ships are a complex combination of different equipment ranging from hull structure to combat systems with maintenance, training and supply requirements. Some of the requirements are known, and some arise unexpectedly. The study brings out that Navy processes in managing these system life cycles are not efficiently organised and result in seams between ships in new construction and those already in service. The study recommends management processes for improving the readiness of the equipment, increasing its efficiency and reducing sustainment costs. The analysis is carried out in three steps. First, the tends in surface ship LCM is analysed for a class of ships, having large number of ships in service, in all phases of LCM. The next step is to examine new construction processes to identify any challenges that may be complicating LCM. The final step is to assess organizational issues that may be contributing to the identified problems. The study results in recommendations in four areas, the data sets and systems covering data standards and essential

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common data, the maintenance figure of merit system, the incentive structures and the command and organizational structures. Sokri [8] argues that accurate cost estimation of a military equipment pays a crucial role in any military acquisition strategy, bringing out that life cycle costing (LCC) is an economic assessment that looks beyond the initial purchase cost of an item. LCC also includes the cost of operating and maintaining the item over its entire operational life. The LCC can be used as an evaluation tool in two situations, assessing the economic impact of a given investment such as procurement of new equipment, and assist in budget allocation if the decision is already made. With this perspective, LCC is subdivided into three categories: acquisition cost, ownership cost and disposal cost. The ownership cost is composed of operation and maintenance (O&M) costs. O&M cost is an important facet of the total ownership costs of a military equipment and can exceed the cost of capital over the life of equipment. While applying life cycle costing concepts, analysts have been thwarted by the lack of accepted methodology to arrive at appropriate decisions. The study aims to develop a methodology for predicting the ownership costs of a military equipment; optimise the LCC of acquiring, operating and maintaining military assets; and enhance the understanding of life cycle costing, both theoretically and practically. The modelling process is summarised as three stage process. Estimate relationship between O&M costs and equipment’s age; compute the optimal replacement age of the equipment; and compute the O&M LCC of the equipment over its entire optimal life.

3 The Research Gap The brief literature review has clearly brought out the importance of life cycle management (LCM) concept for military equipment, including equipment that go onboard an Indian Naval warship. Indian Navy has aggressive warship and submarine induction plans. These ships and submarines are slated for construction in Indian shipyards and will have indigenous design. However, a number of equipment onboard in these platforms are not manufactured in the country as of now. Indian Navy is dependent upon imports for these equipment and systems. An example is electric propulsion system of ship and submarine that comprises of propulsion motor & control drive, gear box, shaft line, propeller and propulsion auxiliary systems. The technologies involved in these equipment are mature in Indian industry in civil applications. However, an indigenous electric propulsion system solution is not available for a warship application, thus making the country dependent upon imports. A number of Indian companies viz. Larsen & Toubro Limited (L&T), Bharat Forge, to cite a few, are keen to support Indian Navy by manufacturing electric propulsion equipment in the country. Setting up a design, development, manufacture and testing of electric propulsion equipment of a warship involves large capital expenditure, thereby making an indigenous solution much more expensive than an import option, if only acquisition costs are considered in deciding the lowest bidder. The life of a Naval warship equipment is 25–30 years and the operation and maintenance (O&M) costs

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of an electric propulsion system is expected to be significant over the entire life. The O&M costs sometimes can be higher than the acquisition costs of a warship equipment [8]. Therefore, it is essential that the life cycle costs (LCC) of a warship equipment is considered in decision-making process in equipment procurement stage, as mandated by the new policy issued by the Ministry of Defence, Govt. of India [2]. Therefore, it is essential that Indian Navy also move towards introducing life cycle management (LCM) so as to holistically manage and minimise the sustenance costs of an equipment onboard its warships and submarines. A number of studies are underway in NATO countries as well as US Navy for adapting LCM [4, 5, 7]. However, a model for estimating the LCC of a warship equipment is not available, thus making it difficult for procurement agencies to evaluate the bidders on the basis of LCC. This is a crucial gap in the procurement processes of Indian Navy. Another crucial gap is in implementation of LCM processes. Indian Navy has discreet acquisition and logistics processes as of now, and there is a need to move towards LCM, as is the case in modern Western Navies [4, 5, 7].

4 Research Objective Define a roadmap for implementation of life cycle management (LCM) process for electric propulsion system for Indian Naval warships and submarines.

5 Scope and Limitations The study focuses on formulation of a methodology for estimation of life cycle costs for electric propulsion equipment for new shipbuilding projects of Indian Navy in line with stipulations of the Defence Procurement Policy of MoD, GoI. The study also analyses various methodologies for implementation of life cycle management aspects and suggesting way ahead in selection and implementation of the relevant process for Indian Navy.

6 Research Methodology 6.1 Primary Research It is intended to conduct a survey followed by unstructured interviews with subject matter experts from Naval shipbuilding agencies, shipyards and Indian industry in chalking out a roadmap for introduction of LCM into electric propulsion equipment

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of the Indian Naval warships. The concept can then be generalised for warship equipment. This is not considered in the scope of this paper and will be undertaken as part of the main research study.

6.2 Secondary Research Most of the modern Navies world over is moving towards life cycle management as regards equipment and systems of a warship. As brought out in literature review, US Navy has set up a high-level task force for undertaking a research study in implementation of life cycle management (LCM) aspects. The task force has done considerable work in areas of LCM. Some of the concepts and findings of the research study that can be adapted for Indian Naval scenario also are appended in succeeding paragraphs.

6.3 Organisation Structure and Processes of Indian Navy There are two distinct organisations in Indian Navy dealing with acquisition and O&M aspects of a warship equipment. The Staff Branch I, headed by the Vice Chief of the Naval Staff, is responsible for ordering the equipment for newbuild projects of the Indian Navy. The project ordering and execution is carried out by Controller Warship Production and Acquisition (CWP&A), through Design and Ship Production Directorates. The order for the ship or submarine is placed in the respective shipyard and is accounted under ‘Capital Budget’. The responsibility of this set-up is limited up to delivery of the vessels to the operational authority of the Indian Navy, i.e., one of the three commands of the Indian Navy, Western, Eastern and Southern Naval Command. Thereafter, the operation and maintenance of the ship is the responsibility of the Materiel Branch headed by the Chief of Materiel. The shipyard is responsible for delivery of the vessel, including the warranty of the entire ship for a period of one year. This warranty charges are accounted for in the Capital Budget. Thereafter, the operation & maintenance of the ship along with all the equipment for the life cycle of 25–30 years is catered for in ‘Revenue Budget’, by the Materiel Branch of the Indian Navy. This is a grey area and the agency for building the ships and delivering the same is not accountable for O&M of the ship. Even though the draft DPP-2020 has tried to remedy this lacuna of life cycle management of the ship equipment by introducing life cycle support, but is to a limited extent only. The draft DPP-2020 has introduced long-term product support mandatory for 3–5 years by introducing one or more of the following as part of equipment procurement package: • Engineering support package (ESP)

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Annual maintenance contract (AMC) Comprehensive maintenance contract (CMC) Life cycle support contract (LCSC) Performance-based logistics (PBL).

The pros and cons for the above are explained in succeeding paragraphs. It is easy and simple to incorporate ESP, AMC and CMC into the original procurement contract. A number of the equipment procurement contracts include ESP as its integral part. The ESP comprise of on-board spares (OBS), necessary tools, test equipment, jigs and fixtures, carried by the ship for undertaking first level maintenance and defect rectification. Therefore, an ESP is almost always included in equipment procurement specifications and is taken as part of the Capital Budget allocated towards equipment acquisition. The AMC and CMC are not easy to implement as this forms part of the Revenue Budget. Some of the tenders makes it mandatory for the bidders to mention AMC (or CMC) costs for a number of years as a percentage of the equipment cost or in absolute numbers. But these are optional elements of the tender and is not considered in the bid evaluation process. Therefore, the very purpose of binding the supplier, even partly, towards life cycle costs is defeated as there is no incentive to quote a realistic AMC or CMC cost. Therefore, it is left to the Materiel Branch to conclude AMC or CMC separately after expiry of the warranty period. Therefore, this lacuna remains to be addressed. The PBL is a right step towards binding the equipment supplier towards life cycle cost of the equipment. But it has to be linked with an AMC or CMC to be implemented fully. Life cycle support contract (LCSC) is the correct way of binding the equipment supplier throughout the O&M cycle of the equipment. This will include the following: • • • • •

Spares Scheduled maintenance Breakdown maintenance Shore support facility for repairs, testing and training Obsolescence management at least twice in the equipment life cycle of 25– 30 years. One during mid-life update (MLU) of the ship after 10 years of operation and second after 20 years of operation. • Disposal of the equipment However, estimation of life cycle costs including the elements given above is a complex process and involves active interaction with the suppliers in the pre-bid stage and making them aware of the various aspects of the LCC. Further, the two distinct organisations of the Indian Navy, viz, Staff Branch I and Material Branch will have to undergo streamlining of the processes on the principles of life cycle management (LCM) to cater for LCSC for a warship equipment. A number of methodologies are suggested in research studies world over towards estimation of life cycle cost and implementation of life cycle management aspects in Naval equipment. These are briefly explained below.

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7 Findings The secondary research was carried out by extensive study of the research papers pertaining to the advanced Navies world over. A study undertaken for the US Navy in year 1993 [4] is most relevant to the prevailing scenario and environment in Indian Navy. The study established that the integrated logistics support (ILS) costs represent a very large portion of the life cycle costs. Therefore, a great emphasis was placed on ILS costs in program acquisition. Analyses of life cycle costs and design trade-offs have been made mandatory for all acquisition programs of US Navy. A concept of ILS exists in Indian Navy also as part of integrated logistics management system (ILMS). Each equipment is broken down in equipment, assemblies, sub-assemblies and kits (EASK) categories with each element listed in the ILMS document giving the full details of the equipment along with costs. Therefore, it is suggested to implement the practices followed in US Navy in ILS management. The process followed in US Navy is known as logistics support analysis (LCA) process. The objective of the LCA process are to influence system design, identify and specify logistic support resource requirements, avoid duplication of the analysis efforts and assess supportability. Such a process is applicable for warship equipment procurement also, but it is not mature and is, therefore, implemented on a piecemeal basis. It will become evident as we study the elements of the LCA process and establish commonality of each process in Indian scenario. Thereafter, it will be possible to suggest a methodology for implementation of an equivalent process for Indian Navy, that is the objective of this research paper. A block concept of various elements of an ILS is given below [4]. While the ILS elements depicted in Fig. 1 are as applicable to US Navy, it is pertinent to mention here that Indian Navy also has similar elements applicable ILS Requirements

ILS Planning

Design Interface

Logistics Support Analysis Human Factors Quality Assurance Test and Evaluation

Supply Support

Support Equipment

Maintenance Planning Technical Data

Fig. 1 ILS elements

Configuration Management Standardisation Reliability and Maintainability System Safety Survivability

Facilities

Packaging, Handling, Storage & Transportation

Manpower & Personnel

Computer Resource Support

Training and Training Support

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for equipment acquisition and O&M. However, as the responsibility for various elements is divided between the authorities responsible for acquisition and O&M, the overall costs are not considered during equipment induction, thus creating an adverse environment for Indian manufacturers, as explained above. The research studies carried out in NATO Navies and US Navy [5, 7] have also shown similar situation where in life cycle management processes are not followed by all stakeholders in an integrated manner. Acquisition authorities focus on ship delivery and do not take holistic view of the O&M costs. Other studies [6, 8] have laid thrust in life cycle costing and evaluation of life cycle costs during evaluation of various design alternatives as it have been conclusively found out that estimation of life cycle costs during design stage results in most competitive alternative of sourcing the equipment. Estimation of LCC helps in budget allocations during the entire life cycle of equipment. The MoD, GoI has also stipulated consideration of long term product support in the recently promulgated draft DPP [2]. With an aim to link the life cycle cost elements, as applicable to Indian Navy with the well-defined ILS elements as brought out in Fig. 1 above, a table has been made as given below. Table 1 links the elements of ILS with the stipulations of the DPP and suggests methodology for adaptation of the life cycle management process in Indian Navy. Therefore, it is necessary to formulate a unified life cycle management (LCM) process in the acquisition as well as maintenance organisations of the Indian Navy. Further, the life cycle cost (LCC) of a complex equipment viz. electric propulsion equipment is to be estimated in line with the methodology explained in Table 1. This research paper explains the qualitative methodology of adaptation of LCM and estimation of LCC as applicable to electric propulsion equipment of Indian Navy. Further study will be carried out with primary research methodology involving interaction with subject matter experts of Indian Navy, academia and Indian industry in defining quantitative estimation of life cycle cost of an electric propulsion equipment.

8 Recommendation Indian Navy should adapt life cycle management (LCM) processes in organisations of all stakeholders responsible for acquisition as well as operation and maintenance of a warship and its equipment. Evaluation of bidders on the basis of LCC and not acquisition costs alone, will go a long way in providing level playing field to Indian defence industry. Indigenous development of electric propulsion equipment can only succeed if such practices are adapted by Indian Navy in time for procurement of these equipment. The regulatory framework in the form of Defence Procurement Policy-2020 is in place and provides a solid foundation towards this journey.

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Table 1 Life cycle cost elements for Indian Navy ILS element

Link to Indian Navy process

Remarks/suggestions for Indian Navy

ILS planning

Long-term support

Loosely defined as of now. Will get addressed once all other elements of ILS are taken care of during acquisition phase

Design interface

Various elements required to be considered in the design phase of acquisition program viz. configuration management, standardisation, reliability & maintainability, safety, survivability, logistics support analysis, quality assurance, manned systems engineering and the plans, analysis and data required to support these factors are considered during design stage. But these are not quantified

There is a need to quantify these factors with an aim to arrive at contribution of these factors towards life cycle costs. This exercise can only be undertaken when life cycle management aspects are borne in mind in the equipment acquisition phase. The LCM processes are to be followed in a unified manner by the stakeholders viz. acquisition authority and maintenance authority

Maintenance planning

These are linked with ESP, AMC/CMC, PBL, LCSC, as stipulated under the long-term product support, newly introduced in the DPP-2020

Quantifying the maintenance support package as per the DPP and considering the same during quantitative evaluation of bids in acquisition phase will go a long way in encouraging indigenisation of high technology equipment viz. electric propulsion equipment

Support equipment and training equipment

Support equipment for electric Presently, support equipment is propulsion system will include not considered in tender shore-based test and repair evaluation facility for undertaking second-level maintenance. It will also include shore-based training facility for imparting effective training on the equipment

Supply support

A very elaborate supply support organisation exists in Indian Navy under Materiel Branch to ensure logistics processes in making various spare parts available to the ship as well as dockyards, necessary for operation and maintenance of the equipment

Presently, spares for a period of two to three years of O&M are considered as part of acquisition process. This needs to be extended for entire service life of the equipment

(continued)

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Table 1 (continued) ILS element

Link to Indian Navy process

Remarks/suggestions for Indian Navy

Packaging, handling, storage and transportation

Not significant for electric propulsion equipment

Is considered in acquisition phase

Computer resource support

Indian Navy maintains a repository of all software of the system

Is managed well as of now and needs minor streamlining

Technical data

A very exhaustive technical documentation is mandatory along with equipment supply

Although the exploitation documentation is adequately captured in acquisition phase, the maintenance and troubleshooting documentation are not given due importance during acquisition phase. These document will form the basis of estimation of O&M costs under spares, deputation of specialists, etc. Indian Navy is paying exorbitant sums in this element of life cycle costs, and this element is not considered during commercial evaluation. This is a serious lacuna

Facilities and manpower

These are as are considered Not recommended to be separately as manpower budget considered at this stage

Total ILS cost

Life cycle cost (LCC)

The objective of this study is to estimate LCC for electric propulsion equipment

9 Future Scope The future scope includes quantitative research study with primary research methodology involving focused interaction with senior Indian Naval officer in shipbuilding and equipment maintenance organisations, subject matter experts in Indian shipyards, Indian industry and academia.

References 1. A. Bhagwat, P. Chitrao, Life cycle costing model for equipment of Indian Naval shipbuilding programme, in Proceedings of ICT4SD 2019, vol. 1. Advances in Intelligent Systems and Computing, vol. 1077 (Springer, Singapore, 2019). ISBN 978-981-15-0936-0) 2. Draft Defence Procurement Policy (DPP) 2020. Published by Govt. of India, Min. of Def. https:// mod.gov.in/dod/sites/default/files/draftdppnew200320c.pdf

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3. I. Ameer et al., A study into the true life cycle costs of electric motors. Presented in CEED Seminar in year 2009. https://ceed.wa.edu.au/wp-content/uploads/2017/02/10.Irfan_.Ameer_. pdf 4. DCMAO Baltimore, Integrated Logistics Support Life Cycle Cost (1993). https://apps.dtic.mil/ dtic/tr/fulltext/u2/a264270.pdf 5. E. Fajardo et al., Life cycle management in NATO. Presented at the RTO Studies, Analysis and Simulation Panel (SAS) Symposium held in Paris, France, 24–25 Oct 2001. https://apps.dtic. mil/dtic/tr/fulltext/u2/a418689.pdf 6. P. Gualeni et al., Life cycle performance assessment tool development and application with a focus on maintenance aspects. J. Mar. Sci. Eng. (2019). https://www.researchgate.net/pub lication/335252086_Life_Cycle_Performance_Assessment_Tool_Development_and_Applic ation_with_a_Focus_on_Maintenance_Aspects 7. B. Martin et al., An Approach to Life cycle Management to Shipboard Equipment (RAND Corporation, Santa Monica, CA, 2018). https://www.rand.org/content/dam/rand/pubs/research_ reports/RR2500/RR2510/RAND_RR2510.pdf 8. A. Sokri, Life cycle costing of military equipment, in Proceedings of Control, Dynamic Systems, and Robotics, Ottawa, Ontario, Canada, 15–16 May 2014. https://pdfs.semanticscholar.org/e28e/ a1db8770fcc2c96a0f80905781f1ca5ed555.pdf

Framework for Securing IoT Ecosystem Using Blockchain: Use Cases Suggesting Theoretical Architecture Anshul Jain, Tanya Singh, and Nitesh Jain

Abstract Blockchain is a distributed, open record between two customers for all trades. The decentralized nature is possible because each trade is controlled by the understanding of a larger number of customers, who are interested in anything in the system. The authors concentrate and present methods to address real issues of security in IoT. The authors review use cases and organize common issues of security with respect to IoT layered building. Despite the current plans for attacks, hazards and cutting-edge game, we are designing essential IoT security conditions. IoT security problems are also orchestrated against the current game plans discovered in writing and mapped against them. Furthermore, we are trying to discuss how blockchain, which is the fundamental development of Bitcoin, can be the primary enabling impact to deal with immeasurable IoT security problems. Besides, the paper includes open research issues and challenges to IoT security. In this paper, the potential informative applications are focused, along with explorations on how blockchain advancement can be used to address some preparation issues. In the wake of inquiring about security issues, authors present an architecture of IoT blockchain ecosystem. This paper also talks about the highlights and ideal states of blockchain improvement. Keywords Blockchain · IoT · Data security · Network security · Blockchain IoT protocols · IoT security · IoT ecosystem · Blockchain-enabled IoT

A. Jain (B) AIIT, Amity University, Noida, Uttar Pradesh, India e-mail: [email protected] T. Singh ASET, Amity University, Noida, Uttar Pradesh, India e-mail: [email protected] N. Jain JSSATE, Uttar Pradesh Technical University, Noida, Uttar Pradesh, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_21

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1 Introduction Blockchain progress is generally referred to as appropriate record innovation. It allows people to efficiently check transactions performed, exchange and transfer advantages in [1]. A model flow of cryptographic currency blockchain exchange is technique of ease. To initiate a transaction with User B, a shared blockchain is used by Customer A. A cryptographic character check (two or three open keys and private key) is used to make an unusual correlation between Customer A and Customer B in the framework. At this stage, the exchange will be transferred strongly to the memory pool of the blockchain framework for transaction check and approval. The new block is shaped by acquiring a specific measure of the avowed center points; this is called arriving at a consensus. This square incorporates every one of the exchanges that have occurred during this minute. It is “connected” through the modern imprint to the main obstacle in the plan in [2]. The foundation of the accord is practiced utilizing an agreement calculation. This block embodies each of the exchanges taking place at this time. It is “connected” to the main obstacle in the plan through the modern imprint in [2]. The platform of the consensus is practiced using a consensus algorithm. This is referred to as mining industry. In particular, the peer-to-peer plan competes with the present circumstance of the spread record [3]. Each center can cast a vote by denying advances by taking increments or dismissing invalid blocks by their CPU ability to perceive authentic blocks. Any fundamental direction and propelling powers can be executed through this plan of understanding [4]. Any block of exchange is distinguished with a specific time mark. The two blocks are equally interfaced by a timestamp. The blockchain data thus demonstrates a property of time, and the span of the chain is always developing. It implies that the blockchain is a variety of transmitted transactions that upgrade the timestamp organization [5–8]. To create a notable chain of SHA-256 hash limit, explicit hardware is utilized by blockchain, and cryptographic data is used to prevent invincible client data from being adjusted [1]. The organization of this paper is as follows. Section 2 explains different applications in IoT ecosystem using blockchain technology, and it also describes its security requirements. Next Sect. 3 explains different benefits of blockchain which makes it robust and reliable. Section 4 describes a combined and modular architecture of IoTenabled blockchain technology. A brief literature survey is done in Sect. 5 which goes through several papers and writes the outcome of the research done. This section also reviews several papers and presents them in a tabular format representing different IoT blockchain methods proposed, their benefits, shortcomings and future scope. Finally, conclusion is presented in last Sect. 6.

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2 Internet of Things Domain Use Cases Using Blockchain and Their Security Requirements This section includes various IoT applications which explains the problems faced due to lack of security in the study.

2.1 IoT Blockchain-Enabled Financial Services Numerous IoT frameworks are likely to be subject to smaller-scale budget exchanges between computerized articles, requiring the IoT devices be associated in a manner that considers the so-called machine-to-machine (M2 M) economy–which is essentially the money trading between non-human devices. The number of transactions is concerned; countless blockchain schemes have restricted execution every time they can cope with it. This implies most blockchain executions of proof of work and proof of stake blockchain present the limited potential for versatility, making them unacceptable for large-scale handling of M2 M microtransactions. Nevertheless, it is worth mentioning that countless blockchain operations are shifting toward adaptability provisions, such as the Bitcoin lightning network and the Ethereum Plasma [9].

2.2 IoT Security with Blockchain Another safety building that dodges the use of cryptography scheme based on the trade-in symmetric or asymmetric cryptography keys is suggested that is not prescribed in IoT devices due to their handling, storage and energy containment [10]. The suggested scheme relies on the multi-operator being used to guarantee the safety of related objects. With no compelling reason to register keys, these studies modified with specialists can speak to different items. The main goal of this job is to maintain an abnormal state of safety by enhancing the energy usage of IoT devices [11].

2.3 Privacy-Preserving Healthcare Blockchain for IoT Designs are for confirming an unbound IoT-based implementation, especially concerning applications for medicinal services where customer approval is a key issue. The three procedural examples, i.e. Security Goal Documentation, Choose the Right Stuff and enroll from Outsider Pattern, are examined in [12]. Five other examples of approval are shown in this investigation as reference monitor, access

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matrix role approval guidelines and role-based access control, remote authenticator/authorizer and file authentication patterns. For IoT-based E-healthcare applications, a protected gathering-based lightweight validation conspire has been proposed where the proposed model will provide shared validation and energy proficiency and calculation for IoT-based social insurance applications [13], which will use elliptic curve cryptography (ECC) guidelines that provide mention included of the suggested model [14].

2.4 IoT Blockchain Support to LoRaWAN Network Servers An IoT framework model is presented that uses the LoRaWAN convention to transmit information from sensor hubs to our cloud administration and the things network stage that executes the backend administrations of LoRaWAN [15]. The authors have organized a structure that can be adapted and extended to expand new advantages just as other IoT phases are coordinated. It is also equally adaptable, which means that a presentation can be designed simply by creating new server instances (Fig. 1).

Fig. 1 Application and benefits of IoT blockchain security

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3 Benefits of Blockchain Reliability. The databases of the entire transaction records are changed by the decentralized concept. Cloud adds to the reliability where which helps data to be easily accessed and processed with security features like asymmetric encryption [16]. Trust. The blockchain structure is happening as new trust providers with decentralized data. These data is distributed across a system of fixed centers [17]. Security. The blockchain framework uses one-way hash work. The yield has no unmistakable connection to the information. Efficiency. Blockchain advancement could accelerate the clearing and repayment of specific exchanges identified with cash by decreasing the number of middle people included and making the trade-off procedure faster and gradually profitable [18].

4 Literature Review Another IoT access control structure is based on the blockchain’s development. The objective of the study is to provide a reference model for the proposed structure in the IoT objectives, models, architecture and mechanism [19]. In addition, fair access is presented as a full pseudonymous and security-saving executive system authorization that empowers customers to own and regulate their data. For updating the model, the blockchain is used and adjusted to decentralized control of access. A decentralized trust system, called IoT passport, is suggested using innovation in blockchain for coordinated cross-stage efforts [20]. A reference design for shared IoT applications based on blockchain, featuring the main correspondence channels anticipated to interface the IoT layer with the blockchain layer, was shown in [21]. By establishing frameworks and conventions for shared IoT applications based on blockchain, the practicality of the suggested engineering has been explained. A successful and controlled blockchain plot-based transmitted cloud architecture is discussed in CRVANET’s biological system instead of custom cloud engineering to protect driver safety with minimal effort and on-demand detection in [22]. In addition to providing research methodology to identify concerns and arrangements, an efficient survey of existing IoT security and protection systems is provided in [23]. It discusses IoT design, characterizes the distinct areas of safety and security and provides a scientific classification and close investigation to plot safety goals, risks and attacks and agreements suggested late (2015–2018) (Table 1).

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Table 1 Review of proposed methods Reference No

Proposed method

Benefits

Shortcomings

[20]

IoT passport

It helps in commodity user interfaces

It is completely Future work calls dependent on for collaborative trust and is very IoT susceptible to security threats

Future scope

[6]

IoT chain

It provides a flexible authorization

The performance and robustness have not been much evaluated

Private ethereum blockchain network needs upgradation to be used in the PoS-based version of the ledger

[8]

A blockchain-based proxy re-encryption scheme

It provides scalability and security to the automated payments

The number of mined transactions depends on various factors including gas price and limit

Future implementation includes implementation on different blockchain platform, e.g., hyperledger

[24]

An over-the-blockchain firmware update framework

It provides a secure verification to the firmware

The firmware mechanisms are too lengthy to operate

Future work includes the development of a robust and lightweighted protocol

[25]

Unibright

New templates are created to recognize different industries

A huge number Future work needs of risks are to focus mostly on associated with the risks associated it

[26]

A decision framework to support blockchain platforms for IoT and edge computing

It shows the systematic identification of blockchain characteristics suitable for IoT platform

This framework still did not provide any specific

This decision framework can be improved into a recommender tool

5 Proposed Architecture–Blockchain in IoT Ecosystem The architecture of IoT ecosystem as explained in [27] is fully modular, and all the modules can be easily decoupled from one another in order to add a module and keeping the rest of the system intact. [28] Further provides a comparative study on the architecture, which shows security issues and solutions on application layer. Integrity-related problems can be handled by blockchain solutions discussed in this paper.

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Fig. 2 Proposed architecture for blockchain in IoT ecosystem [27, 28]

This paper proposes in Fig. 2 a modification to the architecture discussed in [28], where blockchain is introduced before the application layer which can address some of the security issues addressing our concerns related to confidentiality, integrity and accountability. Several linked devices possessing the capabilities of communication, data storage and computing are present in the IoT physical layer. The connectivity layer provides services which include managing the networks, managing the security and passing of messages. The IoT blockchain service layer provides all services including managing identities, peer-to-peer communication and consensus. The application layer consists of the interfaces which visualize the data from all the physical devices for regulating and manipulating devices.

6 Future Projection of an IoT and Blockchain Authorization Model The Internet of things (IoT) has stretched the Web availability to reach PCs and people, yet most of our population is missing Internet of things. The IoT may be able to combine billions of articles at the same time, which has the effect of improving the sharing of data needs as a result of improving our lives. Even though the advantages of IoT are boundless, due to its concentrated server/customer model, the IoT are facing numerous difficulties. For example, the versatility and security issues that arise as a result of the unreasonably large number of IoT issues in the system. The server model requires all devices to be linked and verified through the server, which is the single point of failure. In this way, moving the IoT framework in a decentralized direction

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could be the right choice. One of the well-known frameworks for decentralization is blockchain. The blockchain is a ground-breaking innovation that decentralizes computation and board forms that can capture a vast number of IoT issues, security. Blockchain innovation is the missing link to solve the problems of versatility, protection and reliability in the Internet of things. Blockchain advances might be the silver slug required by the IoT business. New Innovations in Blockchain technology can be used to track billions of devices, empower exchanges and coordination between devices are considered as significant reserve funds for IoT industry. This decentralized methodology would be used for the sole purpose of disappointment, creating a stronger biological system for devices to keep going. Cryptographic calculations used by blockchains would make purchaser information confidential. Blockchainbased IoT arrangements are appropriate for streamlining business forms, improving customer experience and achieving significant cost efficiencies.

7 Conclusion As we envision the future growth of IoT, we must also discuss about its security as one of the utmost requirements of IoT ecosystem. In this paper, we have addressed security issues in IoT ecosystem using blockchain technology. We have also discussed a few domains like finance, healthcare, network, smart devices, etc., all these domains have their specific security requirements and challenges. This paper discusses the probable models which can be used by these domains to address security issues using blockchain technology. The broad overview provided in this paper also discusses about the combined and modular architecture of IoT and blockchain, and this architecture is designed in such a flexible way that all the devices and layers can be coupled or decoupled as per the domain’s requirement. This paper also sheds some light on the benefits of blockchain which gives us confidence of using this technology in our future deployments. Furthermore, we have done a brief literature survey on the solutions discussed by several technical papers on this technology. Different solutions provided by several writers are compared based on their benefits, shortcomings and future scope on the proposed method. Before concluding this paper, we have also discussed a future projection of an IoT and blockchain-based authorized model, which mentions specific details required for this model. This paper is just a drop in the ocean of blockchain technology, there is long way to go and much more to dive into, but we expect that we are able to address some of the challenges of IoT ecosystem using blockchain technology and we hope that paper will be helpful in researches going to happen in near future. We are hopeful that blockchain is going to be one of the pioneer technologies in addressing security issues.

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References 1. F. Tschorsch, B. Scheuermann, Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutorials 18(3), 2084–2123 (2016) 2. J. Yli-Huumo, D. Ko, S. Choi, S. Park, K. Smolander, Where is current research on blockchain technology?—a systematic review. PLoS ONE 11(10), e0163477 (2016) 3. D. Kraft, Difficulty control for blockchain-based consensus systems. Peer-to-Peer Networking Appl. 9(2), 397–413 (2016) 4. S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system (2008) 5. S. Haber, W. Stornetta, How to time-stamp a digital document, Crypto’90, LNCS 537 (1991) 6. O. Alphand, M. Amoretti, T. Claeys, S. Dall’Asta, A. Duda, G. Ferrari, F. Rousseau, B. Tourancheau, L. Veltri, F. Zanichelli, IoTChain: a blockchain security architecture for the internet of things, in 2018 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE, 2018), pp. 1–6 7. S. Mendhurwar, R. Mishra, Integration of social and IoT technologies: architectural framework for digital transformation and cyber security challenges, in Enterprise Information Systems (2019), 1–20 8. A. Manzoor, M. Liyanage, A. Braeke, S.S. Kanhere, M. Ylianttila, Blockchain based proxy re-encryption scheme for secure IoT data sharing, in 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (IEEE, 2019), pp. 99–103 9. The derivative effect how financial services can make IoT technology pay off. Retrieved from https://www2.deloitte.com/us/en/pages/financial-services/articles/the-derivative-effecthow-financial-services-can-make-iot-technology-pay-off.html. Last accessed 15 Jan 2020 10. B.A. Abdelhakim, B.A. Mohamed, B. Mohammed, B.A.O. Ikram, New security approach for IoT communication systems, in Proceedings of the 3rd International Conference on Smart City Applications (ACM, 2018), pp. 2 11. How to secure the internet of things with blockchain. Retrieved from https://www.devteam. space/blog/how-to-secure-the-internet-of-things-iot-with-blockchain/. Last accessed 15 Jan 2020 12. M. Aydar, S.C. Cetin, S. Ayvaz, B. Aygun, Private key encryption and recovery in blockchain, arXiv preprint (2019), arXiv:1907.04156 13. A. Pazaitis, P. De Filippi, V. Kostakis, Blockchain and value systems in the sharing economy: the illustrative case of backfeed. Technol. Forecast. Soc. Chang. 125, 105–115 (2017) 14. A.D. Dwivedi, G. Srivastava, S. Dhar, R. Singh, A decentralized privacy-preserving healthcare blockchain for iot. Sensors 19(2), 326 (2019) 15. Network Architecture, https://www.thethingsnetwork.org/docs/network/architecture.html. Last accessed 25 Jan 2020 16. Y. Zhang, C. Xu, J. Ni, H. Li, X.S. Shen Blockchain-assisted public-key encryption with keyword search against keyword guessing attacks for cloud storage. IEEE Trans. Cloud Comput. (2019) 17. S. Underwood, Blockchain beyond bitcoin. Commun. ACM 59(11), 15–17 (2016) 18. H. Wang, K. Chen, D. Xu, A maturity model for blockchain adoption. Finan. Innov. 2(1), 12 (2016) 19. S. Moin, A. Karim, Z. Safdar, K. Safdar, E. Ahmed, M. Imran, Securing IoTs in distributed blockchain: analysis, requirements and open issues. Future Generation Comput. Syst. 100, 325–343 (2019) 20. B. Tang, H. Kang, J. Fan, Q. Li, R. Sandhu, IoT passport: a blockchain-based trust framework for collaborative internet-of-things, in Proceedings of the 24th ACM Symposium on Access Control Models and Technologies (ACM, 2019), pp. 83–92 21. G.S. Ramachandran, B. Krishnamachari, A reference architecture for blockchain-based peerto-peer IoT applications. arXiv preprint (2019), arXiv:1905.10643 22. S. Nadeem, M. Rizwan, F. Ahmad, J. Manzoor, Securing cognitive radio vehicular ad hoc network with fog node based distributed blockchain cloud architecture. Int. J. Adv. Comput. Sci. Appl. 10(1), 288–295 (2019)

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Feature Selection for Handwritten Signature Recognition Using Neighborhood Component Analysis Kamlesh Kumari and Sanjeev Rana

Abstract Advance trends adopted in classification of images using deep learning networks have paved opportunities for handwritten signature identification and verification. Convolution neural network has demonstrated to be profoundly proficient in image classification, yet this methodology has a few weaknesses also. Notably, a huge number of images are required for training to achieve high level of a precision for classification. Transfer learning with pretrained deep convolution neural network model can be utilized to conquer these issues. To bring about further enhancements in terms of recognition rate, storage and computational time, feature selection procedure plays a key role by distinguishing the most significant features. In this work, we used a pretrained DCNN model, i.e., GoogLeNet for feature extraction using transfer learning and further used neighborhood component analysis to reduce the irrelevant features. Support vector machine is used for classification. Experiments have been conducted using three signature datasets, i.e., CEDAR, SigComp2011 and UTSig. Keywords AlexNet · Support vector machine (SVM) · GoogLeNet · Neighborhood component analysis (NCA) · Deep convolution neural network (DCNN) · VGGNet · Center of excellence for document analysis and recognition (CEDAR)

K. Kumari (B) · S. Rana Department of Computer Science & Engineering, M.M.D.U, Mullana, Ambala, India e-mail: [email protected] S. Rana e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_22

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Filter Concatenation

1*1 Convolution

3*3 Convolution

5*5 Convolution

3*3 Max Pooling

Previous Layer Fig. 1 GoogLeNet-inception module

1 Introduction Amongst various biometric frameworks, handwritten signature recognition has got distinct fascination throughout the most recent three decades. In recent years, deep convolution neural network-based features show the best result for image classification. So in our research work, we investigate the performance of signature identification and verification utilizing features based on GoogLeNet and further use neighborhood component analysis for feature selection. In AlexNet, there is not any fixed pattern [1]. The convolutions for each layer are chosen experimentally. In VGGNet, you can distinguish a structure obstruct as two convolution layers followed by pooling layers [2]. GoogLeNet has a quite different architecture than AlexNet and VGGNet [3]. It uses inception module that performs numerous convolutions, with various filter sizes, and just as pooling in one layer. Consequently, it makes network wider than deeper. Inception module is limited to filter sizes 1 × 1, 5 × 5 and 3 × 3 as shown in Fig. 1. The outline of this paper is as follows: Neighborhood component analysis is described in Sect. 2. Section 3 shows the related work of the authors who have considered the features extracted from pretrained CNN model. The methodology has been elaborated in Sect. 4. Experimental results and analysis are detailed in Sect. 5. Conclusion of the work is discussed in Sect. 6.

2 Neighborhood Component Analysis NCA is a compelling strategy for choosing critical component that focuses on high-dimensional information [4]. Neighborhood component analysis is used for Alzheimer’s disease [5] and skin lesion classification [6]. The subtleties of the feature section process in context of signature recognition are as per the following: Let us consider signature subtype classification, multi-class classification. Suppose c be the subtype classes and n the number of signature samples. At that

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point, a given training set can be depicted as follows: S = {(x1 , y1 ), (x2 , y2 ), . . . , (xn , yn )}

(1)

whereas x i stands for the r-dimensional vector and yi denotes the class corresponding to vector. The choice of reference point in NCA is based on some probability which is called selection probability pij as described in Eq. (2). If the reference point of x, i.e., x i is closer to x, then the probability pij will have higher value, as measured by the distance function in Eq. (3) and kernel (k). k(dw (xi , yi )) j−1, j=1 k(dw (x i , yi ))

Pi j = n dw (xi , yi ) =

p 

(2)

  wr2 xir − x jr , Wr is a weight assigned to the r th feature.

(3)

r =1

For a given S, the probability of correct classification of signature sample x i is defined by Eq. (4) and is called average leave one out probability pi where yij is 0 for i = y. pi =

n 

Pi j yi j

(4)

i=1, j=i

Probability of correct classification of all signature samples is F(w) =  p λ r =1 wr2 , where λ is regularization parameter.

n i=1

pi −

3 Related Work Architecture similar to inception is used for offline signature verification [7]. Performance of proposed architecture is tested on three public datasets, i.e., UTSig, CEDAR and BHSig260. False acceptance rate of 6.38, 3.38 and 23.34 is obtained for Hindi, Bengali and Persian signature, respectively. Also, performance on CEDAR is 100%. Particle swarm optimization approach to select features is proposed in the work [8]. Database used is GPDS-960, and features used are signet-2048. With this method, 55% feature give same result that was achieved with 2048 feature from signet. This method controls the overfitting. Experiments are performed on signature of thirty users in [9]. Transfer learning approach using two DCNN (i.e., VGGNET and AlexNet) is used. With 50 epochs, outcome is 100%. Three different DCNN models with three, four and five CONV layers are also tried. Result of DCNN with five CONV layer is 99.17% with execution time of 115s. A DCNN with 4 CONV layer and one dense layer with twin structure

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is proposed in [10]. Syntactic signatures are generated to increase the samples. The equal error rate of 4.84% for CEDAR and 3.93 for SigComp2011 are obtained. Performance of features based on pretrained DCNN model is tested on datasets used in signature verification competitions [11]. Investigations are performed on seven sets, i.e., two sets for Japanese, Chinese, Italian and one set for Dutch. Four types of signatures are used, and outcome of SVM with AlexNet-based features is better than DT. Results are expressed in confusion matrix. Accuracy of Japanese and Chinese signature is 99.2% and 100% for one set of each which are better than other two languages, i.e., Dutch and Italian. Investigations are performed on scanned signatures of three datasets using AlexNet-based features and two machine learning algorithms in the research [12]. Two sets are formed each of seven users. Outcome of SVM is better than DT. Performance is expressed in confusion matrix for each user. The recognition rate of 100% and 99.1% is obtained for Bengali and Hindi signatures, respectively. Also, outcome on CEDAR and UTSig dataset is good, i.e., above 90%. Performance of bag of features is also compared with these features.

4 Methodology Flow diagram of proposed approach, i.e., from input to prediction is shown in Fig. 2.

4.1 Preprocessing GoogLeNet recognizes input size of 224 × 224 × 3, so all the scanned signatures of dataset are resized to 224 × 224. Scanned Signature

Genuine / Forged

Preprocessing

SVM

Feature Extraction (GoogLeNet)

Feature Selection (NCA)

Fig. 2 Flow diagram of proposed approach, i.e., from input to prediction

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4.2 Feature Extraction To extract features from any pretrained deep CNN model, it is essential to mention from which layer of pretrained model, we want to extract feature. The length of feature vector depends on the layer from which we are extracting features. Extracting feature from layer named pool5-drop_7 × 7_s1 results in a set of 1024 features for a single image. Whereas, in our work, feature representation of scanned handwritten signature images is extracted from a layer named as loss3-classifier of GoogLeNet that yields a length of 1000 values for each image.

4.3 Feature Selection We used NCA for feature selection. A higher weight value of features indicates better discrimination value. The best λ value is chosen to reduce the number of features through cross-fold validation considering minimum loss value.

4.4 Classification Support vector machine is used. Outcome is expressed in confusion matrix.

5 Experimental Results and Analysis We investigate the performance on two set of seven users on CEDAR [13] and UTSig [14] datasets and Dutch users from SigComp2011 [15] in MATLAB [16]. Table 1 shows the notations used in experiment. Table 1 Dataset and their notations

CEDAR UTSig SigComp2011

CED-I

User 1–7

CED-II

User 8–14

P-I

User 1–7

P-II

User 8–14

DUT

Dutch users

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Fig. 3 Feature weights for Dutch signatures

5.1 Experiment on DUT Feature weights of 1000 features extracted from GoogLeNet for Dutch signature are shown in Fig. 3. Using NCA, we got 176 features and obtained recognition rate of 96.4% as shown in Fig. 4. Plot of loss values for different lambda values is shown in Fig. 5.

5.2 Experiment on P-I and P-II Feature weights of 1000 features extracted from GoogLeNet for P-I are shown in Fig. 6. Plot of loss values for different lambda values for P-I is shown in Fig. 7. Using NCA, we got 97 features for P-I with outcome of 89.5% (as shown in Fig. 8) and 167 features for P-II with recognition rate of 91.7%.

5.3 Experiment on CED-I and CED-II Feature weights of 1000 features extracted from GoogLeNet for CED-I are shown in Fig. 9. Plot of loss values for different lambda values for CED-I is shown in Fig. 10. Using NCA, we got 162 features for CED-I with outcome of 91.8% as shown in Fig. 11 and 159 features for CED-II with recognition rate of 91.8%.

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Fig. 4 Confusion matrix for Dutch signatures

Fig. 5 Average loss values versus lambda values for Dutch signatures

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Fig. 6 Feature weights for P-I

Fig. 7 Plot of loss values for different lambda values for P-I

5.4 Analysis Comparison of performance achieved through NCA is described in Table 2. Outcome of GoogLeNet with NCA is better for Dutch and Persian signatures in terms of

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Fig. 8 Outcome of 97 features selected by NCA for P-I

Fig. 9 Average loss values versus lambda values for CED-I

prediction and storage (due to reduced feature). Outcome of GoogLeNet without NCA is better for CED-I and CED-II.

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Fig. 10 Feature weights of 1000 GoogLeNet features (CED-I)

Fig. 11 Outcome of 162 features selected by NCA (CED-I) Table 2 Comparison of performance with features based on GoogLeNet

GoogLeNet + NCA (%)

Dataset

GoogLeNet (%)

CED-I

92.9

91.8

CED-II

93.9

91.8

DUT

92.9

96.4

P-I

88.7

89.5

P-II

85

91.7

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6 Conclusion Thinking about the ongoing accomplishment of deep learning, we explored a technique for the classification of handwritten signature recognition using NCA for feature selection. In the wake of actualizing this thought, we are in a situation to advance a couple of cases including: (i) further improvement in analyzing feature selection and (ii) dimensionality step to be further decreased that significantly improves the classification results in terms of storage and execution time and recognition rate for both Persian and Dutch signatures. Finally, as future work, more advanced convolutional network models can be used to acquire better results for handwritten signature recognition problems.

References 1. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012) 2. C. Szegedy et al., Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015) 3. K. He et al., Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 4. W. Yang, K. Wang, W. Zuo, Neighborhood component feature selection for high-dimensional data. JCP 7(1), 161–168 (2012) 5. M. Jin, W. Deng, Predication of different stages of Alzheimer’s disease using neighborhood component analysis and ensemble decision tree. J. Neurosci. Methods 302, 35–41 (2018) 6. T. Akram, H.M.J. Lodhi, S.R. Naqvi, S. Naeem, M. Alhaisoni, M. Ali, N.N. Qadri, A multilevel features selection framework for skin lesion classification. Human-centric Comput. Inform. Sci. 10, 1–26 (2020) 7. R.K. Mohapatra, K. Shaswat, S. Kedia, Offline handwritten signature verification using CNN inspired by inception V1 architecture, in 2019 Fifth International Conference on Image Information Processing (ICIIP) (IEEE, 2019), pp. 263–267) 8. V.L. Souza, A.L. Oliveira, R.M. Cruz, R. Sabourin, Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control. arXiv preprint (2020), arXiv:2004.03373 9. A. Hirunyawanakul, S. Bunrit, N. Kerdprasop, K. Kerdprasop, Deep learning technique for improving the recognition of handwritten signature. Int. J. Inform. Electron. Eng. 9(4) (2019) 10. V. Ruiz, I. Linares, A. Sanchez, J.F. Velez, Off-line handwritten signature verification using compositional synthetic generation of signatures and siamese neural networks. Neurocomputing 374, 30–41 (2020) 11. K. Kumari, S. Rana, Offline signature recognition using pretrained convolution neural network model. Int. J. Eng. Adv. Technol. (IJEAT) 9(1), 5497–5505 (2019) 12. K. Kumari, S. Rana, S. Offline signature recognition using deep features, in International Conference on ICT for Intelligent Systems (Springer, 2020) 13. http://www.cedar.buffalo.edu/NIJ 14. A. Soleimani, K. Fouladi, B.N. Araabi, UTSig: a persian offline signature dataset. IET Biometrics 6(1), 1–8 (2016) 15. TC11, I. A. P. R., ICDAR 2011 Signature verification competition (SigComp2011) (2011) 16. http://www.mathworks.in/

Blockchain-Based E-Voting Protocol Shreya Shailendra Chafe, Divya Ashok Bangad, and Harsha Sonune

Abstract Today, each individual voter expects that his vote is counted correctly, and that the election results are not rigged. Every voting process affects voter’s trust in the system. In other words, it is very important that the government is able to successfully hold non-fraudulent, transparent, trustworthy and most importantly, secure elections to affirm the faith of public. In this paper, we propose a system that makes use of blockchain to make the elections secure, maintain anonymity of voter and is decentralized. We propose a new e-voting protocol which adheres to the fundamental e-voting properties and in addition offers to make the process decentralized. Keywords Election · Transparent · Trustworthy · Secure · Blockchain · Anonymity · Decentralized

1 Introduction Election plays an important role in building a nation and determining its fate. Elections permit voters to exercise their democratic power by casting a vote for a meriting candidate who would represent them within the government [1]. However, sadly, voters are being bribed or the ballot machines are being tampered by the parties to win the elections in an unethical manner. There have been many such cases of rigging and fraud in past, across the world. Cases related to election manipulation, election fraud or illegal intrusion with the electoral process have been seen in US Election (2016), Malawi elections (2019), Turkish General Election (2015), Ugandan General S. S. Chafe (B) · D. A. Bangad · H. Sonune Cummins College of Engineering for Women, Pune, India e-mail: [email protected] D. A. Bangad e-mail: [email protected] H. Sonune e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_23

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Election (2006) and many more. Therefore, it is the need of an hour to fabricate an election system free from external interventions. Every citizen’s vote should be autonomous, and he should be able to verify whether or not his vote is counted properly. In this paper, we are proposing a system that aims at creating the present e-voting system transparent, auditable and secure. The current electoral system in India is as mentioned further. Constituencies are created within which people can cast their vote or can even apply to get a ticket to stand in elections themselves. Each constituency consists of an electoral roll that contains the lists of voters. Any Indian national who is eighteen or more can exercise his power to vote. On the day of the election, individuals have to head to the election constituencies to cast their vote. They need to press a button against a candidate of their choice on the electronic mechanical device (electronic voting machine—EVM) to cast their vote. On the day of vote counting, EVMs are checked and unlocked. Then, the counting of votes begins, and a candidate with maximum number of votes wins the election. This method needs to be carried out under the presence of eminent officers. The current electoral system is centralized, which means the system is controlled by a central authority [1]. This could lead to tampering of votes while counting [1]. Sometimes, it might happen that the EVMs are programmed to constantly yield the same output for different inputs. Thus, every voter of India cannot keep track of his vote. Therefore, we can use blockchain to overcome the issues like change of state of the votes and rigged EVMs [1] within the current election system. Online voting can prove to be an efficient solution for this problem [1]. Using blockchain technology, online voting could encourage voter’s involvement and help reinstate the public’s trust in the democracy [2]. Let us take a look at what is blockchain and how it will develop a secure online voting system. Blockchain, also known as distributed ledger technology (DLT) [3], is a peer-to- peer network [4], open to anyone [3]. A blockchain is a chain of blocks that contains information, where each block is linked with each other using cryptography [3]. Each block consists of a hash, a timestamp and the data of transactions that have occurred [3]. A very interesting property of blockchain is that once some data has been recorded, it becomes very difficult (almost impossible) to change it, rendering the records immutable [3, 5]. Other main features of blockchain include validating, consensus and verifiability. Blockchain is used to store data in addition to keeping it safe from any malicious attack while keeping the identity of voters disclosed. Blockchain would shift the current centralized electoral system to a decentralized voting system, giving complete transparency. Thus, implementation of blockchain is a very efficient approach toward a secure and transparent election process.

2 Design Considerations The following are some design requirements that should be taken under consideration [6–8]:

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2.1 Eligibility The system ought to be designed solely on the degree of eligibility of a person who is exercising the power to cast a vote. Eligibility needs should embrace age restrictions and citizenship of the individual.

2.2 Anonymity The system ought to be fabricated in a way that the votes cannot be traced back to a exact voter, thus revealing the identity of the voter, i.e., to possess the flexibility of permitting a citizen to vote anonymously [9].

2.3 Unchangeable Records The system ought to be fabricated in such a way that the votes casted cannot be mutated or modified by anyone [9].

2.4 Single-Cast The system ought to be designed such that a citizen will exercise the power to vote just once and not multiple times.

2.5 Authentication The system ought to be designed such that solely those that had registered previously will vote. In different words, a citizen shall be supplied with some variety of identity (election-card) which can attest them to vote [10].

2.6 Transparency The system ought to be designed in such a way that it provides transparency to avoid any ambiguity, within the style of a verifiable assurance to every citizen that their vote was counted, correctly, besides maintaining anonymous standing of citizen [2].

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3 Properties of Blockchain to be Used in E-Voting 3.1 Smart Contract Smart contract is a small computer code deployed in blockchain nodes. They are immutable and distributed [5]. Being immutable means that no one can tamper with the code of the contract. And being distributed means the code and conditions in the contract are publicly available on the ledger. They are initiated by the message which is embedded in the transaction. These are used to layer the trust infrastructure of the blockchain with logic and computation.

3.2 Boot Node The boot node is assigned a static IP, instead of dynamic IP so that district nodes can locate their peers quicker. A boot node is a coordination and discovery service that helps the district nodes to learn about each other [1, 6].

3.3 District Node Each district will have its own district node, which will have a software mediator that interacts independently with the boot node and manages the life cycle of smart contract and entire balloting process [1, 6].

3.4 Asymmetric Cryptography This uses mathematically related public key and private key. Private key is kept secret, while the public key is shared with everyone. Data encrypted by the sender’s private key and receiver’s public key can be decrypted only with the sender’s public key and receiver’s private key.

3.5 Hashing It is used to verify that the information is not been tampered or modify [9]. Each node in the blockchain has a unique identity which is a 256-bit hash placed on its header, which is calculated using the secure hash algorithm (SHA-256) [11]. Input in all the states of the blockchain is a plain text, i.e., is all the transactions along with the

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previous block hash. The output is the current state of the blockchain encrypted in 256-bit binary value (hash). To generate the hash for the first block (genesis block), the series of the initial transactions is used. This is strictly a unidirectional algorithm, which means we cannot use the same algorithmic program to convert the hash code to plain text. Besides if the data of any transaction is changed, the hash of that block changes and conjointly of any following block. On scrutiny with the original hashes, the change of state is caught easily.

3.6 Consensus Protocol A consensus algorithmic program aims at finding a common agreement among all the nodes of blockchain that is a win for the whole network. There are various types of consensus protocols that are based on different mechanism. Some of the consensus protocols are Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), Proof of Work (PoW) and Delegated Proof of Stake (DPoS). We will implement DPoS in our system, and in this, the stakeholders rather than creating a block themselves give the power to the delegates they support. This reduces the process power of the stakeholders. In some DPoS versions, a delegate should show a commitment by depositing some funds into a time-locked security account. DPoS is sort of a government system, if the delegates miss their turn of creating a block, they will not be allowed, and the stakeholders will select new nodes to exchange their place. DPoS is affordable and high-efficiency consensus protocol as compared to PoW and PoS [5, 12].

4 Requirements for the Elections 4.1 Registration Every eligible voter should register his/her name only once in lifetime. During registration, the individual must provide Aadhar card and permanent account number (PAN) card. Biometrics like fingerprint and iris of an individual ought to be taken and stored in the database. In case of any ambiguity, the biometrics can also be verified with the Aadhar card database. These are unique to a person, and hence, it ensures that someone solely votes against his own name. This would add more security to the system, ensuring that an individual casts vote just once and will do no frauds. Phone number and e-mail id should be provided by the voters during registration. These data fields will be very useful for sending OTP, confirmation messages and some additional details and information.

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After registration, the voters will be provided with voter’s ID card, each of which will contain a unique QR code. This unique code will act as a username for the concerned individual. The QR code will be used for login credentials.

4.2 Blockchains Used Private blockchain Every district will have a private blockchain in which each node will consist of the votes casted by the voters Fig. 1a. Public blockchain Every state will have a public blockchain. This will contain the boot nodes linked to a district node. Figure 1a, b in other words, the total vote tally of each party in each district will constitute this blockchain. The blockchain in every district can be activated that is the voting process in each district will begin only when all the administrators have logged-in in the system. This ensures decentralization. These administrators can be trusted people from NGOs who look after the welfare of the people and run campaigns to fight against the voting frauds [3].

Fig. 1 Representing types of blockchains used

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5 The Proposed Protocol For the actual process of casting votes, the voters are supposed to report to their voting centers, along with their voting IDs. Let us see step-by-step as to how the votes shall be casted.

5.1 Prerequisites Prior to the election, every voter’s cryptocurrency wallet meant exclusively for the election process will be set with few coins [13]. This wallet can be accessed only with a private key that each voter has. Private key is unique to every voter.

5.2 Login The voters have to scan the QR code from the voter’s ID card. After this, the biometrics will be taken as input, to ensure the validity of the voter. If the biometrics and the login details match, only then shall the process proceed further, otherwise the current session shall be aborted.

5.3 Initiating Voting After the identity of the voter is verified, he would be given the authority of creating a node in the private blockchain by the administrators using the DPoS consensus algorithm.

5.4 Displaying the Candidates The list of candidates to be voted for will be displayed along with their party symbol. For the voter to cast his vote, he will have to spend all his coins on the candidate of his choice [14]. The voters can use their private key to access their wallet. While the public key of the voter will be shared with a deserving candidate for whom the voter is willing to spend the coins and wants to vote for.

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Fig. 2 Encrypting transactions

5.5 Cast a Vote When the voter spends his coins to cast his vote, the transaction will be added in the ledger. A hash code will be generated using the hash of the previous block and the voter’s current transaction. Then, this transaction along with the hash code will be encrypted by the voter’s private key, and this is the digital signature. We can say that digital signature is the combination of hashing and encryption of the transaction by the private key of the voter. Digital signature can be defined as the following function (Fig. 2): f (hash code, current transaction) = y encryption(y) = digital signature

5.6 Receipt Generation (not Mandatory) After casting the vote, a receipt will be generated indicating the vote casted by the voter. These receipts can be used to verify the results of the election if needed. Figure 3 illustrates the working of proposed protocol.

5.7 Tally The tally is performed using a blockchain which is a distributed ledger. Each voter can see the entire network of blocks. After results are declared, every individual voter can verify whether his/her vote was counted correctly (i.e., that system was not rigged), and if the total tally matches, the actual votes casted. This ensures that the votes were not tampered with. In case of any tampering, the hash of the block whose vote is tampered with changes and so does the hashes of further blocks. Every voter shall be able to see his own block, showing the vote that he has casted (Fig. 3). For this, the authority that is conducting the elections shall provide

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Fig. 3 Working of proposed system

an interface where a voter can login and see his/her details clearly. The details may include the voter’s name, voter ID number, Aadhar number and the vote that he had casted. Personal details can only be seen by the voter herself/himself. He/she can also be able to see all other votes, casted by other voters, while maintaining their anonymity of other voters. The details of other voters shall include only the hash of the block and the vote casted. This way, anonymous status of other voters is maintained, and the results (total tally of votes) can also be cross-checked by the voters [5]. Hence, the control is not just in the hands of one specific body. If the voter finds out that the vote he had casted is not the same as that shown in the block, he may take legal action and register complaint of vote rigging (Fig. 4).

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Fig. 4 View of voter

6 Discussion One major inconvenience caused by the existing process of elections is its inflexibility. The existing system does not allow any voter to cast his/her vote in case the voter is not present in constituency where he/she has been registered. This rigidity of the system results in decreased participation of the voters. We can have an additional flexibility in the proposed system. As discussed earlier, on the day of voting, administrators in each city must first login into the system to begin a session. These login credentials can be shared with the administrators using a smart contract over the state so that those voters who could not be in their respective constituencies can vote from their current location. This helps in motivating the population of the country to turn up at the voting centers and cast their vote. This also eliminates any possibility of voter fraud as smart contracts cannot be changed. To conclude, the names of the voters should be registered in a smart contract so that these smart contracts can be exchanged between the administrators. Different polling centers should be set up for those who do not belong to the constituency from where they are voting. In this, the administrator in that polling center will make sure that the voter is added to the correct boot node.

7 Result and Conclusion India is the largest democracy in the world, yet EVM fails to provide justice to the citizen’s right to vote [2]. Political instability also prevails as the casted votes are rigged by hacking the electronic machines. Hence, comes the need for a more reliable platform for casting votes and electing a political party for the welfare of country. It is the need of an hour to design a secure and immutable voting system. Blockchain, a technology that offers various features such as transparency, security, decentralization and immutability, is an appropriate approach toward the implementation of such system [2]. We have proposed the use of cryptocurrency wallets in order to invest on the favored politician. Digital signature preserves authenticity of the voter and tampering with the vote. Smart contracts, an important feature of blockchain, make sure that the voter does not spend more than the available amount. Using all these features of blockchain, this paper proposes a working e-voting system.

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References 1. F. Hjálmarsson, G.K. Hreiðarsson, M. Hamdaqa, G. Hjálmtýsson, Blockchain-based E-voting system, in 2018 IEEE 11th International Conference on Cloud Computing 2. A. Menon, V. Bhagat, Blockchain based e-voting system (2020) 3. A. Khandelwal, Blockchain implementation on e-voting system, in International Conference on Intelligent Sustainable Systems (ICISS 2019), IEEE Xplore 4. T. Moura, G. Alexandre, Blockchain Voting and its effects on election transparency and voter confidence, in Proceedings of the 18th Annual International Conference on Digital Government Research (2017) 5. B. Wang, J. Sun, Y. He, D. Pang, N. Lu, Large-scale election based on blockchain, in 2017 International Conference on Identification, Information and Knowledge in the Internet of Things (2017) 6. W. Li, Escape analysis on the confinement-escape problem of a defender against an evader escaping from a circular region (2016) 7. N. Sharma, A. Chakrabarti, V.E. Balas, Data management, analytics and innovation, in Proceedings of ICDMAI 2019, vol. 1 (2019) 8. I.L. Awalu, P.H. Kook, J.S. Lim, Development of a distributed blockchain e-voting system, in Proceedings of the 2019 10th International Conference on E-Business, Management and Economics (2019) 9. R. Hanifatunnisa, B. Rahardjo, Blockchain based e-voting recording system design (2017) 10. M. Pawlak, A. Poniszewska-Maránda, N. Kryvinska, Towards the intelligent agents for blockchain e-voting system, in The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (2018) 11. A.B. Ayed, A conceptual secure blockchain-based electronic voting system (2017) 12. S. Zhang, J.-H. Lee, Analysis of the main consensus protocols of blockchain (2019) 13. K. Patidar, B. Rahardjo, Decentralized e-voting portal using blockchain, in 2019 10th International Conference on Computing, Communication and Networking technologies (2019) 14. F.S. Hardwick, A. Gioulis, R.N. Akram, K. Markantonakis, E-voting with blockchain: an evoting protocol with decentralisation and voter privacy, in 2018 IEEE Conferencess on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, Congress on Cybermatics (2018)

SOLID: A Web System to Restore the Control of Users’ Personal Data Manishkumar R. Solanki

Abstract The concept of Web was conceived to facilitate sharing of information between scientists and researchers in universities and institutes around the world. But this juggernaut has given birth to personal and business data breach, cyber bullying, harassment, hate speech and other cybercrimes. The privacy of a user is always at stake while using Internet due to commercial exploit of private data. This paper gives overview of a project “SOLID (SOcial LInked Data)”, initiated by the inventor of the Web Tim Berners-Lee to give a person the ultimate control of his private information on the Internet. Keywords Angular · Cybercrimes · PODRDF · React · WebID

1 Introduction The harmonization of World Wide Web (www) service and the hardware backbone Internet has significantly changed the living standard of a human across the globe. People can access Internet services at anytime from anywhere with the help of smart phones. Almost every kind of business has the Web presence today. The E-Commerce platform is increasing its user base at every moment due to ease of use and saving of time and money. Millions of business transactions carried out on Internet store consumers’ private data in central repository for commercial purpose. Consumers have lost the control of their own data. Openness of Web has been compromised at great extent. To bring back it on its track, the founder of www, Tim Berners-Lee has started to build a system named “SOLID”. This paper gives overview of Solid and its working. The goal of this paper is to introduce the functioning of the Solid project, especially how it can prevent the misuse of personal information of a user and to give him the M. R. Solanki (B) Shri Bhagubhai Mafatlal Polytechnic, Mumbai, India e-mail: [email protected]

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_24

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ultimate control of the same. Section 2 describes the exploitation of the present Web, Sect. 3 introduces the working principles of Solid project, and Sect. 4 concludes the paper.

2 Exploitation of the Present Web Tim Berners-Lee, a British scientist, invented the World Wide Web (WWW) in 1989, while working with Robert Cailliau at CERN [1]. The purpose of inventing the Web was to enable seamless information sharing between researchers and scientists residing across the globe. The Web began to flourish with the release of its’ open license in 1993. It sparked a global wave of creativity, collaboration and innovation. Tim moved from CERN to the Massachusetts Institute of Technology in 1994 to found the World Wide Web Consortium (W3C), an international community devoted to developing open Web standards [2]. The free, open and accessible nature of Web is being compromised due to corporate exploit of information, hate speech, state-sponsored hacking and attacks, criminal behavior and online harassment and other such activities taking place on the Internet [2]. The world has witnessed huge data breaching victims, i.e., Adobe, Adult Friend Finder, Ebay, Linkedin, Mariott International, Yahoo, Facebook and other business giants in the last decade. The secret and sensitive data of millions of users have been exposed in the public area for the commercial exploit. Such breach of valuable data has raised the questions on reputational of brand, loss of money, legal ramifications and security of intellectual property. During the inauguration of Web Summit, in Lisbon, Portugal, on November 5, 2018, Tim Berners-Lee launched a campaign to persuade governments, companies and individuals to sign a “Contract for the Web” designed to defend a free and open Internet [3].

3 Basic Components of Solid System This section discusses about basic components of Solid system and demonstrates the working of a demo app developed with Solid.

3.1 POD and WebID Solid provides a distributed data service by extending the present Web standards. Individuals and organizations keep their data in personal online data stores (PODs). They can store photos, comments, address books, calendar events, bank details, documents and other personal information on a POD as shown in Fig. 1. PODs are

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Fig. 1 Personal data stored in POD [5]

designed to be secure, and the owner can provide fine-grained access to portions of their data, as well as revoke it as needed [4]. PODs can be stored either in a public cloud storage service, i.e., Solid Community or Inrupt, or self-hosted. WebID provides globally unique decentralized identifiers, Solid uses WebID instead of usernames which internally includes POD of a user. The data and link types are written in standardized resource description framework (RDF) form, multiple applications can use the same data. So, the data being stored in the POD can be given access to all applications, i.e., Facebook, Twitter, Google, Amazon, etc., instead of separately giving them the data and remembering to manage the same across all apps. The application development is underway for it now. The demo app to retrieve the user profile is developed in Angular and React and is described on Solid URL.

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3.2 Setting up the Environment for the Demo Application In this paper, I am showing the application development using Angular. The following steps should be executed on command line in Windows OS. 1. Install Yeoman: npm install-g yo Yeoman is a generic scaffolding system allowing the creation of any kind of app. Yeoman is language agnostic. It can generate projects in any language, i.e., Java, Python, C#, etc. [6] (Fig. 2). After installation, we will get the Yeoman Doctor message as shown in Fig. 3. 2. Install solid-angular generator: Generators are basically plug-ins in the Yeoman environment. 3. Now, navigate to the project folder where you want to create your app. In my case, the project folder is SOLID which resides in drive d: yo @inrupt/solid-angular (Fig. 4). It will ask the application name or folder name, i.e., in my case d:\SOLID\solidapp folder, so I gave the name as “solidapp” as shown in Fig. 5. After successful installation of necessary resources in the “solidapp” application, “welcome to solid” message will be displayed as shown in Fig. 6. 4. Install the Angular CLI: The Angular CLI is a command line interface tool that you use to initialize, develop, scaffold and maintain Angular applications (Fig. 7). 5. Running the application: Make solidapp as your present working directory and run the following command: If you get an exception as shown in Fig. 8, then refer the reference [7] and employ Approach 2 to fix the issue.

Fig. 2 Installation of Yeoman

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Fig. 3 Successful installation of Yeoman

Fig. 4 Successful installation of solid-angular generator

Fig. 5 Setting application name to store necessary files and folders

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Fig. 6 Successful installation of necessary resources in the application npm install -g @angular/cli

Fig. 7 Successful installation of Angular CLI (can be done in the beginning also)

Fig. 8 Receiving “could not find implementation for builder….exception”

After resolving the issue, we get “Compiled successfully” run the application on Web browser using http://localhost:4200/.

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3.3 Running the Application The first and foremost requirement to get ownership of data, the user has to set up his POD by registering at Solid Community, Inrupt or he can have his own setup. After registering, the user will get his WebID which he can share to allow to the apps wants to access user data as shown in Figs. 9 and 10, i.e., https://mrsolanki1903.inr upt.net/profile/card#me. When the application is being run on the browser using 4200 port (as in our case), the user will be asked his POD provider and has to set permissions of his data to be accessed by the app as shown in Figs. 11 and 12. The user can see his profile and permissions given to the data at the provider site, i.e., Inrupt by logging in his account as shown in Figs. 13 and 14. The app can see the user data as shown in Fig. 15. As the user has given modification permissions to the app, it can modify the user data, i.e., the role of a user from “Employee” to “Lecturer”. As the save button is pressed, “Success!” message is displayed at the top-right corner.

Fig. 9 Getting POD from solid providers [8]

Fig. 10 User WebID with included POD

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Fig. 11 Selection of POD provider

Fig. 12 Setting personal data permissions

Now, if the user changes the permission “Read” only as shown in Fig. 16, then the app cannot modify the role, i.e., from “Lecturer” to “Senior Lecturer”, and an error message “All Required Access Modes not Granted” would be displayed at top-right corner as shown in Fig. 17. This demonstration shows that before accessing the users’ personal data, an app will have to take permission from the user, and user will not sign up on the application Web site or it will not even perform social sign in instead with his WebID at POD he can grant permission for his personal data.

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Fig. 13 User profile at Inrupt

Fig. 14 User profile at Inrupt with trusted applications

4 Conclusion Companies and governments dip into the personal information of user data in increasingly and innovative ways, specially tracking what they are, what they do, to whom they interact and where they go. The commercial exploitation of personal data has defeated the working philosophy of www at great extent. By proposing the Solid project, Sir Tim Berners-Lee has initiated a revolutionary action to give back the user to have control of his own data. As the system is in development phase, it

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Fig. 15 Modification of role from “Employee” to “Lecturer”

Fig. 16 Changing access modes to “Read” only

would be challenging task for Solid team to succeed in a space dominated by giant corporations who make a living by harvesting and selling user data.

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Fig. 17 “Required Access Modes Not Granted” error

References 1. The Birth of the Web, https://home.cern/science/computing/birth-web 2. A. Hern, Tim Berners-Lee on 30 years of the world wide web: ‘We can get the web we want’, https://www.theguardian.com/technology/2019/mar/12/tim-berners-lee-on-30-years-ofthe-web-if-we-dream-a-little-we-can-get-the-web-we-want 3. Technology News November 6, 2018/ 12:23 AM/ A Year Ago, Web creator Berners-Lee launches contract for better internet, https://www.reuters.com/article/us-portugal-websummitberners-lee/web-creator-berners-lee-launches-contract-for-better-internet-idUSKCN1NA2CX 4. D. Cardinal, Tim berners-lee’s solid project: can it save the web? (2018), https://www.extrem etech.com/extreme/281334-tim-berners-lees-solid-project-can-it-save-the-web 5. https://solid.inrupt.com/how-it-works 6. Getting started with yeoman, https://yeoman.io/learning/ 7. https://thecodebuzz.com/resolved-could-not-find-the-implementation-for-builder-angular-dev kit-build-angularbrowser/ 8. Solid Pod Providers, https://solid.inrupt.com/get-a-solid-pod

Enhanced Three-Dimensional DV-Hop Algorithm Abhinesh Kaushik

and D. K. Lobiyal

Abstract Numerous applications of Wireless Sensor Networks (WSNs), need location information for various purposes, such as identification of the location of objects, improve the routing process, etc. The accuracy of the location information also plays a significant role in these applications due to which localization of WSNs has emerged as an important field of research. Among the various algorithms designed for localization, DV-Hop is a favourite among the researchers. Lots of improved adaptations of DV-Hop are suggested in the literature as a result of its simple usage. In this paper, we have proposed an enhanced three-dimensional DV-hop algorithm to enhance its location accuracy. The proposed algorithm uses only the beacon nodes in close proximity for localization. Further, the concept of coplanarity is used to select an optimal set of beacon nodes around an unidentified node for its location estimation. Furthermore, to reduce the propagation error, we use a new method to solve the system of distance equations. The simulations illustrate that our proposed technique outclasses the DV-Hop algorithm. Keywords WSNs · DV-Hop · Localization

1 Introduction Wireless sensor networks comprise various tiny sensor nodes appropriated over an enormous zone to quantify the physical properties like temperature, pressure, humidity, motion, etc. [1]. These are multifunctional, low-cost sensors having powerful sensing capability along with a capacity to communicate over small distances. They are capable of forming self-organizing, distributed, and multi-hop wireless communication network [2]. WSNs have numerous applications in the field of target tracking [3], remote sensing, smart homes, medical care, military affairs, natural disasters, intelligent transportation, precision agriculture, and so forth [4]. Location of the place where A. Kaushik (B) · D. K. Lobiyal School of Computers and Systems Sciences, Jawaharlal Nehru University, New Delhi, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_25

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the event occurred is an important aspect. The data received from these sensors is not of much use without the location information. Therefore, this makes location estimation a primary requirement in countless applications of the WSNs. Most of the commonly used methods for location estimation are designed based on Global Positioning System (GPS) [5]. In GPS based methods, nodes are equipped with GPS supporting hardware chips [6]. These GPS chips make the sensor nodes, bulky, costly, and power-intensive. GPS also restricts the sensor nodes usability in indoors and in an underground scenario as it requires to communicate with the satellite [7]. Therefore, designing a localization algorithm with minimal hardware, minimal cost, and limited power is a challenging task. These localization algorithms use techniques like Distance Vector, Approximate point in triangulation, centroid [8], received signal strength indicator (RSSI), etc. for localization. DV-hop algorithm is among the most frequently used algorithms for localization because of its simple usage and vast scope for improvement in location accuracy. Consequently, researchers have proposed scores of improved variants of DV-Hop algorithm [2, 9–11] in the literature. But most of them are restricted to two-dimensional scenarios. This manuscript is arranged as follows. In Sect. 2, we discuss DV-hop algorithm and its improved version. Section 3 elaborates the proposed work. Simulation results and functioning of the system is discussed in Sect. 4. Whereas, conclusion of the paper is deliberated in Sect. 5.

2 Correlated Work The DV-hop algorithm [12] is designed for a two-dimensional scenario. It consists of three steps. The first step, involves flooding coordinates of all the beacon nodes (nodes knowing their positions) and hop count to all the nodes (beacon and unidentified) in the network. In second step, an average hopsize is calculated using the information obtained from the first step. It also involves, estimation of distance between the beacon and unidentified node. Whereas in the last step, the information from the preceding steps is used to localize the unidentified node by solving a set of distance equations. The biggest shortcoming of this algorithm is that it provides poor accuracy. The algorithm is developed for a two-dimensional scenario, whereas most of the real-life applications involve a three-dimensional scenario. A three-dimensional DV-Hop localization algorithm based on RSSI in [13] (3DVR) is proposed to upgrade the existing DV-Hop algorithm. The second step of 3DVR algorithm involves the concept of RSSI to find the average hop size. An error correction factor is also added to the distance. 3DVR does improve the location accuracy when compared with the DV-Hop but introduces its own limitations due to the usage of RSSI. The most common limitation of the model is shadowing, fading, and path loss. The algorithm also consumes more power in contrast to the other improved renditions of DV-Hop algorithm. There are few more modified versions of DV-Hop like one in [14] where the author uses a method of intersecting circles (of communication radius) between the

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unidentified node and the beacon node. Further, these intersecting circles are used to localize the unidentified node. Although the method makes calculation easier but compromises on the location accuracy.

3 Proposed Work In this paper, we propose an enhanced three-dimensional DV-Hop algorithm(E3DVHop) for enhancing location accuracy of the DV-Hop. The proposed algorithm is broadly working in three steps. In the Step 1, E3DV-Hop algorithm works in the same way as first step of the DVhop algorithm. In the Step 2 of the algorithm, we determine a beacon node’s average hop size. To find the hop size, we use hop count and location information from step 1. The hopsize, thus computed, helps in computing the distance between the nearest beacon node and the unidentified node. Before calculating the distance, we locate the nearest beacon nodes. To get a more accurate estimation, we use the beacon nodes within the close proximity of one to three hop count from the unidentified node. On finding the nearest nodes, we check for degree of coplanarity [15], which helps in selecting the best topology for location estimation. In the step 3, we restrict the total beacon nodes used for localization, which leads to a reduction in the number of distance equations for localization of unidentified node. Further, in the step 3, we use a distinctive method to solve the set of distance equations meant for computing the location of an unidentified node. There is a reduction in the propagation of error because of the new method used for solving equations. The simulation results also support our proposed method in terms of betterment in location accuracy. The proposed technique works in the subsequent three steps: Step 1 In this step, the beacon nodes present in the network broadcast their location information in the wireless sensor network so that all other nodes get coordinates of the beacon nodes and the hop count. Step 2 In this step, we calculate every beacon node’s hopsize (average) by using the coordinates information of beacon nodes and the hop count received from step 1, using Eq. (1),  Hopsizei =

i= j



xi − x j

2 

 2  2 + yi − y j + z i − z j

i= j

hopsi j

(1)

After calculating the hopsize, we now find the beacon nodes in the close proximity of the unidentified node to be localized. To decrease the network utilization, we use beacon nodes, which are one to three hop away from the unidentified node. There shall be a minimum of 4 beacon nodes around the unidentified node to use the multilateration method for localization of unidentified node. After finding the beacon nodes in close proximity, we check them for coplanarity. Beacon nodes that

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are present in the close proximity to unidentified node and non-coplanar are the only ones used for localization. The degree of coplanarity helps us in checking the coplanarity of four neighboring beacon nodes. A tetrahedron is formed by joining the lines connecting the four non-coplanar beacon nodes in a 3D space. The following Eq. (2) represents the radius ratio of the tetrahedron, ρ=

3rin rout

(2)

where ρ is the radius ratio of the tetrahedron, and rin , and rout are the radius of the inscribed and the circumscribed sphere represented using Eq. (3), and Eq. (4), respectively. 3σ rin = 3 i=0

rout

(3)

μi

√ (a + b + c)(a + b − c)(a + c − b)(b + c − a) = 24σ

(4)

where volume of the tetrahedron is σ , and μi represents the surface areas of the four sides. The edges of the tetrahedron are denoted by a, b and c. 216σ 2 √ i=0 μi (a + b + c)(a + b − c)(a + c − b)(b + c − a)

ρ = 3

(5)

Therefore the radius ratio can be calculated by using Eq. (5), where the value of ρ varies from 0 to 1. The value of ρ approaching 0 represents that the four points are coplanar, whereas a value approaching 1 denotes that a tetrahedron is formed out of the four points. Therefore, the degree of coplanarity is stated as:  Degree of Coplanarity =

0 coplanar ρ otherwise

The degree of coplanarity helps us in finding the topologically optimal set of beacon nodes around the unidentified node for localization. After getting the beacon nodes in the close proximity and checking for the coplanarity, the beacon nodesbroadcast their hopsize in the network. The unidentified node u picks the hopsize hopsizei from the nearest beacon node i and evaluates the distance between the beacon nodes and itself by using Eq. (6) dup = hopsizei × hopsup

(6)

where hopsup is the least hop count between uth unidentified node and pth beacon node.

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Step 3 In this step, to decrease the propagation of error, we introduce a new method of solving the distance equations. In this method, we subtract all the distance equations from the largest distance equation and then square the resulting equations to solve further. Subtraction before squaring leads to a reduction in propagation of inherent error as compared to the squaring and then subtracting method, which increases the error rapidly. We assume the unidentified node u has (xu , yu , z u ) coordinates, and (xn , yn , z n ) are the coordinates of the nth node, and the distance between unidentified node u and the last beacon node n, which is not more than three hops away, is denoted by dn . The coordinates of unidentified node are calculated using Eq. (7)  (xu (xu . (xu . (xu

− x1 )2 + (yu − y1 )2 + (z u − z 1 )2 = d1 − x2 )2 + (yu − y2 )2 + (z u − z 2 )2 = d2

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ (7)

− xm )2 + (yu − ym )2 + (z u − z m )2 = dm ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭ 2 2 2 − xn ) + (yu − yn ) + (z u − z n ) = dn

Subtracting all the n equations from the largest distance equation (mth) having distance dm   (x − xm )2 + (yu − ym )2 + (z u − z m )2 − (xu − x1 )2 + (yu − y1 )2 + (z u − z 1 )2 = dm − d1  u  (xu − xm )2 + (yu − ym )2 + (z u − z m )2 − (xu − x2 )2 + (yu − y2 )2 + (z u − z 2 )2 = dm − d2 . .  (xu − xm )2 + (yu − ym )2 + (z u − z m )2 − (xu − xn )2 + (yu − yn )2 + (z u − z n )2 = dm − dn

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(8)

Squaring both sides and solving Eq. (8) and further simplifying the set of equations, ⎫ −2(xm + x1 )xu − 2(ym + y1 )yu − 2(z m + z 1 )z u + 2r = dm2 + d12 − (tm + t1 ) ⎪ ⎪ ⎪ ⎪ −2(xm + x2 )xu − 2(ym + y2 )yu − 2(z m + z 2 )z u + 2r = dm2 + d22 − (tm + t2 ) ⎪ ⎬ . ⎪ ⎪ ⎪ . ⎪ ⎪ −2(xm + xn )xu − 2(ym + yn )yu − 2(z m + z n )z u + 2r = dm2 + dn2 − (tm + tn ) ⎭ (9) where r = xu2 + yu2 + z u2 and t = xi2 + yi2 + z i2 , ∀ i = 1, 2, 3 . . . ..n where n is the last beacon node in the close proximity. Matrix form of the system of n − 1 equations in Eq. (9) is as follows: AX = B

(10)

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where A, X, and B are defined as: ⎡

−2(xm + x1 ) ⎢ −2(xm + x2 ) ⎢ A=⎢. ⎣ ..

−2(ym + y1 ) −2(ym + y2 ) .. .

−2(z m + z 1 ) −2(z m + z 2 ) .. .

2 2 .. .

⎤ ⎥ ⎥ ⎥ ⎦

−2(xm + xn ) −2(ym + yn ) −2(z m + z n ) 2 ⎡ 2 ⎤ dm + d12 − (tm + t1 ) ⎢ d 2 + d 2 − (tm + t2 ) ⎥ 2 ⎢ m ⎥ B = ⎢. ⎥ . ⎣. ⎦ dm2 + dn2 − (tm + tn ) ⎡ ⎤ xu ⎢ yu ⎥ ⎥ X =⎢ ⎣ zu ⎦ r

Least square method is used to solve Eq. (10), and Eq. (11) is used to find the location coordinates of the unidentified node,  −1 Z = AT A AT B

(11)

where A T denotes the transpose of A.

4 Simulation Results and Performance Analysis To assess the performance of the proposed algorithm, it is simulated on MATLAB 2018b. In this section, we examine the localization error (LE) with respect to various parameters. We have defined localization error in Eq (12) as one of the performance indicators of the location accuracy. N LE =

j=M+1



x uj − x aj

2

2  2  + y uj − y aj + z uj − z aj

C × (N − M)

(12)

Where (x uj , y uj , z uj ), (x aj , y aj , z aj ) are the computed and the actual position coordinates of the jth unidentified node, respectively, N denotes the overall number of nodes in the WSN, M is percentage of beacon nodes, and C denotes the communication radius of a sensor node. In order to provide a uniform environment for all the algorithms we have kept all the parameters as constant for all the algorithms. We have also extended a third dimension to the DV-Hop algorithm to simulate it in a three-dimensional space.

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Fig. 1 Communication radius versus localization error

The simulation is carried out in a random network area of 100 m × 100 m × 100 m, and results are averaged over a thousand trials.

4.1 Localization Error Versus Communication Radius Figure 1 shows the effect of change in communication radius of the sensor node on the localization error. In this trial, the overall number of sensor nodes has been fixed at 400; the percentage of beacon nodes is fixed at 20%, and the communication radius is varied from 15 m to 45 m. The figure shows that LE for E3DV-Hop is smaller in contrast to DV-Hop and 3DVR algorithms for same values of communication radius. We noticed that the fall in LE for E3DV-hop is steeper in every case as compared to that of the other two algorithms.

4.2 Localization Error Versus Percentage of Beacon Nodes Figure 2 depicts the effect of change in the percentage of beacon nodes on the localization error. Here also, the overall number of sensor nodes considered is 400, communication radius is selected as 25 m, and the percentage of beacon nodes alters from 5 to 35% of the overall number of nodes. The figure shows that LE for E3DVHop is smaller in contrast to DV-Hop and 3DVR algorithms for the same value of

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Fig. 2 Percentage of beacon nodes versus localization error

percentage of beacon nodes. We also observe that LE for E3DV-Hop remains lower by about 3–10% as compared to other two algorithms.

4.3 Localization Error Versus Total Number of Nodes Figure 3 depicts the effect of variation in the total number of sensor nodes in the network on the localization error. The percentage of beacon nodes is kept constant at 20% of the total number of nodes in the network, communication radius is taken as 25 m, and the total number of sensor nodes are altered from 100 to 800. This figure shows that LE for E3DV-Hop is smaller in contrast to DV-Hop and 3DVR algorithms for the identical value of total number of nodes. We also observe that LE for E3DV-Hop remains lower by about 10% as compared to the other two algorithms.

5 Conclusion Results obtained from the simulation proves that E3DV-Hop algorithm reduces the localization error. The selection of closed proximity beacon nodes and a check for coplanarity selects the topologically optimal set of beacon nodes. This leads to better localization accuracy over the other two compared algorithms. Whereas a distinctive

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Fig. 3 Total number of nodes versus localization error

method used for solving the set of distance equations leads to a reduction in propagation error. The performance evaluation of E3DV-Hop, 3DVR, and DV-Hop algorithms indicates that E3DV-Hop algorithm is appreciably superior over the other variants of DV-Hop. To be more precise, the localization error of these three localization algorithms DV-Hop, 3DVR, and E3DV-Hop is 55%, 42%, and 25%, respectively.

References 1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002) 2. S. Kumar, D.K. Lobiyal, An advanced DV-Hop localization algorithm for wireless sensor networks. Wireless Pers. Commun. 71, 1365–1385 (2013) 3. S. Biswas, S. Gupta, F. Yu, T. Wu, A networked mobile sensor test-bed for collaborative multi-target tracking applications. Wireless Netw. 16, 1329–1344 (2010) 4. G. Han, J. Chao, C. Zhang et al., The impacts of mobility models on DV-hop based localization in mobile wireless sensor networks. J. Network Comput. Appl. 42, 70–79 (2014) ˇ 5. S. Capkun, M. Hamdi, J.P. Hubaux, GPS-free positioning in mobile ad-hoc networks, in Proceedings of the Hawaii International Conference on System Sciences 255 (2001) 6. R. Nagpal, H. Shrobe, J. Bachrach, Organizing a global coordinate system from local information on an ad hoc sensor network, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2634, pp. 333–348 (2003) 7. B. Hofmann-Wellenhof, HC.J.E. Lichtenegger, Global Positioning System: Theory and Practice (Springer Science & Business Media, 2012) 8. C.C. Chen, C.Y. Chang, Y.N. Li, Range-free localization scheme in wireless sensor networks based on bilateration. Int. J. Distrib. Sens. Netw. (2013)

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9. S. Kumar, D.K. Lobiyal, Power efficient range-free localization algorithm for wireless sensor networks. Wireless Netw. 20, 681–694 (2014) 10. O. Cheikhrouhou, G.M. Bhatti, R. Alroobaea, A hybrid DV-hop algorithm using RSSI for localization in large-scale wireless sensor networks. Sensors 18, 1–14 (2018) 11. S.P. Singh, S.C. Sharma, A modified DV-Hop algorithm for wireless sensor network localisation. Int. J. Commun. Netw. Distrib. Syst. 21, 187–201 (2018) 12. D. Niculescu, B. Nath, Ad hoc positioning system (APS), in GLOBECOM’01. IEEE Global Telecommunications Conference (Cat. No.01CH37270). IEEE, pp. 2926–2931 13. P. Gou, F. Li, X. Jia, et al., A three-dimensional DV-Hop localization algorithm based on RSSI, in Proceedings—2018 3rd International Conference on Smart City and Systems Engineering (ICSCSE, 2018), pp. 830–833 14. X. Yu, R. Xiao, Y. Liu, et al., Modified DV-Hop localization algorithm based on intersecting circle and hops revaluation, in Proceedings of the 3rd International Conference on Intelligent Information Processing, pp. 33–39 (2018) 15. Y. Xu, Y. Zhuang, J.J. Gu, An improved 3D localization algorithm for the wireless sensor network. Int. J. Distrib. Sens. Netw. (2015)

Synthesis of VLSI Structural Cell Partitioning Using Genetic Algorithm P. Rajeswari, Theodore S. Chandra, and Amith Kiran Kumar

Abstract The explosive growth in VLSI Design technology has been juxtaposed with the complexities, hence requiring a more efficient and faster way of miniaturization, with little to none human efforts. The way to go is to explore novel methodologies to find out the best as well as the least Time-Consuming method to find the optimum solution without compromising with quality. Here this paper describes the use of the genetic algorithm to find the most optimum path in a smaller number of iterations by the judicial use of controlled mutation at various stages, all the more so with the least impact on the optimization result with a robust structure. Hence enabling large VLSI circuits to be miniaturized with least cross over, by which circuit efficiency, as well as Cost-Saving, can be achieved in the least time possible with high accuracy at the expense of a very least possible trade-off between time and cost. Keywords Genetic algorithm · VLSI · Circuit integration · Partitioning

1 Introduction Due to phenomenal growth experienced in the VLSI field and there are expectancies for its exponential growth, adding on further there are no signs of saturation in the near feature. Due to the flooding of deep Sub-micron technologies in the field of VLSI, the Self-partitioning Algorithm has become more and more important. As

P. Rajeswari (B) · T. S. Chandra · A. K. Kumar Dayananda Sagar Institutions, Bangalore, Karnataka, India e-mail: [email protected] T. S. Chandra e-mail: [email protected] A. K. Kumar e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_26

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VLSI Circuit design is hampered by a significantly large and wide range of issues, they are design complexities, time complexity, increase in time delay, large power consumption, large area consumption and high cost are some of the common areas of concern. Circuit partitioning techniques can be used to breakdown the large VLSI circuits effectively and logically in to a large number of smaller interacting Sub-elements for better and easy handling. It’s the most efficient way possible to address the difficulties that have shown up due to highly dense VLSI circuit chips in unearthing the design tradeoffs in order to make a more effective design in feature circuits we require a better, faster, accurate, partitioning algorithms to provide a low cost high efficient chips to market in a very short time [1]. This makes VLSI circuit partitioning of the physical design of VLSI system an integral part of VLSI technology. Universities and Industries are working continuously to further fortify and improve the algorithms as well as finding new alternative algorithms to break these large VLSI design into small Sub-parts None the less, partitioning of these highly dense chips is a tedious as well as a challenging task. It requires a lot of time and effort in arriving at a satisfactory solution in a reasonably fair time. From the past observation, it is noted that partitioning is harder to a Non-deterministic polynomial level. This encourages the advent of the use of heuristic algorithms and new methodology that would arrive at a reasonably fair and acceptable solution in the shortest and allocated amount of time. Even though there were a lot of other subsidiary techniques they have some shortcomings. The algorithms have now started to be built based on nature’s inspiration such as from neural network, bee colony, honeycomb, and now finally the genetic algorithms. It is said there are VLSI circuit designs that if they are drawn on a paper would cover the entire earth thrice. Now due to the use of genetic algorithms, we can build such unimaginably bulky circuits today with less human efforts.

2 Motivation and Background 2.1 Genetic Algorithm Due to the efforts of vigorous and deep studies and research in the area of VLSI circuit partitioning to meet the enormous technology scaling, the advent of new and powerful technique such as heuristic procedure has been developed for better partitioning. Among all these algorithms, genetic algorithm has been proven as a very promising algorithm for the partitioning of VLSI circuits. This is the base on this the paper is being developed. Genetic algorithms are nothing but heuristic algorithms constructed on the basis of Charles Darwin’s theory of evolution employing a natural selection of the stronger species, and introduction of occasional mutation to combat the attainment of local minima and arrive at a global maximum. It’s also important to choose the level of mutation and the extent of mutation to create an ideal partitioning algorithm a highly mutated algorithm may miss some of the local or possibly the

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global minima and a very low mutated algorithm take a long time to arrive at the global minima [2–5]. There is always a Trade-off between time and efficiency in the partitioning algorithm, the important thing is how minimum is the Trade-off and robust is the algorithm. Creation of very high mutation would increase the accuracy but it would also take longer time which of course is affordable for smaller VLSI circuits but, when it comes to practical demand there is a lot of restrains related to the time it may take years to build a perfect partition. So, there is a requirement to restrict the number of mutation possible in a circuit.

2.2 Fitness Parameter and Finding Global Maxima The fitness parameter is designed to find the most optimum result or outcome of an individual circuit and it represents how well or how efficient this floorplan or the built circuit is, that is it describes how well a chromosome can survive. In a genetic algorithm after each iteration, in which multiple possible combinations of the circuit partitioning returns the chromosomes that have the local maxima value as its fitness, fitness parameter is checked and then that result showing the level of fitness is passed as an argument to the genetic algorithm where it forms a distributed record of the data (as shown in Fig. 4) and would proceed further with mutations at a random interval [6, 7]. It means to say first the different possible combination of partitioning is started by doing random changes. There are some times when the partitioning might not have reached the most optimum result, yet it might have reached the optimization limit, so the mutations are introduced to avoid getting into local minima and reach maxima. It makes a list of a parameter such as local maxima which are then finally combined to form a global maximum of the function, the global maxima are the final intended result of the program (Fig. 1). The fitness function is designed to find the optimum cost, area, and time. Please note that the first paragraph of a section or subsection is not indented. The first paragraphs that follow a table, figure, equation etc. do not have an indent, either. Subsequent paragraphs, however, are indented. Fn = −(α ∗ F(a) + β ∗ F(t) + γ ∗ F(c))

(1)

f n = ((Fn − Fn (min)) ∗ (U ))/(Fn (max) − Fn (min))

(2)

where F n the nth optimization level of VLSI circuit, F(a) is the area function, F(t) is the time function and F(c) is the cost function, α is the weight of area function, β is the weight of time function and γ is the weight of cost function.

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Fig. 1 Distribution of the local maximums and minimums in a function

When it’s mentioned as time function it is not the time taken by the genetic algorithm, it is the time delay experienced in the VLSI circuit. The negative sign indicates fitness function value decreases as the cost, time and area function values increases [8]. The Eq. (2) is used to map the fitness function to a positive value in the Scale-Up to U. where U is the upper limit, F(min) is the minimum optimization level required and F(max) is the maximum optimization level noticed. After evaluation of Eq. 2, all the negative readings are turned to positive values.

3 Flow Chart and Algorithm The proposed design first import the VLSI circuit data from the V, .itf, or .lib files and make them as chromosomes. It then finds the fitness of the circuit if fitness doesn’t meet the required standard and the iteration count limit n (max) is not reached, introduce mutation at every 1000 steps of iteration and then select a particular few chromosomes with the best fitness to perform Crossing-Over to get new set of population, and send it again to find the fitness [9, 10]. This is performed iteration or fitness is satisfied, once it is satisfied, it stops and gets the global maxima. The flow diagram of the proposed genetic algorithm is shown in Fig. 2.

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Fig. 2 Flow diagram of the genetic algorithm

4 MATLAB Implementation 4.1 Selection of Chromosome Location The VLSI circuit data is taken from the .lib file and the chromosome locations are chosen from that and these locations are represented as a matrix and are then plotted on a graph after that the chromosome data is imported for finding the cost, time and area function (Figs. 3 and 4).

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Fig. 3 Chromosome location from import data

Fig. 4 Distribution graph of maxima and minima

4.2 Data Representation To find the global maxima the weight of each trial is represented in the distribution graph. The graph records all the distribution of local maxima and local minima’s which helps in determining the global maxima once the number of iteration limit is reached, the time allotted is used up or the most possible best solution is obtained.

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5 Results The proposed solution in the genetic algorithm is implemented using MATLAB and the results are obtained. [10] All the crossing-overs are optimized to get the better cost area and time function and the final partitioned circuit is displayed in the GUI display (Table 1). From the table, it is clear that the genetic algorithm is almost four times better than the brute force partitioning technique and as the complexity and the volume of the circuit increases, this difference also increases it’s more obvious in the larger circuit (Fig. 5). Figure showing the structuring of VLSI circuit by partitioning using Genetic algorithm the number of crossing-over is reduced to achieve the most optimum results. The graph has been obtained after over 3200 iterations for more complex circuit a greater number of iterations are required (Fig. 6). As we see from the graph as the number of Iterations increases along the x axis the fitness is improved and has reached a constant mean to say it has reached the global maxima. Table 1 Comparison table for the efficiency of genetic algorithm versus brute force partitioning technique

Fig. 5 Genetic algorithm crossing-over in matrix

Comparision Partitioning technique

Fittness value Number of iteration

Brute force partitioning

401.29

4290+

Partitioning using genetic 100.36 algorithm

1320

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Fig. 6 Cost, area, time function on the number of trials in the genetic algorithm

6 Conclusion In this paper, several papers have studied, researched implemented and modified that have given may insight for the partitioning of VLSI circuit identification. The study briefly depicts that most of the methods concentrate on studying various way on improving efficiency, cost, time and performances. Since VLSI has a limitless future, more research work should evolve. Those future works should study not only individual aspects but also minimize the tradeoffs, thereby boosting the performance capacity of the system. Feature works have to be focused on Multi-Dimension circuits and possible solutions to carbon nanotube technology. Hence this paper has helped to build a robust algorithm for VLSI partitioning.

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5. R. Pavithra Guru, V. Vaithianathan, An efficient VLSI circuit partitioning algorithm based on satin bowerbird optimization (SBO). J. Comput. Electron. (2020). https://doi.org/10.1007/s10 825-020-01491-9 6. M. Rapoport, T. Tamir, Best response dynamics for VLSI physical design placement, in 2019 Federated Conference on Computer Science and Information Systems (FedCSIS) 7. T.N. Bui, B.-R. Moon, Genetic Algorithm and Graph Partitioning 8. S.K. Roy, C. Giri, H. Rahaman, Optimization of test wrapper for TSV based 3D SOCs. J. Electron. Test. 32, 511–529 (2016). https://doi.org/10.1007/s10836-016-5610-4 9. G.K. Chellamani, P.V. Chandramani, An optimized methodical energy management system for residential consumers considering price-driven demand response using satin bowerbird optimization. J. Electr. Eng. Technol. 2020, 955–967 (2020) 10. N. Arya, A.P. Singh, A fault avoidance approach with test set generation in combinational circuits using genetic algorithm, in 2018 2nd International Conference on Inventive Systems and Control (ICISC)

Identification and Classification of Foreign Bodies from Rice Grains Using Digital Image Processing Rajlakshmi Ghatkamble

Abstract This research article presents the method for the identification and classification of foreign bodies from rice grains using the digital image processing approach. Any matter other than the rice grains in the rice images is considered as the foreign bodies in the current research work. The foreign bodies can be in terms of weed, stones, soil lumps, pieces of stems, plant leaves, or other types of grains. The amount of the foreign bodies decides the quality of the rice grains which in turn may be helpful to determine the quality of the rice grains. In the manual inspection of grains, the foreign bodies are evaluated based on the manual inspection which may not be so accurate in every inspection. Whereas the machine vision system automatically determines the amount of the foreign bodies present in the rice grains which can be helpful to farmers in sowing the seeds and also marketing the rice grains. The digital image analysis algorithms are developed using machine learning techniques using MATLAB and Python programming languages to determine the foreign bodies present in the rice grain samples using a neural network method. The foreign bodies in the rice grain image samples are determined based on the color texture and morphological features using a digital image processing method. All three above mentioned features are presented to the neural network for training purpose and this trained network is later used to identify the foreign bodies in rice grains. Keywords Foreign bodies · Neural network · Image acquisition · Pre-processing · Feature extraction

R. Ghatkamble (B) School of Research and Innovation, Department of Computer Science, C. M. R University, Bangalore, Karnataka, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_27

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1 Introduction The current research article depicts the method for identification and classification of foreign bodies from rice grain image samples using the digital image processing approach. In the current situation, the machine vision inspection of agricultural products is emerging as an important area of research interest [5]. The variability in the appearance of grains and foreign bodies makes it a challenging task for any machine vision system to recognize and classify all the biological entities of rice grains as well as foreign materials [5]. There is a lot of research undertaken till now in determining the potential of morphological features of rice grains as well as impurities using pattern recognition techniques [3, 4]. In the current situation, the amount of foreign materials present in the container of rice sacks affects the value of their products and there is a chance for the human inspectors to inspect the rice grains to play mischief during the evaluation of rice grains. Also, the decision-making capabilities of human inspectors can be biased based on their mental health or work pressure. So to avoid such circumstances occurring in rice grain evaluation grain the current research work helps in estimating foreign bodies by analyzing the rice grain image samples using machine learning techniques which can be done based on digital image processing [3]. This paper is organized into sections mentioned below which are proposed methodology which is concerned with image acquisition, preprocessing, image enhancement, image segmentation, feature extraction, knowledge base, and the last section is the conclusion of the work.

2 Proposed Methodology The below-mentioned block diagram illustrates the procedure of identifying and classifying foreign materials from rice grain image samples using digital image processing as shown in Fig. 1. And the methodology is as given in algorithm 1. Algorithm 1: Identification and Classification of Foreign Bodies from Rice Grains Input: original 24-bit color image. Output: Classified foreign materials. Image Acquisition

Knowledge Base Fig. 1 Flow chart of the system design

Pre Processing

Feature Extraction

Image Enhancement

Image Segmentation

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Start Step 1 Acquire rice grain images with impurities. Step 2 Pre-processing of image samples. Step 3 Enhance the grain image with impurities to remove noise and blurring by filtering an image. Step 4 Do the sample image segmentation. Step 5 Extract morphological, color, and texture features. Step 6 These extracted features of rice grain and impurities are analyzed and identified using a probabilistic neural network. Stop (1) Image Acquisition In this stage, the rice grain image samples along with impurities are captured under standardized lighting conditions. The images are acquired using a color digital camera with 4.0 megapixels fixed to a tripod stand at a fixed distance of approximately 700 mm with an orientation of 0° and a focal length of 3.2 mm. During image acquisition the camera is placed at a location situated with a plane normal to the path of the object so that the captured images are of good quality to further process the imaging technique which is used to collect the data of a rice grain samples with impurities from the image. The green simple plain background is used to capture the image samples (Fig. 2). The images acquired are 3264 * 2448 pixels in size. Rice images along with impurities are captured and stored in JPEG format automatically. With the help of data cable, the captured images are transferred and stored in the disk managing device in a proper sequence (Figs. 3 and 4). The proposed system determines the aspects of the ratio distribution of the image sample. These samples are analyzed and examined based on their color, morphological as well as texture features. This technique of identifying the impurities from rice grain image samples can be done using the machine vision technology that is by using a probabilistic neural network algorithm. The current research work also attempts to give the computerized results to analyze and classify the foreign bodies Fig. 2 Block diagram of image acquisition

Rice grain image with impurities

Images stored in hard disk

Digital camera

Grain image stored in jpeg format

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Fig. 3 Image acquisition

Fig. 4 Rice grain image with foreign bodies

present in rice grains and helps in determining the quality of rice grains up to some extent, whose accuracy pertains to be higher. (2) Pre-processing Color image pre-processing of image sample and its segmentation are the classical examples of the multichannel information processing concerned with digital images. Pre-processing is a stage that is also called as data preparation step which is helpful in contrast enhancement, noise reduction, or filtering and edge enhancements of the given input image. Image filtering operations involve the removal of small objects or the noise terms from an image. (a) The grayscale image is a sample input image where each pixel in the image is concerned with the pixel of rice and impurities holding the input image. Every pixel in an image depicts the intensity information of the image which can be either in terms of the black or white image after grayscale processing of the image. It has an only black or white color in which black depicts as weakest intensity and white as the strongest intensity.

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Fig. 5 Pre-processed image

(b) RGB to gray processing is a stage where each pixel in an image comprising of the red, green, and blue pixel which comprises of an array of single, double, or whole pixel values specifying intensity values. This phase converts the RGB to grayscale values. (c) Binary image processing is a phase that converts the grayscale image into a binary image. The output rice grain image replaces all the pixels in the input image with the luminance greater than the level with the value as one that is rice grain and replaces all the other pixels with the value 0 which is below the luminance value lesser is considered as foreign bodies. The function gray thresh is used to estimate the argument level automatically to separate the object such as rice grains and impurities in the image from the background. The color of the rice grain whose threshold value is more than the set values and is termed as a rice grain and the rest is separated as impurities and background of the image (Fig. 5). (3) Image Enhancement This stage is one of the most appealing and simplest forms of methods used in digital image processing. The idea behind this technique is to bring out the hidden details from the image or highlight the features of interest from the rice sample image with impurities. This phase is a subjective area of image processing. Image enhancement techniques are used to improve the quality of the degraded image [1]. Grayscale manipulation, filtering, and histogram equalization schemes are some of the various image retrieval techniques used in the current situation. The histogram equalization method is used to remap the gray levels of an image and hence it is considered as the most convenient image enhancement technique. (4) Image Segmentation In the image segmentation process, the thresholding plays an important role in clustering the objects in an image. The image segmentation process is a method of subdividing an image into different objects or segments using the thresholding technique

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Fig. 6 Segmented and cropped rice grains

which is considered as the first step in image processing [2]. The image is usually subdivided until the rice grains and impurities are isolated from their background. The image segmentation has generally two approaches in which one approach is based on the discontinuity of gray level values and the other approach is based on the similarities of the gray level values. The first approach uses the method of the partition of an image based on the abrupt changes in the gray level of an image. The second segmentation approach uses the thresholding method, region growing, merging, and region splitting (Fig. 6). (5) Feature Extraction Algorithms are developed in the windows environment using python and MATLAB programming language which is used to extract the morphological features, color, and texture features of the rice grains as well as foreign materials. The extracted morphological, color, and texture features in this research work are as mentioned below: (1) Area (mm2 ): This feature extracts the number of pixels inside the rice grain and foreign bodies including their boundaries which is later multiplied by the calibration factor (mm2 /pixel). (2) Major axis length (mm): It is the distance between the endpoints of the longest line that can be drawn through the rice grain and the foreign bodies. The major

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(4) (5) (6) (7) (8)

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axis endpoints are found by computing the pixel distance between every combination of border pixels of the rice grains as well as the impurities boundary and hence finding the pair with maximum length. Minor axis length (mm): It is the distance between the endpoints of the longest line that could be drawn through the rice grain as well as the impurities while maintaining perpendicularity with the major axis. Aspect ratio: Major axis length/Minor axis length. Red mean: It is the average or means the value of red color or pixel in the rice grain and the impurities present in the input image. Green means: It is the average or means the value of green color or pixel in the rice grain and the impurities present in the input image. Blue means: It is the average or means the value of blue color or pixel in the rice grain and impurities present in the input image. Entropy: Entropy value of red, green, and blue color in rice grain, as well as foreign materials, is calculated.

(6) Knowledge Base The knowledge base concerned with this research work is an organized set of data elements or values using a model of vertical columns and horizontal rows of data and a cell being the unit where the column and row intersect. All the extracted features of the rice grains and foreign bodies extracted from the input image are stored in this knowledge base during the training of the sample images and this knowledge base is later used to classify the rice grains and impurities during the testing phase of probability neural network.

3 Conclusion In the current research work, the digital imaging approach is devised to analyze and classify the foreign bodies from rice grain image samples using a digital image processing method. The algorithm developed here helps in identifying the rice grains and foreign bodies from the input image samples. The rice grain and impurity classification accuracies are found to be 80% and above respectively. The increased presence of foreign materials in the rice grains samples reduces the capacity of classification accuracies of the varieties of rice grains and hence it also affects the quality of the rice grains which is based on the purity of rice grains. According to the study if the percentage of the foreign materials in the rice grain samples is above 50%, then the identification and classification of the mixed rice grain samples become an impossible task.

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4 Future Scope Future work may include rice image acquisition in bulk amount and with varying backgrounds.

References 1. S. Shantaiya, U. Ansari, Identification of food grains and it’s quality using pattern classification. Int. J. Commun. Comput. Technol. 2(2,3,4) (2010) 2. B.S. Anami, D.G. Savakar, Improved method for identification and classification of foreign bodies mixed. ICGST 9(1) (2009) 3. B.S. Anami, D.G. Savakar, Effect of foreign bodies on recognition and classification of bulk food grains. J. Appl. Comput. Sci. 6(3) (2009) 4. T. Jiang, Y. Zhang, F. Cai, J. Qian, Optical coherence tomography for identifying the variety of rice grains. Adv. Optoelectron. Micro/nano-opt. 5. A. Mehrez, D. Ali, A. Mohamed, Hybrid method for cereal grain identification using morphological and color features. IEEE

Improving Collaboration Between Government and Citizens for Environmental Issues: Lessons Learned from a Case in Sri Lanka Mohamed Sapraz

and Shengnan Han

Abstract The collaborations between government and citizens are very crucial for addressing environmental issues, but how such a collaboration can be developed in practice is an open question. An interpretative case study is conducted to investigate ‘Wilpattu’ deforestation, one of the recent controversial environmental issues in Sri Lanka. The paper studies citizens’ activities in the social media platforms and the responses of government authorities regarding the case. The results indicate the root cause is lack of proper collaboration and coordination between government and citizens and other stakeholders. The study also reveals the societal, political factors, as well as governing structures of government authorities, exacerbate the environmental problem. By understanding the lessons learned from the study, a Digital Collaborative Platform (DCP) is proposed for potentially improving collaborations between government and citizens for addressing environmental issues in future research. Keywords Digital collaborative platform · Environmental problems · E-government · Co-production

1 Introduction Environmental sustainability is fundamental to humanity’s sustainable development [1] and it is a dominant challenge at present. We have seen massive environmental problems such as climate change including global warming, rising sea levels, ocean acidification, dramatically expanding droughts, and loss of biodiversity [2]. The global environmental conditions are changed at an unprecedented level, in which developing nations like Sri Lanka are still struggling to adapt. Sustainability focuses on the interrelatedness of social, economic, and environmental aspects, and is often M. Sapraz (B) Faculty of Computing, NSBM Green University Town, Homagama, Sri Lanka e-mail: [email protected] M. Sapraz · S. Han Department of Computer and Systems Science, Stockholm University, Kista, Sweden e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_28

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discussed in terms of the triple bottom line [3] where the key ideal is that all three aspects need to be harmonized for something to be sustainable. The collaboration between government and citizens is crucial in addressing environmental problems. Collaboration is one form of citizen engagement and it is defined as ‘citizens and administrators collaborate with one another to solve problems and get things done’ [4]. With the recent development of new ICT technologies such as Web 2.0 tools, mobile-related technologies, and social media platforms, improve and provide a new avenue for the collaboration between government and citizen in e-governance [5]. Due to the scarcity of government resources and financial pressures, more focus appears in citizen co-production (a form of collaboration) of public services. Co-production improves the collaboration between citizens and government, improve efficiency and trust. However, effective collaboration into solving environmental problems remains uncertain and still, it is an open question [6, 7]. Citizens’ engagement plays a pivotal role in environmental decision making [8]. Further, empowerment and citizen participation play a key role in achieving sustainability [7]. Citizens are much eager than ever to engage in environmental protection and they use ICT as a channel to raise their voice against environmental issues [9]. It is required to conduct further research to explore the possibilities of the practical application of collaboration into practice in addressing environmental sustainability with the aid of ICT [10]. The study focuses on adopting an interpretive case study to investigate a case from Sri Lanka. The case is based on deforestation taken place at ‘Wilpattu,’ the largest National Park in Sri Lanka. The results indicate the root cause is lack of proper collaboration and coordination between government and citizens and other stakeholders. The study also reveals the societal and political factors, as well as governing structures of government authorities, exacerbate the environmental problem. Hence, a Digital Collaborative Platform (DCP) is proposed for potentially improving collaborations between government and citizens for addressing environmental issues in future research.

2 Background: E-Government Services and Collaboration The term ‘Electronic government (e-government)’ or ‘Digital government’ is a multifaceted concept and can be defined in different ways. The World Bank has defined Egovernment as ‘government-owned or operated systems of information and communications technologies (ICTs) that transform relations with citizens, the private sector and/or other government agencies to promote citizen empowerment, improve service delivery, strengthen accountability, increase transparency, or improve government efficiency’ [11]. For developing countries, ICT can be an effective solution among different stakeholders including government and citizens in information interchange. [12]. E-government can facilitate and provide better ways of collaboration and information interchange to offer enhanced public services [13].

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Three levels of citizen engagement in e-government services are identified as; 1. The government shares the information with citizens (Government as a platform); 2. Both the government and citizens make a productive dialogue(interaction); 3. Collaboration (Co-production/Citizen-government joint service delivery) [14]. Coproduction in this level of engagement is, ‘The public sector and citizens making better use of each other’s assets and resources to achieve better outcomes or improved efficiency’ [15]. In the past, governments had a constraint and limited ability to collaborate with citizens and citizens to self-organize. But the digital platform is an option to increase interactivity and enable co-production [16]. With the development of DCP for co-production, citizens allowed to identify and discuss problems, propose innovative solutions, and provide crowdsourcing solutions. Overall, e-government services could be designed and facilitate major revolutions in enhancing democratic citizen participation, adopt co-production, and offering innovative solutions through crowdsourcing [17]. There are practical applications of collaborative e-government projects (Environmental Exchange Network, ParticipateDB, GovLoop, MuniGov2.0, Joined-Up, etc. [13]) and Digital participatory platforms [14] currently in use in different countries. Applications specially designed for addressing the environmental problems are very few and previous empirical studies are evidence to suggest that most of the existing e-government systems and platforms remained mostly in information dissemination rather than focus on the effective use of government-citizen collaboration and citizen participation for practical problem-solving [18, 19].

3 Research Method The study adopts an interpretive case study approach to investigate why collaborations between government and citizens cannot be set up in the case in Sri Lanka. Interpretive case studies suggest starting with existing theories, yet allow ‘…a considerable degree of openness to the field data, and a willingness to modify initial assumptions and theories’ [20].

3.1 Case Description—‘Wilpattu’ Deforestation The case is based on ‘Wilpattu’—a forest located in the Northwest coast lowland dry zone of Sri Lanka and it had become controversial due to deforestation by different stakeholders. ‘Wilpattu’ (Willu-pattu) means ‘Land of Lakes’ and the ‘Wilpattu’ Forest Complex is a land area of 528,200 acres and is the largest remaining forested land in Sri Lanka [21]. Illegal deforestation taking place and threatens the reserved forest since early in the year 2009. Unauthorized land settlements and encroachment in the area have resulted in the deforested area.

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4 Data Collection 4.1 Citizens Comments from ‘Twitter’ and ‘Facebook’ Citizens’ comments on ‘Wilpattu’ deforestation had been retrieved from ‘Twitter’ through a Web crawler. ‘The crawler is a computer program that will crawl a site or set of sites, analyze the pages fetched and then report its results’ [22]. In retrieving data, the following hashtags are used as inputs to the ‘Web crawler.’ During the process, ethical consent is obtained from the ‘Twitter’ through submitting the details and purpose of analyzing the data. #wilpattu #Wilpattu #wilpaththu #Wilpaththu #wilpattu deforestation #wilpaththu deforestation #wilpattudeforestation #save wilpattu #Save wilpattu #savewilpattu #Savewilpattu #deforestation sri lanka #SriLanka deforestation Web crawler generated 623 ‘Tweets’ and content is studied for the relevancy of the case. After exclusion, 558 ‘Tweets’ are filtered for further analysis. From Facebook, data retrieved from 15 active user profiles (8 males and 7 females between the age of 20 and 50). The data retrieved from Facebook is limited to 15 users as we need to get the consent of the users to retrieve the data available in the user profiles. As this case is controversial, many of the Facebook users reluctant to share the information posted on their personal Facebook account. With the consent of the users, in each Facebook profile, users are requested to search using the keywords ‘Wilpattu’ and ‘Wilpattu deforestation’ (or similar) for text posts and text comments. The process of retrieving the data from profiles was completed in front of the author by Facebook users. Initial results generated 328 posts and comments and it is reduced to 235 different posts and comments after excluding the comments and posts not directly related to the case.

4.2 Interviews of the Government Authorities Semi-structured interviews had performed with 5 different government officers who are directly or indirectly responsible for environmental issues of the country to gather data about the case. The author visited the government authorities in the October 1st week of the year 2019 and conducted interviews and recorded the interviews with the consent of interview participants. Interestingly, none of the interviewees are willing to disclose their job identity to the public, as ‘Wilpattu’ is a controversial issue in the country. As an ethical practice, the aim of the study is well explained to the interviewees’ and their consent to record, analyze, and publish gathered information is obtained through a formal consent form. Four interviews were conducted as face to face interviews and the other one is a telephone conversation. Interview questions are broadly based on the ‘Wilpattu’ case incident, citizen and government engagement, and collaboration in the case and what to do to improve the current situation. Three

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Table 1 Profiles of interviewees’ Organizations

Duration (min)

Gender

Department of Forest Conservation (DFC)

40

Male

Department of Wildlife Conservation (DWC)

45

Male

Central Environmental Authority (CEA)

40

Female

Sri Lanka Army

40

Male

Information Communication Technology Agency (ICTA)

45

Male

Table 2 Results from Twitter and Facebook comments Category

Number Percentage (%)

(1) Role of the citizens in engage in environmental protection (citizen attitude, behavior, responsibility, etc.)

345

42

(2) Blame and urge the government to take necessary actions

645

78

(3) Problem is identified as a racial issue (positive and negative)

449

55

(4) People invite others to gather and organize protests

180

22

*A single Tweet or comment may belong to more than one category

government officers are interviewed in the native language (‘Sinhala’) and the other two are in English. Recorded interviews are transcribed for the data analysis (Table 1).

4.3 Data Analysis The content analysis is used to analyze the collected citizens’ data (‘Twitter’ and ‘Facebook’). And, it is useful in eliciting meaningful findings by analyzing the collected data and output can be presented in a quantifiable form [23]. All the related Tweets and comments are stored in separate excel sheets for the analysis. The collected comments are thoroughly reviewed manually by reading the content for the relevancy and excluded from the study if it is not directly related to the. Another round of review carried out by an independent reviewer to validate the categorization and the process of analysis. The transcribed interviews were analyzed using inductive thematic analysis. It is a qualitative study to identify, analyze, and reporting patterns from the data collection to elicit meaning and describe the data in detail [24].

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5 Results The social media content analysis of citizen comments exhibits (Table 2), citizens’ enthusiasm, and a strong willingness to collaborate in environmental protection (Category 1 and 4). Most of the citizens believe the government is accountable and they blame for not taking timely actions to stop deforestation (Category 2). Surprisingly, the case study is directed and viewed by certain citizens as a racial issue (Category 3). Results of government interviews (Table 3: Themes and explanation are provided) highlight reasons for their inability to stop deforestation and their challenges.

5.1 Citizens Comments in Social Media Role of the Citizens who Engage in Environmental Protection Out of the total 558 comments analyzed 345 (42%) tweets are toward citizens’ role in engaging in stop ‘Wilpattu’ deforestation (or similar in future). People oppose and suggest their views toward deforestation. In such comments, people already are speculating the root causes of the problem and emphasize the ‘role of the public.’ Ex: ‘Controlling wildlife would be easier than controlling the wild attitude of those people who are destroying flora and fauna, not only in Sri Lanka but also in other parts of the world’; ‘It’s not only Wilpattu, but all other jungles and forests of Table 3 Themes and their associated meaning Theme

Explanation

(1) Issues faced by environmental authorities in addressing issues

Drawbacks of certain policies, interferences of certain politicians, and inter-governmental communication gaps are certain obstacles for the environmental authorities

(2) Officers accept the fact citizens made a strong voice via Social media platforms

Social media strongly impact in penetrating the message, but it could not resolve an issue

(3) Realize the need for ICT based platform for All the officer believes the presence of an ICT communication between the stakeholders based collaborative platform could bring consensus/solve/assist authorities and citizens communication addressing environmental issues (4) Government authorities lack information to provide solutions to the environmental issues

Officers of environmental authorities could not access the required information to make certain decisions and concerns raised by different stakeholders

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Sri Lanka should also be protected’; ‘number of social responsibilities entrusted in the public…’ Blame and Urge the Government to Take Necessary Actions From the dataset, 78% believe and blame the government for not taking proper actions in addressing this issue. Citizens publicly comment about their voice against politicians and government actions toward this deforestation issue. Ex: ‘Not only the politicians but even the government officers are also spineless.’; ‘Useless talking about Wilpattu. Nobody takes any action. Administrators are afraid of those Culprits’, ‘…the entire matter should go to the courts to get the matter cleared without any doubt. Some politicians cannot survive without such issues being raised….’ Problem is Identified as a Racial Issue (Positive and Negative) Unfortunately, 55% of the dataset proves this problem has led to unrest among ethnic groups, and it brings misunderstanding and racism among people. It became a humungous generator of heated arguments, rivalry, and fights among different races. Ex: ‘In sum, the conversational domain around #Wilpattu is mired in overwhelming toxicity, with rabid, racist, extremist content aimed at a specific community, faith, and culture.’; ‘Have to reiterate that Wilpattu deforestation is a NATIONAL issue. Please don’t infuse race or religion into it.’; ‘SaveWilpattu is just pure racism-based propaganda.’ People Invite Others to Gather and Organize Protests Another 22% comments were about inviting and gathering other citizens in organizing protests and gathering people in taking actions against ‘Wilpattu’ deforestation. Although most of the protests were on social media platforms, some were organized by different so-called environmentalists and third parties who gain benefits from intragovernmental issues and racial issues. Ex: ‘We will organize an island-wide trade union action against Wilpattu issue’; ‘Those who are against the deforestation of Wilpattu should get together, visit Wilpattu and see what we can do.’; ‘So yeah let’s get together for #savewilpattu,’ ‘Someone good at this, please start organizing I will gladly commit myself for that.’

5.2 Analysis to Understand Environmental Authorities’ Response All five interviewees were aware of the reality of the ‘Wilpattu’ incident and had varied expressions toward the case based on their professional points of view.

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6 Lessons Learned from the Case Study The interpretative case study results are vital in understanding the failure of the case. Discovered failures are useful in finding feasible solutions in similar environmental problems in the future. Study results show the main failure of the case is the lack of collaboration and coordination between citizen-government, internal and intergovernment authorities in terms of providing timely actions. Citizens’ social media analysis reveals their readiness to collaborate with the government in providing reliable information, evidence, and suggestions to stop deforestation. They are depressed with the actions and decisions executed under alleged patronage of both officers and politicians staying in Colombo and had never visited or does not aware much about either the factual truth or societal issues. Administrative officers miserably failed to perform legal duties and obligations mandated by the law and there are several cases filed against authorities by environmental organizations (Including bribery and corruption). Some of the information published in mass media and social media is ‘bias’ and ‘false information,’ distorted reality, and published and compromised for political survival, popularity, and monetary gains. This was leading to dire implications and perceptions about the case among the citizens. Some citizens questioned about their ‘freedom’ to disclose ‘truth’ to the public. There is a socio-political debut between the right of the people displaced by the war to resettle in their original villages in ‘Wilpattu’ and the right to destroy the environment. As a result, deficiencies in these miscommunications, the environmental issue had converted into a racism-based ethnic issue. Controversially, it has not only affected the ‘Wilpattu’ area in the north, but it has also disturbed the peace and harmony of the societies of the whole country. Citizens’ comments further reveal, their ability to self-organize and execute many endeavors to protect the forest and to assist the government in environment-related decision making. Overwhelming inputs and campaigns of citizens through social media platforms could not derive solutions, but they were able to create the awareness among the majority of the citizens in the country, Sri Lankan ex-pats (citizens never heard about even the term ‘Wilpattu’) and government including head of the state to take actions to stop ‘Wilpattu’ deforestation. Ultimately, the government lost citizen ‘trust’ as they are unable to enforce effective measures toward the conservation of natural resources by using administrative and legal power. Government struggle to provide a solution to ‘Wilpattu’ catastrophe as it is a complicated issue inextricably interwoven with political, social, and environmental aspects. Addressing environmental sustainability is a complex societal problem as many stakeholders and issues revolve around. But they failed to collaborate and communicate with all these stakeholders (government, citizen, politicians, offices of different authorities, etc.) in deriving consensus or solutions. They have faced the challenge of decision making due to the overlap of institutional policies and political interferences. Though laws and regulations exist for the conservation of state-owned forest resources, officers face practical difficulties in law enforcement due to various factors. Certain conflicts in the case could have been easily avoided if substantive

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environmental information is available with officers. Due to the ‘unavailability of information’ and government processes are ‘not transparent’ and ‘not well-defined,’ certain politicians and some other officers had misused the facts their benefits. In the summary, failure of the case is mainly due to collaboration and coordination problems and other different societal, political, and administrative issues that had been affected in the case.

7 Conclusion This paper presents the case of a serious environmental problem in Sri Lanka. The lessons learned from this case emphasize the critical importance of improving the collaboration between government and citizens in a developing country, e.g., Sri Lanka. Therefore, we aim to continue the research and design a digital platform to potentially improve this collaboration. The study had a limitation of increasing the number of citizens and government officers’ comments as studied ‘Wilpattu’ deforestation is a controversial issue, some are reluctant to participate in the study. A digital platform could provide co-production, democratic participation and engagement, crowdsourced solutions and to improve transparency and accountability. ICT is an enabler to increase the interactions and relationships closer and more frequent between citizens and government. Collaboration is critical to design public services, co-production of government, and citizens to provide solutions for many of the societal, political, and governance issues related to environmental problems.

References 1. B. Moldan, S. Janoušková, T. Hák, How to understand and measure environmental sustainability: Indicators and targets. Ecol. Indic. 17, 4–13 (2012). https://doi.org/10.1016/j.ecolind. 2011.04.033 2. R. Gholami, R.T. Watson, A. Molla, H. Hasan, N. Bjørn-Andersen, Information systems solutions for environmental sustainability: how can we do more? J. Assoc. Inf. Syst. 17, 521–536 (2016). https://doi.org/10.17705/1jais.00435 3. T.F. Slaper, The triple bottom line: what is it and how does it work? The triple bottom line defined. Indiana Bus. Rev. 86, 4–8 (2011) 4. K. Callahan, Citizen participation: models and methods. Int. J. Public Adm. 30, 1179–1196 (2007). https://doi.org/10.1080/01900690701225366 5. R. Sandoval-Almazan, J.R. Gil-Garcia, Are government internet portals evolving towards more interaction, participation, and collaboration? Revisiting the rhetoric of e-government among municipalities. Gov. Inf. Q. 29, S72–S81 (2012). https://doi.org/10.1016/j.giq.2011.09.004 6. M. Cuthill, Exploratary research: citizen participation, local government and sustainable development in Australia. Ann. Assoc. Econ. Geogr. 45, 167–168 (1999). https://doi.org/10.20592/ jaeg.45.2_167_2 7. T.M. Koontz, Collaboration for sustainability? A framework for analyzing government impacts in collaborative-environmental management. Sustain. Sci. Pract. Policy 2, 15–24 (2006). https:// doi.org/10.1080/15487733.2006.11907974

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8. D.M. Konisky, T.C. Beierle, Innovations in public participation and environmental decision making: examples from the great lakes regions. Soc. Nat. Resour. 14, 815–826 (2001). https:// doi.org/10.1080/089419201753210620 9. G. He, I. Boas, A.P.J. Mol, Y. Lu, E-participation for environmental sustainability in transitional urban China. Sustain. Sci. 12, 187–202 (2017). https://doi.org/10.1007/s11625-016-0403-3 10. C.J.A.M. Termeer, A. Bruinsma, ICT-enabled boundary spanning arrangements in collaborative sustainability governance. Curr. Opin. Environ. Sustain. 18, 91–98 (2016). https://doi.org/10. 1016/j.cosust.2015.11.008 11. World Bank: E-government, http://documents.worldbank.org/curated/en/527061468769894 044/pdf/266390WP0E1Gov1gentina1Final1Report.pdf, last accessed 2020/03/20 12. G. Viale Pereira, M.A. Cunha, T.J. Lampoltshammer, P. Parycek, M.G. Testa, Increasing collaboration and participation in smart city governance: a cross-case analysis of smart city initiatives. Inf. Technol. Dev. 23, 526–553 (2017). https://doi.org/10.1080/02681102.2017.1353946 13. S.A. Chun, L.F. Luna-Reyes, R. Sandoval-Almazán, Collaborative e-government. Transform. Gov. People, Process Policy 6, 5–12 (2012) 14. E. Falco, R. Kleinhans, Digital participatory platforms for co-production in urban development: a systematic review. Int. J. E-Plann. Res. 7, 1–27 (2018). https://doi.org/10.4018/IJEPR.201 8070105 15. T. Bovaird, E. Loeffler, From engagement to co-production: the contribution of users and communities to outcomes and public value. Voluntas 23, 1119–1138 (2012). https://doi.org/ 10.1007/s11266-012-9309-6 16. D. Linders, From e-government to we-government: defining a typology for citizen coproduction in the age of social media. Gov. Inf. Q. 29, 446–454 (2012). https://doi.org/10.1016/j.giq.2012. 06.003 17. J.C. Bertot, P.T. Jaeger, J.M. Grimes, Promoting transparency and accountability through ICTs, social media, and collaborative e-government. Transform. Gov. People Process Policy 6, 78–91 (2012) 18. L.A. Brainard, J.G. McNutt, Virtual government-citizen relations: informational, transactional, or collaborative? Adm. Soc. 42, 836–858 (2010). https://doi.org/10.1177/0095399710386308 19. W. Olphert, L. Damodaran, Citizen participation and engagement in the design of e-government services: the missing link in effective ICT design and delivery. J. Assoc. Inf. Syst. 8, 491–507 (2007). https://doi.org/10.17705/1jais.00137 20. G. Walsham, Interpretive case studies in IS research: nature and method. Eur. J. Inf. Syst. 4, 74–81 (1995). https://doi.org/10.1057/ejis.1995.9 21. Wipattu National Forest (2010) https://www.wilpattunationalpark.com/visiting_wilpattu_nat ional_park.php?lId=02, last accessed 2010/02/03 22. M. Thelwall, A web crawler design for data mining. J. Inf. Sci. 27, 319–325 (2001). https:// doi.org/10.1177/016555150102700503 23. M. Bengtsson, NursingPlus open how to plan and perform a qualitative study using content analysis. NursingPlus Open 2, 8–14 (2016). https://doi.org/10.1016/j.npls.2016.01.001 24. V. Braun, V. Clarke, Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

A Study on Blockchain Scalability Manjula K. Pawar, Prakashgoud Patil, and P. S. Hiremath

Abstract Blockchain is an emerging technology for various business and industrial applications because of its characteristics like security, immutability, and transparency. But still, blockchain faces the main issue of scalability, which influences several factors such as throughput, which is defined as the number of transactions processed per second (tps), the limited size of the block, cost, and capacity (size of the blockchain) and latency. In this paper, a survey of literature on the various solutions for scalability issues, namely cashing, side chain, off chain, inter-chain, child chain, on chain, and also deep learning methods along with their pros and cons. Keywords Blockchain · Scalability · Cashing · Side chain · Off chain · Inter-chain · Child chain · On chain · Deep learning

1 Introduction Satoshi Nakomoto developed blockchain technology in the year 2008 [1], which gained popularity due to its application in cryptocurrency, like Bitcoin [2]. This technology is utilized in many fields [3, 4] where immutability, transparency, and security are the requirements. Of late, many banks do their transactions quickly with the help of this technology. Several countries have come up with blockchain for bank transactions [5]. Though it has several advantages, it has some disadvantages, such as low throughput and poor scalability [6]. Ethereum and Bitcoin are two blockchain platforms that process 20 and 7 transactions per second, respectively, which is much

M. K. Pawar (B) · P. Patil · P. S. Hiremath KLE Technological University, Hubballi, Karnataka, India e-mail: [email protected] P. Patil e-mail: [email protected] P. S. Hiremath e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_29

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less compared to common systems, like PayPal and Visa, that process 193 and 1667 transactions per second, respectively. In this paper, the solutions for improving blockchain scalability represented in the literature are discussed. Scalability can be expressed in terms of throughput, cost, and capacity [7, 8]. As the size of the block is limited, transactions wait for more time for completion, which affects throughput. As and when transactions are created, the fees have to be paid to the minors, which generates the blocks. This is burdensome even if the transactions are lighter since they have to pay micro values. This causes the cost issue. As the number of blocks increases in the chain, which results in difficulty in managing the blockchain, it causes the capacity issue. Solutions to overcome these issues are: (1) Cashing method [5], which attempts to improve scalability and throughput by adapting FPGA(Field Programmable Gate Array) key value for storing in gigabit Ethernet Network Interface Controller, and this consumes low resources and low power; (2) on chain, where all the transactions are performed in the main blockchain only; (3) off chain, where only results are stored in main blockchain and transactions are performed outside the main blockchain; (4) child chain, where the results are stored in main blockchain and transactions, are performed in child chain; (5) side chain [9], where different assets and functions from different blockchains are exchanged; (6) inter-chain [7], which is a technique for communication between blockchains; (7) Deep learning method, which improves the scalability by having two layers, namely a base layer and branch layer. The methods, namely cashing, on chain, off chain, child chain, side chain, interchain, and deep learning, are described in detail in the following section.

2 Scalability Methods 2.1 Cashing Cashing method improves the scalability and throughput by adapting the FPGA(Field Programmable Gate Array) network interface controller (NIC). The cashing technique uses customized SHA-256 for performing actual hashing operations only when the request parameter is not a hashed value. This saves time of hashing executions and thus improves the performance. It adapts [10] the method having NIST (National Institute of Standards Technology) recommendation, such as SHA-256, that helps provide collision-free solutions. It uses the SHA-256 hash function that avoids the collision and improves the hash table, which in turn improves the efficiency and performance of the system. SHA-256 is a standard recommended by NIST [10], which is used with the Bitcoin core server through remote procedure calls (RPC) Jansson and curl libraries developed in [5]. Finally, it showed better performance with more than 160 GB data in the lab. The results show a better improvement, i.e., nearly 103 times of system cache hit. It has a small area FPGA (Field Programmable Gate Array) that uses less power consumption.

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2.2 On Chain The on chain technique involves increasing the size of the block by making size as unlimited, wherein the transaction limit is higher, and also it reduces the cost of transmission. But it has a disadvantage, specifically, as the size increases speed is reduced, and the probability of generating orphan blocks will be more and thus causes the maintenance cost to be more, and thereby it leads to throughput issues. Due to these disadvantages, increasing the size of the block is not the right solution [7]. The alternative solution offered is Merkelized Abstract Syntax Tree [11], which is developed in BIP-114 by combining abstract Syntax Tree and Merkle Tree. Merkle tree is a kind of binary tree that concatenates the two transactions of their hash values of a block. Later it hashes the concatenated two transactions. The abstract Syntax tree is the one that connects each function until all the dependencies in the program are mapped. Figure 1 shows a segment of a Bitcoin script example, and its corresponding MAST is shown in Fig. 2. When MAST performs its operation, one branch is hashed, as shown in Fig. 3a, b. The main advantage of this is that (i) it reduces the size of the block since it grows with a log scale due to the binary structure, and (ii) it increases the privacy as one branch is hidden and its information is not visible. But the disadvantage of this method is that it doesn’t provide complete privacy as another branch is not hidden. The next solution is Segregated Witness (Segwit) [12]. Transactions also consist of digital signatures that occupy 70% of space. In order to solve this issue, signatures are stored in a separate data structure called Witness, which separates them from transactions. This also solves the problem of malleability, in which attacker creates OP_IF

OP_CHECKSIG OP_ELSE

OP_CHECKSEQUENCEVERIFY OP_DROP

OP_CHECKSIG OP_ENDIF

Fig. 1 Bitcoin script example: code 1

OP_CHECKSIG Fig. 2 MAST of code 1 example in Fig. 1

OP_CSV OP_DROP OP CHECKSIG

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H

Included in chain

H

Not included in chain

H

H

OP CHECKSIG

OP_CSV OP_DROP OP_CHECKSIG

(a) MAST case1

(b) MAST case2

Fig. 3 MAST cases of code 1 example in Fig. 1

his transaction, which is similar to the original transaction but with different transaction Id (TX_Id). The attacker includes this in the next block earlier to the original transaction to make it appear as if payment not made [12]. To solve this problem, it removes the signature from data structure while generating TX_Id. It increases the complexity of code. The number of transaction processing increases as capacity usage also gets increased. This also leads to fungibility problems [12]. Another solution incorporated in this technique is Sharding, which is popular in distributed processing systems [13]. As the chain size increases, the burden on nodes increases as they need to store all block data, which is troublesome. Sharding is a method that groups the nodes to form a shard, and each shard processes different blocks instead of storing all blocks. This technique improves the throughput by parallelizing the transaction processing. However, it becomes vulnerable to attack (by about 1%), as an attacker may gain control over the entire shard.

2.3 Off Chain In the off chain method, the transactions are processed outside the blockchain. This is done by storing the main chain state and applies the last processed state that has been done on the other channel. This method is called a state-channel chain. This also lowers the load of the main blockchain, and hence, improves the throughput. The general example of off chain is the Lightning Network of Bitcoin [14]. Usually, in Bitcoin, transactions need to pay the transaction fee even for micropayment transactions and need to wait until they enter the block. In the Lightning Network, one opens the channel together with a payment user. A user puts the coin in a multi-sig address so that they both can share the channel. Once the channel is open, both of them can use it. Hence, the transaction fee is paid only in the main chain to open the channel. After this transaction is processed, the main chain doesn’t record these events, and the waiting time is also zero. Once the channel is closed, its state will be maintained in main chain just as in on chain. For example, consider that John

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and Merry establish a channel, and also Merry and Kelvin maintain a channel each. Then John and Kelvin can also have a connection with this. Thus, it can enhance scalability. The advantages of the method are: fewer burdens on the main chain, transaction fee reduced, and standby time is reduced. By applying this method, the solution network called Raiden Network [15] is obtained, which is similar to Lightning Network except that, Raiden network handles a different variety of transaction types, e.g., the Ethereum supports smart contracts and IoT, etc.

2.4 Side Chain The side chain method helps in exchanging the properties of multiple blockchains among each other. This approach aims to bring the functionalities of one blockchain into another [9]. If someone wants to use bitcoin, he can put this in another blockchain as its cryptocurrency is of high value, limited function, and slow. Suppose one wants to use Bitcoin on Ethereum, then he can use the Ethereum smart contract with Bitcoin. For example, to operate the bitcoin in another blockchain, it can be implemented as follows: first, the amount of Bitcoin that can be operated need to be transferred to a special account and freeze it. Now it creates coin in another blockchain and operates through it. After all the transactions are completed after the freeze, the remaining amount is available. If this is possible, interoperability is possible; that is, it can be used from another blockchain. But this requires controlling of cryptocurrencies since the values of currency keep changing on and on.

2.5 Child Chain The solution of the child chain method is parent and child structure. In this technique, child chain processes all the transactions, and the parent chain records all the results. It is applied in the Ethereum plasma chain [16], where the plasma chain recursively creates child chains. Transactions are processed in child chain. All transactions in the generated block are transformed to a Merkle hash root, as shown in Fig. 4a. The same is recorded in the parent chain. The initial stage is the same as that of off chain. Once the child chain is created, it is going to become a parent chain in the plasma, and it deposits coin through a smart contract. To use this child blockchain, it receives the token. During transactions, if fraud occurs, then the bad node (the node where the fraud occurred) will become slower than the owner of the token to withdraw the token. In such cases, the owner will connect to any other parent, as shown in Fig. 4b. Hence, the deposit is lost by the bad node. The verification process is carried out by any child chain. The benefit of the method is that it is lighter since only Merkle hash roots of child chains are stored in parent chains. The disadvantage of this method is that verification by child chains is

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Plasma Blockchain (Ethereum)

A->B 2ET

Plasma Blockchain (Ethereum

Plasma Blockchain (Ethereum)

Root (Ethereum) (a) Plasma example

Plasma Blockchain (Ethereum)

Plasma Blockchain (Ethereum)

Plasma Blockchain (Ethereum)

Root (Ethereum) (b) If the parent chain is invalid

Fig. 4 Plasma chain example

expensive because it has to keep checking the honesty of all parent chains. It is also troublesome, as transforming all the transactions of the child chain to a Merkle hash needs to be valid in the parent chain and then recursively to the root.

2.6 Inter-chain The inter-chain approach provides communication between blockchains, just like the Internet. This is the same as the side chain. In another word, inter-chain provides the infrastructure for side chain. Let us consider the case study of this method as Atomic-swap [15]. Consider that Harry uses Lightcoin, and John uses Bitcoin. Harry wants 30 light coins and John wants 2 bitcoins to be exchanged among each other. Then they can use inter-chain as follows: First, John creates a transaction of 2 bitcoins with Harry’s digital signature, sets the number k which only he knows, or he gets back it as coin after a certain time. Next, John creates the public key for k and broadcasts that to network. Harry, after receiving this message, creates the transaction 30 light coins using John’s digital signature or gets back the coin after a certain time, which is smaller than John’s expiration time, which he had set. Usually, the expiration time for Harry is shorter than John’s expiration time; this is because if expiration time for Harry is larger than John’s expiration time then John receives the 30 lightcoins just before the end expiring time and coins return to John before he takes 2 bitcoins. The advantage of

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this approach is that the exchange of properties between different blockchains and disadvantages are that all blockchains need to implement the same algorithms such as POW hashing and so on. If one of the users is lazy among all, then exchange gets completed almost when expiration time is reached.

2.7 Deep Learning Method In Deep Learning method, it needs big data for training and builds an effective deep learning model. Deep Learning [17] consists of two-layer blockchain architecture, such as the Base Layer and Branch layer. Base Layer can be implemented with Bitcoin or Ethereum. It can have several branch layers. Validation of each branch layer will be done in the base layer. Merkle trees implement branch layers. Merkle tree helps in achieving efficient and secure verification of transactions in a distributed environment. The root of every Merkle tree is stored in the base layer, which ensures that trees stored in branch layers are unchangeable. If any modification happens in the tree, then its validation fails, since it doesn’t match with root stored in the base layer. The advantage of the approach is that it helps in providing features like POET (Privacy, Ownership, Efficiency, Transparency). It is implemented by using the Hash graph and FBA (Federated Byzantine agreement) protocols that improve the throughput and reduce the latency. Further, it uses fewer resources but requires high bandwidth. The hash graphs work only privacy granted or permissioned network.

3 Analysis Herein, the solutions discussed in the previous section are compared by considering factors that are influenced by the scalability, namely throughput, cost, and capacity. Table 1 provides a performance comparison of all these solutions. It is observed that all the methods increase throughput, except that child chain and off chain reduce the capacity by reducing the load on main chain. These also reduce the cost by handling the transactions outside the main blockchain. Increasing the block size does not solve the issue of blockchain. Hence, the on chain method, such as MAST method, slightly modifies the Merkle tree and solves the capacity issue. Segwit on the other hand separates signature from the transaction and consumes more capacity, which is a structural solution. Sharding, on the other hand, provides a distributed database system and solves both capacity and cost issues. Inter-chain, which is similar to the side chain, hence another row for side chain is not shown in the table. It achieves a functional part of scalability by connecting different blockchains. Cashing improves the performance by using FPGA with less power consumption, and hence cost is reduced, and throughput is improved. Deep Learning, by using two layers (base layer

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Table 1 Comparison of perforance of different methods in terms of throghput, cost and capacity Category

Method

Tps

Capacity

Cost

Advantage

Disadvantage

On chain

MAST

–

Low

–

Provides high privacy

Privacy is not completely provided

Big block

High

High

Low

Transmission limit is high

Increases orphan blocks, centralization of mining pools

Segwit

High

–

Low

Possibility to apply to Bitcoin with various solutions

Leads to Fungibility problems

Sharding

High

Low

–

Parallelized processing, the low burden on the capacity

It leads to 1% attack enhancement

Lightning network

Low

Low

Low

No cost for transition and waiting time is almost zero

Only payment channel

Raiden network

Low

Low

Low

General-purpose channel

Child chain

Plasma

Low

Low

–

Parent and child structure blockchain

Verification is expensive

Inter- chain

Atomic-swap

–

–

–

Communication among blockchains

Bounded applicable situations

Deep learning

DeepLinQ

High

Low

Low

Provides privacy, ownership, accountability, transparency

Require rigorous verification, the hash graphs are more suitable for private or permissioned chain

Cashing

FPGA

High

–

Low

Less power consumption, the result is improved over 103 times of average

Off chain

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and branch layer), improves the throughput. Besides, by considering the Merkle tree in Baselayer, it enhances the cost.

4 Conclusion Blockchain is an emerging trend in the industry, as well as in business applications. But, to use this in real-world applications, it needs better scalability, which is lacking. The standard platforms of the blockchain are Ethereum and Bitcoin. Ethereum processes 20 transactions per second and Bitcoin processes seven transactions per second, whereas real-world applications need more number of transactions to be processed per second. Throughput, latency, cost, and capacity are the factors which affect the scalability. Hence, to improve the scalability, there are several approaches, namely cashing, on chain, off chain, side chain, child chain, inter-chain, and deep learning. These solutions are compared in terms of performance metrics, such as throughput, cost, and capacity. From the present literature survey, it is observed that to apply the blockchain for real-life applications, and it is necessary to study existing methods and suggested new solutions in view of scalability. The future scope of the work reported on the studies of blockchain presented in the survey points to consideration of the latency factor, along with the number of nodes in the blockchain, in the development of new efficient solutions for the implementation of blockchain technology in real-world applications. Acknowledgements The authors are grateful to the reviewers for their helpful comments and suggestions, which improved the quality of the paper.

References 1. S. Nakamoto, Bitcoin: a peer-to-peer electronic cash system. Cryptography at https://met zdowd.com (2009) 2. S.D. Lerner, RSK white paper overview (2015) 3. C. Khan, A. Lewis, E. Rutland, C. Wan, K. Rutter, C. Thompson, A distributed-ledger consortium model for collaborative innovation. Computer 50(9), 29–37 (2017) 4. J. Ray, A next-generation smart contract and decentralized application platform. Ethereum Wiki 5. A.I. Sanka, R.C.C Cheung, Efficient high performance FPGA based NoSQL caching system for blockchain scalability and throughput improvement, in 2018 26th International Conference on Systems Engineering (ICSEng) (IEEE, 2018), pp. 1–8 6. H. Kohad, S. Kumar, A. Ambhaikar, Scalability issues of blockchain technology. Int. J. Eng. Adv. Technol. IJEAT. 9(3) (2020). ISSN: 2249-8958 7. S. Kim, Y. Kwon and S. Cho, A survey of scalability solutions on blockchain, in 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, 2018, pp. 1204–1207

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What Influences Consumer Behavior Toward Information and Communication Technology Applications: A Systematic Literature Review of UTAUT2 Model Jick Castanha , Subhash Kizhakanveatil Bhaskaran Pillai , and Indrawati

Abstract Success of any Information and Communication Technology (ICT) application, whether developed for industrial or individual use, depends on the level of acceptance by the consumers and usage of the same on a regular base in the most sustainable manner. Result of the content analysis on UTAUT2 model reveals that this model have more predictive ability than earlier models. The variance explained on behavior intention was of 61–80% and on use behavior was of 41–60%. The external factors which are applied newly, namely, trust, risk, innovativeness, privacy, security, self-efficacy and information are found to be good predictors. Various stakeholders, especially those entrepreneurs who are developing new as well as existing owners of ICT applications consider these constructs for adoption and continuous use among consumers. The present scenario around the world is going to result in more and more use of ICT applications by the people for all their needs. Keywords UTAUT2 · Technology adoption · Behavior intention · Content analysis

1 Introduction During the last one decade, exponential development and growth are being witnessed around the world in the field of ICT applications, technology adoption is being one of the major areas in almost all fields of business which completely changed the lifestyle of people, and the success completely depended on the level of acceptance and continued use of the ICT applications by the consumers. Both public and private J. Castanha (B) · S. K. B. Pillai Goa University, Goa, India e-mail: [email protected] S. K. B. Pillai e-mail: [email protected] Indrawati Telkom University, Bandung, Indonesia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_30

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players are showing great interest and huge investments for the development and introduction of new technologies for the use of various stakeholders in all fields of business. Unless and until these innovations in ICT applications are not successfully adopted and efficiently used by the end-users, time and money spent on these innovations become useless, hence researchers around the world started focusing on understanding and identifying what makes a consumer adopt and use technology. Technology adoption is a stage in consumer buying behavior where in the selection of appropriate technology among the alternatives available for the use by an individual or an organization [1]. Technology adoption is not only related to technology but also related to the user’s attitude [2], personal and performance-related benefits [3], social influence [4], facilitating conditions [5] and also other factors. This being the case, researchers in technology adoption and diffusion are considered to be more mature areas of exploration [6] which led to the development of various theories and models by various scholars and tested in different systems and in a different context. During the last 52 years period (1960–2012), researches in this domain of technology adoption identified new conceptualizing factors that influence the behavior of users to adopt the technology which led to the development of various consumer behavior theories and models, namely, Diffusion of Innovation Theory (DOI) by Everett Roger in 1960, Theory of Reasoned Action (TRA) by Martin Fishbein and Ajzen in 1975, Social Cognitive Theory (SCT) by Bandurra in 1986, Technology Adoption Model (TAM) by Davis in 1989, Theory of Planned Behaviour (TPB) by Icek Ajzen in 1991, Model of Personal Computer Utilization (MPCU) by Thompson and Higgins in 1991, Motivation Model (MM) by Davis, Bagozzi and Warshaw in 1992, Extended Technology Adoption Model (TAM2) by Venkatesh and Davis in 2000, Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh, Morris, Davis G and Davis F in 2003, and Model of Acceptance with Peer Support (MAPS) by Sykes, Venkatesh and Gosain in 2009 [6–8]. The most recent model being the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by Venkatesh, Thong and Xu in 2012. The presence of many theories, each having pros and cons leads confusion among the researchers, as they are forced to pick from a wide variety of competing models and theories which suits the study characteristics [8]. To sort this confusion, Venkatesh, Morris, Davis and Davis developed a unified model – The Unified Theory of Acceptance and Use of Technology in 2003, popularly known as UTAUT, by systematic review and consolidation of various constructs used in earlier models developed during the period 1960–2000 [6]. The UTAUT considered four main constructs, namely Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI) and Facilitating Condition (FC) which are direct determinants of Behavioral Intention (BI) and ultimate Use Behavior (UB) leads to actual system use. However, this UTAUT model was having inherit limitations such as it does not represent the external factors that can affect the performance of the behavior, behavior intention has weak ability to predict behaviors that are not completely within an individual control [9], hence Venkatesh, Thong and Xu extended the original UTAUT model with three more main constructs [10], namely, Hedonic Motivation (HM),

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Fig. 1 Extended unified theory of acceptance and use of technology (UTAUT2). Source Venkatesh et al. [10]

Price Value (PV), and Habit (H) which are direct determinants of behavioral intention and this model is commonly known as UTAUT2, having three moderators, namely, Age (A), Gender (G) and Experience (E), the detailed model is shown (see Fig. 1). The original UTAUT Model and the modified UTAUT2 are shown together (see Fig. 1), the shaded constructs (HM, PV and H) and the three moderators (A, G and E) are the addition made over UTAUT to make UTAUT2 which resulted in increased predictive capability. The orange lines indicate that all the seven constructs or independent variables (PE, EE, SI, FC, HM, PV and H) influences the construct or dependent variable (BI) which in turn influences the final resulting construct or dependent variable (UB), which is shown in the blue line. Also the two constructs or independent variable (FC and H) shown in blue line directly influences UB. The moderating variables are shown in dotted green lines. It was found that the UTAUT model explained the variance of 56% on behavior intention but in the UTAUT2 it explained 74% which is a good predictor model of behavioral intention. With respect to technology use, UTAUT explained variance of 40% whereas UTAUT2 explained 52% [10]. Hence, UTAUT2 was widely accepted by the researchers and also is been tested in a different context and hence the scope

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for exploring the applicability of UTAUT2 in assessing consumer behavior when it comes to ICT applications in different fields of business around the world. The goal of this research is to analyze the research works carried out on the UTAUT2 model during the period 2012–2020 to see how best this model is applied in different areas of ICT applications around the world so as to see those critical factors which are considered significant and influencing consumer behavior. Subsequent sections discuss (1) the methodology followed for the content analysis; (2) findings and discussion on the result of the content analysis; (3) conclusion of the analysis and the implications which provides some insights on the applicability of UTAUT2 model and finally (4) limitations of the work as well as future research directions. As far as the authors’ knowledge, no similar study has been carried out so far addressing the above-mentioned issues, which makes this paper unique because it throws some light on an otherwise unexplored area which needs to be studied in detail and how best the developers and also existing organizations of ICT application can improve the services to have complete customer satisfaction and improved brand loyalty and, therefore, the study fills the gap by adding valuable knowledge, new perspectives, and presents possibilities for consideration. The paper offers valuable inputs for academicians and researchers, developers of ICT applications, business entities using ICT applications, the governments, and other stakeholder groups.

2 Methodology The study aim was to conduct the systematic review of all the research works carried out during the period 2012 till February 2020 on the application of UTAUT2 model, mainly for assessing and identifying the most and least influencing constructs, namely, the independent and dependent variables and also the moderating demographic variables. A comprehensive search was conducted on all research database, namely, Emerald, Elsevier, Inderscience, JSTOR, MDPI, Sage, Springer, Taylor and Francis, Google Scholar and other open sources by using keywords such as “Extended Unified Theory of Acceptance and Use of Technology” or “UTAUT2.” The search was limited to only research articles and conference proceedings. After the identification of papers, the criteria for selecting the papers were established. The papers which used any of the extended UTAUT2 constructs (HM, PV, and H) along with the original UTAUT constructs (PE, EE, SI, and FC) have been selected for further analysis. Finally, 204 articles were considered for carrying out content analysis. These articles were then analyzed in series of aspects, namely, methodological details, types of relationship found between model constructs, explained variance by model constructs, moderating variables, and external variables, which are in line with the earlier works [8, 11, 12]. For the purpose of assessing the inter-relationships and the predictive capability of the constructs, weight analysis was carried out. A total of 204 articles were reviewed where in 186 studies were quantitative in nature, of which 12 studies made use of UTAUT2 more than once in the same study due to different time span or user

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type, bringing about a total of 221 different occurrences of the relationship between the variables. In order to better understand the predictive power of each independent/dependent variable pairs, a weight analysis was performed as suggested by Jeyaraj [13]. The predictor’s weight of each UTAUT2 construct (PE, EE, SI, FC, HM, PV, H on BI and UB) was obtained by dividing the number of times each of these constructs has been found to be significant on behavior intention and on use behavior by the total number of times that relationship has been examined across all studies [8]. A weight of “1” indicates that the relationship between two pairs is significant across all studies, whereas “0” indicates that this relationship is non-significant across all the studies. The Independent variable is said to be the best predictor, if its weight is 0.80 or more [13].

3 Findings and Discussion Three way analysis is being carried out on the final 204 research papers, namely, demographic characteristics of the research papers, methodological aspects used in the research papers and the analysis of constructs used in the UTAUT2 model. The findings of each category have been discussed in the following sections.

3.1 Demographic Characteristics Content analysis result of the demographic characteristics of 204 papers on UTAUT2 during 2012–2020 reveals certain interesting facts about frequency of research works carried out over the years, research collaboration, the most prolific author, geographical concentration of the work, prominent publishers as well as application area/industry. Though the UTAUT2 model was developed in the year 2012, the initial three years (2012–2014) very few researchers adopted this model in their research (only 4%) as this new model was not considered superior over the earlier models when it comes to studying consumer behavior in ICT applications. But over the next five years (2015–2010) researchers identified the superiority of the UTAUT2 model and started applying it extensively (96%) and found that this model explains consumer behavior better than earlier models. Another interesting aspect is about the collaboration among the researchers, where only less than 9% of the research works are being carried out by individual authors whereas more than 91% of the research works are the result of the collaboration of at least 2 researchers. This clearly indicates that among the researchers also, the technology adoption and use is increasing, whereby geographical distance is not an issue for sharing ideas. The most prolific authors who published at least 4 research works using the UTAUT2 model in different fields of business along with their respective institutions are listed in Table 1. These eight

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Table 1 Most prolific authors Authors (Years of sample works)

University

No.

Dwivedi [14]

Swansea University, UK

12

Alalwan [15]

King Abdulaziz University, Saudi Arabia

9

Oliveira [16]

New University of Lisbon, Portugal

9

Williams [17]

Swansea University, UK

7

Indrawati [18]

Telkom University, Indonesia

6

Tarhini [19]

Sultan Qaboos University, UK

5

Rana [20]

Swansea University, UK

5

Baabdullah [21]

King Abdulaziz University, Saudi Arabia

4

Source Authors Compilation

authors collectively published around 57 (28%) collaborative research works out of the total 204 papers. When it comes to geographical concentration, the Asia Pacific region accounts for around 40% of the research works, followed by Europe (24%), the Middle East and Africa (23%), North America (10%) and Central and South America (3%). When the country-wise analysis result is considered, concentration is more seen in India (25%) and Malaysia (22%), followed by the USA (13%), Jordan (12%) and Spain (12%). The result of publisher-wise analysis shows that the majority of the papers are published by Emerald (22%) and Elsevier (21%) and the remaining 57% of the research papers are shared by Springer, Taylor & Francis, Inderscience, Wiley, Sage, JSTOR, MDPI, etc. The result of the application area/industry where the UTAUT2 model is applied reveals that Education (16%), General purpose (14%) and Healthcare (10%) are the three toppers having double-digit figures. Those with a share of between 5-9% shares include Banking (9%), Mobile Technology (8%), Shopping (7%), Payment (5%) and Travel (5%). The result also reveals that almost all the area/industry are being studied by the researchers to identify the consumer behavior toward adoption and use of ICT applications.

3.2 Methodological Aspects Content analysis result of the methodological aspects provides insights on the methodology adopted for identifying and collecting sample data and various statistical techniques used for analyzing the sample data. For the purpose of data identification and collection, most of the researchers used exploratory research (66%) that was based on an online survey (50%) using convenience sampling (25%) with a sample size of 201–400 (48%) using Likert’s scale for assessing the influencing factors of consumer behavior toward ICT applications. The majority of the studies

What Influences Consumer Behavior Toward Information … Table 2 Statistical techniques used

323

Technique name

%

Confirmatory factor analysis (CFA)

31.14

Structure equation modeling (SEM)

30.56

Descriptive analysis

27.47

Regression analysis

4.26

Exploratory factor analysis (EFA)

3.87

Correlations

1.55

Analysis of variance (ANOVA)

0.58

Meta-analysis

0.39

Chi-square test

0.19

Source Authors Compilation

indicated a high response rate, 13% of the research between 61 and 80% response rate and 19% of the research between 81 and 100%, which clearly shows that the result of the research works will have a high degree of reliability. Statistical techniques used for analyzing the data is shown in Table 2. Since the majority of the research identified and collected multivariate data, data analysis is carried out mostly using confirmatory factor analysis (31%) and structural equation modeling (31%). This clearly shows that complexity involved in identifying and assessing the consumer behavior for the purpose of understanding whether there is any pattern emerging out of the data which indicates the most and least influencing constructs/variables when it comes to adoption and use of ICT applications with the help of the UTAUT2 model.

3.3 Construct Used in UTAUT2 Model This section provides a discussion about the different constructs/variables used in analyzing and testing the UTAUT2 model and their inter-relationships, reliability statistics, moderating variables used, and finally, use of external variables and their impact. The UTAUT2 model has seven constructs (see Fig. 1) as independent variables, namely, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (H). The additional two more constructs treated as dependent variables are Behavioral Intention (BI) and Usage Behavior (UB). The UTAUT2 model (see Fig. 1) focuses on 10 inter-relationships (shown as orange and blue arrows), namely, PE-BI, EE-BI, SI-BI, FC-BI, HM-BI, PV-BI, HBI, FC-UB, H-UB, and BI-UB. Those constructs having weight above 0.80 are considered to have the best predictors when it comes to finding inter-relationships, remaining all constructs with weight less than 0.80 do not have good predictive ability [13]. Weight analysis of independent variables (refer Table 3) indicates that

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Table 3 UTAUT2 variables relationship and weight analysis Relations

Significant

Non-significant

PE-BI

151

22

EE-BI

79

97

SI-BI

89

FC-BI HM-BI PV-BI H-BI

Not tested

Total

Relation examined

Weight of predictors

48

221

173

0.87

45

221

176

0.45

82

50

221

171

0.52

82

76

63

221

158

0.52

124

47

50

221

171

0.73

84

50

87

221

134

0.63 0.84

115

22

84

221

137

FC-UB

40

26

155

221

66

0.61

H-UB

53

9

159

221

62

0.85

BI-UB

82

5

134

221

87

0.84

Source Authors Compilation

only four pairs of the UTAUT2 are said to be best predictors, namely, PE-BI (0.87), H-BI (0.84), H-UB (0.85) and BI-UB (0.84), whereas the other variables did not meet this requirement, the fifth closest predictor being HM-BI, with weight being 0.73. This provides a generalized result that only PE and H are the two constructs in the UTAUT2 model which allows prediction of BI and UB when it comes to adoption and use of ICT applications. When it comes to the explained variations, considered as an indicator for reliability (R2), earlier studies on UTAUT model having six main variables 56% of the variations are explained w.r.t BI and UB of 40% [6], whereas the extension proposed in UTAUT2 produced a substantial improvement in variance explained in BI of 74% and UB of 52% [10]. Result of the content analysis of the 204 research works on UTAUT2 also reveals that the variance explained (R2) in BI is found to be between 61 and 80% of the 52% of the studies and in UB between 41 and 60% of the 42% of the studies, which is similar to the UTAUT2 models explained variations. This is a clear indication that the UTAUT2 model is superior over the earlier models on consumer behavior when it comes to the adoption and use of ICT applications. About the influence of the demographic variables on the constructs, only 29% of the research applied and the most common moderator was gender (25% of the research) and age (24% of the research). Few researchers also examined the moderating role of income, nationality, employment, marital status and also cultural factors. Apart from those seven constructs/variables, researchers also used additional external constructs/variables. The result shown in Table 4 shows that trust is the most frequently applied construct, followed by risk, innovativeness, privacy, security, selfefficacy and information. Among these seven constructs, self-efficacy is having more significance (67%), followed by trust (58%), risk and security (54%) and innovativeness (48%). Privacy and information have less significance. Analysis of the original as well as the external constructs used and the moderating variables reveals that the

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Table 4 Significance of external variables Relations

Significant

Non-significant

Not tested

Total

Trust—BI

25

7

11

43

Risk—BI

13

4

7

24

Innovativeness—BI

10

4

7

21

4

3

7

14

Privacy—BI Security—BI

7

3

3

13

Self-Efficacy—BI

8

2

2

12

Information—BI

4

3

3

10

Source Authors Compilation

UTAUT2 model does have superiority over the earlier consumer behavior models developed during 1960–2012.

4 Conclusion and Implication Though various models of consumer behavior on the adoption and use of ICT applications are developed from 1960 till 2012, there was no systematic literature review was carried out on the UTAUT2 model developed in 2012, which was considered superior over other previous models. Two such systematic literature was carried out so far was on TAM [11] and UTAUT [8], which formed the basis for the present research work. Acceptance of the UTAUT2 model and its use in academic research was very limited during 2012–2014 but got acceptance and witnessed an exponential increase taken place during 2015–2020 as the model is having superior predictive power. The Asia Pacific is the top region and India and Malaysia are the two countries where researchers applied the UTAUT2 model for assessing the consumer behavior model when it comes to ICT applications in various business fields, most prominent ones being education, general purpose and health-related ICT applications. This is an indication that business entities are trying to capture the markets in the Asia Pacific region. By using weight analysis, it was found that only four out of ten pairs are said to be best predictors, namely, PE-BI, H-BI, H-UB and BI-UB. The majority of the studies also gave a similar variance explained (R2 ) with were in line with the findings of Venkatesh [10]. Gender, age and experience were the most commonly used moderators. The most significant external constructs used by the researchers were found to be self-efficacy, trust, risk, security, and innovativeness. Developers of ICT application must consider the attributes, both from the original UTAUT2 model as well as those which are added subsequently, to improve the existing application and also incorporate these attributes in new upcoming applications for the purpose of ensuring that existing and potential future consumers are adopting and using for

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all future purposes. Trust is found to be one of the best external construct, and it is related with other external constructs, namely level of risk, level of privacy and the level security associated with the ICT application.

5 Limitations and Future Research Direction Every research is bound to have some limitation and the present study is not an exception. First of all, efforts are taken to cover all the relevant content from the existing research works carried out during 2012–2020, but there is always the possibility of losing relevant content, hence scope for future research work. Second, articles reviewed are only the research papers and conference proceedings whereas dissertation, thesis, unpublished work and other work being not taken into consideration, thus providing further scope for the research. Third, the present study is not taken into consideration the limitations faced in the studies of Lee and William [8, 11], thus one can make a detailed study of the constraints faced by those researchers. Fourthly, many researchers might have focused and used UTAUT2 but lack the keywords in title, hence become scope for further research. Lastly, all studies using UTAUT2 construct were studied in general, thus, once can study the relationship between each construct under each ICT application industry which influences the behavior of user toward technology adoption.

References 1. J.V.H. Carr, Technology adoption and diffusion. http://www.icyte.com/system/snapshots/fs1/ 9/a/5/0/9a50b695f1be57ce369534ac73785801745a8180/index.html, last accessed 2020/05/07 2. M. Fishbein, I. Ajzen, Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (Addison-Wesle, Reading, MA, 1975) 3. A. Bamdura, Self-efficacy: towards a unifying theory of behavioral change. Psychol. Rev. 84(2), 191–215 (1977) 4. I. Ajzen, The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991) 5. R.L. Thompson, C.A. Higgins, Personal computing: towards a conceptual model of utilization. MIS Q. 15(1), 125–143 (1991) 6. V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User acceptance of Information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003) 7. R. Sharma, R. Mishra, A review of evolution of theories and model of technology adoption. Indore Manage. J. 6(2), 17–29 (2014) 8. M.D. Williams, N.P. Rana, Y.K. Dwivedi, The unified theory of acceptance and use of technology (UTAUT): a literature review. J. Enterp. Inf. Manage. 28(3), 443–488 (2015) 9. V. Venkatesh, S.A. Brown, L.M. Maruping, H. Bala, Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Q. 32(3), 483–502 (2008) 10. V. Venkatesh, J.Y.L. Thong, X. Xu, Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)

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11. Y. Lee, K.A. Kozar, K.R.T. Larsen, The technology acceptance model: past, present and future. Commun. Assoc. Inf. Syst. 12(1), 752–780 (2003) 12. V. Kumar, Z. Rahman, A.A. Kazmi, Sustainability marketing strategy: an analysis of recent literature. Glob. Bus. Rev. 14(4), 601–625 (2013) 13. A. Jeyaraj, J.W. Rottman, M.C. Lacity, A review of the predictors, linkages, and biases in IT innovation adoption research. J. Inf. Technol. 21(1), 1–23 (2006) 14. Y.K. Dwivedi, M.A. Shareef, A.C. Simintiras, B. Lal, V. Weerakkody, A generalised adoption model for services: a cross-country comparison of mobile health (m-health). Gov. Inf. Q. 33, 174–187 (2016) 15. A.A. Alawan, Investigating the impact of social media advertising features on customer purchase intention. Int. J. Inf. Manage. 42, 65–77 (2018) 16. P. Rita, T. Oliveira, A. Estorninho, S. Moro, Mobile services adoption in a hospitality consumer context. Int. J. Cult. Tour. Hosp. Res. 12(1), 143–158 (2018) 17. H.R. Marriott, M.D. Williams, Developing a theoretical model to examine consumer acceptance behavior of mobile shopping, in Social Media: The Good, the Bad, and the Ugly 2016, vol. 9844, ed. by Y. Dwivedi et al. (LNCS, Springer Cham, 2016), pp. 261–266 18. N.R. Indrawati, Predicting acceptance and use behavior of consumers towards indihome services by using extended UTAUT model (a case study in Bandung). Int. J. Sci. Res. 6(5), 1860–1865 (2017) 19. A. Tarhini, R. Masadeh, K.A. Al-Busaidi, A.B. Mohammed, M. Maqableh, Factors influencing students’ adoption of e-learning: a structural equation modeling approach. J. Int. Educ. Bus. 10(2), 164–182 (2017) 20. K. Tamilmani, N.P. Rana, N. Prakasam, Y.K. Dwivedi, The battle of brain versus heart: a literature review and meta-analysis of hedonic motivation use in UTAUT2. Int. J. Inf. Manage. 46, 222–235 (2019) 21. A.M. Baabdullah, A.A. Alalwan, N.P. Rana, H. Kizgin, P. Patil, Consumer use of mobile banking (M-Banking) in Saudi Arabia: towards an integrated model. Int. J. Inf. Manage. 44, 38–52 (2019)

Secure and Energy-Efficient Remote Monitoring Technique (SERMT) for Smart Grid Sohini Roy and Arunabha Sen

Abstract Monitoring and automation of the critical infrastructures like the power grid are improvised by the support of an efficient and secure communication network. Due to the low-cost, low-power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSN) became an attractive choice for the Information and Communication Technology (ICT) system of the smart grid. However, the energy efficiency and security of WSN depend highly on the network design and routing scheme. In this paper, a WSN-based secure and energy-efficient remote monitoring technique (SERMT) is proposed by demonstrating a WSN-based ICT network model for pervasive monitoring of the generation and transmission part of the power network in a smart grid system. The performance of the proposed network model designed for a smart grid of IEEE 118-Bus system coupled with the secure routing technique is tested during cyber-attacks by means of NS2, and the simulation results indicate that it performs better than existing smart grid monitoring methods like Lo-ADI (Dhunna and Al-Anbagi in IEEE Sens J 19(13), 2019 [1]) with respect to packet drop count and throughput. Keywords Information and Communication Technology · Wireless sensor network · Cyber-attacks · Remote monitoring · Smart sensor nodes · Smart grid

1 Introduction A sustainable lifestyle for human race is reliant upon critical infrastructures like power network, communication network, water supply system, transportation system, etc. Therefore, pervasive monitoring [2] and control over such critical systems are very important. It is to be noted that only an improved Information S. Roy (B) · A. Sen Arizona State University, Tempe, AZ 85281, USA e-mail: [email protected] A. Sen e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_31

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and Communication Technology (ICT) can transform every such system into a smart system and thus can form a smart city. Smart grids are an inherent part of a smart city, and it is obtained by incorporating features like full-duplex communication between the ICT entities, automated metering in the smart homes, power distribution automation and above all intelligent decision making by means of pervasive monitoring of the power system and securing its stability. Therefore, it is beyond any question that the ICT network of a smart grid must be accurate, scalable and secure enough to instantly identify any kind of abnormal behavior in the entities of the power network, securely communicate that information to the control center (CC) and thereby help in taking necessary and timely action to ensure uninterrupted power supply. Although, designing of a robust ICT network for smart grid has become a boiling topic of research, the best-suited ICT design for a smart grid is still not very clear. In [3], only a crude idea of the design of a joint power communication network is given using a test system consisting of 14 buses. While the authors of [4] have come up with a realistic design of the ICT for smart grid by taking help from a power utility in the U.S. Southwest, their ICT system relies completely on wired channels that either use SONET-over-Ethernet or Ethernet-over-dense wavelength division multiplexing. A completely wired ICT system is neither cost effective nor energy saving. Every communication entity in [4] draws power, and thus a huge amount of power is devoted for monitoring the power network itself. Moreover, addition/isolation of ICT entities for hardening purpose or fault tolerance/during a failure or a security threat is extremely difficult and costly in a wired system. Smart sensor nodes like phasor measurement units (PMUs) are popular in remote monitoring system for smart grid. Power generation and transmission, power quality, equipment fitness, load capacity of equipment and load balancing in the grid can also be monitored by data sensing techniques. As a result, wireless sensor networks (WSNs) are currently acquiring attention of the researchers for the purpose of designing the ICT network for a smart grid system. WSNs are comprised of lowpowered sensor nodes with easy installation process, lesser maintenance requirement, low installation cost, low power profile, high accuracy and scalability. All these have convinced the researchers that WSNs are a very good choice for the designing of the ICT of a smart grid. Therefore, in this paper, a new design of the ICT network for a smart grid is proposed using WSN. Despite all these advantages, the most common drawback of a sensor node is that it is battery powered and battery replacement is difficult. As a result, energy conservation becomes important. In secure and energy-efficient remote monitoring technique (SERMT), energy efficiency is obtained by energy-aware routing and use of more expensive rechargeable energy harvesting sensor nodes (EHSNs) for MU and PMU data transfer, respectively. Also, the sensor nodes as well as the wireless channels are vulnerable to common cyber-attacks [5] like sinkhole, wormhole, Sybil attack, packet eavesdropping, flooding attack and most importantly false data injection attack by means of node compromise. SERMT aims at securing the data transfer together with energy conservation by means of lightweight security protocols

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used in [6] like elliptic curve public key cryptography (ECC), elliptic curve Diffie– Helman key exchange scheme (ECDH), nested hashed message authentication codes (NH-MAC) and RC5 symmetric cypher. The rest of the paper is structured as follows. Section 2 gives an overview of the ICT network setup phase for SERMT. A realistic risk model for the SERMT network design is given in Sect. 3 of this paper. Mitigation of the threats discussed in Sect. 3 by adopting a secure and energy-efficient routing technique is explained in Sect. 4. Section 5 does performance analysis of the proposed scheme SERMT by means of comparing the simulation results with an existing secure remote monitoring technique for power grid named as Lo-ADI [1]. Section 6 concludes the paper and discusses the scope for future works.

2 Overview of the ICT Network Setup Phase for SERMT In order to provide a reliable remote monitoring for the smart grid, a generic ICT network design is proposed by SERMT that can be applied on any given power network. In this section, the network setup phase for SERMT is divided into four steps. Each step is illustrated with the help of IEEE 14 bus system as the power network. In the first step, a given power grid is divided into several substations. The buses connected by transformers are placed in one substation with the assumption that transformers remain within a substation only [7]. This process is repeated until no transformers are left. Then each bus is placed in a unique substation. In Fig. 1, the IEEE 14 bus system is divided into 11 substations denoted by Si where i is the substation ID. After the substation division, the distance between all pairs of substations (Si & S j ) is calculated [4]. Now, starting from a substation Si with the maximum connectivity among the substations at the network borders, all substations which are within a given distance of it are marked as substations of a common monitoring region Rx . Then the next substation which is the closest to Si but beyond the given distance and which is not yet placed in a monitoring region is selected and the same process is repeated. This process is continued till every substation is placed within a monitoring region. For example, in the IEEE 14 bus system, substations S3 , S4 , S5 , S6 , S7 , S8 and S10 are at the borders of the smart grid area. Among them, S4 has the maximum connectivity; therefore, the region division starts from it. Figure 1 shows that after this process is completed, the smart grid network for IEEE 14 bus system is divided into four regions. SERMT considers two different types of smart sensors for monitoring the power network entities. The first type is the measuring unit (MU)-based smart sensors [8]. The MU-based smart sensors have a sensing unit consisting of sensors like current transformer (CT) and voltage transformer (VT) and a merging unit for performing signal amplification, conditioning and analog–digital conversion. There is an internal

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Fig. 1 Region division and sensor node placement in a smart grid of IEEE 14 bus system

clock in the merging unit that can timestamp every sample and thereby time synchronize them with an external UTC source. The MU-based sensor has a network communication module which helps it to communicate with intelligent electronic devices (IEDs) or applications. The second type of sensors used for SERMT is the PMUbased smart sensors. The sensing module of a PMU has sensors like voltage and current sensor which provide analog inputs for the PMU. Current and voltage phases are sampled at a rate of 48 samples per cycle in a PMU using a common time source for synchronization. It also has an internal clock and a processing module responsible for signal conditioning, timestamped analog–digital signal conversion and computing voltage and current phasors to produce a synchronized phasor and frequency. In this step, MU-based sensors are placed at every bus in the power network. PMU-based sensors are placed at some of the buses using an optimal PMU placement algorithm [7]. Low-cost, non-rechargeable battery-enabled sensor nodes denoted by N j with ID j are randomly dispersed across the network area. These N j s can carry the MU-based sensor data to the CCs in a power-efficient manner. A phasor data concentrator (PDC), responsible for receiving accumulating PMU data from multiple PMUs, is placed at each region of the smart grid and few EHSNs denoted by E S j with j as the ID are randomly deployed across the smart grid region. The idea behind the deployment of the two kinds of sensor nodes is that the cheaper N j s will follow an event-driven hierarchical routing approach to send the MU data from the MUbased sensor nodes to the CCs; and the E S j s will always be active to accumulate synchrophasor data from the substations of each region and send the data to the local PDCs and finally to the CCs. Due to the high volume of PMU data transfer from each substation having a PMU, the sensor nodes carrying them should always be active.

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In Fig. 1, the MU-based sensors are denoted by Mi where i is the ID of the sensor, and it is same as the bus number it is placed at. The PMUs are represented by Pi , and they are numbered in the order they are placed. It is assumed that N j s and E S j s are deployed region wise and are numbered in the order of their deployment. SERMT assumes that there is a main CC and a backup CC. So, even if the main CC is damaged, the backup CC can efficiently monitor the network state. The connectivity of each substation is calculated based on the number of transmission lines connecting that substation with other substations. The substation having the highest connectivity is selected as the main CC and that having the second highest connectivity is selected as the backup CC. After the CCs are selected, all substations are equipped with a router acting as a gateway for the substation servers, and the servers in each CC are access points for the operator. The CC gateways can wirelessly receive data from the sensors and PDCs and send those data to server via a wired LAN connection in control centers. In Fig. 1, S1 is the main CC and S2 is the backup CC.

3 Risk Model and Assumptions for the SERMT Network Setup The WSN nodes are vulnerable to several attacks that are explored by researchers over the ages. In SERMT, some of the common attacks on WSN are considered. The threat model for SERMT predicts that a compromised entity of the ICT can pretend as a CC gateway and congest the network with bogus communications to launch a flooding attack which can result in denial of service (DoS) by the ICT entities. Another common attack of the WSN is the Sybil attack [5] where a legitimate node of the network gets compromised and deceits the other nodes by providing multiple fake location information of itself. In SERMT network setup, any ICT entity except the substation entities can impose a Sybil attack and oblige other entities to forward data to an entity that does not exist in that location resulting in increased packet drop. In a node compromise attack, the attacker physically gets hold of the node and reprograms it to launch attacks like wormhole attack and packet eavesdropping attack [5]. These attacks can also be launched by an outsider’s node deployed in the network. The Sinkhole attack can be launched by attackers with high-quality equipment, and they can inform the ICT entities about a high-quality entity in their sensing zone, so that all the other entities select it to forward the data to the CC. This node gathers all the data the CCs do not receive any information about the system state from the network. In order to provide security to the ICT infrastructure for the smart grid, the following assumptions are made in the proposed work: 1. The substation equipments like the server and the gateway are trustworthy, and it is impossible for an attacker to compromise those components. The servers authenticate themselves to the sensors or PDCs to avoid flooding attack [5].

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2. Each entity of the ICT network is provided with a global key GBK which an attacker cannot get hold of even if the entity is compromised. 3. A unique set of elliptic curves is stored in the memory of each ICT entity for the purpose of ECC and ECDH protocols. Also, in order to achieve those mechanisms, it is assumed that any pair of entities in the network agrees upon all the domain parameters of the elliptic curves stored in them. 4. The two CC servers generate two separate one-way hash chains using SHA-1 hash function [6]. The starting key for each chain is randomly selected by each of the servers. All the ICT entities are provided with same hash functions and the last keys of the two chains so that they can authenticate the control messages from the control center servers and avoid a Sinkhole attack. 5. Each ICT entity in the network is aware of its own ID, location information, region ID and ID of the servers. 6. All the PDCs can communicate with other PDCs in the neighboring regions.

4 Secure and Energy-Efficient Routing Scheme of SERMT The goal of the ICT network for a smart grid is to securely transmit the sensed data from the sensors to the control centers and help in remote monitoring of the power grid. In order to achieve this with the help of a WSN-based ICT network, a secure and energy-efficient routing technique should be followed. In this section, the secure routing scheme of SERMT is divided into four modules.

4.1 Module 1: Initial Trust Value Estimation of the ICT Entities In the first module of SERMT, the main control center server (Server1 ) sends a number of test messages to the sensor nodes and PDC in its own region to calculate their trust value (TVi ). The message format is Server1 → ∗ : MSG||K x ||HMAC(GBK; MSG||Server1 ID||K x ) where * denotes a set of ICT entities, MSG is the test message, K x is a random key from the sequence of keys generated by the hash function, and this key is never used again. A hash-based message authentication code (HMAC) is generated over the MSG, server ID and the key using the shared global key GBK and appended with the message, so that any non-legitimate node which does not have the GBK and the server ID cannot separate the HMAC from the original message and therefore cannot overhear the message from the server to the other entities. The TVi is calculated by the server for each entity i using Eq. 1. TVi =

MSGdelivered ∗ 100 MSGsent

(1)

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Entities having a TVi of more than 40% are selected as trusted nodes, and any legitimate node having a lower TVi is added to the threat list of the server. Now, the node X i with the highest TVi in that region is selected by the CC server to forward a request message to the nodes closest to it in the adjacent regions. The format of the request message is Server1 → X i : RQM||||K x ||HMAC(GBK; MSG||Server1 I D||K x ) where RQM is the request to calculate TVi , K x is a new random key from the key chain, and HMAC is generated and appended with the message. First, the TVi of the nodes of adjacent regions closest to X i are calculated by X i in the same manner. If those nodes have a TVi greater than 40%, then the RQM is forwarded to them. These nodes now calculate the TVi of the nodes in their respective regions and send a list of blocked node IDs having low TVi to the CC server via X i . The message format is ei → Server1 : Blocked_List||ei I D||HMAC(Blocked_List||ei I D). The Server1 updates the threat list using this data. This process is repeated till the threat nodes of each region are identified by Server1 . Server1 shares the threat list with Server2 via a shortest path consisting of nodes in the trusted list. After both the servers get the list of trusted nodes in the network, the trust value of each node is shared with the substation gateways by Server1 , via a shortest path to the substation consisting of the most trusted nodes. This whole process is repeated at a regular time interval by alternate CC servers. Each time the new trust valuesare assigned to the ICT entities, both the servers compute a public–private key pair UServeri , vServeri and share their public key with all the entities in the network by broadcasting.

4.2 Module 2: Data Sensing by the Smart Sensor Nodes and Data Forwarding by Substation Gateways The next phase of SERMT is the data sensing phase. In this phase, the smart sensors collect data from the power entities and forward that to the substation gateways. The substation gateway is assumed to be trusted. The CC gateways forward the data directly to the connected servers, but they still need to send data to the other CC. The gateways work differently for sending MU-based and PMU-based sensor data to the CCs. In order to send the MU-based sensor data, the gateways send a forward request message to each jth node (N j ) of that region at one-hop distance from it. The format of the request is GWi → N j : forw_ RQM||GWi I D||HMAC(GBK; forw_ RQM||GWi ID). Each N j receiving the forw_RQM from the gateway sends back an acknowledgement (ACK) appended   and its connectivity with with information like their remaining battery power BP j   sender in the format: N → G othernodes in the same region C j to the j i :   ACKBP j C j ||HMAC GBK; ACKBP j C j . In case a node receives forw_RQM from multiple gateways in that region, it selects the one closest to it. The substation gateways calculate a data forwarding score DF j for all the nodes sending back ACK to it using Eq. 2.

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DF j = BP j ∗ T V j ∗ C j

(2)

 Now, each gateway GWi generates a key pair having a public and a private key UGWi , vGWi using ECC and forward the public key UGWi to the jth sensor node having the highest DF j calculated by itself using the message format: GWi → N j : UGWi ||GWi I D||HMAC(GBK; UGWi ||GWi I D). The Nj receiving  the public key from the GWi also generates a private–public key pair v N j , U N j using ECC and sends back the public key and its ID in the similar format to GWi . GWi now computes a point in the elliptic curve (xk , yk ) = vGWi .U N j and N j computes a point (xk , yk ) = v N j .UGWi where xk , the x coordinate of the computed point becomes their shared secret. This is ECDH key exchange scheme where the shared secret calculated by both parties is same—vGWi .U N j = vGWi .v N j .G = v N j .vGWi .G = v N j .UGWi , where G is the generator of the elliptic curve. This shared secret xk is the temporary key for exchanging encrypted data between the GWi and N j . GWi now encrypts the aggregated MU data from the buses of that substation using RC5 symmetric cypher with xk and forwards that to N j by using NH-MAC in the format: GWi → N j : EMD||GWi ID||HMAC(GBK; HMAC(xk ; EMD||GWi ID)). The gateways keep on forwarding the encrypted MU data to the same node until the servers initiate a new round of module 1. At the same time, the PMUs also sense data from the power entities and send that to the corresponding substation gateway. Each PMU containing substation gateway now forwards forw_RQM to the EHSNs of that region within its range. In the similar way, the ES j s receiving the forw_RQM sends back ACK to the gateway with only the connectivity information. Now, the gateway recalculates the TV j for each ES j like module 1 and selects the node with the highest current TV j . The same key exchange scheme is established with the most trusted ES j as it was done with the N j s and the RC5 encrypted PMU data is forwarded to E S j in the similar format with NH-MAC. GWi s recalculate the TV j of the ES j s at regular intervals to repeat this process of key and data exchange and do not wait for the servers to estimate TV j of the ES j s. However, if a server reinitiates module 1, all the substation gateways wait till the process is complete and they are updated with the new trust values of all the nodes in their region. The CC gateways also use the same process to send data to the other CC.

4.3 Module 3: Data Forwarding to the Control Centers In this module, two separate routing techniques are followed by the N j s and ES j s to forward data to the CCs. N j s receiving data from the GWi s first decrypt them (join_RQM) is broadcasted using the same temporary key xk . Then, a join request  in the format: N j → ∗ : join_ RQM N j I D HMAC(GBK; join_ RQM||N j ID) to all other N s in that region by that N j . Malicious nodes present in the network receiving the join_RQM cannot decode the HMAC as they do not have the GBK and thereby they cannot join any legitimate data-carrying N j . Each

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N j receiving ACK from other N s provides a cluster ID to each of the N s in the format RegionID||Timestamp_for_ current_T Vi ||List_ of_ SubstationIDs. The server assigned timestamp value for current TVi is same for all the nodes in a common region, and the list of substation IDs denotes the data carried from which of the substations in that cluster. Therefore, all the nodes getting the common cluster ID are considered as nodes of a cluster. Multiple clusters can be formed in each region of the smart grid. After the cluster formation in every a cluster head (CH) is selected for   region, each cluster using a candidate value CV j calculated using Eq. 3. by each node j in the cluster and shared with all other nodes in the cluster. CV j = BP j ∗ TV j ∗ Cn j

(3)

In Eq. 3., BP j is the remaining battery power of the node, TV j is the current trust value of the node, and Cn j is the connectivity of that node with the nodes of other adjacent regions. The Cn j of a node is determined based on the number of nodes in other regions that are within the sensing radius of the node j. Now, the CH generates a public–private key pair like GWi and share the public key with each of the nodes in the cluster. The cluster member nodes (CMNs) also generate the key pair, send their public key to the CH and compute their own xk using ECDH. Each CMN now forwards its data to the CH by encrypting the data with the xk using RC5 and generating NH-MAC just like each GWi did. The CHs after receiving the data from the CMNs decrypt them with the xk for that CMN. Now each of the CHs aggregates the  dataobtained from the member nodes and encrypts that with the public key UServer1 of the main CC server using ECC-based public key cryptography. In case, the main CC fails, the network is informed about that and all data is forwarded to the backup CC. Now, a CH of the main CC region directly forwards the encrypted-aggregated data to the main CC gateway. Other CHs use a modified version of Dijkstra’s shortest path algorithm to send  aggregated data to the main CC gateway. In this algorithm, the  the weight values Wi j of the link between two nodes i and j are determined by Eq. 4. Wi j = Di j /(BP j ∗ TV j )

(4)

In Eq. 4., Di j stands for the distance between the sender i and the receiver j, B P j is the remaining battery, and TV j is the trust value of the node j. The same route is followed by the nodes for sending data to the main CC until new nodes are selected by GWi s for data forwarding. The main CC gateway generates the HMAC over the received message to check its authenticity, and if a match is found, the data is forwarded to Server1 . Server1 periodically sends backup to the Server2 via the shortest path from Server1 to Server2 , determined using the same modified Dijkstra’s algorithm. In order to forward the PMU-based sensor data to the CCs, each data-carrying ES j node checks if the PDC of that region is adjacent to it. Each ES j is updated with the location information and the current TV j of other ES j s and the PDC in that region

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by the GWi s of that region. If a PDC is adjacent to an ES j , then the ES j first decrypts the data and generates a public–private key pair to share the public key with the PDC. The PDC also does the same and share back its public key with the ES j to complete the ECDH key exchange. The node ES j now forward the encrypted NH-MAC-ed data to it. ES j s which are not near a PDC, select the shortest path consisting of the most trusted ESs from itself to the PDC in that region to send their public key, and the same route is followed by the PDC to send back its own public key to the ES j . Now, the encrypted data is forwarded through the same path to the PDC. The PDC of each region acts in the similar way as a CH. It decrypts the data from each ES j using the unique xk for that node and aggregates the whole data. A shortest path consisting of PDCs only from each PDC of the network to both the control center gateways is calculated only once and is never repeated until a PDC of a region is replaced or a PDC fails. In case, the TVi of a PDC drops below the threshold, the Server1 adds it to the blocked list and selects the most trusted ES j node to perform the function of a PDC, until the actual PDC is replaced by a new one or its fault is diagnosed and recovered by the operator. Now, the aggregated data is encrypted using the public key of the Server1 by each PDC and forwarded to the main CC gateway. The same process is repeated for Server2 . Both the CC gateways authenticate the received data and forward it to their connected servers for evaluation. The energy consumed by both the N nodes and ES nodes in transmitting a message (E T ) is the summation of energy consumed by their power amplifier (E A ), and it is a function of distance from which the message is sent, baseband DSP circuit (E B ) and front-end circuit (E F ). The energy consumed in receiving a message (E R ) is the summation of E B , E F and the energy consumed by low-noise amplifier E L .

5 Simulation Results In this section, a smart grid network of IEEE 118 bus system is considered. The total network region is divided into eight regions, and the power grid is divided into 107 substations. Substation 61 is selected as the main CC, and it consists of three buses—68, 69 and 116. Substation 16, consisting of buses—17 and 30, is selected as the backup CC. The main CC is in region 1, and the backup CC is in region 2 of the smart grid. In order to analyze the performance of SERMT in this network setup, a total of 500 N type nodes, 300 ES type nodes and 8 PDCs (high-quality EHSNs with longer communication range) are deployed in the network area and simulation is performed using NS2.29 network simulator. The simulation results are compared with the Lo-ADI [1] technique that incorporates a secure token exchange mechanism among the WSN nodes. The simulation parameters are given in Table 1. In Fig. 2, the average battery power consumed per hour by all types of sensor nodes is shown for different attack intervals in both Lo-ADI and SERMT. It is observed that the average battery power consumption of SERMT is a little higher than that of Lo-ADI, and this battery power consumption does not decrease if the attack interval is 1 s as in Lo-ADI due to connection revoke and packet drops, rather its battery

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Parameter

Description

Operating system

Fedora

Simulator

NS2.29

Compiler

TCL

Rechargeable battery for EHSNs

NiMH

Battery capacity of EHSNs

2000 (mAh)

Initial battery power for all nodes

150 (mAh)

Fig. 2 Avg. BP consumed versus attack interval

power consumption increases if the attack interval is less as SERMT immediately adds the malicious node into its threat list and resends the packet to some other trusted node. Moreover, due to the presence of ESs, the average residual energy in the network will always be more than that of Lo-ADI. In Fig. 3, it is proved that even though SERMT consumes more power than that of Lo-ADI because of using several power-consuming security mechanism computations and key exchange protocols, it wins in terms of offering security to the smart grid. Figure 3 shows percentage of packets dropped in presence of different number of malicious nodes in the network, and it is observed that in SERMT, the packet drop percentage is much lesser than that of Lo-ADI even when there are 35 malicious nodes in the network. Fig. 3 Number of malicious nodes versus packet drop percentage

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6 Conclusion and Future Works The region-based remote monitoring adopted by SERMT helps in easy identification of a failure in the power grid or an attack in the communication network of the smart grid. Security of sensor data is of utmost importance in the smart grid as any alteration of the data can lead to wrong decision by the operator, and this might badly harm the smart grid. Data privacy is obtained in SERMT by the encryption/decryption mechanisms, data integrity is verified by the hashed message authentication, and authentication of data is done by the one-way hash chain; therefore, all the data security measures are addressed by SERMT. One drawback of SERMT is that it cannot deliver the sensor data from the sensors to the control centers in a very short time due to the security measures it follows and this can be explored in future research how the time delay be reduced in a secure communication technique for smart grid. Designing a threat model with attacks on MUs, PMUs, gateways or servers and analyzing the effect of cyber-attacks on power grid can be another direction of future work.

References 1. G.S. Dhunna, I. Al-Anbagi, A low power WSNs attack detection and isolation mechanism for critical smart grid applications. IEEE Sens. J. 19(13) (2019) 2. K. Basu, M. Padhee, S. Roy, A. Pal, A. Sen, M. Rhodes, B. Keel, Health monitoring of critical power system equipments using identifying codes, in International Conference on Critical Information Infrastructures Security (CRITIS 2018), Kaunas (2018), pp. 29–41 3. W. Zhu, J.V. Milanovi´c, Cyber-physical system failure analysis based on Complex Network theory, in IEEE EUROCON 2017—17th International Conference on Smart Technologies, Ohrid (2017), pp. 571–575 4. S. Roy, H. Chandrasekharan, A. Pal, A. Sen, A new model to analyze power and communication system intra-and-inter dependencies, in IEEE Conference on Technology for Sustainability (SUSTECH 2020) (2020), Santa Ana 5. C. Karlof, D. Wagner, Secure routing in wireless sensor networks: attacks and countermeasures. Ad Hoc Netw. Elsevier J. 1, 293–315 (2003) 6. S. Roy, Secure cluster based routing scheme (SCBRS) for Wireless Sensor Networks, in 3rd International Symposium On Security in Computing and Communications (SSCC), Kochi (2015), pp. 368–380 7. A. Pal, C. Mishra, A.K.S. Vullikanti, S.S. Ravi, General optimal substation coverage algorithm for phasor measurement unit placement in practical systems. IET Gener. Transm. Distrib. 11(2), 347–353 (2017) 8. E.Y. Song, G.J. FitzPatrick, K.B. Lee, Smart sensors and standard-based interoperability in smart grids. IEEE Sens. J. 17(23) (2017)

Balanced Accuracy of Collaborative Recommender System Akanksha Bansal Chopra

and Veer Sain Dixit

Abstract Recommender systems (RSs) have emerged as a vital tool to handle the overloaded and huge information very effectively, caused by exceptional growth of means on Web. Collaborative recommender system (CRS) is one of the types of RSs that are being used tremendously by majority of e-commerce industry in present scenario. CRS help the users to opt for an item of their own interest easily from largely available options on Internet. CRS use the ratings given by similar users to make predictions to the interested users. However, these ratings are not always true and authentic. These un-authentic ratings are the biased ratings that lead to various attacks, such as Push and Nuke Attacks (P-N Attacks). In this work, P-N Attacks are, first, injected on available data and then the effect of these P-N Attacks are explored on the performance of CRS by using balanced accuracy (BAC) and other state-of-the-art measure metrics. Keywords Recommender systems · Collaborative recommender systems · Push Attacks · Nuke Attacks · Mean Absolute Error · Balanced accuracy

1 Introduction RSs are primarily being used by various e-commerce websites. Amazon,1 Youtube,2 Spotify,3 Netfilx4 are some of the famous commercial websites that use RS approach. 1 http://www.amazon.com. 2 http://www.youtube.com. 3 http://www.spotify.com. 4 http://www.netflix.com.

A. B. Chopra (B) Department of Computer Science, SPM College, University of Delhi, New Delhi, India e-mail: [email protected] V. S. Dixit Department of Computer Science, ARSD College, University of Delhi, New Delhi, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_32

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RSs are decision support systems that make the task easy and convenient to the users for selecting an item from a wide range of available items by filtering the information on Web [1]. There are two main approaches of RSs—Collaborative recommender system (CRS) [2] and content-based recommender system (CBRS) [3]. In CRS, ratings for an item are collected on the rating scale from users. A user-item matrix is then created for the collected ratings [4, 5] and similar users are identified by applying any available similarity coefficient. Pearson correlation coefficient (PCC) is one the popular similarity coefficients that can be used for finding similar users. CBRS work on contextual information based on item’s features and user profile both. Hybrid recommender system (HRS) is another approach that works on collective principle of CRS and CBRS approaches [6]. Data is most vulnerable part in present era. So we need to secure it from different attacks. When users are rating an item, they are expected not to disclose their private information. The private information available on network is helpful for malicious users. Various attacks can be injected by these malicious users resulting in inappropriate recommendation list. The attacks, thus degrade the performance and accuracy of RS. Shilling attack and Sybil attack are two common types of attacks that lead to breach in privacy of recommender system [7]. In Sybil attack, multiple fake identities are created by malicious user and false recommendations are injected resulting in inappropriate recommendation list. Shilling attack is a type of attack where misleading recommendations are injected and recommendation list is altered [8–10]. Push Attacks and Nuke Attacks (P-N Attacks) are two types of shilling attacks. In Push Attacks (P-Attacks), higher ratings (HRs) are injected by malicious users to increase the recommendations for an item and alter the actual and genuine recommender list. On contrary, in Nuke Attacks (N-Attacks), lower ratings (LRs) are injected by malicious users to decrease the recommendations of an item. In present commercial industry, P-N Attacks are injected to either escalate the reputation of own firm (by injecting P-Attacks) amongst its users or to destroy the reputation of rival firm (by injecting N-Attacks). In conventional RSs, user-item ratings are collected to generate recommendations. No emphasis was given to identify any biased ratings (HRs, LRs), if injected to the CRS. As in CRS, ratings highly contribute in prediction of items, it is therefore necessary to identify and classify the biased ratings. Actual and authentic ratings increase the system’s efficiency, whereas biased ratings (HRs, LRs) affect the system’s accuracy adversely and lead to false recommendations to its users. Once the user-item ratings are entered, it is expected the system should predict true recommendations for an item. The problem here is defined as to analyse the performance of CRS, when P-N Attacks are present. P-N Attacks alter the ratings of items by injecting HRs and LRs, to the system which leads in false recommendations of items to the users. The effect of P-N Attacks on performance of CRS is analysed by evaluating various measure metrics, such as precision, recall, F-measure, accuracy and balanced accuracy, at different threshold values. This Tpaper is structured in five sections. In Sect. 2, the related works done by various researchers are discussed. Section 3 explains various measure metrics that are

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used in paper. The datasets and experimental evaluation results along with validation of evaluations are shown in Sect. 4. Finally, Sect. 5 discusses the conclusion and outlook for future work.

2 Related Works Authors of paper [11] reviewed various applications of recommender systems (RSs) in e-commerce, e-shopping, etc., and related recommendation techniques are discussed. They also examined dimensions such as collaborative filtering and BizSeeker that are used to develop a recommender system. Paper [12] proposed an algorithm named as, TasteMiner that identifies partial users’ interest for efficient use of neighbourhood space. In another paper, [13] used Pareto dominance method for pre-filtering of less promising users from k-neighbour selection process keeping on promising users for recommendations. Challenges such as scarcity of data, scalability, shilling attacks, grey sheep, etc., with their solutions are discussed by other authors in their research work [14]. In research paper [15], authors have surveyed the privacy issues related to recommender systems and have proposed algorithms to improve performance of recommender system. The authors emphasized to provide enhanced solution to privacy issues. In another paper, authors have used trust-based collaborative filtering algorithm to recommend tourism-related features such as hotel, restaurant, etc., at one single portal accurately [16]. Security risks in Web applications have been analysed by the authors of research work [17]. The authors have proposed design patterns for vulnerabilities. The cost and efforts associated with privacy are also discussed in the paper. The authors of paper [18] have discussed privacy and the risks involved in sharing private information while making recommendations in their work. In another paper [19], the authors have identified threats to information systems and suggested solutions through policy decisions and awareness programs. How the privacy in recommender system affects user psychology is discussed by the authors in their paper [20]. The authors of paper [21] presented a review about ethics and security in their paper. They have proposed security mechanism in compliance to best ethical practises. A model for attack strategy considering constraints on attack attributes and attack order has been presented to provide error–tolerant graph and techniques to measure similarities between attack strategies [22]. Logical attack graphs are generated that represent logical dependencies and configuration information among attacks [23]. The attack paths have been identified by the authors and a recommender system is proposed that could classify cyber attacks in future [1, 24]. Research in paper [25] proposed a new similarity model for an improved recommender system. The authors considered local information of users and global user behaviour to develop the model. In another paper [26], authors studied the effect on performance of recommender system in their research using Mean Absolute Error metric. In this research, balanced accuracy metric is used to support and extend the state of art research. Authors of paper [27] have discussed various algorithms—user

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based, and item based, to improvise the recommender system’s performance and predictive accuracy of recommender system is focussed. F-measure is used to evaluate the quality of recommender system. Authors have used cross-domain approach to improve performance of recommender system [28]. The authors have focused on to provide highly accurate recommender list and to protect user’s private data while making recommendations in their research [29]. In paper [30], the authors have thrown light on limitations of presently being used recommender systems. They have also discussed to improve recommendations and make recommender system more widely available. In paper [31], authors have reviewed more than 80 existing approaches available for academic literature recommendations. The authors evaluated and discussed about various shortcomings and weaknesses of these approaches. The study shows and concludes that there exists no agreement to compare and evaluate existing academic literature recommender approaches. Authors of paper [32] have proposed a CF approach using Entropy and Manhattan distance similarity measures for an improved recommender system.

3 Measure Metrics Used 3.1 User-Item Matrix Construction Let us construct a user-item matrix model for collaborative recommender system (CRS). Let us assume that ‘uth’ user gives rating ‘r’ in the rating scale for ‘ith’ item. Then, ru,i = U . I where ru,i = rating for item ‘i’ given by user ‘u’, U = {u 1 , u 2 , . . . , u n } holds ‘n’ number of users, and I = {i 1 , i 2 , . . . , i N } holds ‘N’ number of items. 

Next, R be defined as the accumulated user–item ratings, such that 

ru,i = I ; R = ru,i = I T Finally, the recommendations for Ith item are generated by finding average values of nearest neighbours. Mathematically, degree of similarity between user-item ratings can be calculated by using Pearson’s correlation coefficient (PCC).

Balanced Accuracy of Collaborative Recommender System

PCC N

=  N

n

a =b=1

N

n

a =b=1

2 i=1 ra,i

−



N

n a =b=1

i=1 ra,i rb,i −

 n

a =b=1

N

i=1 ra,i

2 

N

N

i=1 ra,i

345

 n

n

a =b=1

a =b=1

N

2 i=1 rb,t



N

i=1 rb,i

−

 n

a =b=1

N

i=1 rb,i

2 

(1) where ra,i is ‘ath’ user rating for ‘ith’ item and rb,i is ‘bth’ user rating for ‘ith’ item

3.2 Mean Absolute Error Accuracy is a measure used to find how diligently recommendation knots with user’s likings. It is a key metric that helps to improve recommendation eminence. Mean absolute error (μ E ) is one of the metric that can be used to compare recommendation scores with true user rating. μ E is calculated as an average of expected and actual rating values. Mathematically, μE =

n 1 |ei − ai | n i=1

(2)

where ‘ei ’ is expected rating and ‘ai ’ is actual rating. The lesser value of μ E implies higher performance.

3.3 Mean Accumulated Error In our study, we have used Book-crossings (BX) dataset. There are 1.1 million ratings in BX dataset. Since, it is hard to work with so many single rating values, therefore, to handle this huge data, we find mean accumulated ratings (μ A ), defined as μA =

n 1 ri n i=1

(3)

where ‘r i ’ is total number of ratings given for a particular book ISBN.

3.4 Precision and Recall In recommendation systems, it is desired to recommend only top n items to the user. To achieve this, precision at threshold n (P@n) and recall at threshold n (R@n) metrics are computed by considering only topmost results and performing evaluation at a given cut-off rank. In notion, they may be defined as

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A. B. Chopra and V. S. Dixit

P@n =

tp tp ; R@n = tp + f p t p + fn

(4)

3.5 F-measure Precision and recall are sometimes used harmonically together in F measure. The significance of using F measure is that harmonic mean in comparison to average mean, penalises extreme values. Thus, F-measure at threshold n (F@n) is defined as F@n = 2 ∗

P@n ∗ R@n P@n + R@n

(5)

3.6 Accuracy and Balanced Accuracy Accuracy (ACC) is a metric used to evaluate classification models. Accuracy is defined as proportion of number of true recommendations to total number of recommendations. Mathematically, accuracy is computed by formula ACC =

τ p + τn τ p + τn + f p + f n

(6)

where τ p is true positive, means an interested item and is recommended by user, τn is true negative, means an un-interested item and is not recommended by user, f n is false negative, means an interested item but not recommended by the user, f p is false positive, means an un-interested item but recommended by the user. ACC may not yield better results when datasets are imbalanced. In such situation, balanced accuracy (BAC) is used. BAC measures the overall performance of the system using the formula BAC =

TR P + TR N 2

(7)

where ‘TRp ’ is True Positive Rate and ‘TRn ’ is True Negate Rate and are defined mathematically as TR P =

τp τn and TR N = . τ p + fn τn + f p

(8)

Balanced Accuracy of Collaborative Recommender System

Following algorithm is developed and implemented for the work.

347

348

A. B. Chopra and V. S. Dixit

4 Dataset and Experiment To conduct the experiment, a dataset, namely Book-crossings (BX) is taken [33]. The dataset holds 270,000 books and 90,000 users. There are 1.1 million ratings for the available books in the BX dataset. The dataset has explicit and implicit ratings. The explicit ratings are given on a scale of ‘1–10’, where rating ‘1’ is the lowest rating and rating ‘10’ is the highest rating. Implicit ratings are expressed by ‘0’. There are three tables in BX dataset, namely BX-Users, BX-Books and BX-Book-Ratings that store the Users, ISBN values of Books and Book Rating information, respectively.

4.1 Evaluation Results The experiment is performed in two steps. In first step, actual book ratings are considered. Then, Push Attacks (P-Attacks) are randomly injected to the dataset. The effect of P-Attacks is observed at various proportions and performances of CRS after injecting the attacks is compared with the performance of original CRS. Performance is evaluated using measures: Precision, recall, F-measure, accuracy and balanced accuracy. Similarly, Nuke Attacks (N-Attacks) are arbitrarily injected at different proportions in the dataset and performance pattern of CRSs is observed. The algorithms for P-N Attacks are repeatedly applied to different proportions of datasets and results are evaluated. It is imperative to observe that in our experiment, users’ numbers are not significant. The main emphasis is on book ratings only. The average (medium) book rating is taken as ‘5’ while rating ‘10’ (highest) and rating ‘1’ (lowest) is considered as malicious. In the experiment, the mean accumulated ratings (μ A ) are calculated by summing up the ratings for same ISBN in original dataset. Next, P-N Attacks, at different proportions are arbitrarily injected with ratings ‘10’ and ‘1’, respectively. Table 1 shows evaluative results for mean absolute error. Corresponding to results, Fig. 1 shows the pattern of deviation of ratings after being attacked from actual rating value. Various performance measures are evaluated at a threshold value n. The dataset is injected with P-N Attacks to generate biased recommendations. Injection of P-N Attacks also made the dataset unbalanced. So to analyse the performance of CRS, Table 1 Results for actual book ratings and μ E for injected P-N Attacks Attack proportion

Actual 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0%

μ A (P-Attacks) 4.77

4.72

4.73

4.74

4.75

4.75

4.75

4.78

4.79

4.80

4.81

μ E (P-Attacks) –

0.05

0.04

0.03

0.02

0.02

0.02

0.01

0.02

0.03

0.04

μ A (N-Attacks) 4.77

4.70

4.71

4.72

4.73

4.73

4.69

4.68

4.68

4.68

4.67

μ E (N-Attacks) –

0.07

0.06

0.05

0.04

0.04

0.08

0.09

0.09

0.09

0.10

Balanced Accuracy of Collaborative Recommender System Fig. 1 Mean absolute error pattern for P-N Attacks

349

Mean Absolute Error wrt P-NAttacks 0.15 0.1 E 0.05 0

Push AƩacks Nuke AƩacks

Attack Proportions

Table 2 Results of performance after injecting P-Attacks Measures

P-Attacks proportions 0.1–0.6%

0.7%

0.8%

0.9%

1.0%

P@n

0.25

0.50

0.50

0.50

0.50

R@n

0.25

0.14

0.13

0.11

0.10

F@n

0.25

0.22

0.20

0.18

0.17

ACC@n

0.45

0.36

0.27

0.18

0.09

BAC

0.41

0.45

0.40

0.31

0.05

Table 3 Results of performance after injecting N-Attacks Measures

N-Attacks proportions 0.1–0.6%

0.7%

0.8%

0.9%

1.0%

P@n

0.50

0.50

0.50

0.50

0.50

R@n

0.20

0.17

0.14

0.13

0.10

F@n

0.29

0.25

0.22

0.20

0.17

ACC@n

0.55

0.45

0.36

0.27

0.09

BAC

0.52

0.48

0.45

0.40

0.05

balanced accuracy (BAC) is calculated. Tables 2 and 3, show the results for the performance of CRS when P-N Attacks are present.

4.2 Validation The purpose of validation is to ensure the quality of experiment performed for given dataset and data-subsets. To confirm the correctness of results, the experiment is repeated for different proportions—1/2th, 1/4th, 1/40th and 1/64th rating values of dataset. At different proportions of dataset, mean absolute error (E ) is calculated for P-N Attacks. The original dataset is divided into data-subsets. This approach is helpful for comparative study of results. The evaluative results for actual book ratings and E for injected P-Attacks at various proportions are shown in Table 4. To validate

1/64th

1/40th

–

4.42

–

MAR(N-Attacks)

MAE(N-Attacks)

–

MAE(N-Attacks)

MAE(P-Attacks)

4.49

MAR(N-Attacks)

4.49

–

MAR(P-Attacks)

4.92

–

MAE(N-Attacks)

MAE(P-Attacks)

4.84

MAR(P-Attacks)

–

MAR(N-Attacks)

–

MAE(N-Attacks)

MAE(P-Attacks)

4.97

MAR(N-Attacks)

4.84

–

MAE(P-Attacks)

MAR(P-Attacks)

4.99

MAR(P-Attacks)

1/2th

1/4th

Actual

Attack proportions

0.00

4.42

0.01

4.50

0.00

4.49

0.02

4.94

0.00

4.85

0.01

4.86

0.00

4.97

0.02

5.00

0.1%

0.01

4.41

0.02

4.51

0.00

4.49

0.02

4.94

0.00

4.85

0.03

4.87

0.00

4.97

0.03

5.02

0.2%

Table 4 Results for actual book ratings and E for injected P-N Attacks

0.00

4.42

0.04

4.52

0.00

4.49

0.04

4.96

0.00

4.85

0.04

4.89

0.00

4.97

0.05

5.04

0.3%

0.01

4.41

0.04

4.52

0.01

4.50

0.06

4.98

0.01

4.85

0.06

4.90

0.01

4.97

0.06

5.05

0.4%

0.01

4.43

0.05

4.54

0.01

4.50

0.06

4.98

0.01

4.85

0.07

4.92

0.01

4.97

0.08

5.07

0.5%

0.01

4.43

0.06

4.55

0.01

4.50

0.08

5.00

0.01

4.85

0.09

4.93

0.01

4.98

0.09

5.08

0.6%

0.02

4.44

0.07

4.56

0.01

4.50

0.10

5.02

0.02

4.86

0.10

4.95

0.01

4.98

0.11

5.10

0.7%

0.01

4.43

0.08

4.57

0.01

4.50

0.11

5.03

0.02

4.86

0.12

4.96

0.01

4.98

0.12

5.11

0.8%

0.02

4.44

0.10

4.58

0.01

4.50

0.13

5.05

0.02

4.86

0.13

4.98

0.01

4.98

0.14

5.13

0.9%

0.01

4.43

0.12

4.61

0.01

4.50

0.16

5.08

0.02

4.86

0.15

4.99

0.02

4.98

0.16

5.14

1%

350 A. B. Chopra and V. S. Dixit

Balanced Accuracy of Collaborative Recommender System

351

Table 5 Results of performance after injecting P-Attacks Datasets 1/2th

1/4th

1/40th

1/64th

Measures

P-Attacks proportions 0.1–0.6%

0.7%

0.8%

0.9%

1.0%

P@n

0.50

0.50

0.50

0.50

0.50

R@n

0.25

0.14

0.33

0.50

1.00

F@n

0.33

0.22

0.40

0.50

0.67

ACC@n

0.64

0.36

0.73

0.82

0.91

BAC

0.55

0.45

0.60

0.69

0.95

P@n

0.50

0.50

0.50

0.50

0.50

R@n

0.25

0.14

0.33

0.50

1.00

F@n

0.33

0.22

0.40

0.50

0.67

ACC@n

0.64

0.36

0.73

0.82

0.91

BAC

0.55

0.45

0.60

0.69

0.95

P@n

0.50

0.50

0.50

0.50

0.50

R@n

0.25

0.14

0.33

0.50

1.00

F@n

0.40

0.22

0.40

0.50

0.67

ACC@n

0.73

0.36

0.73

0.82

0.91

BAC

0.60

0.45

0.60

0.69

0.95

P@n

0.33

0.50

0.50

0.50

0.50

R@n

0.33

0.14

0.33

0.50

1.00

F@n

0.33

0.22

0.40

0.50

0.67

ACC@n

0.64

0.36

0.73

0.82

0.91

BAC

0.54

0.45

0.6

0.69

0.95

performance and accuracy of these datasets, evaluations are presented in Tables 5 and 6, respectively. Table 5 shows the evaluation results of performance after insertion of P-Attacks at different proportions and results for N-Attacks are shown in Table 6. The results in Tables 5 and 6 are calculated by using various measures—Precision, recall, accuracy and balanced accuracy, at threshold value n. The results in Table 4 show that the balanced accuracy (BAC) of the CRS may vary from 50 to 90% approximately, depending upon the size of dataset and size of injected attacks. The experiment also validates that performance and accuracy of any recommender system have direct relation with the size of attack (P-Attacks or N-Attacks) injected to it. The more injected P-N Attacks lead to more biasness in the recommendations predicted for an item. Pictorially, the interpretation for Table 4 is depicted through Fig. 2 for P-Attacks and through Fig. 3 for N-Attacks, respectively. The figures show variations in performances of CRS at different proportions due to push and nuke attacks injected. The results for various performance measures at different proportions of dataset at a given threshold value n, when P-N Attacks are injected are given in Tables 5 and 6, respectively. The datasets at 0.1–0.6% are considered as small sampled and

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A. B. Chopra and V. S. Dixit

Table 6 Results of performance after injecting N-Attacks Datasets 1/2th

1/4th

1/40th

1/64th

Measures

N-Attacks proportions 0.1–0.6%

0.7%

0.8%

0.9%

1.0%

P@n

0.83

0.83

0.83

0.83

0.83

R@n

1.00

0.50

0.50

0.50

0.50

F@n

0.91

0.63

0.63

0.63

0.63

ACC@n

0.91

0.45

0.45

0.45

0.45

BAC

0.92

0.25

0.25

0.25

0.25

P@n

0.86

0.80

0.80

0.80

0.80

R@n

0.40

0.40

0.40

0.40

0.40

F@n

0.53

0.53

0.53

0.53

0.53

ACC@n

0.36

0.36

0.36

0.36

0.36

BAC

0.20

0.20

0.20

0.20

0.20

P@n

0.75

0.88

0.88

0.88

0.88

R@n

1.00

0.70

0.70

0.70

0.70

F@n

0.86

0.78

0.78

0.78

0.78

ACC@n

0.91

0.64

0.64

0.64

0.64

BAC

0.94

0.35

0.35

0.35

0.35

P@n

0.83

0.83

0.83

0.83

0.83

R@n

1.00

0.50

0.50

0.50

0.50

F@n

0.91

0.63

0.63

0.63

0.63

ACC@n

0.91

0.45

0.45

0.45

0.45

BAC

0.92

0.25

0.25

0.25

0.25

Mean Absolute Error for P-Attacks

Fig. 2 Mean absolute error pattern for P-Attacks

0.2

1/2th Dataset

0.15 E

1/4th Dataset

0.1 1/40th Dataset

0.05

1/64th Dataset

0

Attack Proportions

collective evaluations are performed. Greater than 0.6% proportions are considered as large samples and evaluations are performed separately for each dataset proportion. Corresponding to Table 5, Fig. 4 depicts the pattern of performance of CRS for injected P-Attacks at different proportions of datasets. The figure shows the variation in performance of CRS using various mathematical measures at different proportions

Balanced Accuracy of Collaborative Recommender System

353

Mean Absolute Error for N-Attacks

Fig. 3 Mean absolute error pattern for N-Attacks

0.025 0.02 0.015 E 0.01 0.005 0

1/2th Dataset 1/4th Dataset 1/40th Dataset 1/64th Dataset

Attack Proportions

Performance Analysis for P-Attacks at different dataset proportions

0.1% - 0.6% 0.70%

Datasets

1/2th

1/40th

0.90%

ACC@n

R@n

F@n

1/4th

bACC

P@n

ACC@n

R@n

P@n

F@n

bACC

0.80% Performance

1.2 1 0.8 Attack 0.6 Proportions 0.4 0.2 0

1.00%

1/64th

Fig. 4 Performance analysis of CRS injected with P-Attacks

of datasets with different sizes of P-Attacks injected to these datasets. Similarly, corresponding to Table 6, pattern of performance of CRS for injected N-Attacks at different proportions of datasets are shown in Fig. 5. The figure shows the variation in performance of CRS using various mathematical measures at different proportions of datasets with different sizes of N-Attacks injected to these datasets. Performance Analysis for N-Attacks at different dataset proportions 1.2 1 0.8 Attack 0.6 Proportions 0.4 0.2 0

0.1% - 0.6%

1/2th

1/4th

1/40th

Fig. 5 Performance analysis of CRS injected with N-Attacks

bACC

F@n

ACC@n

R@n

P@n

bACC

ACC@n

F@n

P@n

R@n

bACC

ACC@n

F@n

R@n

P@n

bACC

F@n

ACC@n

P@n

Datasets

R@n

Performance

0.70%

1/64th

0.80% 0.90% 1.00%

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5 Conclusion A recommender system requires being efficient and accurate so as to generate correct recommendations of items for users. In our research, a CRS is constructed by collecting user-item ratings a matrix model is constructed. P-N Attacks are then injected to the original dataset and various mathematical measure such as—Mean absolute error, precision, recall, accuracy at threshold value n are evaluated to analyse the performance of CRS. The results show, the P-Attacks escalates the accuracy of CRS where N-Attacks degrades CRS’s accuracy. It is to note that, when P-N Attacks are injected to the original dataset, the dataset became biased. Balanced accuracy is, therefore, used to evaluate the effect of performance on RSs. The study also shows the effect of performance on CRS at various attack proportions. While conducting the experiment, it is concluded that at least 1/64th of dataset values are required to reach at results. This means that the experiment may not yield good results on very small datasets. In this paper, only P-N Attacks from non-authentic users are studied and no acquaintance about background of users is recognized. Our future work will address this critical issue along with contextual information given by users while rating and will try to identify and classify P-N Attacks injected to dataset available on Web. Our study will also try to classify P-N Attacks in group recommender system by enhancing multi-expert system [34].

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33. Dataset: http://www2.informatik.uni-freiburg.de/~cziegler/BX/ 34. R. Meena, S. Minz, Group recommender systems—an evolutionary approach based on multiexpert system for consensus. J. Intell. Syst. 1 (2018). (ahead-of-print)

Using Transfer Learning for Detecting Drug Mentions in Tweets Laiba Mehnaz and Rajni Jindal

Abstract Drug mention detection in tweets is defined as a binary classification task that aims to distinguish tweets that mention a drug from those which do not. In our work, we aim to use transfer learning for automatic classification of tweets mentioning a drug. To our knowledge, we are the first to use transfer learning in drug detection using the UPennHLP Twitter Pregnancy Corpus. We find this corpus challenging as the positive class(i.e., mentioning a drug) is only 0.2% of the total number of tweets, leading to a highly imbalanced dataset. We carry out several experiments using pre-trained language models such as BERT, BioBERT, ClinicalBERT, SciBERT, BioMed-RoBERTa, ELECTRA, and ERNIE 2.0. We further analyze the performance of each of the pre-trained models based on the training data distribution, training data size, and different pre-training objectives. According to our results, BioMed-RoBERTa achieves the highest F1 score of 0.85 for the positive class. Keywords Social media · Transfer learning · Drug name detection · Pharmacovigilance

1 Introduction Recent research [1] reveals that Twitter is being increasingly used by adults to post tweets regarding their personal and current medical conditions. The report also states that adults using twitter also get influenced by online trends or discussions related to medication and health. Hence, Twitter acts as an excellent resource for researchers to use it for various medical applications. Detecting drug or medication mentions from tweets helps in multiple tasks and fields such as pharmacovigilance, syndromic surveillance, and monitoring drug abuse. Pharmacovigilance [2] activiL. Mehnaz (B) · R. Jindal Delhi Technological University, New Delhi 110042, India e-mail: [email protected] R. Jindal e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_33

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ties range from detecting, assessing, understanding, and preventing adverse effects of any drug-related problem. Adverse drug reactions produce devastatingly undesirable effects on the body after the drug intake. In 1999, for the treatment of type 2 diabetes mellitus, a drug called Rosiglitazone [3] was introduced, which was later withdrawn due to concerns of a potential risk of increased fatal myocardial infarction, also known as a heart attack. And since tweets pose a big challenge of containing misspellings and informal words for clinical terms, building robust systems that can detect drug mentions from tweets can further help in detecting adverse drug reaction from tweets as well. We define the task of drug detection as a binary classification task, distinguishing tweets that mention a drug from those that do not. The definition of a drug is as per the definition provided by the FDA. The dataset that we use for drug detection is called the UPennHLP Twitter Pregnancy Corpus [4]. It is a recently released dataset for drug detection, released during the shared task of the Fifth Social Media Mining for Health Applications 2020. We are specifically interested in this corpus as it reflects the naturally occurring imbalance in tweets between both the classes. For example, “Shoulda took some more NyQuil.” represents a tweet mentioning a drug, and “I see all these dads with their kids and it makes me sad for my baby :(” represents a tweet that does not mention a drug. The only work done on this corpus, to our knowledge is done by [4], where they use Kusuri [4], a model which is an ensemble of deep neural networks. In our approach, we use transfer learning by using pre-trained language models and finetuning them on our dataset. We analyze the performance of these pre-trained models on a dataset that is highly imbalanced and also is in the medical domain. We analyze the effect of the size as well as the domain of the dataset used while pre-training these models. We also analyze the effect of different pre-training objectives. We carry out experiments with BERT [5], BioBERT [6], ClinicalBERT [7, 8], SciBERT [9], BioMed-RoBERTa [10], ELECTRA [11] and ERNIE 2.0 [12].

2 Dataset To create the dataset, [4] collected 112 timelines of women on twitter that had declared their pregnancy and tweeted throughout their pregnancy. The authors manually annotated the tweets mentioning a drug and tweets that did not. This dataset is highly imbalanced between the two classes, with only 0.2% of the tweets mentioning a drug name. We particularly chose this dataset as we wanted to analyze the performance of transfer learning on the class imbalance. As the task is still going on and the test set has not been released, we downloaded the training dataset and the validation dataset as available. We use the validation dataset as our test set, as we have the ground truth labels, and we can evaluate the performance of our pre-trained models. The original training dataset provided to us from the organizers of the SMM4H 2020 Task 1 contains 55419 tweets. Out of the total number of tweets, only 146 tweets mention a drug, and the rest 55,273 tweets do not mention any. Due to this class imbalance in the dataset, we oversampled the positive tweets(i.e., mention a

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Table 1 Breakdown of tweets in UPennHLP Twitter pregnancy corpus, after the oversampling and train-dev split on the training dataset Dataset Mentioning a drug Not mentioning a drug Training Development Test

49,803 5470 35

49,688 5585 13,818

drug) by simply copying them. After oversampling, our training dataset consisted of 110,546 tweets in total. We randomly split 90% of the dataset into the training set and the rest 10% as the development set. We use the validation dataset provided by the organizers as the test set. The test set consists of a total of 13,853 tweets, of which only 35 belong to the positive class(i.e., mentioning a drug), and the rest 13,818 do not. Table 1 shows the breakdown of the tweets in all the datasets.

3 Method Transfer learning is an approach in which large neural networks are pre-trained on data-rich tasks and then are fine-tuned on various downstream tasks. Transfer learning is supposed to “transfer” the common and general-purpose abilities to these downstream tasks. This approach has been responsible for achieving state-of-the-art in most of the NLP benchmarks [5]. Transfer learning has not been used on our dataset as of now, and we wish to explore the performance of the same. We use the following list of pre-trained language models: BERT, BioBERT, ClinicalBERT, SciBERT, BioMed-Roberta, ERNIE 2.0, and ELECTRA.

4 Experiments In this section, we describe the various pre-trained models used during our experiment. BERT is a pre-trained language model used for various downstream tasks in NLP after fine-tuning it on the specific task. It uses the Transformers [13] architecture. We use the BERT-base uncased model, implemented in PyTorch [14] via the Transformers library [15] using the same hyperparameters as the original model [5]. We use BioBERT, ClinicalBERT, SciBERT, and BioMed-RoBERTa through the Transformers library. BioBERT is a domain-specific language model that further trains BERT with a corpus that is specific to the medical domain. We use BioBERTbase v1.0 which is trained on Wikipedia, BooksCorpus [16], PubMed abstracts and PMC full-text articles. ClinicalBERT is also a domain-specific pre-trained language

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model, which further trains BioBERT on the MIMIC-III V1.4 [17] dataset, which is a collection of approximately 2 million clinical notes. SciBERT also uses the same architecture as used by BERT. It trains BERT on scientific publications in various domains. The training data consists of a random sample of 1.14M papers from Semantic Scholar [18]. Reference [9] have released two kinds of models. One, where they train BERT further on the scientific publications corpus and one, where they train BERT from scratch on the scientific publications corpus with its separate vocabulary, called scivocab. We use the second model, SciBERT-scivocab-uncased, which is trained from scratch. BioMed-Roberta trains RoBERTa [19] further on 2.68M full-text papers from S2ORC [20]. ELECTRA is a pre-trained language model that aims to make the pre-training objective of BERT computationally more efficient. In BERT, only 15% of the input tokens are masked. And hence, BERT ends up learning only from 15% of the input tokens by predicting the word for that particular index. Whereas, given the same input tokens, ELECTRA learns from every token, by distinguishing original tokens from synthetically generated tokens. It is trained as a discriminator as compared to BERT, which is trained as a generator. We use the implementation of ELECTRA provided by the authors [11]. We have used two models, ELECTRA-small and ELECTRA-base to understand the impact of the model size and the number of parameters on the performance on our task. ERNIE 2.0 is another pre-trained model which essentially differs from the rest of the models used so far. It aims to capture lexical, syntactic, and semantic information as well from the training data, instead of just capturing knowledge about the co-occurrence of words and sentences. It uses continual pre-training, where multiple pre-training tasks are incrementally constructed, and the pre-trained model learns via continual multi-task learning. We use ENRIE 2.0’s implementation provided by the authors [12]. For all the models mentioned above, we have used the hyperparameters used in their respective original papers.

5 Results and Discussion We have chosen to use the F1 score for the positive class (i.e., tweets that mention medications) to evaluate the performance of the classifier. The results on the UPennHLP Twitter Pregnancy Corpus are given in Table 2. As can be seen, BioMedRoberta performs the best, and ELECTRA-small performs the worst. BioMedRoBERTa’s success can be attributed to its improved training strategies over BERT itself. RoBERTa improves on several pre-training strategies, from training the model for a longer time over additional data to removing the next sentence prediction pre-training objective and dynamically changing the masking pattern applied to the training data. As well as, further pre-training RoBERTa on domain-specific data, which in this case is a corpus of scientific papers. Another interesting point to note here is that the scientific papers used to pre-train the model consist of 18% of papers from the computer science domain and 82% from the broad medical domain. It shows the benefit of training on a dataset that is of a similar domain if the source data and

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Table 2 Results: F1 score for the “positive class” (i.e., mentioning a drug) Pre-trained model F1 score for the positive class BERT BioBERT ClinicalBERT SciBERT BioMed RoBERTa ERNIE 2.0 ELECTRA small ELECTRA base

0.78 0.8 0.8 0.83 0.85 0.83 0.73 0.78

the target data happen to be from different domains and do not happen to share the vocabulary. On the other hand, ELECTRA-small has the worst performance of all, which we speculate to be because of the difference in the domain of the source data and the task data. We also notice that ELECTRA-small contains much lesser parameters compared to the BERT-base and all the other pre-trained models in our list, and decreasing the number of parameters has hurt the performance drastically. This list of experiments is not at all comprehensive, but within these experiments as well, we can see that large compute models do translate to better performance. We see BERT-base and ELECTRA-base, both achieve the exact score, not benefiting from ELECTRA’s replaced token detection pre-training objective. We again speculate that this could be because our dataset is in the medical domain and maybe this could be the reason that ELECTRA-base did not benefit from its replaced token detection pre-training objective. Maybe if compared on a target domain that is very similar to the training data used while pre-training both BERT and ELECTRA, then can we hope to see a difference in the performance due to the difference in choice of the pre-training objective. We see a consistent increase in the F1 score of the pre-trained models as soon as we train them further on a similar domain as the fine-tuning task. This proves a very strong point that immediately applying the knowledge learned from a large general corpus does not benefit as much as, first updating the weights for the specific taskrelated domain and then fine-tuning for our task. It also raises an interesting question of whether we can have universal pre-trained models that can perform competitively on a variety of domains. BioBERT, ClinicalBERT, SciBERT, and BioMed-RoBERTa, all consistently improve their performance over BERT, while sharing the same architecture and size. SciBERT has a higher F1 score than both BioBERT and ClinicalBERT. This increase could be because of two reasons. According to the increase in performance, it looks like the multi-domain scientific corpus suits the need of our fine-tuning task better than PMC full-text articles, PubMed abstracts, and MIMIC-III dataset, on which BioBERT and ClinicalBERT are trained on respectively. It could also be because SciBERT-scivocab-uncased has been trained from scratch and has a different vocabulary from that of BERT’s, built from the Semantic Scholar corpus.

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SciBERT’s vocabulary has the same size as that of BERT-base’s vocabulary and is considerably disjoint. The overlap between both the vocabularies is just 42% showing the difference in the distribution of words in the scientific domain and the general domain. And also, that the distribution of words in the training corpus in SciBERT is more similar to our task data than compared to. The most interesting result is that of ERNIE 2.0. This model scores the same result as SciBERT without being trained on data which is even close to the domain of scientific papers. Without being trained on any medical data, it still performs better than BERT in understanding our task data, which highlights the fact that ERNIE 2.0 is truly capturing other aspects of language such as lexical, syntactic, and semantic information and not just co-occurrence of words and sentences. This makes it easy for ERNIE 2.0 to classify drug mentioning tweets even when there is a class imbalance present in the dataset. ERNIE 2.0 learns representations from a variety of tasks and is truly able to capture these aspects of information simultaneously, effectively, and efficiently without forgetting the previously learned knowledge by leveraging continual multi-task learning. ERNIE 2.0’s additional data collected from Reddit and the usage of Discovery data [21] while pre-training, gives it an almost equal boost in performance as compared to training BERT on a similar domain before fine-tuning on the particular task. Providing us with information, that to increase the performance and understanding of a model toward a particular domain, pre-training with huge unlabeled data in the similar domain is not the only solution. This pretrained model gives us a ray of hope on having a universal pre-trained model that works competitively on various domains and has a general “understanding”.

6 Conclusion In this paper, we have analyzed the performance of several pre-trained language models on the task of detecting drug mentions in tweets, the dataset for which presents a massive class imbalance and ambiguity. Our work is the first work that uses transfer learning on the UPennHLP Twitter Pregnancy Corpus for detecting drug mentions in tweets, showing that transfer learning works well even when there is a huge class imbalance present in the dataset. It also works well with the informal structure of tweets. Through our analysis, we show that BioMed- RoBERTa performs the best out of all the models we have used. We illustrate the power of contextualized embeddings, further training on domain-specific unlabeled data, and the effect of different pre-training tasks in these models. We also find that reducing the size of the model and the parameters drastically reduce the performance as well. As future work, we aim to explore more pre-trained language models that use various pre-training objectives, and also look into incorporating information from knowledge graphs for drug detection as well.

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A Comprehensive and Critical Analysis of Cross-Domain Federated Identity Management Deployments Tejaswini Apte and Jatinderkumar R. Saini

Abstract Sharing the identity in cross-domain application deployment requires a controlled environment to ensure privacy of the user or request. The paper presents various protocols for Federated Identity Management, available in literature for identity sharing in cross-domain application deployment. Also, the implementation and limitations of each protocol is discussed. Considering cloud as an application of crossdomain deployment, the study covers the entity communication channel along with identity visibility in cloud. During the deployment, the communication channel for identity sharing can be designed with multiple protocols. The challenge is to protect privacy of identity while maintaining the compatibility among multiple protocols. Keywords Federated Identity Management (FIM) · SAML · OAuth · OpenID connect · Service Provider (SP) · Identity Provider (IdP) · OpenStack

1 Introduction In cross-domain application deployment where identities are getting shared with required attributes among various applications; assembling and disassembling the critical information for authentication and authorization in controlled manner is a need of the hour. Transmission of the sensitive identity information over the insecure network has proven itself a challenging risk. Literature has discussed the concept of Centralized and Decentralized Identity Management to control the exposure of identity among various applications [1, 2]. Identity Management (IM) provider service maintains the identities sharing mechanism across various service domains and distributed environments. Authentication T. Apte (B) · J. R. Saini Symbiosis Institute of Computer Studies and Research, Symbiosis International Deemed University, Pune, India e-mail: [email protected] J. R. Saini e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_34

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engine of IM provider verifies the identity and then provides respective authorization for accessing the various services. Policy driven access mechanisms are used to control the access to a given resource [3]. However, sharing the identity attributes for cross-domain application deployment poses the risk of privacy. Federated Identity Management (FIM) minimizes the risk by controlled attribute sharing. It uses the required policies to share the relevant attributes across the network channels. Thus, with same attribute sharing user can access secured information from various service domains. These domains confirm the users’ identities by trusted policies to confirm the authorization scope. Hence, the need of sharing of various attributes to access different applications over the network is drastically reduced; resulted in improved usability and security. Some of the challenges related to processes, technology and privacy in FIM are mentioned in the literature. Further, the “trust framework” design with the required approaches is proposed to mitigate the risk while sharing the identities across cross-domain application deployment [4–6]. Various policies and protocols are available in the literature to share the identities in cross-domain deployment. Section 2 briefly outlines the same. Section 3 exemplifies the comparative assessment and entities (User, Service Provider, Identity Provider, EndPoint) communication required for various FIM protocols such as the SAML, OAuth and OpenID and its implementation in cloud. Further the conclusion is presented in Sect. 4.

2 Literature Review Literature reveals, various Identity and Access Management (IAM) policies are implemented across cross-domain deployment of an application. Role Based Access Control (RBAC) policy is a most acceptable service solution to provide security feature in cross-domain infrastructure. By using specific role hierarchies and constraints the respective security policies can be expressed in a wider range based on the function assigned to the user. These RBAC policies promote the authorization of various users using Separation of Duties. The RBAC policy model defines basic elements such as users, roles, permissions, operations, objects and their relation type. The objective of RBAC policy model is to strictly define the boundary of each role and their hierarchy with respect to CRUD operations. The assignment from the user and permissions to respective roles can be from one to one or many to many [7]. Xenserver provides six predefined roles; in which the permissions can be changed based on requirements. However, no new roles can be added or removed; which results in limited scalability in a distributed architecture environment [8]. Literature also reveals the extension of authorization model which can be extended to have multiple entities according to the tenancy, for example single tenancy covers a tuple of (Subject, Privilege and Object); which can be extended to multi-tenancy by creating a tuple of (Issuer, Subject, Privilege, Object). Further, it can also be extended to support multiple objects. However, this model is not suitable for the requirement of orchestrating the infrastructure; as the dynamic extension by model affects the

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performance negatively [9]. The distribution of role based access control over various Internet technologies is also introduced to overcome the limitation of dynamic extension, but this approach does not cover support for various policy models of different components in cross-domain application deployment [10]. To provide the security over distributed infrastructure, the protection mechanism for securely transferring the attribute is proposed in CloudSeal [11]. It uses various symmetric key algorithms for efficient attribute transformation including the revocation broadcast with the limitation of group membership control for revocation library. The key policy along with attributes based encryption provides more scalability and accountability to the user with respect to access privileges for revocation [12]. On the other hand, the latency of an attribute transfer request is a major concern for privacy. The centralized access control policies are also discussed to implement access control to minimize latency [13]. AcaaS is based on providing the “Access Control as a Service.” It delegates the responsibility of access and policy management to Service Provider (SP); these policies are then converted to enforceable policies to individual cloud provider. The enforcement of policies and its level is determined by the user. Further, on behalf of the user; SP accepts these policies and enforcement for Identity Provider (IdP) to verify user credentials. However, it doesn’t reflect flexible delegation mechanism and revocation requirements [14]. To support flexible delegation mechanism, Federated Identity Management (FIM) provides access to cross-domain application deployment by Single Sign On (SSO). It shares the attributes of identity with the service being accessed after a successful login. Attributes are shared securely and do not contain any private or sensitive information. It is architected by two entity roles viz Service Provider (SP) and Identity Provider (IdP). Service Provider transmits authentication request from user to IdP. Identity Provider shoulders validation of user credentials [15]. The strong collaboration and trust is required by FIM to validate credentials across organizational boundaries. These boundaries encapsulate the trust and relationship among multiple IdPs. In order to have seamless transactions there exist number of FIM standard protocols viz. Security Assertion Markup Language (SAML), Open Authentication (OAuth), and OpenID Connect (OIDC). The communication in SAML is architected by XML to exchange authentication and authorization data across organizational boundaries. In SAML, the IdP authenticates users and generates the SAML assertion, which is then transmitted to browser along with the authorization. This SAML assertion later is presented to the organizational boundary to support identity sharing across cross-domain deployment. However, it is not optimized for the large data transactions via multiple API calls [16]. Also, SAML centrify signature which are not signed by Certification Authority are prone to cloning [17]. OAuth defines a protocol to enable user to grant authentication to the respective services [Relying Parties (RP)] to access other authorized services [Identity Provider (IdP)] with a required access token. The token contains the authorization scope and timeline to access the required resource for the user’s accounts. In OAuth architecture, the access tokens are prone to leak from RP through direct communication with IdP. OAuth enables the ease for sharing the data by providing the transparent environment

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along with a decision about validity of token to the user. Due to the said ease, the phishing attacks are more common in authentication via OAuth [18, 19]. OpenID connect is lightweight protocol as compared to SAML and OAuth. The objective of OpenID connect is to provide a defined interface for user authentication along with RP registration, signing, encryption of messages, and logout [20, 21]. Though, Federated Identity Management (FIM) is in use by many cross-domain services; but it is greatly limited by users’ ability to interact with multiple IdPs per session. In addition, federated identity has introduced many security and privacy requirements as it promotes the sharing of credentials over loosely coupled networks. FIM governs the Single Sign On (SSO) across cross-domain network. SSO regulates sharing of identity information as per the domain requirement. However, sharing identity information across the domains is a greater challenge to manage privacy. Attribute aggregation in FIM implemented by David W. Chadwick and George Inman, provides the ease to link all IdP accounts. The accessibility of the respective service can use such accounts to automatically authorize the required service [22]. In this view, we have explored cross-domain application, i.e., cloud (OpenStack) to investigate the implementation of FIM protocols. In OpenStack, every component is authenticated and authorized by keystone (Identity Service). Keystone promotes role based access policies. These policies are then used to generate token for authorization. The authorization information contains user credentials and respective project information. The authentication information is transmitted via SAML or OpenID connect protocols to support FIM in OpenStack [23]. Federated Identity Management (FIM) ease the user to access the resources for cross-domain deployment; with no additional logins. The said ease also introduces the risk of unauthorized access to every resource. In order to mitigate the risk the FIM protocols are explored to share only required attributes for cross-domain deployment. However, during the transmission of credentials the privacy preservation among multiple protocols is a significant limitation. The said protocols are required to be compatible and interoperable in the crossdomain communication for identity attribute sharing with no privacy conflicts. There exists a need to architect a protocol; which can preserve privacy across cross-domain deployment by providing the compatible layer for existing protocols. To maintain the privacy, the entity information transmission across the domain is a significant challenge; the subsequent Sect. 3 presents the entity communication in FIM protocols SAML, OAuth and OpenID connect and their adoption in OpenStack cloud deployment.

3 Methodology Entity communication in FIM protocols has major impact on privacy of identity across the domains. We have investigated various research papers available in literature to examine and evaluate entity communication pathway for identity sharing. The subsequent paragraph explains the same.

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Various entities or roles are responsible to build communication channel in SAML, OAuth and OpenID connect, which in turn drives privacy of identity attribute sharing. SAML promotes the communication from user to Service Provider (SP) to access required resource; the said request also discovers the Identity Provider (IdP). Service Provider then diverts the user request to Single Sign On (SSO) process for validation of user identity with IdP. Once the user identity is validated, an assertion is generated to access the targeted resource by the user. In addition, the Service Provider is then responsible for granting the resource access to the user; this grant exists till it is explicitly removed. OAuth enables users to share resources among multiple SP, which includes the IdP as well. As a result, OAuth is not suitable to implement Federate Identity Management (FIM). In order to support FIM, OpenID is developed to support the layer of authentication to OAuth. User requests are forwarded to OpenID endpoint for authentication to OpenStack by successfully validating the user identity with IdP. Identity Provider then redirects OpenID auth response to the user to access required resource [3, 18, 23, 24]. During the communication with the endpoints, user authentication data is exposed to various vulnerabilities introduced by communication channel. Table 1, briefly assesses the privacy preservation in these protocols along with the implementation in OpenStack deployment. As depicted in Table 1, to support Federated Identity Management, the protocols SAML, OAuth and OpenID connect implements RBAC policies with XML and JWT. Also, multiple entities are coordinating with each other to share identity details. In order to address privacy preservation in cross-domain applications deployment, these protocols must process the identity information with maximum compatibility while honoring the privacy of said protocols. These protocols are inherited in OpenStack deployment as external plug-in to support FIM.

4 Conclusion We have assessed the concept of identity management and the respective policies and protocols to implement cross-domain applications. Firstly, it describes available Identity Management policies including Federated Identity Management and its protocols. Then, the respective protocols are assessed and compared with respect to privacy and implementation in OpenStack. During our assessment we have discovered that SAML shares the identity attributes with no consent from user and user authentication remains valid even if explicitly removed in some cases. Hence, it opens up larger threat for sharing the identity attributes in cross-domain deployment; whereas in OAuth and OpenID user identities are visible to third parties after required user consent and also revoked immediately after removing the access to the user for specified resources. Further, in cross-domain deployment these protocols are working together; where identity leaks are frequently recurring. In this view, there is a necessity to devise a protocol to implement compatibility with no privacy conflicts in cross-domain deployment.

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Table 1 Assessment of SAML, OAuth, OpenID connect to deploy Cross Domain Applications S. No.

Reference

Parameter

SAML

OAuth

OpenID Connect

1

Maler and Reed [24] Naik and Jenkins [21] Kyungoh and Hunjin [20]

RBAC Token Generation format and Parameters

XML, (User Identity encoded in token)

XML, JWT, (Authorization (client_id, client_secret encoded in token)

JWT, (User Identity is encoded in token)

2

Naik and Entities Jenkins [21] Sun and Beznosov [18]

Identity Provider, Service Provider, User

Relying Party, Identity Provider, User

Identity Provider, Service Provider, User, Endpoints for communication

3

Maler and Privacy and Reed [24] Sharing of user Naik and attributes Jenkins [21]

Tokens are Encrypted and user attributes are share with other domain applications without user consent; and user authentication remains valid even if explicitly removed in some cases

Token are Encrypted In OAuth user attributes are visible to third parties with User Consent. Authentication revoked immediately, once explicitly removed

Cryptographically Signed token; The user attributes are visible to third parties with User Consent. Authentication revoked immediately, once explicitly removed

4

Maler and Federated Promoted Reed [24] identity Support Naik and Jenkins [21] Sun and Beznosov [18] Brian and Rhee [19]

Less promoted

Promoted

5

OpenStack Implementation Deployed [23] in OpenStack SAML as Naik and Deployment Integrated Jenkins [21] module with Brian and WebSSO and Rhee [19] regsite. It is Sun and not optimized Beznosov for the large [18] data transactions via multiple API call

Deployed OAuth as plug-in in Identity Service of OpenStack. It is vulnerable to phishing attacks

Deployed as light weight protocol plug-in on top of OAuth 2.0 in OpenStack. It is more often used due to its light weight nature

A Comprehensive and Critical Analysis of Cross-Domain …

371

References 1. A.A. Pandey, J.R. Saini, Online Identity Management techniques: identification and analysis of flaws and standard methods. Int. J. Innov. Res. Dev. 1(3), 06–21 (2012) 2. A.A. Pandey, J.R. Saini, An analysis of online identity management techniques. Int. J. Res. Comput. Appl. Manage. 1(3), 53–55 (2012) 3. Y. Yang, X. Chen, G. Wang, L. Cao, An identity and access management architecture in cloud, in Seventh International Symposium on Computational Intelligence and Design (2014) 4. A.A. Pandey, J.R. Saini, An investigation of challenges to online federated identity management systems. Int. J. Eng. Innov. Res. 1(2), 50–54 (2012) 5. A.A. Pandey, J.R. Saini, Identity management in e-governance. Int. J. Emerg. Trends Technol. Comput. Sci. 2(5), 51–56 (2013) 6. A.A. Pandey, J.R. Saini, Development of privacy protecting identity management system for E-Government in India. Int. J. Adv. Netw. Appl. 94–100 (2014) 7. D.F. Ferraiolo, R. Sandhu, S. Gavrila, D. Richard Kuhn, R. Chandramouli, Proposed NIST standard for role-based access control. ACM Trans. Inf. Syst. Secur. 4(3), 224–274 (2001) 8. Citrix, Modifying Default Role based Access Control Permissions for XenServer, Citrix (14 Apr 2016). https://support.citrix.com/article/CTX126442. Accessed 19 Apr 2020 9. J.M. Alcaraz Calero, N. Edwards, J. Kirschnick, L. Wilcock, M. Wray, Toward a multi-tenancy authorization system for cloud services. IEEE Secur. Priv. 48–55 (2010) 10. L. Hu, S. Ying, X. Jia, K. Zhao, Towards an approach of semantic access control for cloud computing. Cloud Computing, CloudCom (2009), pp. 145–156 11. H. Xiong, X. Zhang, D. Yao, X. Wu, Y. Wen, Towards end-to-end secure content storage and delivery with public cloud, in Proceedings of the second ACM conference on Data and Application Security and Privacy (2012), pp. 257–266 12. S. Yu, C. Wang, K. Ren, W. Lou, Achieving secure, scalable, and fine-grained data access control in cloud computing, in Proceedings IEEE INFOCOM (2010) 13. V. Echeverr´Ia, L.M. Liebrock, D. Shin, Permission management system: permission as a service in cloud computing, in IEEE 34th Annual Computer Software and Applications Conference Workshops (2010) 14. R. Wu, X. Zhangy, G.-J. Ahn, H. Sharifi, H. Xieyz, AcaaS: access control as a service for IAAS cloud, in International Conference on Social Computing (2013) 15. S. Koussa, Comparing the Top 3 federated identity providers: OpenID, OAuth, SAML, software secured (November 2, 2018). https://www.softwaresecured.com/federated-identities-openidvs-saml-vs-oauth/. Accessed 18 Apr 2020 16. PingIdentity, An introduction to identity federation and the SAML standard, PingIdentity. https://www.pingidentity.com/en/lp/saml-101.html. Accessed 18 Apr 2020 17. Swisskyrepo, SAML Injection, github (June 9 2019). https://github.com/swisskyrepo/Payloa dsAllTheThings/tree/master/SAML%20Injection. Accessed 18 Apr 2020 18. S.-T. Sun, K. Beznosov, The devil is in the (implementation) details: an empirical analysis of OAuth SSO systems, in Proceedings of the 2012 ACM conference on Computer and Communications Security (2012), pp. 378–390 19. O.M. Brian, K.-H. Rhee, A secure social networking site based on OAuth implementation. J Korea Multimedia Soc. 19(2), 308–315 (2016) 20. H.-K. Oh, S.-H. Jin, The security limitations of SSO in OpenID, in 10th International Conference on Advanced Communication Technology (2008) 21. N. Naik, P. Jenkins, Securing digital identities in the cloud by selecting an opposite federated identity management from saml OAuth and OpenID Connect, in 11th International Conference on Research Challenges in Information Science (RCIS) (2017) 22. D.W. Chadwick, G. Inman, University of Kent has proposed. Attribute Aggregation In Federated Identity Management. Computer 42(5) (2009)

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23. OpenStack, 2019-07-22, OAuth1 1.0a, OpenStack. https://docs.openstack.org/keystone/latest/ admin/oauth1.html/. Accessed 19 Apr 2020 24. E. Maler, D. Reed, The venn of identity options and issues in federated identity management. IEEE Secur. Priv. 6(2) (2008)

Concentric Morphology Model in Detection of Masses from Mammogram: A Study Madhavi Pingili, M. Shankar Lingam, and E. G. Rajan

Abstract Breast cancer is a victor among the most comprehensively saw malignancies among ladies around the world. Clump territory from mammogram helps in right on time affirmation for breast tumor. A PC-aided detection structure which will see also perceive the subverting lumps in person’s breast in an unmistakable and fiscally able way is required. It is the fantasy and reason for experts to have a CAD framework with most important affectability and low fake positives per picture (FPI). Affirmation of clump from person’s breast is badly arranged considering its plenteous geomorphological attributes and questionable edges. Clump territory execution can be overhauled by utilizing productive preprocessing instrument and geomorphological features of the clump area. As a clump makes, it enrages the breast parenchyma and widens by stirring up different coaxial layers. Geomorphological assessment of these coaxial layers is a foundation in clump conspicuous verification figuring. Particular CAD structures utilizing coaxial morphology show exist as a bit of the creation. In this article, an endeavor has made to plot a piece of the current CAD frameworks which utilize coaxial morphology appear for precisely on time and exact zone of clumps. General Terms Biomedical · Automated system Keywords Breast cancer · Mammography · PC-aided detection · Clump detection · Coaxial morphology model M. Pingili (B) CMR Engineering College, Hyderabad, India e-mail: [email protected] Research Scholar, University of Mysore, Mysore, India M. Shankar Lingam University of Mysore, Mysore, India e-mail: [email protected] NIRDPR, Hyderabad, India E. G. Rajan RG-ISIT, MGNIRSA, Hyderabad, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_36

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1 Introduction Breast cancer becomes the most imperative contamination among ladies both in making and made nations. Global flourishing connection encounters projects that for all intents and purposes 360,000 individuals flop pitiably a year and basically 900,000 newly emerged cases are spoken to dependably. In the event that recognized at a starting time, it is conceivable to fix breast mischief and passing rate can be decreased considering. Mammogram assessment is the most extensively seen and gifted technique that is explored for perceiving clumps. Mammography is the course toward utilizing less thick abundancy X-Rays to see human being’s breast and is utilized as a symptomatic gadget. Radiotherapist appraisals mammogram and recognizes lumps. In prior days, radiotherapists used to look over the mammograms to curb blunders in recognizing verification. Despite the way that twofold investigating was done, human slips up were incredibly typical and it is expensive. So as to vanquish it PC-aided detection (CAD) and PC-aided diagnosis (CADx) structure had presented. PC users arrangement perceived the greater part and solicitation of clumps into kind and risky clumps were finished by CADx framework. PC bolstered structure framework was conveyed as a manual for the radiotherapist and it helps the radiotherapist in obvious affirmation of vibrant assortments from the norm and hinders the measure of missed injuries. Eventually, CAD and CADx structures are treated as a total framework which will perceive and depict clumps. It is finished for precisely on time and distinct separating verification of breast ailment yet now there isn’t to such an extent as a solitary CAD and CADx framework with no sham positives and counterfeit negatives. Therefore, heaps of examines are continuing around there to have a free of charge framework. Clump affirmation from mammographic expects an essential part in breast ailment disclosure. Accurate conspicuous confirmation of clump makes it conceivable to perceive breast advancement at a starting time and it likewise maintains a strategic distance from a patient without peril experiencing pointless clinical shows. Clump affirmation is finished by isolating two perspectives of a human breast: the craniocaudal (CC) see, which is the totally observe, and the mediolateral slanted (MLO) see which will be side-view captured at an edge. Exemplary of CC view also MLO see are appeared in Fig. 1 [1]. Here are two fundamental zones in a mammograph, breast area and non-breast domain. The breast area comprises pectoral muscle, breast tissues, or clump, and the non-breast locale comprises dull foundation and foundation things. A piece of fundamental indications of breast contamination that radiotherapists examine for bunches of downsized scale calcifications, clumps, and compositional turns. A clump may be depicted as an expanse incorporating injury perceived in various projections [2]. Clumps may delineate through their structure and edge properties. Hardenings are little stores of calcium that show up as pitiful marvelous marks on the mammograph. They will depict through their sort and dispersing features. Building turns will be the impacts passed on by the augmentation of mayhem and heartbreaking spots in the mammograph, whereas the checking is finished. Territory of clumps is most

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375

Fig. 1 Two sights of breast: the craniocaudal (CC) sight, mediodorsal oblique (MLO) sight

bothersome than every other improvement responses considering the way that their segments can be progressively clouded or like basic breast parenchyma [2]. The motivation behind this article is to give an outline of the clump territory figurings and also a piece of the current computer-aided design structures. This is in addition needed to make the idea of more researchers to the monstrosity of manifold coaxial layer quantifies in clump affirmation estimations. Whatever is left of the article is filtered through which takes after. By Sect. 2, the generic outline of clump exposure calculations is depicted. Every phase in PCaided detection frameworks and the estimations utilized to evaluating the execution of clump distinctive verification figurings are in addition examined. By Sect. 3, a piece of the current PC sustained detection frameworks are considered. Vitality of clump explicit trademark like manifold coaxial layer models is in like way outlined here. Zone 4 closes the article.

2 Mass Identification Algorithm CAD framework helps the radiotherapist in finding clumps on the mammographs. The broad extent of specialists may taking a shot at the affirmation of clumps in mammograms. Clumps happen fit as a fiddle and structure. Hypothesized clumps have more probability of danger. A theorized clump is portrayed through marks oozing through the edges of the clump. As the dangerous clumps are not conjectured, revelation of non-guessed clumps will likewise fundamental. The basic advances required in the divulgence PC sustained detection and finding (CADx) of diagnostic irregularities is appeared in Fig. 2 [1].

376 Fig. 2 Phases in identification (CAD) and analysis (CADx) of mammographic aberrations

M. Pingili et al. Input Mammogram CAD

Stage One Stage Two Output: Lesions Detected (Marks or ROIs)

CADx Output: Likelihood of Malignancy or Management Recommendation (1)

Preprocessing Feature ExtracƟon Feature SelecƟon ClassificaƟon (2)

All around any clump affirmation calculation comprises two phases. In phase one, the truth is to recognize questionable injuries at a inflated affectability. This phase is called as zone phase. In phase two, the truth is to diminish the measure of fake positives rather diminishing the affectability significantly. Game plan of the questionable district into clump or common tissue is accomplished in phase two. So this phase is known as depiction or breast rate. All of these phases comprise of pretreated, consolidate uprooting, highlight choice and plan phases. In certain approaches, a fragment of the techniques may fuse major procedures or be jumped without a doubt. Preprocessing step includes racket clearing, improvement and division stages. Unsettling influence clearing and improvement are utilized for overhauling the picture quality. The motivation behind the division experience in mammographic picture appraisal is to center zones of intrigue (ROIs) containing all breast assortments from the standard from the regular breast tissue. Another motivation behind the division is to find the questionable injury hopefuls through the zone of intrigue. In the part uprooting experience of the clump disclosure estimation, the segments are figured from the qualities of the zone of intrigue. A fragment of segments disconnected from the zones of energy for the diagnostic picture are not tremendous while watched lonely, yet rather in mix in with different parts, they can be essential for depiction. The best game plan of fragments for getting out fake positives and for

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377

depicting sore sorts as perfect or bargaining are picked in the segment confirmation phase. In the depiction phase, the questionable regions are delegated clump or ordinary tissue on the explanation of picked segments. The affirmation structure yield is the larger part. These clumps or locales of intrigue (ROI) are depicted as duty to the affirmation structure (CADx) [3]. Counts utilized as a bit of conspicuous evidence time of clump divulgence calculation are either pixel based or district dependent. In the pixel dependent methods, parts are disconnected for every pixel and named regular or questionable. In the region dependent procedure, ROIs are divided, and starting now and into the foreseeable future, portions are expelled from every territory, they are thusly used to or breast rate the area as questionable or unquestionable. Affectability and false positives per image (FPI) are the conditions utilized for enumerating the execution of clump distinctive verification tallies. Affectability is the complete sum of confirmed positive etchings among the all out of number of wounds and counterfeit positives per picture (FPI) is the measure of fake positive inscriptions among absolute number of pictures. An ensured positive check is a stamp accomplished by the PC sustained detection framework that relates to the district of a sore. A fake positive stamp is a check made by the PC sustained detection structure that does not relate to the zone of a physical issue. A plot of affectability versus FPI is known as a free-response receiver operating characteristic (FROC) plot and this is commonly explored to account the execution of the territory calculation. A feasible CAD framework will have most unmistakable affectability and least FPI. Moved Database for Screening Mammography (DDSM) and Mammographic Image Analyses Society (MIAS) database are the imperative of all things considered utilized databases of mammogram pictures by specialists with a definitive goal of assessment, affirmation and finish of breast hurt. Any clump zone tally ought to is capable of see all clumps in a mammograph without erroneously picking an average tissue as clump. By and by, the basic tradeoff among affectability and FPI is an endeavor in order to decrease FPI will curb affectability. So the main thought must be taken while endeavoring to diminish FPI. FPI decay must be rehearsed rather decreasing affectability. Neither of the current structure is accomplished 100% unmistakable verification exactness. Different business CAD frameworks are accessible. Despite the way that they have accomplished most vital accuracy in perceiving scaled down scale calcifications, clump affirmation precision must be pushed ahead. Two of the business PC maintained exposure and affirmation structures are: R2 technology copy organizer with a declared clump affirmation execution of 85.7% with 1.42 FPI and Brainy System Software, Inc. (ISSI) with an uncovered clump ID execution of 87.4% with 3.32 FPI [1]. Most unprecedented precision can be gotten by picking fitting means all of the times of clump ID calculation. A large portion of the current checks has clump unmistakable evidence exactness of 90% at 1–10 FPI. So yet there is space for updating the region pace of clumps in PC sustained detection.

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3 Computer-Aided Detection of Clumps from Mammographs There is sweeping arrangement on the progress and examination of PC-aided detection frameworks in diagnosis. Various plans have been made for mammographic clump ID. Unquestionable approaches have been utilized as a bit of these plans. These approaches utilized obvious mix of picture managing structures for understanding the two times of clump conspicuous evidence tally. None of these systems have winning concerning accomplishing a 100% exactness. Bigger part of these methods was concentrating on the picture segments of the mammograph as opposed to seeing attributes of clumps in mammogram, and these structures were not very convincing in accomplishing most preposterous region rate. Early affirmation plans utilized clear overhauling or sifting frameworks. Later, some unusual strategies were made. Brake and Karssmeijer [4] projected clump divulgence plot utilizing single and multiscale styles; Mudigonda et al. [5] broke down the utilization of a thickness cutting philosophy to fragment the section of benefits (ROIs). These techniques have compassions nearly 90% with 1–10 counterfeit positives for each picture (FPI). A piece of the late assessments pondered vital features of clumps for affirmation. Timp and Karssmeijer [6] dissected the break vagaries between two mammographic pictures in include space. They will be profitable in discovering little injuries and compositional turns.

3.1 Custom of MCL Norms in Clump Recognition Movement of a clump bothers the breast parenchyma and distributed by making coaxial layers. Clumps have an emphasized central locale by few unique dimmer coaxial layers. So edge and geomorphological parts are for all intents and purposes every so often utilized for clump attestation. Contemplating this Eltonsy et al. [7] examined the geomorphological portrayal of the layers of clump for stirring up a motorized arrangement for ID of clumps. This procedure centers around the disclosure time of clump affirmation figuring through preview mammograph to pick questionable breast areas that will comprise dangerous clumps. These areas are the open doors for the ensuing phase (gathering) of the PC-aided identification framework [8]. By this store up from the beginning in the wake of beating, the breast locale into 50 areas with various force expands, a framework lead is related with make another picture with decreased amount of pellets. By then, these grainy locales are dissected utilizing the preparing segment to perceive the measure of coaxial layers. At long last, coaxial get-together with at most significant amount of coaxial layers and with wide range of speculation are picked as the last questionable zones. Speculation essential is associated with the grounds that it is clinically settled that frightful clumps will when all is said in done packaging hypotheses [7].

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This technique has been tried utilizing mammographs from Digital Database for Screening Mammography (DDSM). Craniocaudally perspectives of the mammographs have been explored as a bit of this assessment. This procedure when executed utilizing 42 biopsy indicated clumps as a bit of aggregate (21 harmful clumps and the rest of the 21 big-hearted clumps) itemized 85.7% affectability with a normal of 0.53 fake positives for each picture [7]. After progressively far-reaching examination of the structure on a more prominent game plan of mammographs, the watched execution was 92% affectability by 3.26 FPI [9]. Tourassi et al. [9] did a development of the exertion completed by Eltonsy et al. [7]. They projected a sham positive diminishment system utilizing a phony neural structure that cementings highlight and information-based appraisal of questionable mammographic locales. This game plan will go about as the second time of the CAD structure (game plan). The framework plays out the going with strides: (1) Preprocessing and division of questionable areas, (2) feature dependent and learningdependent assessment of questionable region and (3) classification for false positive diminishing. The preprocessing segment utilized the game plan made by Eltonsy et al. [7] in order to see and parcel the questionable region. Highlight evaluation examines both the directional and textural qualities of the questionable zones. Highlight assessment module gets a hero among the most outstanding depictions of dangerous clumps. Comparing to include appraisal, an information relied unit is utilized for purifying the sham positive diminishment handle. This unit utilizes an orientation library where mammographic issues with acknowledged certainty are dealt with, and essential data is utilized as the resemblance model among a solicitation case and recorded case. Information relied assessment passes on a choice record that trials the comparative correspondence among the solicitation case and the chronicled case. The sections of highlight assessment stage and the choice record from learning relied appraisal stage are joined into an extraordinary assurance utilizing a back affecting created neural structure. This assessment utilized mammographs from Digital Database for Screening Mammography (DDSM). Course of action is spoken to have accomplished 87.4% affectability with 1.8 sham positives for each picture [9]. Eltonsy et al. [10] projected an other coaxial layers (MCL) dependent include to perceive clumps in mammographs. The projected region plan is a pick manufacture estimation that depends concerning a geomorphological prototype of breast danger progression. Geomorphological evaluation of the coaxial layer prototype is the foundation of MCL affirmation figuring. The estimation includes three stages: Firstly, the breast areas are pretreated by division and pulverization philosophies. By at that point, the questionable central regions are perceived utilizing learning-based thinking. At long last, two remarkable measures are related to takeout counterfeit positives. In the zone sort out following to limiting the central areas with the questionable morphology, least segment standard is utilized to perform beginning evacuation of questionable districts. By then manifold coaxial layer prototypes are related for picking clumps. False positive decreasing is drilled by the assessment of relative repeat and least segment standard.

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They utilized mammogram pictures from Digital Database for Screening Mammography (DDSM) database for their tests. They picked 270 CC perspectives of mammographic cases with biopsy displayed dangerous clumps. The slightest bit of the cases was utilized for preparing and the other half to test. The execution nitty gritty by the producers is 92% affectability to frightful clumps at 5.4 FPI [10]. Gao et al. [11] presented another strategy which consolidates coaxial morphology prototype (MCL) with geomorphological part analysis (MCA). This framework vanquishes the setbacks in MCL rules. Exploration of MCA for pretreating has broadened the conspicuous verification exactness. Here, MCA is utilized for declining the picture into fragments smooth part and surface section. Promoted preparing is accomplished on piece-wise soft part. By then clumps are perceived utilizing coaxial layer models. Gigantic phases in this figuring are: pretreating, geomorphological part abstraction and regulate relied disclosure. In this strategy, mammograph pictures are pretreated utilizing MCA. MCA is a decay system considering sad depiction of information. This technique depends upon the uncertainty that every pennant is a straight blend of a few nuclear signs of progressively clear commencement. For each nuclear standard lead to be isolated there exists a jargon that empowers its headway utilizing a desolate depiction. In like way, it is ordinary that the different word references are wasteful in tending to exchange practices in the blend. This word reference close by an appropriate pursuit calculation sifting for the sparsest depiction prompts the ideal fragment. Over complete word references are utilized as a bit of this strategy. Undecimated wavelet transforms and local DCT will be the best selection of changes for word reference depictions as it possibly withdraws surface part and segmented smooth section. Piece-wise soft parts are then restricted to various power layers utilizing different force limits. The entirety of the areas in these free layers is utilized for extra preparing. By then, geomorphological parts are ousted through these sheets to pick the questionable sections. Geomorphological fragments utilized as a bit of this strategy join healthiness, eccentricity, degree and differentiation. Supervise-based affirmation stage is utilized for extra launch of superfluous districts. For lessening sham, positives least division perspective and appraisal of relative repeat are utilized [11]. This course of action was tried utilizing 100 merciful and 50 bargaining risks from the Digital Database for Screening Mammography database. It declared the affectability of such course of action is 99% in sabotaging, 88% in liberal and 95.3% in a wide extent of risks with 2.7, 3.1 and 2.83 FPI solely [11]. A synopsis of the execution of techniques portrayed here are making review is given in Table 1. It is ludicrous to cause a connection among these specific figuring’s as they too had not organized and tried identical data records. This might be viewed by the chart that the utilization of various data records with a tantamount figuring offsets undeniable outputs. A CAD will have most outrageous execution if the affectability of the structure is most noteworthy and FPI is least. PC supported structure system chooses the disclosure accuracy of breast development area. Therefore, the prerequisite for a PC-aided identification system through most outrageous affectability and least or

Concentric Morphology Model in Detection of Masses … Table 1 Synopsis of clump identification algorithms exploring MCL norms

Author

381 No. of images Sensitivity (%) FPI

Eltonsy et al., 2004

42

85.7

0.53

Eltonsy et al., 2004

150

92

3.26

Tourassi et al., 2005

150

87.4

1.8

Eltonsy et al., 2005

270

92.1

5.4

Xinbo Gao et al., 2010 150

95.3

2.83

no FPI is maximum. Advent of MCL standards in clump acknowledgment contrive has enhanced the precision of clump recognizable proof. Amid the current scheme’s revelation plot exploring geomorphological module scrutiny and coaxial layer show have most extraordinary affectability by exchanging off FPI. It must be seen that this arrangement has achieved 99% affectability for compromising clumps with 2.7 FPI. Therefore, decline in FPI can enhance the precision [12].

4 Conclusion and Future Work Primary affirmation of breast advancement is essential to diminish the passing frequency. With a specific extreme target to make it conceivable, a robotized territory structure is required. Different clump disclosure plans have been made now a PC-aided identification framework with 100% precision notwithstanding everything stays as a specialist’s fantasy. In this article, an attempt is performed to see a clump affirmation plot which has spurred affectability and declined fake positive rate. Through the making layout, it is originated that the greater part of the present PC-aided identification structures has affectability about 90% with 1–10 counterfeit positives for each picture. Presentation of MCL models has surged the exactness of clump disclosure tallies. The clump affirmation plan utilizing a mix of geomorphological bit assessment (MCA) and diverse coaxial layer rules (MCL) have most exceptional affectability (99% with dangerous clumps with 2.7 FPI). Therefore, this clump territory figuring has unbelievable forthcoming update prospects. Decay of counterfeit positives will update the execution considering. The utilization of clump zone explicit qualities has an incredible part in unquestionable proof and zone of clumps. Perceiving proof of fitting segment will curb the false positives. It is common that the advancement of novel sections, for example, Gaussian allotment qualities of clump areas will lessen false positives as power dispersal of clump locale look like 2D prognosis of Gaussian surface. Utilization of restored word references for MCA can propel redesign the affirmation happens as intended.

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References 1. M.P. Sampat, M.K. Markey, A.C. Bovik, Computer-Aided Detection and Diagnosis in Mammography (Elsvier Academic Press, 2005) 2. B. Senthil Kumar, G. Umamaheswari, A review on pc Aided Detection and diagnosis-towards the detection of breast cancer. Eur. J. Sci. Res. (2011) 3. J. Tang, R.M. Rangayyan, J. Xu, I. El-Naqa, Y. Yang, Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans. Inf. Technol. Biomed. (2009) 4. G.M. te Brake, N. Karssemeijer, Single and multiscale detection of clumps in digital mammograms. IEEE Trans. Med. Imag. (1999) 5. N.R. Mudigonda, R.M. Rangayyan, J.E. Leo Desautels, Detection of breast clumpses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imag. (2001) 6. S. Timp, N. Karssemeijer, Interval change analysis to improve pc aided detection in mammography. Med. Image Anal. (2006) 7. N. Eltonsy, H. Erin Rickard, G. Tourassi, A. Elmaghraby, Geomorphological coaxial layer analysis for detection of suspicious clumps in screening mammograms. IEEE Eng. Med. Biol. Soc. (2004) 8. J.L. Starck, M. Elad, D. Donoho, Redundant multiscale transforms and their application for geomorphological component analysis. Adv. Imag. Electron Phys. (2004) 9. G.D. Tourassi, N.H. Eltonsy, J.H. Graham, C.E. Floyd, A.S. Elmaghraby,. Feature and knowledge based analysis for reduction of false positives in the computerized detection of clumps in screening mammography. IEEE Eng. Med. Biol. (2005) 10. N.H. Eltonsy, G.D. Tourassi, A.S. Elmaghraby, A coaxial morphology model for the detection of clumps in mammography. IEEE Trans. Med. Imag. (2007) 11. X. Gao, Y. Wang, X. Li, D. Tao, On combining geomorphological component analysis and concentric morphology model for mammographic clump detection. IEEE Trans. Biomed. Eng. (2010) 12. Z.A. Sasinas Alias Haritha, R. Shreeja, Use of coaxial morphology model in detection of clumps from mammograms: a review, in Conference on Advances in Computational Techniques (CACT) 2011 Proceedings published in International Journal of Computer Applications® (IJCA)

A Survey on DevOps Techniques Used in Cloud-Based IOT Mashups M. Ganeshan

and P. Vigneshwaran

Abstract Development life cycle involves writing scripts, testing, bug finding, recoding, documentation, and deployment, and this activity consumes more time. DevOps looks at automating the entire process. DevOps is a total software and template delivery system that gives importance to the interaction between operations and development fellows. Choosing the right technologies and crafting an IoT infrastructure need a sophisticated comprehensive methodology which can be difficult. DevOps consists of a set of people-oriented, technology-oriented, organizational methods, and tools that face challenges one after the other so that a smooth, easy, and flexible system is obtained. There are many STS practices involved in the development and crafting cloud-based IOT Mashups. We provide few theoretical points of the major IoT threats mentioned in the latest papers and a brief of research activity undertaken in DevOps world to overcome these demerits. This paper briefs the correlation between DevOps technical practices and the tools and engineering that enable uninterrupted integration and transmission, test mechanization, and quick implemen-

M. Ganeshan (B) Department of Computer Science and Engineering, Jain Deemed to be University, Bengaluru, India e-mail: [email protected] P. Vigneshwaran Department of Computer Science and Engineering, Faculty of Engineering and Technology, Jain University, Bengaluru, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_37

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tation. This paper also gives insight on the tools that unite to give a “tool chain” of technology and services to excel the modular tasks of the template deployment and schedule of activities. Keywords IOT · DevOps · Mashups · Cloud · Technical automation practices

1 Basic Concepts We present a theoretical structure of basic principles of DevOps framework. The goal of our structured plan of study of the papers is to gain an understanding of principles of DevOps, based on the literature, to aid the analysis and understanding of DevOps challenges.

1.1 Scholarly Reviewed Literature The survey questions mentioned in Table 1 are answered and helpful for completion of the survey paper. After selection of papers, the papers are divided into seven groups, such as methodologies, result report, influence, fundamentals, challenges, knowledge, and implementation as shown in Fig. 1. The divisions are explained in the following list: • Methodologies: Gives tools, methods, templates and plans to improve DevOps. These give ground rules for choosing implementation automation [8], metrics values plays important role in deciding the nature of deployment [9], how to aid communication among devices across the world using sophisticated network for cloud based technology [10], and how to reconstruct the old infrastructure to latest supportive platform reducing faults at initial stage and make the system reliable for IoT devices connected on cloud network [11]. Table 1 Survey research questions Survey question

Source

SQ1: What does DevOps culture in IoT Mashups mean?

References [1, 2]

SQ2: What are the problems that lead to the need for implementation of DevOps technology?

References [3–6]

SQ3: What are the major advantages of utilizing DevOps culture in IoT mashups?

References [1, 2]

SQ4: What are the probable major problems/limitations of using DevOps References [3, 4, 6] technology/tools? SQ5 What is continuous integration/continuous deployment?

References [3, 7]

SQ6 What problems are encountered when implementing DevOps?

References [5, 7]

A Survey on DevOps Techniques Used in Cloud-Based IOT Mashups Fig. 1 Review literature categories

385

Qty:15 3 5

Quantity

11 17

Category

19 24 26

Qty:8

27

2

30

6

25

4

Qty:6

32

7

39

14

10

38

12

40

16

15

46

18

41

21

28

1

Qty:3

47

20

42

22

29

8

13

48

33

43

36

31

9

34

50

34

49

37

35

23

45

Qty:7 Qty:7

Qty:4

Knowled Implem Method Result. Influen Fundame Challenges ge entaion oligies report ce ntals

• Result report: Points toward the end result of companies implementing DevOps culture in the deployment of APIs between private and public cloud components. • Influence: Deals with how DevOps culture improves the efficiency in the area of APIs development between the private architecture and cloud-based network architecture [12], there is importance of designing security criteria before the implementation of crafted template [13], or the impact of DevOps culture in the APIs development in specific cases of real-time computation of data research [14]. • Fundamentals: Covers the basic theory needed for cultivating DevOps culture in continuous delivery and data integration using the concept of the deployment pipeline [15]. • Challenges: Explains the threats present in implementation of DevOps culture. Few sources deal with specific threats like DevOps used in old domains and new domains supporting the new technologies [16], most of the devices connected in IoT are embedded systems in nature with multiple computation nature [17], or communication APIs between different IoT mashup devices with heterogeneous characteristics [18]. • Knowledge: Deals with the problems faced while training people in DevOps. Papers in this sub-division introduce teaching styles [19], as wel l as explain the key attributes, abilities and exposure needed for DevOps technical engineers, leads and experts [20, 21]. • Implementation: This sub-division deals with the different prototypes for DevOps culture tools for deployment and scripting the templates [3] and shows how a

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company identifies and shortlists a set of standards tools to implement and gauge respective tools in DevOps activity [22].

2 Role of DevOps in IOT Mashup DevOps, with its focus on reducing delivery cycle times, can increase the speed at which IoT heterogenous devices are available in the technology market throughout the globe. Google states that the number of IoT heterogenous devices will exceed non-IoT by 2025. The technologies and principles of IoT will have a major impact on organizations with regard to planning business, management of risk, and design of architecture and network. Any error occurring in any module regarding security and data integration of IoT Mashup architecture can affect the whole DevOps process.

3 Implementing DevOps Architecture Style The course in the needs of DevOps architecture is the utilization of connected portable microservices with suitable APIs for the micro services engineering along with DevOps methodologies [23]. An appropriate tool to orchestrate virtualization mentioned in Table 2 can be decided by the DevOps team to achieve a continuous integration and a recovery system. Table 2 DevOps tools to orchestrate virtualization technology Tools

Description

CloudSlang

CloudSlang is an open-source tool, we can use or customize ready-made YAML-based workflow

Dockers

Docker, and its open-source its being widely adopted for real-world data backs

OpenVZ

Open-source container-based virtualization for Linux.

Kubernetes

Kubernetes is a free package for container-composition system used in automation of APIs and monitoring of code

Containership

Containership simplify the build/test/deploy pipelines in DevOps

Packer

Packer is user friendly used in automation of machine image creation of different types

Linux Containers

Image building tool for LXC/LXD, Support for a lot of distributions and architectures

Nomads

Nomad is an open-source clustered scheduling engine. It can run different applications of micro-service, batch, containerized and non-containerized applications

A Survey on DevOps Techniques Used in Cloud-Based IOT Mashups

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Table 3 Survey on IoT Mashups security Year

Reference

Contributions

2016

[24]

A brief discussion of vulnerabilities faced by the edge side layer of IoT

2017

[25]

Survey on using end points for IoT devices on cloud computing planform

2017

[26]

Discussion on similarities and differences between IoT orchestration using cloud computing and fog computing

2017

[27]

A brief discussion on most relevant limitations of IoT devices

2017

[28]

Discussion on security key factors to be improved on targeted endpoints in network-based IoT services for heterogeneous devices

2017

[29]

Security issues related to the IoT middle ware

2018

[30]

Security mechanism for IoT security like SDN and NFB

2019

[31]

Trust management techniques for Internet of Things

3.1 DevOps Tools for Template/Scripts/Code Management Script/code management DevOps system is in need of tools mostly intended to improve relationship among developers of different domains. DevOps tools are most required for today’s world of bigdata to deploy continuous integration/delivery system. While using traditional approach in deployment and code management, the delivery concepts cannot match the data handling for present world data/code processing requirement. Even developer and operational team cannot manually conduct source code/data management. Therefore, automation scripts are needed to obtain workflow information that can affect the activities carried out in the work frame. For any IoT Mashup instance, a code error can create a bug which may result workflow disturbance and data leak that affects system security [8] and Table 3 lists some of the IoT Mashups Security issues faced in the years mentioned against them.

3.2 Thingworx Versus AWS in IoT Platform While comparing IoT platforms, choosing a better platform is not an issue since you do not have to worry about choosing among options. You can pick Thingworx [32] for the industry expertise and then use AWS for your cloud platform as an integral solution. Thingworx is a third-party development tool for implementing mechanical drives in industry platform for high-level IoT architecture. AWS IoT Core can automate devices in report generation and limitless logs maintained using S3 services [33]. Management and maintenance are cost effective, with less waiting time. The communication can be upscaled to as many heterogenous devices as required. AWS IoT Core is compatible with protocols like HTTP, MQTT and WebSocket’s [34]. Information security is provided by a protocol TLS [35].

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4 Conclusion DevOps culture in deployment and orchestration automation tools for IoT should be sophisticated to provide information exchange through well-designed APIs to confirm lesser downtime in information transfer between IoT mashup heterogenous devices. Edge location in cloud platform must be well defined by appropriate protocols for specific DevOps tools. In this survey, we have explored the different technologies on DevOps culture to automate the deployment, orchestration, monitoring and recovery approaches for IoT Mashup devices on cloud platform by studying a systematic mapping approach. The references were categorized as shown in Table 4 for conceptual study. Table 5 gives the list of cloud IoT features. Many challenges were realized in building a data mashup service between IoT smart gadgets. But, the number of electronic gadgets and mechanical drives are increasing rapidly and are inter-dependent. Wearable technology in coming days is ready with big data lake for analysis. Every human being has increased his count of personal electronic smart devices that are connected to network for computation. All the smart devices require data computation and the process for analyzing the data needs DevOps culture. This rapid growth of technology and IoT devices needs DevOps culture in deployment and Ochestration to ensure friendly Web services. Technically, well-defined research is required in DevOps culture to provide the services with reliable performance. Cloud-based services and tools for IoT Mashups is helpful for conducting high-end workflow. AWS IoT Core provides end-to-end services and embeds DevOps culture in deployment, monitoring, and recovery to increase workflow reliability and efficiency by applying more focus on the DevOps tool-based automated APIs and devices configuration from cross platform.

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389

Table 4 Study of references Reference

Category

Concepts

Goal

References [36, 37]

Source code management

Automate the versioning on DevOps tools

Human collaboration

Reference [38]

Improve the DevOps culture and methods in scripts/code management

Continuous delivery and recovery using tool for code management

References [18]

Overcoming the barriers between different

Decision support system (DSS) is implemented for specific technologies to obtain desired results

Tool platform

References [1, 20, 36, 39]

Continuous integration

Develop APIs with can handle continuous information flow between domains

Continuous delivery;

DevOps can be used in testing environment

Optimize the method to secure devices in IoT domain, using cross-layered network

Real time monitoring/recovery

Continuous delivery

References [37]

Implement continuous delivery and integration of system

Reliability

References [43–45]

Infrastructure as code Virtualization, containerization cloud services, automation

To improve APIs connectivity for smooth information flow

Monitoring and logging

DevOps tools can improve the scalability of instances. Provide reliable work platform with automatic metric value and alerting

Reliability

IoT as a network of networks

Three layers in technological innovation—cloud-pipe-device

Bridge between cloud and IoT

References [1, 12, 19, 40, 41]

Reference [42]

References [1, 40] References [3, 10, 26, 27, 35]

Reference [24]

Deployment automation

To migrate existing infrastructure to new domain on cloud platform

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Table 5 Survey on cloud IoT features IoT cloud provider

Network protocols

Data store

Push not

Geo

BLOB

Trigger

Amazon

REST/MQTT

✓

✓

✓

✓

✓

Azure

MQTT/AMQP/REST

✓

✓

✓

✓

✓

Google

REST

✓

✓

✓

✓

✓

Cloud-foundry

REST

✓

✖

n/a

✓

✖

Kinvey

REST

✓

✓

n/a

✓

✓

Heroku

MQTT

✓

✖

n/a

✓

✖

Parse

REST

✓

✓

✓

✓

✓

Bluemix

MQTT

✓

✓

✓

✖

✓

Dream-factory

REST

✓

✓

n/a

✓

✓

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Digital Assistant with Augmented Reality Vijay Verma, Aditya Sharma, Ketan Kumar Jain, and Sushant Adlakha

Abstract From CRTs to virtual displays, the perception of the digital world we know is continually evolving. Technologies like computer vision and object recognition can be used in conjunction with augmented reality to create an interactive and enhanced user experience of the real world. People are familiar with digital assistants like Siri on iPhone, Google Assistant on android, Cortana on Windows, or Alexa and Google Home in households. As the digital assistant market increases, so does their functionalities and capabilities. They perform a wide variety of tasks ranging from answering the most basic questions, to playing a song, telling jokes, setting alarms, reading audiobooks, and even take control of IoT devices in a smart home system. With this paper, we aim at incorporating the same in an environment where the user can see and interact with the agent, which increases user satisfaction and enhances the overall experience. Keywords Augmented reality · Virtual assistants · Object tracking · Human–computer interaction · 3D modeling · Computer vision

1 Introduction Augmented reality (AR) is a technique that involves overlaying computer graphics on real-world objects [1]. Unlike virtual reality (VR), which creates an artificial environment, augmented reality uses the existing environment and integrates digital V. Verma (B) · A. Sharma · K. K. Jain · S. Adlakha National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, India e-mail: [email protected] A. Sharma e-mail: [email protected] K. K. Jain e-mail: [email protected] S. Adlakha e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_38

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information with the user’s environment in real time. It tries to detect a marker, which is a distinguishable landmark in the environment over which the digital graphics are rendered. However, the rendered graphics exist in virtual space only and not presented holographically. A closely related term is mixed reality (MR), which, in addition to the above features, allows the user to physically interact with the rendered virtual image. AR can be used in various domains for a variety of purposes. It is used as an assistive system for performing human tasks, thereby enhancing the ease and proficiency of the functions. In surgery [2], AR can be used to identify the patient’s affected anatomy, which gives a much deeper insight in comparison to the two-dimensional scan of a body part. AR can also change the perspective in the military vehicle and aviation training [3], where the cost of failure is high, or the equipment is only available at a select few sites. In product manufacturing, most of the working part remains obscured beneath layers, investigating which might cause permanent damage to the product. Such procedures are critical, expensive, and rarely can be performed in a controlled environment, but can be conveniently taught using AR, where the user can learn in a more intuitive environment and the ability to interact, which would otherwise not be possible. Further, remote teaching and distance learning of the same complex procedures can also become possible without the establishment of infrastructure. The same technique can be brought into the education system [4] to explain complex natural, geographical, or biological phenomena with ease. An average person learns better by observing and listening to something than by merely reading something, and this specific property of the human mind can be exploited to accelerate learning. AR can be leveraged to provide real-time information to the tourists about the various places by using GPS data and the buildings as a reference point [5]. When used in the field of entertainment, AR can provide boundless opportunities to amuse one in a real-time 3D environment setting of games [6], movies, and other forms of media. The concept can also be utilized for virtual tryout of dresses and accessories [7] to allow users to get an insight whether the product is suitable for them before making the transaction. To make AR possible, video cameras are used to grab the visual input and recognize where the graphic overlay is to be rendered. A processing system, possibly a computer, then translates the read image and projects the virtual objects onto the user’s screen, which can be a wearable component. Additional hardware components such as audio systems, infrared sensors for tracking the location of real-world objects, and biosensors might be used to get more information about the user and their environment. Wearable systems come equipped with various electronic sensors which provides a user with greater ease of use. If such a model is backed by an artificially intelligent core, it will serve as the foundation of intelligent virtual assistants, which can perform tasks specific to the user in a one-to-one relationship. Such an agent may learn to perform better with time, just like a human personal assistant would behave. For a digital assistant to be effective, the primary requirements [8] are simplicity, flexibility, and ease of interaction. Hence, the voice-based IO interface serves best, as it requires the least cognitive effort from the user’s end. In this paper, we aim to

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build a virtual assistant which not only is capable of understanding and executing user’s voice commands but also provides a visual embodiment of self, making its actions more affable to the user [9, 10].

2 Related Works The tech giants like Google, Microsoft, Apple, and Facebook have taken a keen interest in enhancing user experience in their current application using AR and MR (mixed reality). In the academic contrast, several researchers have devoted their time to explore viable applications and techniques as well. Saquib et al. [11] presented an approach that assists beginners to generate real-time augmented video presentations with a variety of hand gestures, where the users first have to map the gestures with the graphical objects via a direct manipulation interface, which are later interpreted by the system in real time to generate the required graphics. Reuter et al. [12] proposed to integrate AR using the Microsoft HoloLens [13] into Unified Modeling Language (UML) teaching as 3D views for easier comprehension of the relationships between different models as compared to the conventional teaching methods. Prakash et al. [14] suggested a new technique for dealing with the problem of improper illumination in augmented reality. It primarily observes those objects that tend to reflect light, which is further used for real-time approximation of lightning conditions. This could enable AR developers to work with liquid and shiny metals, etc. For ensuring low computation cost, it is run in parallel as a separate thread. Hartholt et al. [15] introduced an approach to boost morale of persons affected with autism spectrum disorder by presenting a virtual humanoid to help them prepare for a meeting or an interview. The rendered agent is capable of tracking eye gaze movement and flicker, response delay and duration, and provides feedback to the user by normalizing their score on a scale of ten. Kyto et al. [16] proposed to combine head and eye movement together for a better range of selection of deliberate actions. Deshmukh et al. [17] aimed to reinvent the education system by bringing out a low-cost high-fidelity application to the masses by making use of Vuforia SDK with Unity to render a three-dimensional layout of a pre-scanned object, which can then be studied in-depth to understand its function and operation. Gary et al. [18] proposed an approach to create custom levels and content within a given video game by exploring the concept and design of AR game editors using Microsoft HoloLens for building up the AR environment. This allows the users to edit and play community-made maps designed in the setting of a home environment. Schmeil et al. [19] combined the idea of integrating the human interface with a digital location tracking system by rendering a virtual humanoid to guide and navigate the user through the city. Xu [20] introduced a digital image processing algorithm for detection of the marker, which is 11 * 11 two-dimensional array of black and white color pattern, where the image is captured via cell phone camera parallelly taking into account rotation of the marker.

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3 Proposed Approach With our work, we aim to build a digital personal assistant with AR, operable using a voice input–output interface. The assistant would be capable of real-time interaction, understanding and executing user commands, read and identify text, process information, and fetch results based upon the user query. Thus, a user may be able to extract digital assistance in a variety of forms utterly different from each other from a single computing machine. The notion of a digital assistant using AR starts from the recognition of the plane on which the digital assistant would be projected. The use of computer vision allows distinguishing between the varieties of objects captured in the image frame based on their color. The object that we herein take into consideration is referred to as a marker. Preprocessing of the image frame using computer vision libraries allows us to detect edges, contours, and the number of sides present in the contour selected, which help us to obtain the coordinates and the orientation of the marker in the image frame. This shall be used to render the model of the virtual assistant on the plane normal to the plane of orientation of the marker. The camera used for recognition will capture some distortions (both radial and tangential) due to non-conformance of the plane of marker and axis of camera focus; hence, normalizing, i.e., removing distortions from the image shall also be taken into account. The entire framework is designed in Python. FreeGLUT, an open-source alternative to the OpenGL Utility Toolkit, is utilized for creating and managing application window frames with the inherent ability to render 2D and 3D graphics. For image processing, OpenCV is used for the purpose of detecting the marker pattern. When coordinates of the recognized marker are obtained, a 3D model designed using Blender is rendered at those specific coordinates. Features build are highly modular for ease of upgradability.

4 Experimentation To overlay an AR glyph or object on a real-world object, a pattern distinguishable from the environment, viz. marker, is to be identified from the image frame. This can be accomplished using image processing libraries like OpenCV, Scikit-Image, etc. After a few trials, it was found that the camera to be used also needs to be calibrated if better marker designs are to be used. But since the axial plane of the camera lens is not always in coherence with the plane of the marker, which is prone to movement as well, radial and tangential distortions tend to occur. Radial distortions occur when the curvature of line or shape changes, in the sense that straight line appears curved. As our webcam is static in the position, i.e., not parallel to imaging plane or marker plane. Hence, some part of the image which is closer to the webcam appears larger and vice versa, giving a perspective effect, which is referred to as tangential distortion. Camera calibration [21, 22] returns intrinsic parameters like focal length and the

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optical center of the camera lens, along with extrinsic parameters like translational and rotational vectors. The camera is calibrated against a grid of black and white squares, as shown in Fig. 1. These parameters can deeply affect how the image is recognized and the spatial position of an object in the image on the screen. The marker that we consider is a two-dimensional array, which consists of 5 × 5 elements, and each element is either black or white. As shown in Fig. 2, the feature of the marker is fixed with a black horizontal and vertical guide bar (5 elements long) at the edges. For each white cell, we select a value 1, and for each black cell, we select a value 0. This marker data is stored in a file for identifying whether the correct marker is recorded from the image or not. To capture the image frames continuously from the webcam, it is run inside a parallel thread, which gives us the current frame, whenever prompted. Then, the current frame is checked for having a specific marker. For removing the distortion, the camera’s distortion coefficients and other details of the camera were retrieved from camera calibration data processed in the previous

Fig. 1 Calibration of the camera

Fig. 2 Markers used for rendering assistants using a black and white grid

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step. For checking the marker pattern in the image, the contour (a curve connecting all the continuous points with similar color or intensity along the periphery) is found. It is ensured that the selected contour has four sides by using the approxPolyDP function of OpenCV. If the contour has four sides, then the endpoints of the contour are calculated (i.e., coordinates of all four vertices). Then, the contour is divided into nine equal parts comprising of three rows and three columns. From the center point of each of those nine parts, it is determined whether the pixel value lies in black or white color’s threshold range. The black color threshold is set at 100, while that of the white color is set at 155. If the pixel value satisfies the criteria, then an entry is made in a 3 × 3 array, representing nine parts of the pattern under consideration. Then, the 3 × 3 array is matched with the actual marker pattern. If a perfect match occurs, then the recognized pattern is confirmed as a marker, and then translational and rotational vectors are returned. The model view for the virtual assistant, which is to be rendered on the user’s screen, is designed in Blender 3D. Blender is a free and open-source 3D computer graphics software toolset used for creating 3D-printed models, interactive 3D applications, animated clips, motion graphics, and computer games. First, the skeletal framework for the model is designed. Care was taken to model the moving parts for animation. For facial animations, sets of separate action frames were designed to call up the desired effect. Two similar robot-like models were generated, and texture mapping was generated to give them a unique feel. The designed models were then exported as wavefront objects, which provide data regarding the vertices, vertex textures and vertex normals, and how the faces are to be formed. This is a standard file format and is acceptable by most rendering libraries. The designed models were then tested for compatibility with OpenGL [23] in a PyGame host window, to check for proper 3D orientation. OpenGL or Open Graphics Library is a cross-language, cross-platform API for rendering 2D and 3D vector graphics, with inherent support for assembling of vertex points, rasterizing, and shading. OpenGL makes use of the GL display lists generated from the data of the wavefront file to create set of coordinates and their corresponding texture mappings, which are called to generate a frame. The window and event handlers are provided by GLUT. This has a non-returning function calling a display function over and over again, which takes the frame as input and redraws the subsequent frame in the next processing cycle. Once the virtual model is imported into OpenGL, it is rendered at the specific coordinates whenever a marker pattern is recognized by the webcam. Being vector in form, the rendered objects retain their shape and are automatically scaled up or down with change in position of marker without loss in quality of the model. The virtual assistant rendered on screen is able to interact with the user via voice commands (Fig. 3). This is implemented via text to speech and speech recognition libraries. The user may select one of the available features, and the control is transferred to the respective feature module for execution. To provide ease of feature upgradability, the followed approach is highly modular. For any new feature to be added, only the feature handler file is to be modified.

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Fig. 3 Assistant images shown

The interface loaded at startup is a chatbot that can answer to most user queries, but is not able to perform any computation. For specific operations, the user can request a particular feature to be loaded. Some of the basic features implemented are real-time news and weather updates, optical character recognition and language translation, deliver presentations, play audiobooks, stream music, and videos.

5 Results This paper presents an introductory approach to a contemporary problem which is being researched and developed by major tech organizations of the world. The projections hold well in ordinary lighting conditions. The features being modular provide easy upgradability and maintenance with ample scope for the addition of new features. Each feature just performs only one task efficiently that is expected from it. The initial interaction of virtual assistant happens with the user by the medium of a chatbot which is capable of answering basic user queries that does not require any mathematical computation. Features added to the assistant like streaming audio and video, optical character recognition, phase translation are a step toward increasing its viability in the day-to-day requirements of an average user. Some of such features is depicted in Figs. 4, 5, and 6.

6 Conclusion This paper represents a hybrid combination of augmented reality applications along with a personal digital assistant, and it provides the user with ease of interaction and interface of an augmented reality application and a plethora of features of the personal digital assistant. At present, our system is capable of answering the user’s

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Fig. 4 Translating native language to foreign language from voice input

Fig. 5 Streaming of a sample video from Internet. The video overlay is presented over a third marker

Fig. 6 Detecting text from the image in the camera frame

queries via the implementation of chatbot and providing a user an option to stream audios and videos and may even help them to read out or digitize the text, translate phrases in foreign languages and many more. In order to make the assistant understand a wider range of commands, we plan on applying artificial intelligence to the module. We expect that in future iterations we could provide the user the option to customize the looks of his digital assistant along with making it compatible for mobile platform as well.

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References 1. K. Maxwellm Augmented Reality—definition of Augmented Reality, http://www.macmillan dictionary.com/buzzword/entries/augmented-reality.html 2. J.W. Meulstee, J. Nijsink, R. Schreurs, L.M. Verhamme, T. Xi, H.H.K. Delye, W.A. Borstlap, T.J.J. Maal, Toward holographic-guided surgery. Surg. Innov. (2019). https://doi.org/10.1177/ 1553350618799552 3. L. Fan, J. Chen, Y. Miao, J. Ren, Y. Wang, Multi-vehicle cooperative military training simulation system based on augmented reality, in Adjunct Proceedings of the 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2019 (2019) 4. C.S.C. Dalim, H. Kolivand, H. Kadhim, M.S. Sunar, M. Billinghurst, Factors influencing the acceptance of augmented reality in education: a review of the literature (2017) 5. D.D. Han, T. Jung, Dublin AR: implementing augmented reality (AR) in tourism. Inf. Commun. Technol. Tour. 2014 (2013). https://doi.org/10.1007/978-3-319-03973-2 6. J. Hamari, A. Malik, J. Koski, A. Johri, Uses and gratifications of pokémon go: why do people play mobile location-based augmented reality games? Int. J. Hum. Comput. Interact. (2019). https://doi.org/10.1080/10447318.2018.1497115 7. M. Yuan, I.R. Khan, F. Farbiz, S. Yao, A. Niswar, M.H. Foo, A mixed reality virtual clothes try-on system. IEEE Trans. Multimedia (2013). https://doi.org/10.1109/TMM.2013.2280560 8. G. Chollet, J. Boudy, A. Esposito, G. Pelosi, Mines-t, I.: Building the next generation of personal digital assistants, in International Conference on Advanced Technologies for Signal and Image Processing, vol. 1 (2014), pp. 458–463 9. S. Haesler, K. Kim, G. Bruder, G. Welch, Seeing is believing: improving the perceived trust in visually embodied Alexa in augmented reality, in 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (2018), pp. 204–205. https://doi.org/ 10.1109/ISMAR-Adjunct.2018.00067 10. K. Kim, L. Boelling, S. Haesler, J. Bailenson, G. Bruder, G.F. Welch, Does a digital assistant need a body? The influence of visual embodiment and social behavior on the perception of intelligent virtual agents in AR, in 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2019), pp. 105–114. https://doi.org/10.1109/ISMAR.2018.00039 11. N. Saquib, R.H. Kazi, L.Y. Wei, W. Li, Interactive body-driven graphics for augmented video performance, in Conference on Human Factors in Computing Systems—Proceedings (2019) 12. R. Reuter, F. Hauser, D. Muckelbauer, T. Stark, E. Antoni, J. Mottok, C. Wolff, Using augmented reality in software engineering education? First insights to a comparative study of 2D and AR UML modeling, in Proceedings of the 52nd Hawaii International Conference on System Sciences, vol. 6 (2019), pp. 7798–7807. https://doi.org/10.24251/hicss.2019.938 13. Y. Liu, H. Dong, L. Zhang, A. El Saddik, Technical evaluation of HoloLens for multimedia: a first look. IEEE Multimedia (2018). https://doi.org/10.1109/MMUL.2018.2873473 14. S. Prakash, A. Bahremand, L.D. Nguyen, R. LiKamWa, GleaM: an illumination estimation framework for real-time photorealistic augmented reality on mobile devices, in MobiSys 2019— Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (2019), pp. 142–154. https://doi.org/10.1145/3307334.3326098 15. A. Hartholt, S. Mozgai, E. Fast, M. Liewer, A. Reilly, W. Whitcup, A.S. Rizzo, Virtual humans in augmented reality: a first step towards real-world embedded virtual roleplayers, in HAI 2019—Proceedings of the 7th International Conference on Human-Agent Interaction (2019), pp. 205–207. https://doi.org/10.1145/3349537.3352766 16. M. Kytö, B. Ens, T. Piumsomboon, G.A. Lee, M. Billinghurst, Pinpointing: Precise head- and eye-based target selection for augmented reality, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Apr 2018. https://doi.org/10.1145/3173574.3173655 17. S.S. Deshmukh, C.M. Joshi, R.S. Patel, Y.B. Gurav, 3D object tracking and manipulation in augmented reality. Int. Res. J. Eng. Technol. 5, 287–289 (2018) 18. G. Ng, J.G. Shin, A. Plopski, C. Sandor, D. Saakes, Situated game level editing in augmented reality, in Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction, Jan 2018, pp. 409–418. https://doi.org/10.1145/3173225.3173230

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An Experimental Approach Toward Type 2 Diabetes Diagnosis Using Cultural Algorithm Ratna Patil, Sharvari Tamane, and Kanishk Patil

Abstract The performance of cultural algorithm for diabetes prediction has been studied systematically and result has been presented in this paper. The performance of the proposed method is compared with prevailing approaches to measure the efficacy of this method. The study shows that cultural algorithm can be employed for determining the best features for building the type 2 diabetes prediction model. In this experimental study, cultural algorithm is used for searching the best subset of features to enhance the efficiency of the predictive model. The selected subset of features was assessed using artificial neural network to check the quality. Proposed method was tested on diabetes dataset collected from the local hospitals and compared with other metaheuristic approaches. Keywords Artificial neural network · Classification · Cultural algorithm · Feature selection · Knowledge source · Type 2 diabetes

1 Introduction One of the challenges faced by biomedical scientist is to analyze the data which consists of smaller number of samples with multiple features out of which many are irrelevant. Efficient processing of high-dimensional data has increased the research opportunities and improved health care services. Recent research work shows that extracting the most important features from the high dimensional dataset and using it for classification has improved the accuracy of predictive models. Reduced number R. Patil (B) Noida Institute of Engineering and Technology, Greater Noida, India e-mail: [email protected] S. Tamane Jawaharlal Nehru Engineering College, Aurangabad, India e-mail: [email protected] K. Patil J J Hospital, Mumbai, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_39

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of features makes the learning model more efficient and easier to interpret. Selection of features is broadly classified into three methods which are filter, wrapper, and ensemble. Filter approach for selecting features uses statistical tests to find out important features for the classification model. Wrapper approach works together with the classification algorithm and the performance of the predictive model is taken into consideration to choose best feature subset. To obtain the most important feature subset which gives optimal classification performance is a NP Hard problem. To search the best subset in a search space is an exhaustive search process. Alternative to this exhaustive search will be to employ metaheuristic approaches [1, 2]. These algorithms are application independent and produce optimal solution iteratively thereby improving the quality of subsets of features. For tuning the parameters of classifiers and selection of optimized feature subset, metaheuristics-based hybrid feature selection classifiers based on swarm intelligence [3], ant colony, genetic algorithm, cultural and gravitational search algorithm may be developed. The study has been presented in the following manner. Section 2 describes the cultural algorithm and correlated basic concepts. Proposed methodology and its flow diagram are presented in Sect. 3. Experimental results and its analysis have been put forth in Sect. 4 and conclusion drawn in Sect. 5.

2 Cultural Algorithm The cultural algorithm has been presented by Reynolds in 1994. The cultural algorithm (CA) is induced by the growth of the human culture and its effect on individuals as well as forthcoming generations. Process of cultural evolution in human society has invigorating effect on this algorithm. Information is stored in the common area and shared by population which leads to the evolution process in the human society. The shared ideas, knowledge by population are referred as a culture. This shared knowledge directs the behavior and actions of the society. The algorithm has three components as shown in Fig. 1. These are (1) population (2) Belief space (3) Communication protocol for interaction between population and belief space. Figure 1 demonstrates the framework of CA algorithm [4]. The population space has autonomous solution agents. The belief space has global knowledge repository. The acceptance function and influence function are used for interaction between the population and belief space. Belief space encompasses four knowledge sources, namely normative, situational, history, and topographical. 1. Normative knowledge source: Agents during mutation use normative knowledge source. This knowledge source stores the features and the possible values that the feature can take from the training dataset. Minimum and maximum values of numeric attributes are stored by normative knowledge source. Normative knowledge is updated by (1) and (2).

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Fig. 1 Components of cultural algorithm

 fl (t + 1) =  f u (t + 1) =

f k (t) f k (t) < fl (t) fl (t) otherwise

(1)

f k (t) f k (t) > f u (t) f u (t) otherwise

(2)

where lower limit and upper limit of the fitness value at the tth iteration are denoted by fl (t) and f u (t), respectively. Position of kth cultural individual is given by yk (t) and f k (t) is the fitness value of yk (t). 2. Situational knowledge source (SKS): Situational knowledge source contains the best pattern found through the evolutionary development process. It denotes a front-runner for the other entities to follow. In this system, for the crossover operation, instead of arbitrarily selecting individual, agents use the leader. It pushes the offspring nearer to the best point found. Situational knowledge is updated by (3).  S(t + 1) =

S(t) f S (t) < f Yg (t) Yg (t) otherwise

(3)

where, at the time t, the best cultural individual is given by Yg (t). f S (t)) and f Yg (t) are the fitness value of S(t) and Yg (t), respectively. 3. Topographical knowledge source: A map of the fitness landscape of the problem is produced throughout the evolutionary process. It contains a set of cells which store the best individual found in each cell. It produces an ordered list of the best b cells as per the fitness value of the best individual on each of them. In this, the influence function attempts to push the offspring to any one of the b cells in the ordered list.

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Fig. 2 Genetic transmission versus cultural transmission

4. History knowledge source: To search patterns in the environmental changes, history knowledge source is used. The best individual position found preceding to each environmental change is recorded in the form of a list. W is the size of the list mentioned. Human culture covers behaviors, artifacts, and ideas that is cultured and communicated between individuals and changes over time period. This course of transmission and change is reminiscent of Darwin’s principle of parentage with variation through natural selection [1]. Figure 2 shows the genetic transmission versus cultural transmission which transpires in a short span. It has been learned that cultural transmission is more complex than genetic transmission. An offspring can get social individualities not only from its parents as well as from nonparental adults and peers. Hence, the occurrence of a cultural characteristic in the population is relevant beyond just the likelihood that an individual’s parents had that peculiarity. In the literature, it has been proved that in most of the cases, the more common a cultural trait is in the population, the more likely it is for an individual to have the chance to get it through social learning [5]. However, the dimensions of the population may also have its impact on the ongoing transmission, and thus existence, of a cultural trait. The pseudocode for cultural algorithm is given below: 1. 2. 3. 4. 5.

Start Set t = 0. Initialization of population space population(t). Initialization of belief space belief(t). Repeat the following until termination condition is found. a. Variations of individuals in the population space population (t + 1) ← population (t) b. Evaluate population, population(t) c. Adjust (Belief space (t), Accept(population(t))) d. Influence(population(t), belief(t)) e. t = t + 1.

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It is hard and expensive to obtain the optimal solution using conventional methods, specifically for high value of number of features in a dataset. Cultural algorithm is a metaheuristic approach which may be employed to accelerate the search for the best feature’s subset.

3 Proposed Methodology The proposed hybrid approach is used to improve the learning and convergence of the search techniques. The algorithm is functioning at two sages. First stage is a population or cultural stage. Binary form is used for representing chromosomes (one implies that the particular feature is selected and zero denotes not selected). In the first stage, individual candidate solutions are transmitting their characteristic into objective function in the problem domain. In the second stage, the knowledge or belief space records information learnt by generations. This information is made available to the current generation. Figure 3 depicts the flow diagram of proposed approach. The hybrid approach uses cultural algorithm for feature selection in combination with the artificial neural network [6, 7]. The fitness function used for the population space is based on the MSE computed by developing artificial neural network (ANN). The objective function is developed using ANN due to its ability to learn and model nonlinear complex relations between inputs and outputs. Artificial neural network can deduce unseen relations on unknown data and makes the model more generalized.

4 Analysis and Experimental Results As a part of this study, the real-time dataset for developing a model for early diagnosis of type 2 diabetes was collected from local hospitals. The total samples collected are 1133. This dataset has 32 attributes. The dataset was verified from physician. Missing values are handled and outliers are detected in the pre-processing stage. The class variable has three values. They are diabetic, prediabetic, and healthy. The parameter values giving good results were decided and used for the developed predictor model. It is a challenge to search the best predictor features out of 32 attributes. In this study, cultural evolutionary algorithm is used for searching the problem space to find all the prospective feature subsets and artificial neural network is employed for evaluation of the performance of each subset. The proposed approach was implemented in MATLAB and configured to enhance the accuracy. Features selected using proposed approach are “HDL” “No. of times Pregnant” “LDL” “Family History” “Blurred Vision” “PPG” “BMI” “VLDL” “Fatigue” “FPG”. Multi-class confusion matrix output obtained from the developed model is summarized in Table 1. To assess the model’s performance accuracy, error, precision, sensitivity, and specificity are computed as per the formulas. Predictor model’s overall parameters and corresponding values are shown in Table 2.

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Fig. 3 Flow diagram of proposed CA algorithm

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Table 1 Summary of multi-class confusion matrix output Labels

True positive

False positive

False negative

True negative

Actual class 1 (Healthy)

416

56

18

643

Actual class 2 (Prediabetic)

226

36

81

790

Actual class 3 (Diabetic)

358

41

34

700

Table 2 Overall values

S. No.

Parameter

Value

1

Accuracy

0.8826

2

Error

0.1174

3

Sensitivity

0.8693

4

Specificity

0.9403

5

Precision

0.8804

6

False positive rate

0.0597

7

F1 score

0.8726

8

Matthews correlation coefficient

0.8169

9

Kappa

0.7359

To compute the various performance measures given in Table 2, following terms are used. • • • •

True positive (TP): is predicted to be positive with actual class positive. False negative (FN): is predicted negative with actual class positive. True negative (TN): is predicted to be negative with actual class negative. False positive (FP): is predicted positive with actual class negative. Accuracy of the developed model is computed by Eq. 4. Accuracy =

(TP + TN) (TP + FP + FN + FP)

(4)

Accuracy obtained is 88.26% and depicted in the form of confusion matrix is shown in Fig. 5. Error is a misclassification and computed by Eq. 5. Error =

(FP + FN) (TP + FP + FN + FP)

(5)

Sensitivity is the ratio of the true positive predictions to the total number of actual positives. The best sensitivity is one. It is also called as recall and computed by Eq. 6. Sensitivity =

TP (TP + FN)

(6)

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Specificity is the ratio of true negatives to the total number of Actual negatives. The best value of specificity is one. Equation 7 is used to compute specificity. Specificity =

TN (TN + FP)

(7)

Precision is the ratio of true positives to the total predicted positives computed by Eq. 8. Precision =

TP (TP + FP)

(8)

False positive ratio (FPR) is the ratio of the number of false positive predictions to the total number of negatives. Zero is the best FPR value. FPR is given by Eq. 9. FPR =

FP (FP + TN)

(9)

Harmonic mean of recall and precision is called F1 score. Equation 10 computes F1 score. F1Score =

2 ∗ (precision ∗ recall) (precision + recall)

(10)

Matthews correlation coefficient (MCC) is used to measure the quality of the classifier. It is computed by Eq. 11. Positive one represents a perfect predictive model and negative one represents inverse prediction. (TP ∗ TN) − (FP ∗ FN) MCC = √ (TP + FP) ∗ (FP + TN) ∗ (TP + FN) ∗ (FN + TN)

(11)

The comparison of observed accuracy with an expected accuracy is done by Cohen’s Kappa coefficient and its value is computed by Eq. 12. Kappa value =

(observed accuracy − expected accuracy) 1 − expected accuracy

(12)

The best cost at each iteration is plotted in the form of graph shown in Fig. 4. The X-axis of the graph refers the iteration number and the Y-axis gives the best cost obtained in the respective iteration. A confusion matrix describes performance of the developed classifier in tabular form and is shown in Fig. 5. An epoch is used to indicate the number of passes through the complete raining set. The best validation performance obtained is 0.20712 at epoch 32. Figure 6 shows the validation performance at training, validation, and testing phases.

An Experimental Approach Toward Type 2 Diabetes Diagnosis …

Fig. 4 Best cost versus number of iterations

Fig. 5 Confusion matrix

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Fig. 6 Validation performance

Receiver operating characteristics (ROC) curve is plotted with true positive rate on Y-axis against false positive rate on X-axis. The ROC curve for proposed approach is displayed at Fig. 7. The test is more efficient when the graph is closer to the top left corner. This curve tells us how much the model is skilled in distinguishing among classes. Therefore, this predictive model has shown a better result.

5 Conclusion In this experimental study, cultural algorithm in combination with artificial neural network is developed for optimization of feature selection problem in a diabetes dataset. The accuracy obtained for the developed prediction model for type 2 diabetes is 88.3%. The developed model will detect the prediabetic stage. Sedentary lifestyle, obesity, and consuming junk food are known to play a huge role in the pathophysiology of type II diabetes mellitus. Detection of the prediabetic stage will therefore serve as a wake-up call for individuals to initiate lifestyle modifications as suggested by their physician and prevent further progression of the disease. Lifestyle modifications include daily exercise and limiting intake of food high in saturated fat, trans fatty acids. In the future, the social knowledge derived from the individuals in the cultural algorithm may be applied to solve the computer security problems in numerous applications.

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Fig. 7 ROC curve

References 1. O. Oloruntoba, G. Cosma, A Modified Cultural Algorithm for Feature Selection of Biomedical Dat (Springer, Cham, 2019) 2. R.N. Patil, S.C. Tamane, Upgrading the performance of KNN and Naïve Bayes in diabetes detection with genetic algorithm for feature selection. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 3(1), 2456–3307 (2018) 3. X. Wang, W. Hao, Q. Li, An adaptive cultural algorithm with improved quantum-behaved particle swarm optimization for sonar image detection. Sci. Rep. (2017) 4. H.C. Kuo, C.H. Lin, Cultural evolution algorithm for global optimizations and its. J. Appl. Res. Technol. 11, 510–522 (2013) 5. B. Zaeri, Z. Kobti, Improving robustness in social fabric-based cultural algorithms with two new approaches in population and belief spaces, in Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (2017) 6. P. Ratna, S.C. Tamane, PSO-ANN-based computer-aided diagnosis and classification of diabetes, in Smart Trends in Computing and Communications, Proceedings of SmartCom 2019 (2019) 7. X. Xue, M. Yao, R. Cheng, A novel selection operator of cultural algorithm. Adv. Intell. Soft Comput. 123, 71–77 (2017)

Predicting Students Performance Through Behavior and Computational Thinking in Programming Vinayak Hegde, H. N. Meghana, R. Spandana, and M. S. Pallavi

Abstract The advancement and achievement of the students are known as primary success metrics for many universities around the world. Hence, universities invest heavily in resources to reduce the performance gap between academically good performers and low-performing students. The high rate of failure in computer programming courses can attribute several factors. This paper explores the educational history of students, their fields of study, and their approaches to learning applied to programming courses. Considering students’ attendance, assignment marks, program practice at home, willingness to work, interaction in class predicts student’s behavior. By considering students’ lab test marks, willingness to apply a new algorithm predicts student’s computational thinking. The data are collected from undergraduate students by conducting lab tests and surveys among teachers. Finally, considering student behavioral and computational thinking parameters, and by using simple linear regression, the attributes which are supported and which are not supported for predicting student performance can be finalized. Keywords Computational thinking · Student behavior · Predicting student performance · Education computing · Educational data mining

V. Hegde (B) · H. N. Meghana · R. Spandana · M. S. Pallavi Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham Mysuru Campus, Mysuru, Karnataka, India e-mail: [email protected] H. N. Meghana e-mail: [email protected] R. Spandana e-mail: [email protected] M. S. Pallavi e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_40

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1 Introduction This paper works on predicting student performance in programming through students’ behavior and computational thinking. Most of the students in computer programming show poor performance in their academics. Student background plays a major role like age, gender, attendance level at the University, programs they are pursuing and pursued at the high school, and also, it depends on the number of times they have taken a particular programming course and some of the students will be from the non-computer background, and it will be very difficult for them to learn programming language. So, we should take opinions on the difficulties in learning programming, and also, they will be facing problems in writing, reading as well as designing a simple programming code. To overcome the problem, in this study, we have followed the protocol as Vo Ngoc Hoi research paper [1]. We try to predict student computational thinking and their behavior by giving tests on programming language which include algorithms and executable programs and taking feedback from the teachers on students’ regularity, assignments, internal marks, and interaction in class. Computational thinking attributes like lab test marks, willingness to work, and behavioral attributes like attendance, assignment marks, program practice at home, willingness to work, and interaction in class are considered for both computational thinking attributes and behavioral attributes, and converting them to a common ‘n’ form is shown in the Table 3. Then apply the correlation matrix for Table 3 data. After getting the result of the correlation matrix from Tables 4 and 5, apply principal component analysis (PCA) to get eigenvector values. After applying the PCA, screen plot, active variables, the active component graph will be displayed. For the eigenvalues from Tables 6 and 7, simple linear regression will be applied to find out which all attributes are supported and which are not. The positive values which we get by applying simple linear regression will be considered as supported, and negative values which we get will be considered as not supported.

2 Literature Review It presents the thought of foreseeing scholarly execution dependent on computational thinking. The exhibition of 900 and eighty-two understudies in forty segments were evaluated over the two-year time frame. These outcomes have likewise inferred that the appraisal of computational thinking can be utilized as an early intercession apparatus to improve the understudies’ maintenance, movement, and graduation rates in STEM-related orders [2]. Computational thinking is viewed as a general skill, right now further expound on what computational thinking is and present instances of what should be instructed and how. First, we position computational thinking in paper’s work with LOGO and point out difficulties in characterizing computational thinking and examine the center and fringe parts of a definition [3]. The explanation

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between computational intuition assignments in a single side, understudy’s focused on aptitudes, and the sorts of issues they expect to fathom, we led a little scope pilot contextual analysis with two gatherings of understudies for the basic and center grades. We found a divergence between the sorts of abilities evaluated, the effortlessness of the undertakings, and the age gatherings, which makes it hard to show up to some steady end [4]. Numerous variables influence this scholarly presentation, and therefore, it is important to establish a prescient knowledge digging model for the exhibition of understudies to discern the contrast between high students and moderate student understudy. The rough information was preprocessed to top off missing qualities, turning qualities into another and important characteristic/variable determination in one single structure [5]. The proof of quality for the new content mining plans owed exceptional physical, articulation, and commotion. However, the information mining works with viably, the update finds designs having issues on the open research particularly in the content mining as space. Thus, the information preparing apparatus, for the most part, works in the forecast and characterization of information [6]. Right now, mark forecast models have been created utilizing institutional inside databases and outside open information sources. The consequences of observational investigation for college understudies’ first-year point expectation show that forecast models dependent on the institutional interior and outside information sources furnish better execution with increasingly precise models contrasted with the models dependent on just institutional inner information sources [7]. As a consequence of the rapid expansion of knowledge in instructive context, instructive knowledge mining resulted in the development of techniques to investigate the interesting kinds of information emanating from instructive settings and the use of such strategies to obtain understudies more readily and the settings in which they learn. [8]. Worldwide the design of schools has a relatively high whittling down the pace. Approximately 35% of the main year understudies in different design programs do not usually make it into the next year. The purpose behind this test is to identify the elements that fill in as great indications of whether an understudy is going to drop out or flop the programme [9]. Numerical models have been used to anticipate understudies’ exhibitions in the expectation of illuminating the help group to mediate at the beginning time of the in danger understudy at the college. Right now, utilized a mix of institutional, scholarly, segment, mental and financial variables to anticipate understudies exhibitions utilizing a multilayered neural system (NN) to characterize understudies’ degrees into either a decent or essential degree class [10]. The extent of this paper is to the calculation to foresee the evaluation of understudies to give convenient and proper admonition to understudy the individuals who are in danger. A study cum exploratory technique was adopted in the present analysis to construct a database, and it was created from both an important and an optional source. This work will assist the instructive foundations with identifying the understudies who are in danger and to and give better extra preparing to the powerless understudies [11].

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V. Hegde et al. Attendance

Behavior

Interaction in Class Assignment Marks

Predicting

Computational Thinking

Willingness toWork Program Practice in Home

Willingness to Apply New Algorithm

Lab Test

Fig. 1 Research model for predicting students performance through behavior and computational thinking in programming

3 Methodology 3.1 Research Model and Hypotheses This study sought to predict the student’s computational thinking and behavior in class and laboratory. Figure 1 depicts the research model with relevant attributes like attendance, interaction in class, assignment marks, willingness to work, program practice at home are the parameters considered for predicting student behavior. Lab test and willingness to apply the new algorithm are the parameters considered for predicting student computational thinking. Behavior and computational thinking are the two paths for the prediction of student overall performance.

3.2 Attendance Attendance is the concept of students, to know whether the students are present in class or not. It plays a major role in predicting student’s behavior. The educational success of a student is based on student attendance [12]. Participation inspiration is a great extent affected by fulfillment with the activity circumstance and different internal and outer external pressure to attend [13]. H1: Attendance has a positive significant effect on behavior.

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3.3 Interaction in Class Interaction is the concept of knowing student attentiveness in class, by clarifying doubts with the teachers, answering questions. The instructor mainly used positive engagement to demonstrate unity and to maintain a friendly relationship with the students, records to provide fair and unambiguous guidance, negative good manners to reduce the students’ pressure [14]. H2: Interaction in class as a positive impact on behavioral significance.

3.4 Assignment Marks Assignments are given to make students better on particular topics. In training, it shows that self-assessment is conceivable and is a possibly important guide in educating and learning [15]. H3: Assignment marks have a positive significant effect on behavior.

3.5 Willingness to Work Students who are motivated in certain works show more interest in doing those works. Not all students show a willingness to work as the subject interest will be less. Students should show eagerness is to learn the subject in the classroom. This examination utilizes a subjective technique, especially the contextual analysis with the utilization of perception, survey, and meeting to gather the information [16]. H4: Willingness to work has a positive significant effect on behavior.

3.6 Program Practice at Home Learning the things at college cannot be remembered by students, but practicing it at home can. The more practice they do, the quality of the knowledge increases. Practicing programs at home to study the program carefully so that they know a lot about it and they can try to apply new algorithms. H5: Program practice at home has a positive significant effect on behavior.

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3.7 Lab Test The laboratory test is a concept to assess students’ practical knowledge in a specific course. Conducting lab tests for students makes it easier to predict their computational skills. The technical research tools as part of cooperative online learning helps to improve the development of logical competencies [17]. A laboratory environment motivates students to understand and conduct well in examinations. The approach must be obtained positively by students at the college [18]. H6: Lab test has a positive significant effect on computational thinking.

3.8 Willingness to Apply the New Algorithm It is a concept of creative thinking and applying own logic. People with more computational skills will try to find out new things rather than getting stuck to the existing ones. Great software engineers are essential than ordinary ones in terms of showing a willingness in software creation [19]. H7: Applying a new algorithm has a positive significant effect on computational thinking.

4 Materials A survey was conducted among teachers to collect students’ behavioral data, and a test was conducted among students to collect computational data. A survey had attributes like attendance, interaction in class, assignment marks, and willingness to work, program practice at home. The test consisted of basic programming questions, where students were supposed to write the proper logic and expected output. A total of 60 students’ data were collected.

5 Results By specifying the student’s name, the third person can know the private data of a student. In order to maintain privacy for student’s data, specific code is given for each student so that we can predict their data privately, without revealing their name for the third person (Tables 1 and 2). The Table consists of a student’s code followed by seven parameters. V1, V2, V3, V4, and V5 are the behavioral attributes, the data of this attributes are collected by conducting survey. V6 and V7 are the computational thinking attributes, the data are collected by conducting a test. Teachers were expected to offer ratings out of 5,

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Table 1 Replacing each student’s name with a specific code Name

Code

Student 1

A

Student 2

B

Student 3

C

Student 4

D

Student 5

E

Table 2 Displays the parameters for predicting student’s behavior Code

V1

V2

V3

A

76

Good

7

B

89

Very Good

8

C

94

Excellent

>8

D

77

Good

6

E

73

Good

7

V4

V5

V6

V7

Average

Average

10

Yes

Average

Poor

12

Yes

Excellent

Excellent

14

Yes

Average

Poor

9

No

Average

Poor

10

Yes

where 5 was regarded as excellent, 4 as very good, 3 as good, 2 as poor, and 1 is average. By taking all of the above criteria into consideration, teachers were meant to offer an overall ranking for each student out of 5. V1—Attendance, V2—Interaction of students in class and laboratory, V3— Assignment marks, V4—Willingness to work, V5—Program practice at home, V6—Lab test, V7—Willingness to apply new algorithm. Table shows the model measurement performance. Nearly, seven parameters were considered. We need to convert all the attributes to the common ‘n’ form. Here, in Table 3, behavioral attributes are converted into three entries, and computational thinking attributes are converted into two entries. The threshold values are taken as per the higher education requirement. A matrix of correlation is a table showing the coefficients of correlation between collections of variables. To get the correlation matrix, we have considered Table 3 data. Table 4 is the correlation matrix for behavioral attributes. Table 5 is the correlation matrix for computational thinking attributes. In principal component analysis (PCA), we can pick either the matrix of covariance or the matrix of correlations for components. Here, we have already chosen the correlation matrix in Tables 4 and 5. By applying PCA to the correlation matrix, we will get eigenvector values as shown in Tables 6, 7 and 8.

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Table 3 Displays measurement process performance Constructs

Items

Attendance

19, 28 mod 19 is performed as step 3) 28 mod 16 = 12, step 4) 12 − 3 = 9. 6. Exit loop when partial product is less than 2n + 3. 7. All the partial products are added. If it is more than 2n + 3, the procedure of truncation followed by subtraction may be used as one final step.

Table 1 Difference between modulo 2n and 2n + 3 for the partial products, n = 4 k Range

0

1

2

3

2n+k

16

32

64

128

2n+k + 3

19

38

76

152

Difference

3

6

12

24

600

N. R. Aiholli et al.

13 mod 19

=

13

0 mod 19

=

0

52 mod 19

=

14

104 mod 19

=

9

+

36

Same steps 3 to 5 may be repeated to obtain final result. 36 mod 19 = (26 + 10) mod 19 = {(26 mod 19) + (10 mod 19)}mod 19. 26 mod 16 = 10, followed by subtraction 10 − 3 = 7 ∴ 36 mod 19 = 7 + 10 mod 19 = 17 Note that when any partial product is larger than 2n + 3, then the procedure of truncation followed by subtracting the difference can be applied till final partial product is obtained. Final result matches with the conventional squarer method. A flowchart with the steps of algorithm is illustrated in Fig. 2.

3 Comparison with Earlier Architectures In the previous paper [20], the authors have worked on modulo 2n − 3 squarer architecture implementation for residue number system. Figure 3a illustrates the partial product bits after multiplication. Bits beyond n bits are mapped once normal and then with one bit left shift. Figure 3b shows the mapped partial product bits. Initially the bits in the matrix are arranged as per the partial product bits obtained. Bits in the subsequent mapped partial product matrix are rearranged such that there is no vacant space left in between the consecutive bits as compared to the previous matrix bits. Each row is converted into its equivalent decimal number expressed in terms of bit vector as the method employs operation on bits rather than on integer. Modulo reduction operation is performed on each row. Obtained bits are added with a CSA (Carry save adder) tree. Top three rows are added in the first row and then one row at a time. The column is stopped at that level, if no bits are remaining to be added. CPA (Carry propagate adder) is used to add final sum and carry vectors. The sum obtained is subjected to modulo reduction. Final value is equivalent to that obtained from conventional approach. As compared to the previous architecture, present architecture proposed is on modulo 2n + 3 squarer implementation for residue number system using the same simulation tool set as the previous architecture [20]. There is certainly comparable difference between the two in terms of delay requirement and hardware with the present one having an edge over that of the previous one [20]. This is illustrated in the result section that follows.

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Fig. 2 Flowchart of squarer

4 Hardware Simulation and Testing Hardware can be constructed directly from the algorithm presented above in Sect. 2 (Fig. 4). Xilinx ISE design suite 14.7 has been used as the design tool with Verilog programming. Proposed algorithm is synthesized using the same tool as that for the previous architecture of the author [20]. Xilinx FPGA viz. Virtex 6 XC6VLX75T in FF484

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Fig. 3 Mapping the partial products and rearranging for modulo computation

Fig. 4 Block diagram showing steps used in proposed squarer design

package and -3 speed grade is the target implementation device under Xilinx 14.7 working environment. The simulator used is Integrated SIM (ISIM) for behavioral verification. The speed measured in terms of delay and areas in terms of the number of LUTs are considered as parameters of measurement respectively. Simulation waveform is as shown in Fig. 5a. Multiplicand and multiplier values used for simulation are: multiPlicand[7:0] = 10101010 = 17010 multiPlier[7:0] = 10101010 = 17010 Expected result of the squarer is 170×170 = 28900 mod 259 = 151, which is observed in Fig. 5a above as resf [8:0] = 10010111 = 151(decimal). The RTL schematic obtained during synthesis is represented as shown in Fig. 5b; (Tables 2, 3 and 4). The device utilization summary report gives number of LUTs utilized, number of LUT-FF pairs and IO blocks used. Offset out after for clock delay is 0.562 ns. As compared to the delay of the proposed work for squarer, delay of the 8 bit squarer of paper [20] is 22.188 ns and that of Jaberipur [21] is 6.5 ns. Jaberipur’s work is on custom IC design. But, the proposed work is on FPGA architecture. Still results of the work carried out in this paper are better than that of paper [20] and Jaberipur [21] in terms of delay. The power estimated using Xpower analyzer is 1.293 W.

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Fig. 5 a Simulation results of proposed algorithm b RTL schematic of 8 bit proposed squarer Table 2 Device utilization summary of the proposed work of 8 bit squarer

Logic utilization

Used

Available

Utilization (%)

Number of slice LUTs

1055

46,560

2%

Number of used LUT-FF pairs

67

1109

6%

240

10%

Number of bonded 26 IOBs Table 3 Device utilization summary of the proposed work of 12 bit squarer

Table 4 Device utilization summary of [20] of 8 bit squarer

Logic utilization

Used Available Utilization

Number of slice LUTs

3066

46,560

6%

Number of used LUT-FF pairs 145

3155

4%

Number of bonded IOBs

240

15%

38

Logic utilization

Used Available Utilization

Number of slice LUTs

484

46,560

1%

Number of used LUT-FF pairs 0

484

0%

Number of bonded IOBs

240

11%

28

604

N. R. Aiholli et al.

5 Conclusion Squarer implementation for modulo 2n + 3 is the main focus of this paper. Design for 4, 8 bit and 12 bit units is demonstrated here, but is expected to also work for higher bits. The future work may be to implement the design on Virtex 6 low power and Virtex 7 FPGA’s. The main objective of this paper was to show that new method is comparable to the conventional architectures in terms of delay requirement. All the Verilog code used for simulation is available from github project https:// github.com/nagarajaiholli.

References 1. S. Mayannavar, U. Wali, Design of hardware accelerator for artificial neural networks using multi-operand adder, in Proceedings of the International Conference on Information, Communication and Computing Technology, ICICCT (2019) 2. N. Szabo, R. Tanaka, Residue arithmetic and its applications in computer technology, McGraw Hill (1967) 3. M. Soderstrand, G. Jullien, W. Jenkins, F. Taylor, (eds.) Residue Number System Arithmetic: Modern Applications in Digital Signal Processing IEEE Press (1986) 4. A.P. Mohan, Residue number systems: algorithms and architectures, Kluwer (2002) 5. A. Omondi, A. Premkumar, Residue number systems: theory and implementation, (Imperial College Press, 2007) 6. P.A. Mohan, New Reverse converters for the moduli set {2n − 3, 2n + 1, 2n -1, 2n + 3}”, AEU, 2008, vol. 62, pp 643–658 (2008) 7. L. Huihua, L. Lei, Z. Wanting, High-speed and high-efficient modulo (2n − 3) multipliers, in International Conference on Education, Management, Computer and Society (EMCS 2016) 8. M. Sheu, S. Lin, C. Chen, S. Yang, An efficient VLSI design for a residue to binary converter for general Balance moduli (2n - 3, 2n -1, 2n + 1, 2n + 3), IEEE Trans. Circ. Syst. Express Briefs 51, 52–55 (2004) 9. R. Muralidharan, C. Chang, C. Jong, A low complexity modulo 2n + 1 squarer design, in Proceeding of IEEE Asia Pacific conference on Circuits and Systems (2008), pp. 1296–1299 10. D. Adamidis, H. Vergos, RNS multiplication/sum-of-squares units. IET computers Digit Tech 1, 38–48 (2007) 11. A. Spyrou, D. Bakalis, H. Vergos, Efficient architectures for modulo 2n − 1 squarers, in Proceeding of IEEE International Conference on DSP 2009, (2009), pp. 1–6 12. A. Strollo, D. Caro, Booth Folding encoding for high performance squarer circuits. IEEE Trans CAS, Part II 50, 250–254 (2003) 13. D. Govekar, A. Amonkar, Design and implementation of high speed modified booth multiplier using hybrid adder, in Proceedings of the IEEE International Conference on Computing Methodologies and Communication, ICCMC (2017) 14. S. Sinthura, A. Begum, et al, Implementation and analysis of different 32-bit multipliers on aspects of power, speed and area, in International Conference on Trends in Electronics and Informatics (ICOEI 2018) 15. M. Sharma, R. Verma, Disposition (reduction) of (negative) partial product for radix 4 booth’s algorithm, in World Congress on Information and Communication Technologies (2011) 16. N. Aiholli, R. Rachh, U. Wali, Design of arithmetic unit for RNS using 2n − 3 as base, in Proceedings of the IEEE International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques ICEECCOT (2018)

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17. R. Muralidharan, C. Chang, Fixed and variable multi-modulus Squarer architectures for triple moduli base of RNS, in Proceeding of IEEE ISCAS (2009), pp. 441–444 18. H. Vergos, C. Efstathiou, Efficient modulo 2n + 1 squarers, in Proceeding XXI Conference on Design of Circuits and Integrated systems, DCIS (2006) 19. P. Matutino, R. Chaves, L. Sousa, Arithmetic units for RNS moduli {2n − 3} and {2n + 3} operations, in 13th Euromicro conference on Digital System Design: Architectures, methods and Tools (2010) 20. N. Aiholli, U. Wali, R. Rachh, Implementation of arithmetic unit for RNS using 2n − 3 as base. Int. J. Innov. Technol Exploring Eng. 9, 254–258 (2019) 21. H. Ahmadifar, G. Jaberipur, Improved modulo—(2n + 3) multipliers, in International Symposium on Computer Architecture and Digital Systems, (CADS), (2013), pp. 31–35

Hybridization of Artificial Bee Colony Algorithm with Estimation of Distribution Algorithm for Minimum Weight Dominating Set Problem Sima Shetgaonkar and Alok Singh

Abstract In this paper, we have proposed a hybrid approach combining Artificial Bee Colony Algorithm (ABC) with Estimation of Distribution Algorithm (EDA) for Minimum Weight Dominating Set (MWDS) problem. By combining ABC algorithm with EDA, we are able to guide the search process toward more promising solutions in comparison with any of the two constituent approaches. We have also used a solution improvement method which further trims down the solution in order to minimize its fitness value. Computational results on standard benchmark instances of the MWDS problem show that the hybrid ABC-EDA performs as good as or better than existing approaches in terms of solution quality on most of the instances. Keywords Minimum Weight Dominating Set · Artificial Bee Colony Algorithm · Estimation of Distribution Algorithm · Swarm intelligence

1 Introduction For an undirected graph, a dominating set is a subset of vertices such that each vertex of the graph is either a member of the dominating set or connected by an edge to a vertex in the dominating set. The vertices belonging to the dominating set are called dominating vertices, and the rest of vertices is called dominated or non-dominating vertices. Given an undirected graph, the Minimum Dominating Set (MDS) problem seeks a dominating set of minimum cardinality on this graph. The Minimum Weight Dominating Set (MWDS) problem is a generalization of MDS problem. Given a vertex-weighted undirected graph, the MWDS problem seeks a dominating set with minimum sum of weights of its constituent vertices. Both MDS S. Shetgaonkar (B) · A. Singh School of Computer and Information Sciences, University of Hyderabad, Hyderabad 500046, India e-mail: [email protected] A. Singh e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_59

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and MWDS problems are N P-hard [3]. Throughout this paper, we will use node and vertex interchangeably. Both MDS and MWDS find real-world applications in various domains from text summarization to formation of a virtual backbone for routing in wireless network [7]. Detection and prevention of attacks over a network can be done effectively using the concept of dominating set [6]. Gateway placement in wireless mobile networks is another application [1]. All these applications involve computing a MDS or a MWDS depending on whether the underlying graph consists of homogeneous or heterogeneous nodes. A number of approximation algorithms, heuristics, and metaheuristics have been proposed in the literature. Due to paucity of space, we will survey only a few of them. Two hybrid approaches based on Ant Colony Optimization (ACO) and Genetic Algorithm (GA) have been presented in [5]. An Artificial Bee Colony (ABC) algorithm is presented in [4] which outperformed the hybrid approaches of [5]. An evolutionary algorithm with guided mutation (EA/G) approach is presented in [2]. EA/G combines Estimation of Distribution Algorithm (EDA) with a mutation operator [8]. This EA/G approach also outperformed the two hybrid approaches of [5]. Motivated by the success of ABC and EA/G approaches for MWDS, we have proposed a hybrid approach combining ABC algorithm with EDA and which we will refer to as ABC-EDA. The ABC algorithm searches through the entire search space for an optimal solution utilizing the location information of a population of solutions and does not exploit the global statistical information about the search space which is available to it as byproduct of search process. On the other hand, an EDA utilizes the global statistical information about the search space to produce offspring instead of utilizing the location information of solutions. Location information of a solution uniquely identifies that solution in the search space. For MWDS, set of dominating vertices uniquely identifies a solution. The ABC algorithm uses this information to produce a neighboring solution. An EDA on the other hand makes use of a probability model representing the distribution of promising solutions in the search space, and offspring are produced by sampling this model. This probability model is updated at each generation according to the population of solutions in that generation. By combining ABC algorithm with an EDA, we can exploit location information of

Algorithm 1: Hybridized ABC-EDA Algorithm 1 2 3 4 5 6 7 8 9 10 11

Initialize Population () ; Bestglobal = Calculate Best Solution amongst the Population () ; Initialize EDA () ; repeat Employed Bee Phase (); Onlooker Bee Phase (); Update EDA (); if Bestiteration is better than Bestglobal then Bestglobal := Bestiteration ; until termination condition is satisfied; return Bestglobal ;

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population of solutions as well as global statistical information to produce a new solution. This may guide the search process toward more promising solutions. Our proposed approach can be considered as a cross between ABC approach & EA/G approach and utilizes several features of both. Computational results on standard benchmark instances of the problem show the effectiveness of ABC-EDA. The remainder of this paper is organized as follows: Sect. 2 describes our ABCEDA approach. Computational results and their analysis will be presented in Sect. 3. At the end, Sect. 4 outlines some concluding remarks. Algorithm 2: Initialize Population 1 2 3 4 5 6 7 8 9 10 11

Population size N p Solutions S0 , S1 , S2 , . . . , S N p −1 Fitness of solutions f 0 , f 1 , . . . , f N p −1 S0 ← ∅; Repair (S0 ); Calculate fitness (S0 ); S1 ← Include all the vertices in the graph; Improve (S1 ); Calculate Fitness (S1 ); for i := 2 to N p − 1 do Init(Si )

Algorithm 3: Employed bee phase 1 n e is the number of employed bees. 2 for i := 0 to n e − 1 do

6

Sˆ ← Generate neighboring solution of Si using GM operator; ˆ Repair and improve S; ˆ if S is better than Si then ˆ Si ← S;

7 8

else if Si has not changed over the last Ntrials iterations then Scout Bee Phase (Si );

3 4 5

9 10

if Si is better than best then best ← Si

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Algorithm 4: Onlooker bee phase 1 n o is the number of onlooker bees. 2 for i := 1 to n o do 3 Pi ← Select and return Index S0 , S1 , S2 ...Sn o ; 4 Oi ← Generate neighboring solution of S Pi using GM operator; 5 Repair and improve Oi ; 6 if Oi is better than best then 7 best ← Oi ; 8 for i := 1 to n o do 9 if Oi is better than S Pi then 10 S Pi ← Oi

Algorithm 5: Scout Bee Phase (Si ) 1 Randomly generate a number rand1 in [0, 1]; 2 if rand1 < μ1 then 3 Init (Si ) 4 else 5 Si = ∅ foreach v ∈ best do 6 Randomly generate a number rand2 in [0, 1] if rand2 < μ2 then 7 Si = Si ∪ v 8

Repair Si

2 Proposed Hybridized Approach ABC-EDA for MWDS Problem The proposed algorithm has the components of the ABC algorithm along with the features of EDA which helps in directing the search toward the promising solutions. The ABC searches through the search space for an optimal solution utilizing a population of solutions. The EDA builds a probabilistic model using the global statistical information and directs the search toward the more promising solutions. We have also used a solution improvement method which further trims down the solution in order to minimize its fitness value. In Algorithm 1, we have explained the working of the ABC-EDA algorithm. The algorithm starts by initializing a population of solutions. The solutions are represented using subset encoding and binary encoding both as such a policy facilitates efficient implementation of various methods. The termination condition is the maximum number of iterations. In Algorithm 2, we explain how we generate the initial population. Solution 0 and 1 are generated by using the repair and improve operators, respectively. These operators will be described subsequently. These two solutions are

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added since they may represent some of the best solutions in the solution space and it would be ideal to explore the region around them for the optimal solution. The Init function generates remaining solutions randomly by adding nodes to the solution until it becomes a dominating set. The fitness of each solution is calculated by adding the weight associated with each node present in the solution.

2.1 ABC Algorithm The ABC algorithm has been used for a number of optimization problems. Its simplicity and self organizing nature make it desirable while solving complex problems. It basically consist of three phases, i.e., employed, onlooker, and scout bee phase. Employed Bee Phase: In this phase, each employed bee is associated with a solution. Each employed bee generates a neighboring solution using guided mutation which is described subsequently in Sect. 2.4. If the neighboring solution is infeasible, then it repairs it by adding nodes until it becomes a dominating set as described in Sect. 2.3. It then improves it by eliminating nodes from the solution which do not affect its feasibility as explained in Sect. 2.5. If the original solution does not improve over a certain period, then it calls the scout bee phase as shown in Algorithm 3. Onlooker Bee Phase: In Algorithm 4, each onlooker bee randomly selects a solution associated with the employed bee and explores it even further. It generates a neighboring solution using GM, which is then repaired and improved. If the solution is better than the best solution, then it is replaced. There is a chance that the same solution is selected by more than one onlooker bee. Scout Bee Phase: It either initializes the solution completely randomly or takes part of the best solution depending on some defined probability, i.e., μ1 . The parameter μ2 controls contribution of the best solution in the newly generated solution as shown in Algorithm 5.

2.2 EDA The EDA algorithm is a population-based algorithm. In this approach, it exploits the global statistical information of the population provided by the search process performed by the ABC algorithm. Initialize EDA: This method initializes the probability vectors using the initial population as shown in Algorithm 6. It is based on the idea that if a node is present in many of the good solutions, then there is a high possibility that it might be present in the optimal solution. Some percentage of best solutions are selected from the population, and the probability vector is initialized.

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Update EDA: After every iteration, this method is run in-order to update the probability vectors based on the new population as shown in Algorithm 7. Algorithm 6: Initialize EDA Np best solutions from the population; 1 Select d 2 Pi is the probability vector for node i; 3 for i := 1 to V do 4 Ni ← Count of best solutions containing node i; 5 if Ni = 0 then Ni 6 Pi ← N p ; (d ) 7 8

else Initialize Pi to some small value.

Algorithm 7: Update EDA Np best solutions from the population; 1 Select d 2 Pi is the probability vector for node i; 3 for i := 1 to V do 4 Ni ← Count of best solutions containing node i; Ni 5 Pi ← (1 − λ)Pi + λ N p ; (d )

2.3 Repair This methods converts an infeasible solution into a feasible one. Our repair method is same as the one used in [4]. It keeps on adding a new node until the solution becomes a dominating set. The node to be added is determined by using one of the heuristics mentioned below: d(u) (1) v ← arg maxu∈V −Si w(u) W (u) w(u)

(2)

W (u) × d(u) w(u)

(3)

v ← arg maxu∈V −Si v ← arg maxu∈V −Si

where w(u) is the weight of the node u, d(u) is the degree of the node u, W (u) is the sum of the weights of the non-dominated neighbors of u, and V is the complete

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set of nodes. The heuristic to be applied is selected randomly. These heuristics are introduced in [5].

2.4 Guided Mutation Algorithm 8 shows how the neighboring solution is generated in the ABC algorithm. The neighboring solutions are generated partly by taking elements from the existing solution and partly by using the probability vector.

2.5 Improve The improve algorithm is explained in Algorithm 9. If a solution remains a dominating set even after the elimination of a node from the solution, then the node is said to be a redundant node. Our improve algorithm is an iterative algorithm where during each iteration it determines a set of redundant nodes R and selects the node from R to be eliminated from solution using the heuristic given below: v ← arg maxu∈R

w(u) d(u)

(4)

where w(u) is the weight of the node u and d(u) is the degree of the node u. Thus, it selects a node which has higher weight and lower connectivity. This heuristic is introduced in [5] and is also used in [2, 4] for redundant node removal. The selected node is eliminated, and the next iteration begins. This process is repeated as long as the set of redundant nodes is non-empty. Algorithm 8: Guided mutation (Si ) 1 G ← ∅; 2 foreach v ∈ V do 3 Randomly generate a number rand1 in [0, 1]; 4 if rand1 < β then 5 Randomly generate a number rand2 in [0, 1]; 6 if rand2 < Pv then 7 G ← G ∪ {v}; 8 9 10

else if v ∈ Si then G ← G ∪ {v};

11 return Si ;

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Algorithm 9: Improve (Si ) 1 R ← ∅; 2 foreach v ∈ Si do 3 if Si \ v = DS then 4 R ← R ∪ v; 5 while R  = ∅ do 6 7 8 9 10 11

v ← argmaxu∈R w(u) ; d(u) Si ← Si \ v; R ← ∅; foreach v ∈ Si do if Si \ v = DS then R ← R ∪ v;

3 Computational Results 3.1 Dataset We have used the same dataset as used in [4, 5] to test our approach. This dataset consist of three types of graphs, viz Type I, Type II, and UDG. The number of nodes varies from 50 to 1000 in these graphs. Each combination of nodes and edges has ten instances. The solutions are computed for all the ten instances of a combination, and then, the average value is reported for that combination. In Type I graphs, the weight of every node is between [20, 70]. In Type II graphs, the weight of each node v is between [1, d(v)2 ], where d(v) is the degree of the node v. UDG instances consists of randomly distributed nodes in a space of 1000 × 1000 units. The transmission range of every node is defined as 150 or 200 units, and there is an edge between two nodes if they are within transmission range of one another.

3.2 Parameters All parameter values were chosen empirically. The number of iterations is taken as 700 for Type I and II, and 90 for UDG; number of trials for the scout bee phase Ntrials as 25 for Type I and II, and 30 for UDG; number of employed bees = 50, number of onlooker bees = 20; number of solutions selected to generate the probability vector d = 5; learning rate for probability vectors λ = 0.6; contribution of the probability vectors β = 0.2; probability that the solution will be generated randomly or using best solution μ1 as 0.5 for Type I and II, and 0.85 for UDG; probability which decides

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the contribution of best solution while generating a solution in scout bee phase as 0.5 in Type I and II, and 0.25 in UDG.

3.3 Results We have implemented our ABC-EDA algorithm in C and executed it on a Linux system with 3GHz Intel Core 2 Duo CPU with 4 GB RAM. We have not included HGA, ACO-LS, and ACO-PP-LS approaches of [5] in our comparisons since the approaches EA/G, EA/G-IR proposed in [2] and ABC in [4] are superior. Tables 1, 2 and 3 present the results of our approach along with EA/G, EA/G-IR, and ABC approaches. Best results are presented in bold font. ‘*’ and ‘**’ indicate the solution value obtained by ABC-EDA is equal to ABC and better than ABC, respectively. Data for EA/G, EA/G-IR, and ABC approaches are taken from their respective papers. Tables 1, 2 and 3 were analyzed, and following observations were made based on the results. For Type I and Type II instances, the ABC-EDA performs better than EA/G-IR, EA/G, and ABC algorithm in most of the cases. It gives worse results than the best known result for some cases by a very small margin. As the number of nodes increase, we can see a pattern emerging. We see that the ABC-EDA gives worse values as the density of the edges increases, but with very small margins. On the other hand, the improvement provided by the approach for other instances is much more for many of the cases, e.g., in Table 1, for instance 500 × 1000 the solution improved from 3695.2 to 3652.1. The degree of improvement increases as the number of nodes increases. It provides better results than the known best values for all the instances with 800 nodes and 1000 nodes in case of Type I, and 1000 nodes for Type II. The ABC-EDA obtained the known best results for all the instances of UDG dataset except for one instance where it improves the known best result by a small margin. For Type I and II instances, the ABC-EDA gives best results in most of the cases at the expense of higher execution time. For UDG instances, the computation time given by ABC-EDA is better than that of EA/G-IR except for one instance. Except for the first two small instances, ABC-EDA outperforms the ABC algorithm in terms of execution time.

4 Conclusion and Future Work In this paper, we have proposed a hybrid approach ABC-EDA combining ABC algorithm with EDA for MWDS problem. We have compared the ABC-EDA approach with EA/G, EA/G-IR, and ABC approaches available in the literature on standard benchmark instances for the MWDS problem. For a few instances, ABC-EDA algorithm produces slightly worse results than the best known values with some small margins. But for most of the large instances, it provides better results than the known best results. ABC-EDA performs better than ABC algorithm in most of the cases,

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Table 1 Results for Type I instances Nodes

Edges

EA/G-IR Avg

EA/G Time

Avg

ABC

ABC-EDA

Time

Avg

Time

Avg

Time

50

50

532.9

0.21

531.8

0.15

534

0

531.3**

0.41

50

100

371.5

0.23

371.3

0.15

371.2

0

370.9**

0.39

50

250

175.7

0.18

175.7

0.12

175.7

0

175.7*

0.33

50

500

94.9

0.16

94.9

0.13

94.9

0

94.9*

0.32

50

750

63.3

0.1

63.3

0.13

63.1

0

63.1*

0.31

50

1000

41.5

0.1

41.5

0.11

41.5

0

41.5*

0.23

100

100

1065.5

0.76

1065.4

0.45

1077.7

1.3

1061.4**

1.49

100

250

620

0.72

620.8

0.39

621.6

1

621.1**

1.29

100

500

355.9

0.6

357

0.33

356.4

0

355.8**

1.1

100

750

256.7

0.52

257.9

0.31

255.9

0

258

1.07

100

1000

203.6

0.49

204.8

0.32

203.6

0

205.2

1.01

100

2000

108.1

0.45

107.9

0.35

108.2

0

107.9**

0.93

150

150

1587.4

1.64

1594.6

0.81

1607.9

4.1

1583.8**

3.63

150

250

1224.5

1.71

1240.3

0.78

1231.2

2.8

1223.2**

3.43

150

500

755.3

1.45

762.3

0.67

752.1

1.8

747.5**

2.73

150

750

550.8

1.27

552.7

0.61

549.3

1

549.2**

2.35

150

1000

435.2

1.11

439.3

0.56

435.1

1

434.8**

2.21

150

2000

241.5

0.88

242.1

0.55

242.2

1

243.7

2.02

150

3000

168.1

0.82

168

0.59

167.8

1

169.3

2.08

200

250

1924.1

3.68

1951.7

1.26

1941.1

6.6

1916.7**

7.24

200

500

1251.3

3.3

1269.5

1.11

1246.9

4

1243.4**

6

200

750

927.3

2.78

935.3

1.01

923.7

3

924.2

5.26

200

1000

731.1

2.39

736.5

0.94

730.4

2.8

729**

4.56

200

2000

417

1.68

422.4

0.82

417.6

2

417.9

3.63

200

3000

294.7

1.42

297.8

0.81

294.4

2

298.1

3.48

250

250

2653.7

4.65

2695.5

1.85

2685.7

15.7

2647**

12.78

250

500

1835.3

4.66

1909.3

1.67

1836

8

1817.9**

11.33

250

750

1399.2

4.25

1436.9

1.52

1391.9

5.9

1382.6**

10.04

250

1000

1114.9

3.69

1137.3

1.42

1115.3

5

1107.7**

8.77

250

2000

637.5

2.65

657.4

1.22

630.5

3.8

628.6**

6.16

250

3000

456.3

2.16

459.5

1.15

454.9

3

457.1

5.81

250

5000

291.8

1.85

295.5

1.16

292.4

3

297.4

5.39

300

300

3213.7

8.73

3288.3

2.38

3240.7

23.6

3193**

20.53

300

500

2474.8

7.21

2574.9

2.18

2484.6

13.9

2461.3**

18.99

300

750

1896.3

6.48

1977.6

1.93

1901.4

10.1

1876**

16.91

300

1000

1531

5.7

1604.3

1.8

1523.4

8.1

1516.7**

15.16

300

2000

880.1

4.03

904.4

1.46

875.5

5.9

878.8

10.33

300

3000

638.2

3.27

645.6

1.34

635.3

5

636.8

8.55

300

5000

415.7

2.59

419.6

1.3

411

4.4

417.7

7.32

500

500

5380.1

37.52

5630

6.02

5480.1

78.1

5370.7**

82.83

500

1000

3695.2

26.36

3990.2

5.11

3707.6

38.7

3652.1**

71

500

2000

2264.3

25.54

2427.3

4.05

2266.1

22.8

2225**

53.8

(continued)

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Table 1 (continued) Nodes

Edges

EA/G-IR Avg

500

5000

500

10,000

800

1000

7792.2

800

2000

5160.7

800

5000

2561.9

800

10,000

EA/G Time

Avg

ABC Time

ABC-EDA

Avg

Time

Avg

Time

1083.5

9.02

1128

3.06

1070.9

15.3

1073.5

29.41

606.8

6.08

625.7

2.77

596

13.1

605.8

21.37

129.94

8426.6

14.63

7907.3

208

7755.7**

310.5

102.09

5641.4

11.76

5193.2

95.1

5079.1**

269.85

53.01

2702.4

2548.6

50.9

2492.2**

140.94 81.02

1497

31.17

7.11

1559.6

5.12

1471.7

40.6

1440**

1000

1000

10771.7

249.82

11599.1

23.76

10992.4

589

10766.4**

585.73

1000

5000

3876.3

107.41

4163.5

13.03

3853.7

95.8

3757.4**

331.43

1000

10,000

2265.1

63.22

2365.8

9.02

2215.9

69.8

2180.8**

180.46

1000

15,000

1629.4

45.86

1682.6

7.44

1603.2

64

1573.6**

131.36

1000

20,000

1299.9

36.35

1333.7

6.57

1259.5

60.4

1242.1**

113.69

Table 2 Results for Type II instances Nodes

Edges

EA/G-IR Avg

EA/G Time

ABC

ABC-EDA

Avg

Time

Avg

Time

Avg

Time

50

50

60.8

0.19

60.8

0.16

60.8

0

60.8*

0.42

50

100

90.3

0.27

90.3

0.15

90.3

0

90.3*

0.42

50

250

146.7

0.23

146.7

0.14

146.7

0

146.7*

0.34

50

500

179.9

0.09

179.9

0.14

179.9

0

179.9*

0.3

50

750

171.1

0.07

171.1

0.14

171.1

0

171.1*

0.27

50

1000

146.5

0.06

146.5

0.12

146.5

0

146.5*

0.23

100

100

123.5

0.86

123.7

0.46

124.4

2

123.5**

1.64

100

250

209.2

0.92

209.6

0.43

209.6

1.1

209.6*

1.42

100

500

305.7

0.78

305.7

0.38

305.8

1

305.7**

1.11

100

750

384.5

0.7

384.5

0.36

384.5

0.3

384.5*

0.99

100

1000

427.3

0.67

427.3

0.35

427.3

0

427.3*

0.93

100

2000

550.6

0.54

550.6

0.39

550.6

0

550.6*

0.87

150

150

184.5

1.85

185.4

0.82

185.9

6.1

184.5**

4.02

150

250

232.8

2.03

233.5

0.82

233.4

5

233.1**

4.01

150

500

349.7

1.95

351.3

0.75

349.5

3.3

349.5*

2.96

150

750

452.4

1.78

453.6

0.7

453.7

2.4

452.8**

2.51

150

1000

548.20

1.61

551

0.68

547.8

2

547.2**

2.29

150

2000

720.1

1.2

720.1

0.64

720.1

1

720.1*

1.82

150

3000

792.4

1.07

793.2

0.68

793.2

1

792.4**

1.74

200

250

272.3

4.38

274.8

1.31

273.5

11.9

271.7**

8.22

200

500

388.4

4.51

389

1.22

387.6

8.6

386.8**

6.76

200

750

497.2

4.18

501.7

1.14

498.5

6.5

497.3**

5.58

200

1000

598.2

3.89

599.2

1.08

599.3

5.2

597.3**

4.88

200

2000

885.8

2.78

885.8

0.98

885.5

3.5

884.6**

3.46

200

3000

1019.7

2.16

1023.8

0.95

1021.3

2.8

1022.4

2.98

(continued)

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Table 2 (continued) Nodes

Edges

EA/G-IR Avg

EA/G Time

Avg

ABC Time

ABC-EDA

Avg

Time

Avg

Time

250

250

306.5

5.26

312.5

1.9

308.6

23.7

306.1**

14.54

250

500

441.6

6.1

448.5

1.8

442.6

18

440.8**

13.16

250

750

569.2

6.09

579.9

1.7

569.9

13.7

568.2**

11.35

250

1000

671.7

5.89

677.4

1.61

670.3

11.5

669.5**

9.77

250

2000

1010.3

4.23

1019.6

1.41

1010.4

6.9

1010.2**

6.45

250

3000

1250.6

3.5

1253.8

1.35

1251.3

5.6

1250.6**

5.25

250

5000

1464.2

2.59

1470.3

1.34

1464.7

4

1464.2**

4.4

300

300

370.5

9.01

380

2.41

373.5

41.1

369.9**

23.38

300

500

480

8.83

491

2.32

481.6

28.2

478.4**

21.7

300

750

613.8

7.57

631.5

2.17

617.6

26.1

613.9**

19.06

300

1000

742.2

8.96

767.1

2.06

743.6

20.2

738.1**

16.9

300

2000

1094.9

6.67

1107.7

1.72

1095.9

11.6

1095.2**

10.83

300

3000

1359.5

5.41

1371.9

1.58

1361.7

9.1

1361.3**

8.22

300

5000

1683.6

4.02

1686.2

1.5

1682.7

6.9

1682.7*

6.55

500

500

625.8

31.06

650.9

6.36

630.4

162.3

623.7**

98.25

500

1000

906

28.27

955.7

5.69

906.7

108.3

901.6**

83.89

500

2000

1376.7

23.41

1441.3

4.58

1383.6

66

1367.3**

59.56

500

5000

2340.3

17.36

2412.5

3.17

2337.9

31

2342.1

28.71

500

10,000

3216.4

10.8

3236.9

2.38

3211.5

21.1

3214.5

19.32

800

1000

1107.9

132.36

1187.6

14.58

1119.2

505

1104.5**

357.18

800

2000

1641.7

111.84

1797.5

12.49

1656.4

338.9

1636.1**

306.02

800

5000

2939.3

68.14

3200.4

8.64

2917.4

127.3

2888.1**

154.42

800

80.72

10,000

4155.1

40.15

4353.5

6.02

4121.3

70.9

4121.5

1000

1000

1240.8

202.08

1333.1

23.57

1256.2

1173.7

1239.4**

745.57

1000

5000

3222

132.94

3586.2

15.05

3240.7

303.6

3194.4**

387.94

1000

10,000

4798.6

84.82

5247

10.91

4781.2

150.6

4748.1**

190.3

1000

15,000

5958.1

61.64

6364.4

8.96

5934

111.9

5897.6**

132.03

1000

20,000

6775.8

59.2

7066.8

7.78

6729

75.3

6691.5**

107.38

but at the expense of higher computational time. ABC-EDA is better than all the approaches considered in comparison with most of the instances in terms of the quality of the solution. As a future work, we intend to explore new tie-breaking rules for existing constructive heuristics and altogether new constructive heuristics to improve the performance of our approach. Similar approaches combining ABC algorithm with EDA can be developed for other combinatorial optimization problems.

Hybridization of Artificial Bee Colony Algorithm … Table 3 Results for UDG instances Nodes Range EA/G-IR Avg Time 50 50 100 100 250 250 500 500 800 800 1000 1000

150 200 150 200 150 200 150 200 150 200 150 200

393.9 247.8 449.7 216 293.7 118.7 169.9 68 113.9 40.8 121.1 45.9

0.23 0.2 0.47 0.47 2.3 1.77 3.43 2.34 10.23 7.86 13.76 5.42

619

EA/G Avg Time

ABC Avg

Time

ABC-EDA Avg Time

393.9 247.8 449.7 216 293.9 118.7 170.5 68 114.5 40.9 121.5 46.8

393.9 247.8 449.7 216 293.9 119 170.7 68 114 40.8 121.4 46.1

0.01 0.01 0.3 0.22 2.6 2.1 6.7 5.9 13.9 12.56 28.4 23.5

393.9* 247.8* 449.7* 216* 293.7** 118.7** 169.9** 68* 113.9** 40.8* 121** 45.9**

0.13 0.12 0.36 0.31 1.16 1.03 2.48 2.51 2.99 5.42 6.71 3.45

0.05 0.05 0.19 0.15 0.98 0.67 3.36 2.3 8.4 6.36 13.16 10.4

References 1. B. Aoun, R. Boutaba, Y. Iraqi, G. Kenward, Gateway placement optimization in wireless mesh networks with QoS constraints. IEEE J. Sel. Areas Commun. 24(11), 2127–2136 (2006) 2. S.N. Chaurasia, A. Singh, A hybrid evolutionary algorithm with guided mutation for minimum weight dominating set. Appl. Intell. 43(3), 512–529 (2015) 3. M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NPCompleteness (W. H. Freeman, New York, NY, 1979) 4. C.G. Nitash, A. Singh, An artificial bee colony algorithm for minimum weight dominating set, in 2014 IEEE Symposium on Swarm Intelligence (SIS 2014) (IEEE, 2014), pp. 313–319 5. A. Potluri, A. Singh, Hybrid metaheuristic algorithms for minimum weight dominating set. Appl. Soft Comput. 13(1), 76–88 (2013) 6. D. Subhadrabandhu, S. Sarkar, F. Anjum, Efficacy of misuse detection in ad hoc networks, in 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004 (IEEE, 2004), pp. 97–107 7. Y. Wang, W. Wang, X.-Y. Li, Distributed low-cost backbone formation for wireless ad hoc networks, in Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM, 2005), pp. 2–13 8. Q. Zhang, J. Sun, E. Tsang, An evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans. Evol. Comput. 9(2), 192–200 (2005)

Privacy Preservation for Data Sharing with Multi-party Access Control in Cloud Computing: A Review Paper Shubhangi Tanaji Khot, Amrita A. Manjrekar, and Rashmi J. Deshmukh

Abstract The cloud computing is the advancement to shared volume of information through the network. There are lots of techniques that are used to providing security for data in cloud. But current techniques are not as better related to the ciphertext. So here, we propose information gathering, sharing and restrictive distribution plan with multi-owner privacy preserving in cloud. Here, data owner can impart private information to group of clients through cloud in secure manner with identity-based broadcast encryption (IBBE) technique. Keywords Data sharing · Multi-owner · Identity-based encryption · Privacy conflict

1 Introduction Cloud computing is the combination of various services like servers, storage, databases, networking, over the Internet to giving fastest intelligence, flexible resources and economies of scale. Cloud computing offers pervasive and on-demand access to the fully featured applications. Cloud computing is web-based processing so the data will be always available to the client. It means cloud computing is done by storing local data of user into cloud server so that user can access their own data whenever they needed [1]. Cloud computing is brilliant condition for industry world regarding cost and giving services. It is for quite a while envisioned vision of handling as an utility where data owners can S. T. Khot (B) · A. A. Manjrekar · R. J. Deshmukh Department of Technology, Computer Science and Technology, Shivaji University, Kolhapur, Maharashtra, India e-mail: [email protected] A. A. Manjrekar e-mail: [email protected] R. J. Deshmukh e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_60

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remotely store their data in a cloud to acknowledge on-demand amazing applications and organizations from a pooled configurable registering assets. The cloud computing is very famous because of its rich storage resources and immediate access at anyplace [2]. It adds up to the advantages of preparing structure and after that gives on-demand benefits over the Internet. Many popular organizations are as of now giving open cloud authority, for example, Amazon, Google and Alibaba. These authorities enable individual clients and enterprise clients to upload information (e.g. photographs, documents and videos) to cloud specialist as cloud service provider (CSP), to get to the information whenever in anyplace and distribute the information to others, especially with different types of searching of data over the cloud [3] and [21]. To ensure the security of clients related to data, generally cloud administrations accomplish access control by keeping up access control list (ACL). Along these lines, clients can decide to either distribute their information to anybody or award access rights simply to their affirmed individuals. However, the security dangers have brought worries up in individuals, because of the information is put away in plaintext structure by the CSP. When the information is presented on the CSP, it is out of the data owner’s control [4]. However, combining privacy selection of data owner and co-owner is a big task, so that privacy conflicts occur. Privacy conflict happens because of owner and coowner has opposite permissions of accessing the data, so anyone can easily access that data. All existing systems were based on plaintext. To deal with this problem, we propose system with multi-party access control mechanism. The proposed system supports group sharing of secured data and conditional distribution of data with multi-owner in cloud. This system will also support multiparty access control mechanism. The system will be based o group sharing with respect to ciphertext among various users and collect all the functionality of multiparty access control. In this paper, we discuss about brief literature review with some key parameters. The review focus is on also different group sharing cryptographic schemes and comparison between them.

2 Literature Survey Cloud computing is a very secure storage which gives customers very easy and simple access towards their data. It allows customers to distribute data to another by functioning as collecting the data. It faces the challange as maintaing integrity and furthermore privacy conflicts. Several schemes which ensure the integrity of the shared group data with multi-owner cloud environment are reviewed here. In the paper, creator proposed a few private information sharing strategies in cloud computing [2]. In this paper, they characterized that information proprietor sends private information to the cloud provider by giving record of customers, along these lines just approved customers in the record can get decryption key and further interpret the private data.

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The author gives in-depth analysis about various security attacks and threats related to the data integrity with proper solution [5]. They proposed an independent and data secrecy scheme in cloud. This technique shows that attribute-based encryption with ciphertext policy (CP-ABE) scheme is less secure than RBAC-CPABE because access authorization is only achieved by data itself. The structure discussed as MONA in [1] accomplishes fine grained get to control and which user has not priority to access the shared private information are come in category of revoked users and will not have permission to share again. The framework is exibited to accomplish secured and adaptable information bunch transfer in [6]. In this paper, they utilize the method as quality and planned discharge proxy re-encryption to guarantee that private information provider who satisfies the entrance strategy of encoded information and just exchange it to different gatherings in the wake of liberating time by dispensing a re-encryption key to cloud server. Here creator utilized the paper in which framework permits a customer to send a scrambled message to a recipent by utilizing cloud storage server [7]. The sender just has to know the personality of the beneficiary; however, no other data (e.g. public key or its certificate). The recipient needs to have two things so as to decode the ciphertext. The primary thing is his/her secret key put away in the PC. The subsequent thing is an extraordinary individual security gadget which interfaces with the PC. In paper, they proposed the system which has two sides: one is cloud and other is data owner side in cloud storage [8]. This is also combined with the different attacks and provide resource consumption accounting. In paper, creator presented new idea as privacy preserving and effective information sharing help on cloud [9]. In this plan, creator joined two methods to be specific CP-ABE and IBE where client opens key which is tight with client secret key to acknowledge completely impact secure and protection saving. This is secure and related with execution, and P2E has low overhead and high effectiveness. Table 1 is based on the criteria of above summary of literature review where it focused on some major papers. This table contains information which is used to impart the private data to group of users through cloud with secure manner. It Table 1 Summary of literature survey Criteria

Data security

Data efficiency

Data sharing techniques

Data integity

Yan [2]

Yes

Yes

ABE and proxy re-encryption (PRE)

No

Lang [5]

Yes

Yes

CP-ABE

No

Boyang Wang [10]

Yes

No

Multisignature

Yes

Huang [6]

Yes

No

ABE and time relevant—conditional PRE (TR-CPRE)

Yes

Liu [1]

No

Yes

Dynamic broadcast encryption (DBE)

Yes

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also gives the information about various data sharing techniques over the years are used. The main techniques illustrated in the following Table 1 are data security, data efficiency, data sharing techniques and data integrity. Data security model is used for secure private data, only authenticated users can show it, data efficiency model is suitable for flexible data access, data sharing techniques model shows what are the techniques used to communicate in the cloud over the years, and data integrity model is checking mechanism on data owner side.

3 Different Cryptographic Scheme Figure 1 shows different cryptographic schemes are developed over the time. The first scheme is introduced in 2006, and here we analyze the ABE and IBBE scheme. For sharing the data in the group, IBBE has access control mechanism to achieve secure data sharing.

3.1 Attribute-Based Encryption (ABE) Scheme Attribute-based encryption scheme is utilized for secure and fine-grained information sharing. In this plan, both the client secret key and their separate ciphertext are relating to set of qualities.This scheme uses access policies for encrypted data with decryption key. This scheme focuses on one to many encryption methods. The real-time example: Consider the College has attributes: {“Student”, “Teacher”, “HOD”, “Admin”}. If the user A is a student, he has the attributes{“Teacher” ∧ “HOD”}. If admin encrypts the file with attributes{“Colllege” ∧ (“Teacher” ∨ “HOD”)}, the student will not be allowed to decrypt the data. If the attributes are {“Colllege” ∧ (“Teacher” ∨ “HOD” ∨ “Student”)}, the students are allowed to decrypt the data. 2010

Year

Fig. 1 Different cryptographic schemes

2008 2006 2004

Different Cryptographic Schemes

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3.2 Proxy Re-Encryption(PRE) Scheme The PRE scheme is same as traditional symmetric and asymmetric encryption scheme. The asymmetric PRE comes with two basic function as unidirectional forwarding and bidirection forwarding. (1) Unidirection forwarding: This is one-way communication. For example, message can be re-encrypted from teacher to student but not the reverse. (2) Bidirection forwarding: This is two-way communication where message can be re-encrypted from teacher to student and vice versa. This plan is utilized to accomplish secure information dissemination in distributed computing with the assistance of re-encryption key related to the new recipients to the Cloud Service Provider (CSP). But it disseminates a particular document.

3.3 Conditional Proxy Re-Encryption (C-PRE) Scheme In the PRE scheme, data dissmination depends on sender’s and receiver’s public key. So the new procedure is presented as C-PRE plot with bilinear paring. In C-PRE, intermediary additionally needs to have the correct condition to move the ciphertext. In this plan, information proprietor can be re-scrambled information with comparing re-encryption key which fulfilling explicit condition.

3.4 Identity-Based Broadcast Encryption (IBBE) Scheme In this plan, client can impart their scrambled information to different recipients one after another and open key of beneficiary can be viewed as any substantial strings. It will take minimal effort key administration to broadcasting information to explicit collectors in cloud computing. This scheme is known as semi-structured because ciphertext is not modifying uploaded by data owners.

3.5 Comparison of ABE and IBBE Algorithms The below Table 2 shows the comparison between ABE and IBBE algorithm based on following parameters as Setup, Key Generation, Encryption and Decryption. In ABE scheme, client secret key and their separate ciphertext are relating to the set of qualities. So this technique takes high cost for key management and access policies as compared to IBBE technique. The Table 3 shows the notations of algorithm.

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Table 2 Comparison of ABE and IBBE algorithm S. No.

Parameters

Algorithms ABE

IBBE

1

Setup

(MKa , PKa )

(MKi , PKi )

2

Key Generation

(MKa , PKa , Sa )

(MKi , PKi , IDi )

3

Encryption

(Ta , PKa , Ma )

(IDi , PKi , Mi )

4

Decryption

(AKa , PKa , CTa )

(SKi , PKi , CTi )

Table 3 Notations

S. No.

Notations

Description

1

MKa , MKi

Master key of ABE and IBBE

2

PKa , PKi

Public parameter key of ABE and IBBE

3

Sa

A set of attributes of ABE

4

Ta

Access policies of ABE

5

M a , Mi

Message of ABE and IBBE

6

AKa

Attribute set of ABE

7

CTa , CTi

Ciphertext of ABE and IBBE

8

IDi

Identity key of IBBE

9

SKi

Secret key of IBBE

4 Proposed Work: Privacy Preservation for Data Sharing with Multi-party Access Control in Cloud Computing As cloud becomes an efficient way for the data storage and sharing services, people are cooperating as a gathering to impart the information to one another. Cloud is the best solution for sharing, maintaining and handling data. In the recent year, cloud is used in large system to store data and retrieve data online. Using the cloud computing, the user accesses the data anywhere, anytime and any device. Cloud computing is a most recent and quickly developing innovation which depends on the utilization of assets. These resources are provided dynamically via the Internet. The service provided by service provider is used by multiple customers. The customer cannot fully trust for storage service provided by cloud service provider; hence, instead of storing data as it is, customer prefers to store encrypted data. The problem arised due to this is searching required data from encrypted data. Existing research work manages security issue through encryption and access control model to the redistributed information. These benefits are only suitable for single owner. In single-owner framework, only group admin has priority of read, write and update any task. But multi-owner is more adaptable than single owner. In multiowner, each group member has read, write and update rights. The current member of

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group can deny the group at any time. All the group members have their own unique identity in group so data sharing is done by a secure manner.

5 Conclusion There are different cryptographic techniques available in cloud computing. These cryptographic techniques are used for sharing data in group. This paper provides a review of attribute-based encryption (ABE), proxy re-encryption (PRE), conditional proxy re-encryption (C-PRE) and identity-based broadcast encryption (IBBE) techniques. The comparison table shows that IBBE technique is better than ABE. It is easy to implement. IBBE scheme always preferred the users who can share their encrypted data with multiple receivers. This scheme allows low-cost key management and also takes less complexity for encryption and decryption than ABE. In IBBE, approved client can perform searching legitimately on encoded information without sharing mystery key. The information proprietor just needs to scramble one ciphertext for every approved client.

References 1. X. Liu, Y. Zhang, B. Wang, J. Yan, Mona: secure multi-owner data sharing for dynamic groups in the cloud. IEEE Trans. Parallel Distrib. Syst. (TPDS’13) 24(6), 1182–1191 (2013) 2. Z. Yan, X. Li, M. Wang, A.V. Vasilakos, Flexible data access control based on trust and reputation in cloud computing. IEEE Trans. Cloud Comput. 5(3), 485–498 (2017) 3. P.A. Tamgave, A.A. Manjrekar, S. Khot, Regular language search over encrypted cloud data using token generation: a review paper, in: Innovative Data Communication Technologies and Application, ICIDCA 2019, ed. by J. Raj, A. Bashar, S. Ramson (eds). Lecture Notes on Data Engineering and Communications Technologies, vol 46. (Springer, Cham, 2020) 4. A.S. Anila, M.J. Manoj, Multi-owner data sharing in cloud. IJCSMC 4(6), 36–48 (2015) 5. B. Lang, J. Wang, Y. Liu, Achieving flexible and self-contained data protection in cloud computing. IEEE Access 5, 1510–1523 (2017) 6. Q. Huang, Y. Yang, J. Fu, Secure data group sharing and dissemination with attribute and time conditions in Public Clouds. IEEE Trans. Serv. Comput. (2018) 7. F. Wang, J. Mickens, N. Zeldovich, V. Vaikuntanathan, Sieve: cryptographically enforced access control for user data in untrusted clouds, in NSDI, pp. 611–626 (2016) 8. K. Xue, W. Chen, W. Li, J. Hong, P. Hong, Combining data owner-side and cloud-side access control for encrypted cloud storage. IEEE Trans. Inf. Forensics Secur. 13(8), 2062–2074 (2018) 9. A. Jhari, S. Fernandes, Techniques for secure multi-owner data sharing in cloud. Int. J. Eng. Sci. Comput. (2017) 10. B. Wang, H. Li, X. Liu, F. Li, X. Li, Efficient public verification on the integrity of multi-owner data in the cloud. IEEE J. Comm. Netw. 16(6), 2014 11. B. Wang, S.S.M. Chow, M. Li, H. Li, Storing shared data on the cloud via security-mediator, in IEEE 33rd Internation Conference on Distributed Computing System, 2013 12. C. Delerablée, Identity-based broadcast encryption with constant size ciphertexts and private keys, in Proceeding of International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT ‘2007), pp. 200–215 (2007)

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13. Z. Yan, X. Li, M. Wang, A.V. Vasilakos, Flexible data access control based on trust and reputation in cloud computing. IEEE Trans. Cloud Comput. (2015) 14. X. Dongt, J. Yut, Y. Luot, P2E: privacy-preserving and effective cloud data sharing service, in IEEE Global Communications Conference (GLOBECOM), 2013 15. B. Wang, H. Li, Efficeint public verification on the integrity of multi-owner data in the cloud. J. Commun. Netw. 16(6) 2014 16. L. Li, Y. Zheng, X. Li, KeyD: secure key de-duplication with identity-based broadcast encryption. J. Latex Files 14(8) 2015 17. J. Bethencourt, A. Sahai, B. Waters, Ciphertext-policy attribute based encryption, in IEEE Symposium on Security and Privacy (SP), (IEEE, 2007), pp. 321–334 18. K. Ren, C. Wang, Q. Wang, Security challenges for the public cloud. IEEE Internet Comput. 16(1), 69–73 (2012) 19. J. Hong, K. Xue, W. Li, Y. Xue, TAFC: time and attribute factors combined access control on time-sensitive data in public cloud, in Proceeding of 2015 IEEE Global Communications Conference (GLOBECOM 2015), pp. 1–6 (2015) 20. M. Sepehri, S. Cimato, E. Damiani, C. Yeuny, Data sharing on the cloud: a scalable proxy-based protocol for privacy-preserving queries, in Proceedings of 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2015) 21. A. Hanumant Bhosale, A.A. MAnjrekar, Attribute-based storage control with smart dedepulication filter using hybrid cloud, in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (Pune, India, 2018), pp. 1–6. https://doi. org/10.1109/ICCUBEA.2018.8697399.

Pixel-Based Attack on ODENet Classifiers Nitheesh Chandra Yaratapalli, Reethesh Venkataraman, Abhishek Dinesan, Ashni Manish Bhagvandas, and Padmamala Sriram

Abstract With rapid improvements in deep learning, one of the challenging open issues is the exposure of neural systems to adversarial attack. Many competitions explored various attacks and prevention strategies against these attacks. Another worldview of profound neural systems with persistent inward activations was presented at NeurIPS 2018 that likewise acquired the best paper grant. With the neural ODE block, all discrete transformations can be parametrized as a continuous transformation that can be solved using a ODE solver. We break down picture/content classifiers with ODE Nets, upon subject to ill-disposed assaults and contrast it with standard profound models. Neural ODEs are more robust towards black-box attacks. Some of their intrinsic properties, such as accuracy–efficiency trade-off and versatile calculation cost, allow us to build powerful models further. Keywords Ordinary differential equations · Adversarial attacks · Deep model learning · One-pixel attack · ODEnet

N. C. Yaratapalli (B) · R. Venkataraman · A. Dinesan · A. M. Bhagvandas · P. Sriram Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] R. Venkataraman e-mail: [email protected] A. Dinesan e-mail: [email protected] A. M. Bhagvandas e-mail: [email protected] P. Sriram e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_61

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1 Introduction The adversarial attack is a strategy utilized in the domain of machine learning/deep learning which tries to trick a model through malicious input made by subtle modifications negligible to a human to the input data. This technique is commonly used to attack detection/classification models. The real-life implications of such attacks can be severe. For example, modification of reviews of a topic in social media can trick content moderation systems. This could lead to display and spread of misinformation or copyrighted data onto these platforms [1]. Currently, no approach, however, is capable of a completely robust approach towards adversarial inputs. Today’s widely used models consist of discrete layers, which use gradient-based methods to train itself. The transformations of data happen in layers in a layered approach. At each layer, features will be transformed and passed to the next layer. Generally, deeper the model, more the training data required to achieve acceptable results. Deep learning has widespread applications such as text classification, autonomous vehicles, speech to text models, anomaly detection, decision-making and breast cancer detection [2, 3]. With improvements in technologies like GANs, models are even able to generate life-like faces and perform a style transfer. The investigation into the adversarial examples on trained models is actively being worked upon by the research community [4]. The characterization of the techniques provides various insights to identify and differentiate adversarial examples from authentic inputs. Just recognizing the “cause and effect” is good enough to validate the usage and regulations of these models in critical systems. The goal of our work is to pick up insights from a paperback, which lately proposed a new family of deep learning architectures based on and derived from the similarity of the parametrize equation of ResNets with ordinary differential equations (ODEs) [5]. Instead of a set of discrete transformations in traditional neural networks that follow a layered approach, neural ODEs have continuous transformations in the direction of the output. Any adaptive ODE solver like variants of RK4 [6–9]. Most of the solvers provide attributes which are used to handle trade-off between speed and precision of the system. Due to the generality and independence of ODE nets, they can easily be used in existing models and integrated in new applications. Neural ODE networks are still differential models meaning they use gradient descent. Adversarial sample generation algorithms [10, 11], which are used in the existing differential models, can be applied to neural ODEs. Current work includes the following contributions: Comparison of the following architectures—a ResNet and two ODE-based models, along with a variant WRNs— and their robustness to adversarial attacks on data sets: Kaggle CIFAR-10 [12] and ImageNet [13]. Along with stability against adversarial attacks with tweaks to precision–cost trade-off, which is made possible by adaptive ODE solvers having tolerance parameters, decreasing the solver threshold brings about a decline in the success percentage of the attack with only small degradation in classification accuracy of the model.

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2 Related Work 2.1 Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey The paper presents a survey on a plethora of techniques used for attacking NLPbased deep learning models. The works presented in this paper date back to 2017 when the concept was first proposed. Unlike CV-based models, NLP presents a lot of challenges because of the intrinsic differences between CV models like CNNs and NLP models. The paper selects, summarizes, discusses and analyses 40 representative works in a comprehensive way along with covering a lot of preliminary knowledge required to get into the field [14].

2.2 Ensemble Adversarial Training: Attacks and Defences In this paper, Tramer et al. (2017) find a novel black-box attack and a defence mechanism for black-box attacks in general. They claim that overfitting on the training loss of the model is more common when models with adversarial training are one-shot attacked. They also experimentally proved that the network changes the gradient a little locally to confuse the attacker [15].

2.3 Spatially Transformed Adversarial Examples The stAdv attack [16] aims to prove that perturbation deviation metrics (such as L 2 ) are not effective. Instead of pixel value alteration, the spatial position is minimally altered. The loss function is same as the one used in Carlini and Wagner [11].

2.4 Adversarial Training Methods for Semi-Supervised Text Classification The adversarial and virtual adversarial methods proposed in this document perform better than previous models not only in terms of text classification tasks but in terms of word-embedding training. This can provide valuable insights into many other research topics such as machine translation and question answering systems. The proposed methods could also be used for other sequential tasks such as voice and video. One of the records is the large movie review record, often referred to as the IMDB record. 88.89% accuracy was reached [17].

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3 Robustness of NeuralODEs Neural ODE-based classifiers are networks using an ODE block. We studied the robustness of classifiers with an ODE block against different variations. We considered three architectures: with baseline as regular ResNet (RES), a combination of residual and ODE block which we refer to as a (MIX) mixed model and a pure ODE block-based model (ODE-only network, OON). All models are trained on public data sets with adversarial attacks on test sets of those corresponding data sets. Attack success rate is the metric being used for comparison.

3.1 Model Architectures The model architectures we used (Fig. 1) are as described.

3.1.1

Residual Network (RES)

A series of ResNet blocks, where input will be added to outputs of layers, is also with skip connections. This was used as baseline for the results. This model has a

Fig. 1 Model structures [stride]; padding = 1. K = 256

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kernel size of 3 × 3 with 64-filter convolutional layers as ResNets [18, 19]. Model consists of eight ResNets with one convolution skip connection.

3.1.2

Wide Residual Network (WRN)

Similar to a regular ResNet, a number of layers between skip connections are more, thus becoming higher-order ResNet blocks. Using a model similar to RES, but double the width, model consists of eight ResNets with two convolution skip connections [19, 20].

3.1.3

Mixed ResNet-ODE Network (MIX)

In both RES and WRN, excluding the first two ResNet blocks, replace all others combined with one ODE Block, akin to the ODENet architecture defined by Chen et al. [5].

3.1.4

ODE-Only Network (OON)

All the ResNet blocks combined have been replaced with a single ODE Block. This allows for a drastic drop in model parameters.

3.2 One-Pixel Attack Differential evolution(DE) method, by encoding perturbation to an array with combinations of values, where each combination has a fixed perturbation count, is with elements: (x, y, r, g, b). x-y coordinates with pixel RGB value. Each perturbation only modifies one pixel. Each perturbation will be following the equations: xi (g + 1) = xr 1 (g) + F(xr 2 (g) − xr 3 (g))

(1)

r1  = r2  = r3 ,

(2)

here xi is the candidate element solution, and r1 , r2 , r3 are the results, scale parameter F using 0.5 and index of generation g [21]. Robustness with adversarial examples on classifiers can be calculated using the attack success rate, which can be analysed with various attack parameter configurations.

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4 Experimental Set-up Evaluate the success of one-pixel attacks on the neural networks models and variants mentioned in the previous section.

4.1 Data Sets We have used the data sets most common with low resolution images: ImageNet [13] and CIFAR-10 [12]. CIFAR-10 consists of around 60,000 images of size 32 × 32 spread across ten different classes [22]. ImageNet is similar but significantly larger in number.

4.2 Training Details Number of filters in each convolution block represented by K is set to 256. A dropout probability of 0.5 has been used for fully connected layers, and SGD momentum equals 0.9 with α(learning rate) equals 0.1, along with batch size of 256 [23]. Dormand–Prince fifth-order Runge–Kutta ODE solver version [24] was used for models with ODE Blocks, with tolerance τ taken as 10−3 . if τ is less than step error, then integration will be ignored and the solver continues to the next step which is used package provided by [5] https://github.com/rtqichen/torchdiffeq. In Table 1, the classification accuracy for each model and data set is mentioned along with total parameters. ODE-based models are tested with various τ . τ is inversely proportional to precision of the integration. This causes reduction in accuracy, but with only a small effect on overall accuracy. The worst deterioration occurs for the ODE-only network where accuracy difference is noticeable. Decrease in number of parameters can also be observed.

4.3 Adversarial Attack Details No consideration was given to naturally misclassified images. In one-pixel attack, an evolutionary technique black-box attack, we have used with different amount of modified pixels 1, 3, 5. Iterations will be stopped as soon as a misclassification occurs [23]. Also verified for targeted attacks, i.e. trying to misclassify as a targeted category. source reference https://github.com/Hyperparticle/one-pixel-attack-keras.

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Table 1 Classification accuracy and number of trainable parameters on test data sets of Kaggle CIFAR-10 and ImageNet for (RES)ResNet, (MIX)mixed and (OON) ODE-only net. The ODE solver tolerance τ is also used for architectures with ODE blocks ImageNet

CIFAR-10(Kaggle)

τ

RES

(RES) MIX

10−4

73.54%

10−3

–

10−2 10−1

(RES) OON

RES

(RES) MIX

73.28%

72.98%

77.42%

75.33%

74.16%

73.15%

73.12%

–

75.33%

74.16%

-

72.96%

72.92%

–

75.29%

74.03%

-

72.70%

72.03%

–

75.16%

73.56%

100

–

72.43%

71.84%

–

75.03%

73.25%

101

–

72.17%

71.42%

–

74.91%

73.01%

Params

270K

250K

240K

190,154

177,845

142,458

ImageNet

(RES) OON

CIFAR-10

τ

WRN

(WRN)MIX

(WRN)OON WRN

(WRN)MIX

(WRN)OON

10−4

76%

75%

75%

79%

77.33%

77%

10−3

–

75%

74%

–

77%

76%

10−2

–

74%

74%

–

77%

76%

10−1

–

74%

73%

–

77%

75%

100

–

73%

73%

–

76%

75%

101

–

73%

72%

–

76%

75%

Params

3.4M

3.1M

2.9M

414,978

378,945

326,503

5 Results The observations for the models with different configurations are as follows (Table 2). Our results imply that neural ODE-based models have an attack success rate consistently lower or equal in comparison with regular residual or wide residual networks. For similar changes to the original image, the attack success rate was almost halved. When the τ tolerance of the ODE solver is set to 0.1, attack success rate was almost halved while the classification accuracy only changed by 2%. Allowing higher significance in feature extraction process makes the decision counts flexible, thus bringing down the effects of adversarial inputs with neural ODE integration. When classifying a perturbed or pristine image with neural ODE, analysis of the internal states is as follows: let the continuous hidden state trajectory for normal image be h(t) and by an adversarial sample of the same image be hadv (t). h(t) = |h(t) − hadv (t)|2

(3)

The differences are averaged across all test samples, using same τ tolerance for each segment in adversarial examples measured by h(t). By setting a higher tolerance τ , the adversarial effect weakens in respect of internal state changes.

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Table 2 Pixel attack success rate for different models with number of pixels to be modified for untargeted and targeted cases Untargeted Targeted Model 1 pixel 3 pixel 5 pixel 1 pixel 3 pixel 5 pixel (%) (%) (%) (%) (%) (%) ResNet Wide ResNet MIX(Res)τ = 0.1 MIX(Res)τ = 0.01 MIX(WRN)τ = 0.1 MIX(WRN)τ = 0.01 OON(Res)τ = 0.1 OON(Res)τ = 0.01 OON(WRN)τ = 0.1 OON(WRN)τ = 0.01

34 19 27 24 21 21 23 22 19 19

79 58 46 3 46 40 43 42 43 39

79 65 56 48 52 44 49 47 48 42

14 1 11 9 0.3 0.3 9 9 0.1 0.1

21 18 17 15 15 13 15 13 12 9

22 22 20 20 19 18 19 17 17 15

6 Conclusion We proved the robustness of neural ODEs, a neural architecture in comparison with three types of variations on two different architectures (ResNet, WRN), using the ImageNet, Kaggle CIFAR-10 data sets. We concluded that neural ODEs are robust against pixel attacks with 1, 3 and 5 pixels. Furthermore, ODE solver’s error tolerance parameter has minimal effect on classification accuracy. Robustness towards adversarial attacks has been increased, i.e. lower success rate for adversarial attacks. Different variations of ResNets with ODE blocks and pure ODE-based networks can be extended to any larger real-world high resolution image or video data sets. They also can be extended to integrate ODEP-based networks into advanced models. As our work focuses on black-box attacks, future work can apply white-box attacks, which can analyse the internal states of attacks. Robustness of the models is yet to be verified in the white-box attack context and can also focus on text-based models.

References 1. R. Vinayakumar, M. Alazab, K.P. Soman, P. Poornachandran, A. Al-Nemrat, S. Venkatraman, Deep learning approach for intelligent intrusion detection system. IEEE Access 7, 41525– 41550 (2019) 2. M.A. Anupama, V. Sowmya, K.P. Soman, Breast cancer classification using capsule network with preprocessed histology images, in 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0143–0147 (2019) 3. N.S. Rani, P. Rao, P. Clinton, Visual recognition and classification of videos using deep convolutional neural networks. Int. J. Eng. Technol. (UAE) 7, 85–88 (2018)

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4. S. Sriram, K. Simran, R. Vinayakumar, S. Akarsh, K.P. Soman, Towards evaluating the robustness of deep intrusion detection models in adversarial environment, in Security in Computing and Communications, ed. by S.M. Thampi, G. Martinez Perez, R. Ko, D.B. Rawat (Springer Singapore, Singapore, 2020), pp. 111–120 5. T.Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, Neural Ordinary Differential Equations. CoRR abs/1806.07366, http://arxiv.org/abs/1806.07366 (2018) 6. E. Süli, D.F. Mayers, An Introduction to Numerical Analysis (Cambridge University Press, Cambridge, 2003) 7. F. Lu, Two implicit runge-kutta methods for stochastic differential equation. Appl. Math. 3, 1103–1108 (2012) 8. N.A. Ahamad, S. Charan, Study of numerical solution of fourth order ordinary differential equations by fifth order runge-kutta method. Int. J. Sci. Res. Sci. Eng. Technol. 6, 230–237 (2019) 9. Wikipedia: Runge–Kutta methods—Wikipedia, the free encyclopedia. http://en.wikipedia. org/w/index.php?title=Runge%E2%80%93Kutta%20methods&oldid=956618157 (2020), Online; Accessed 30 May2020 10. I.J. Goodfellow, J. Shlens, C. Szegedy, Explaining and Harnessing Adversarial Examples (2014) 11. N. Carlini, D. Wagner, Towards Evaluating the Robustness of Neural Networks (2016) 12. A. Krizhevsky, Learning Multiple Layers of Features from Tiny Images (University of Toronto, Toronto, 2012) 13. J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database, in CVPR09 (2009) 14. W.E. Zhang, Q.Z. Sheng, A. Alhazmi, C. Li, Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019) 15. C. Xiao, B. Li, J.Y. Zhu, W. He, M. Liu, D.X. Song, Generating adversarial examples with adversarial networks, in IJCAI (2018) 16. C. Xiao, J.Y. Zhu, B. Li, W. He, M. Liu, D. Song, Spatially Transformed Adversarial Examples (2018) 17. T. Miyato, A.M. Dai, I. Goodfellow, Adversarial Training Methods for Semi-supervised Text Classification (2016) 18. X. Yu, Z. Yu, S. Ramalingam, Learning strict identity mappings in deep residual networks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4432–4440 (2018) 19. I. Kim, W.,Baek, S. Kim, Spatially Attentive Output Layer for Image Classification (2020) 20. S. Zagoruyko, N. Komodakis, Wide Residual Networks (2016) 21. J. Su, D.V. Vargas, K. Sakurai, One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828–841 (2019). https://doi.org/10.1109/TEVC.2019.2890858 22. Q. Shi, J. Wan, Y. Feng, C. Fang, Z. Chen, Deepgini: Prioritizing Massive Tests to Reduce Labeling Cost (2019) 23. F. Carrara, R. Caldelli, F. Falchi, G. Amato, On the robustness to adversarial examples of neural ode image classifiers, in 2019 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2019) 24. J. Dormand, P. Prince, A family of embedded runge-kutta formulae. J. Comput. Appl. Math. 6(1), 19–26 (1980). https://doi.org/10.1016/0771-050X(80)90013-3

Survey on Advanced Spectrum Sharing Using Cognitive Radio Technique Mohamed Hassan, Manwinder Singh, and Khaild Hamid

Abstract Wireless communications from generation to generation up to the fifth depend on the increase band width output and lower latency, which have been achieved through the development and addition of new technologies on the dedicated channels. The fluctuating nature of the available range and the varied quality of service (QoS) requirements for different applications, limited spectrum, and the spectrum resource allocation policies present challenges for radio networks. Cognitive radio (CR) is one of the keyways to optimize the spectrum to the fullest degree possible; this paper describes CR, analyzes key functions, takes measures to share spectrum, discussing the effect of CR when combined with non-orthogonal multiple access (NOMA) at 5G and its impact on improving the performance of satellite communications. Index Terms Quality of service (QoS) · Cognitive radio (CR) · Fifth-generation (5G) · Non-orthogonal multiple access (NOMA)

1 Introduction The way people live and communicate across wireless networks has changed and this was either positive or negative. This area is still a constant example of many applications that can be developed and developed. A fast survey of the growth and complexity of mobile communications was made of the first 1G wave, distinguished by a single sound service, FDMA (Frequency Division Multiple Access), the channel M. Hassan (B) · M. Singh Department of Wireless Communication, Lovely Professional University, Phagwara 144411, India e-mail: [email protected] M. Singh e-mail: [email protected] K. Hamid Department of Electronics and Communication, University of Science and Technology, Omdurman 22110, Sudan e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_62

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control network with a maximum speed of 1G, 2.4 kbps. The first remarkable development is achieved when mobile phones have switched from 1G to 2G. The principal differences between mobile systems (1G and 2G) are that radio signals from the 1G network are analog, and that the 2G are digital. A secure and reliable communication system, TDMA (Timing Division Multiple Access) as a channel access method and a 144-kbps data interchange rate were the main drivers of this generation. The third-generation sets the standards for WCDMA (Wideband Code Division Multiple Access), whereby Web browsing, e-mail, video downloading, photo sharing, and other smart phone technologies are possible. In 4G aims to give users high speed, high quality, and high capacity, while enhancing security and reducing the rates of speech and service, multimedia and the Internet via an IP possible and existing applications include updated mobile web access and IP telephony, gaming, high definition mobile TV, video conferencing, 3D TV and cloud computing based on OFDM (Orthogonal Frequency Division Multiplexing), maximum speed approximately 1 Gbps. 5G technology seeks to improve the 4G network by increasing data rates even more efficiently, rising connections density, and dramatically reducing latency by, among other steps, improving data speeds. Increasing battery consumption and enhanced wireless coverage by adding a device to device communication functions. The software targets a maximum bandwidth of between 5 and 10 Gbit/s and uses non-orthogonal multiple access (NOMA) radio access technology [1]. The fifth-generation technology will make important changes in this field and reinforce other technologies such as the Internet of Things, all technologies focus on increasing performance and reducing latency, wireless communication technologies are developing rapidly and restricting the limited radio spectrum resources. In accordance with existing spectrum regulations, wireless service providers, including telecommunications, television, and the satellite, have allocated or licensed a large portion of the spectrum available to them as licensed services (or primary service providers). Statistics from the Federal Communications Board (FCC) show that the average use of authorized bands varies between 15 and 85%. The FCC also considered that only 5.2% of the spectrum is used for less than 3 GHz in any location in the USA [2]. Many multiplex technologies such as the frequency division multi-access (FDMA), the time division multi-access (TDMA), and the code division multi-access (CDMA) are being used to maximum effect in order to improve licensed spectrum and continue to support other users with limited spectrum resources. In this article, we provide a survey the basic concepts of cognitive radio (CR) definition, essential characteristics, functions, steps that follow to share the spectrum, explain the effect of CR when used in 5G and satellite communication.

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2 Cognitive Radio Definition Dr. Metulla presented a modern idea of maximizing the gentle level at the end of the last century, and renamed it to cerebral radio CR. It was defined as an intelligent communication technique that knows the surroundings considerably and uses a method of awareness by making it possible to impart from the atmosphere and adjust its internal cases to inspire statistical differences within the incoming radio frequency by making corresponding adjustments to the working parameters in real time [3].

2.1 Attribtes of Cognitive Radio CR’s core characteristics are classified into two groups. a. Cognitive capabilities CR has the potential to identify the dominant domain by utilizing specialized techniques on the radio spectrum and to test correct parameters. The CR jobs are often referred to as the cognitive process and have three key phases. • Observe spectrum: CR is trying to gather information on wasted radio spectrum, to identify the spectrum within the cavity. • Spectrum analytic: frequency observation and evaluation of signal cavity characteristics. • Spectrum resolution: selection of the best possible fixed line contact channel focused on choosing the optimal unused fixed-band link channel according to the customer specifications and features assessed [4, 5]. b. Reconfiguration option It is the ability to acclimatize changing operating parameters during radio operation without any component modulation of the equipment [4, 5]. The parameters used to reconfigure are: • • • •

Frequency use. Switch methodology. Energy transfer. Technology of communication.

2.2 CR Functions The primary role of CR is to monitor, sensitize, and collect information on the environment. The judgment was then taken, and the conditions were established [6]. Spectrum sensing, spectrum management, spectrum division, and spectrum movement are the core features.

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Spectrum sensing: To take cognitive radio operation into account, intelligent sensing and intelligent decision-making methods are needed. Perhaps one of the most critical mechanisms for cerebral radios is frequency identification and one of the most important functions of cognitive radio systems is also the method of frequency detection [7]. Spectrum management: The best available signal frequency, transmitting capacity, consumer numbers, interference, energy efficiency and QoS specifications are selected as well. This method also includes spectrum exchange mechanisms for the best distribution of channels and power and other features apply to the key functions of the layer protocols of medium access controls. It can therefore be included in the MAC layer. However, efficient spectrum sharing challenges are associated with time synchronization and distributed power allocation [7]. Division of spectrum: Access to spectrum with other users is arranged. Switch spectrum: The secondary user exit and switch into another frequency band opening when the first user returns or appears [8].

3 Spectrum Sharing The CR technology allows the reuse of important resources in the spectrum without altering the existing policy. CR’s key concept is to ensure spectrum allocation by dynamic access to the spectrum so secondary users (Sus) can use an idle spectrum of primary users (Pus). The distribution of spectrum usually takes four steps.

3.1 The Detection of Spectrum The first procedure includes collecting information on the available spectral frequency bands, the primary use of existing bands, and the finding of spectrum gabs [9]. It includes various techniques like detection of power, detection of characteristics, consistent discovery, and filtering. It can be divided into white, gray, and black regions according to the status and condition of the frequency spectrum. Applicants for secondary users are gray and white region [10]. We provide a general idea of some of these approaches through the great development in the area of wireless communication and the development of new methods of spectrum sensors. • First non-cooperative sensor frequency range In this model, each user selects the best way to increase the use of available frequencies, to communicate egoistically and in a way that is so different from ideal [6].

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• The second frequency range sensor full duplex (FD) In this model, both primary and secondary users simultaneously take advantage of the spectrum, while ensuring optimum use of the frequencies available [3] in the number of collisions with the principal user. • Third spectrum database frequency range sensor It is used to save old data and to create schedules, which provide information on the currently available frequencies based on frequency setting at the time. • Fourth sensor cooperative range frequency The model is used for sensitive applications, which does not enable delays, for example, in industrial areas, where knowledge sharing between users helps accelerate procedures and distribute opportunities. • Fifth spectrum of compression This model is primarily intended for Internet of things technology, as it is aimed at large populations who exchange information in limited amounts but constantly over and above their low energy consumption [3].

3.2 Allotment of Spectrum It is a vital mechanism that avoids conflict between cognitive radio and primary licensing, enabling wireless radio frequency to have more functionality. Two approaches to do this. a. Spectrum sharing cooperative A data collection center and a control channel between them, or over distributed networks, communicate with each other in this frequency range. The first method is more accurate, but the time is much longer, the implementation is very complex, and energy usage is greater than the second method. The key downside of the second method is its inaccuracy [11]. b. Spectrum sharing non-cooperative This does not allow a kind of user-to-user information exchange, yet a very useful method of communication is available for a small number of cognitive user networks [12].

3.3 Access Types of Spectrum The first type, divided into two types, is called static and assigns the frequency range for a company approved by a government agency. This company is called a service provider and is called primary users for subscribers. The second type, named dynamic, does not have a license to use frequency bandwidth, but it tries to classify the gaps in the allocated frequencies by using CR methods and, on the other hand, its inefficiency is divided into three types, namely: The second type, named dynamics.

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(1) Commons, (2) Models of common use and (3) Models of exclusive use [4]. It will cover every model in some detail. a. Commons: There are no parties allocated to any 2 frequency bands 2.4–5 GHz, so that all users with a specific interest can use them [3, 9]. b. Shared usage type: This type allows frequency distribution between primary and secondary users, but guidelines for the second type of user are given. Three methods have been developed to hit the frequency bands for secondary users: (1) Method of interweaving: These methods allow secondary users to use the frequency band gaps when they are not used by the primary user. If no financial expenses are involved, secondary users are therefore required to leave the bandwidth immediately upon arrival of primary users in search of another domain to convert to this; the difficulty of this process is constantly changing from one bandwidth to another [2]. (2) Access method for spectrum underlay This method allows the secondary user to transmit at the same time with the primary user, according to the main condition of the interference from the secondary users does not exceed to reasonable limit. Therefore, they send very low power, and this affects the coverage area negatively. (3) Type of exposure to spectrum overlays The secondary user searches the most assigned frequency bands with modified methods for knowing the unused places and using them by synchronizing sending with the primary users. If the secondary user knows the presence of the primary users, the features of signal and transmission are changed to avoid any interference [2]. c. Layout for exclusive use The principal frequency service providers would then be leased or loaned by primary service providers, and secondary service providers tend to be the new name, and secondary users, like primary users, have full access rights to the frequency band according to the contract agreement [2].

3.4 Hand-off Spectrum Due to the various spectrum parameters, it is referred to as the handout method and consists of three cases: passive, effective, and hybrid [3] where the secondary users are transferred from one channel to another according to their time and place or returns. This is the following description of the proposed spectrum hand-off policy. For devices with the planned spectrum hand-off, two radio spectrum bands are called. The radio spectrum band which is higher by average off time is allocated to machines to send data packets according to historical details in the radio spectrum band databases. Radio frequency energy can be collected by machines from both spectrum bands. At

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the beginning of a multi-frame, a machine gets details of both radio bands about the busy state or idle state. Then, with higher percent of mean off time depending upon the idle or busy state of the band [12].

4 Game Theory This is a method for simulation and evaluating nodes. It will forecast the positions and actions of all participants and then determine for all participants the best possible choices. One of the best practices, including many of the advantages of the best bandwidth and frequency utilization, is the Nash method or the equilibrium point [11, 13].

5 Commercial Application 5.1 Networks of 5G Cognitive radio has many problems since the emergence of technologies of the fifth generation, contributing to a huge and immense growth in the usage of broad and wide frequency ranges, which ensures that information collection and sensing become more complicated and measured [14]. The NOMA techniques are expected to be integrated with the CR networks to enhance spectral efficiency and serve the number of users. The Noma techniques are useful to secondary users, to cooperate effectively and to gain access to the Primary User (PU). The effectiveness of the SU and PU can be increased simultaneously by implementing a satisfactory CR network coordination system with NOMA [15].

5.2 Satellite Communications Since satellite communication has a wide reach, seamless connectivity and easy Internet access for foreign users, it is considered a key component of future wireless communications [16]. The recent development of satellite communications has seen a major interest in them, since they have been working simultaneously or close to the highperformance cellular communications and have reduced the gap between them. Cognitive radio was suggested to optimize the width of the band assigned to this type of communication, with a downlink speed of 2.4 GHz and uplink speed of 2 GHz [17].

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6 Conclusions By examining the basic concepts of cognitive radio, it became clear to us that this is one of the best ways to solve the issues of limited frequency range. CR technique is a paradigm that enables the frequency band to be utilized by secondary consumers in a proactive way by detecting the unused frequency spectrum, sometimes known as strength gabs. Once the primary user enters, the domain would be empty without the usage of the hierarchical series triggering conflict with other applications. For future research, we would recommend NOMA in a commercial registry defined 5G network that can access frequency bands in all primary consumer cases and undertake a comprehensive device efficiency analysis taking into account the quality of service parameters.

References 1. D. Cohen, S. Tsiper, Y. Eldar, Analog-to-digital cognitive radio sampling, detection, and hardware IEEE Sig. Process. Mag. 137–167 (2018) 2. M. Hassan, G. Karmakar, Exclusive use spectrum access trading models in cognitive radio networks: a survey. IEEE Commun. Surv. Tutorials 19, 2018–2025 (2017) 3. F. Hu, B. Chen, A. Kunzhu, Full spectrum sharing in cognitive radio networks toward 5G: a survey. IEEE 3(3), 15754–15776 (2018) 4. J. Thomas, P. Menon, A survey on spectrum handoff in cognitive radio networks, in International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore (2017), pp. 1–4 5. J. Agarkhed, V. Gatate, Survey on spectrum sensing techniques in cognitive radio networks, in IEEE, International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (2017) 6. M. Manesh, M. Apu, N. Kaabouch, W. Chen Hu, Performance evaluation of spectrum sensingm techniques for cognitive radio systems, in IEEE Conference (UEMCON) (2016) 7. E.U. Ogbodo, D. Dorrell, A.M. Abu-Mahfouz, Cognitive radio based sensor network in smart grid: architectures, applications and communication technologies. IEEE Access 5, 19084– 19098 (2017) 8. N. Muchandi, R. Khanai, Cognitive radio spectrum sensing: a survey, in IEEE, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (2016) 9. D. Alias, G. Ragesh, Cognitive radio networks: a survey, in IEEE WiSPNET Conference (2016) 10. S. Sharma, S. Chatzinotas, L. Bao, Cognitive radio techniques under practical imperfections: a survey. IEEE Commun. Surv. Tutorials 17 (2015) 11. A. Khan, M. Rehmani, M. Reisslein, Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Commun. Surv. Tutorials 18 (2015) 12. S. Tzeng, Y. Lin, L. Wei, Spectrum hand-off for RF energy harvesting in cognitive radio based machine to machine communication networks, in 2018 International Conference on System Science and Engineering (ICSSE), New Taipei, pp. 1–4 (2018) 13. J. Zheng, Y. Wu, Dynamic computation offloading for mobile cloud computing: a stochastic game-theoretic approach. IEEE Trans, Mob. Comput. 18, 757–770 (2018) 14. L. Zhang, M. Xiao, G. Wu, M. Alam, Y. Liang, S. Li, A survey of advanced techniques for spectrum sharing in 5G networks. IEEE Wirel. Commun. 24 (2017) 15. F. Zhou, Y. Wu, Y. Liang, Z. Li, Y. Wang, K. Wong, State of the art, taxonomy, and open issues on cognitive radio networks with NOMA. IEEE Wirel. Commun. 25(2), 100–108 (2018)

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16. T. Liang, K. An, S. Shi, Statistical modeling-based deployment issue in cognitive satellite terrestrial networks. IEEE Wirel. Commun. Lett. 7(2), 202–205 (2018) 17. M. Jia, X. Gu, Q. Guo, W. Xiang, N. Zhang, Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G, in IEEE Wireless Communications, vol. 23, December 2016

An AI-Based Pedagogical Tool for Creating Sketched Representation of Emotive Product Forms in the Conceptual Design Stages Sunny Prakash Prajapati , Rahul Bhaumik , Tarun Kumar , and Unais Sait Abstract This paper presents an AI-based pedagogical solution to aid designers in an appropriate analysis of existing product forms, and to provide feedback to them in the ideation of new forms pertaining to specific emotions and attributes. Sketching is often used as a tool for ideation and communication, especially by artists and designers. It is the non-ambiguity in the sketched representation that is crucial for the effective communication of ideas amongst the stakeholders involved in the product development process. Sketching, therefore, becomes the critical skill that an aspiring designer should acquire in design schools or during the learning phases of their careers. Hence, a pedagogical support system to assist novice designers in developing their ideas unambiguously through sketched representations is necessary. In this study, an image classification-based machine learning (ML) model is trained on a dataset containing images of lines. Further, this model is validated on a set of concept sketches, and a smartphone-based learning application for novice designers is proposed as a pedagogical feedback tool for the conceptual design stage. Keywords Pedagogy · Design sketching · Emotive forms · Form generation · Machine learning · Image classification

S. P. Prajapati (B) · R. Bhaumik · U. Sait PES University, Bangalore, India e-mail: [email protected] R. Bhaumik e-mail: [email protected] U. Sait e-mail: [email protected] T. Kumar Indian Institute of Science, Bangalore, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_63

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1 Introduction 1.1 Emotions and Artworks In the context of user experience, emotion is a complex phenomenon that emerges because of the interactions between a person (user) and an artefact (object of use). All creative forms of work—be it music, visual art, or literature—elicit certain emotions in the perceiver. In the context of written works, there have been attempts to describe its relationship with the emotions through frameworks (and models) which can be dimensional or categorical [1]. The categorical frameworks represent emotions, as a set composed of individual primaries which amalgamate in various combinations to form complex emotions. Plutchik’s theory is a prime example wherein all complex emotions are based out of eight fundamental emotions [2]. The dimensional model presents emotions by mapping them against specific key parameters on continuous scales. For example, emotional states have been mapped to the following parameters: valence (positivity or negativity of emotion) and arousal (alertness and calmness of emotional state), in Russell’s circumplex model [3]. Looking at the categorical framework connecting emotions and words, it is observed that a set of words is highly effective in describing the emotional response of an individual. This connection can be perceived as a relational map between the emotions and the word set, R: Emotion → {words}. The critical aspect of this mapping is the mutual exclusivity amongst the categories of emotions and overlaps in the word sets describing the emotion. This limits it from being studied as a simple, functional model between sets of words and emotions. There have been attempts to formalise this complicated relationship which can be insightful for the present case [4, 5]. The relationship between shapes and emotions is less coherent as compared to words and emotions. Collier’s experiment on matching a word to a shape provides a significant correlation between simple line drawings and universal emotional perception [6]. This experiment is further extended to 3D shapes and emotions [7]. Taking insights from the works of Plutchik, Russel, and Collier [2, 3, 6], a relationship that connects forms to emotions through words can be constructed. This construct is detailed out for the creation of a pedagogical support system, as discussed later.

1.2 Design and Sketching In the process of new form development (in product design), sketching is one of the primary tools used by the designers. Product form development is a part of a larger process which is the design thinking process. Although there are various models of the ‘design thinking’ process, a generalised version has four stages: identification of problem and requirements; ideation; consolidation and refinement of solutions; and testing and selection of solutions [8]. Sketching is not only a mere tool for

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representation but also an important activity during the ideation phase of the design thinking process. Sketching in product design differs from the artistic sketching process, which involves a mere replication of an object or a scene in observation. The design sketching is analogous to an iterative enquiry that helps the designer in bringing about the intrinsic characteristics of the product form [9], which holds good even in the field of architectural form design [10]. Designing products that evoke an emotional response in the user is an essential skill of a designer. The emotional perception of the product can be achieved through modification of the characteristics of the product elements [11]. One of the essential elements which need to be focused is the form or the overall geometry of the product, the others being the colour–material–finish(CMF) of the product, packaging, and functionality. Design sketching is one of the most direct, quick, and cost-effective ways to represent and ideate forms. Hence, it becomes a vital skill for the designer that needs to be acquired in the initial years of design training. A designer initiates the development of a product form through sketches, and iteratively adds ‘meaning’ to it guided by metaphors to establish an emotional connect with the end-user. This activity is usually supervised by an experienced designer or a mentor in institutions offering design education. Feedback of the mentors on the student’s work is an essential aspect of learning, which is not always available at every step of the training process. Considering the promising results shown by the AI-based technologies in 3D form generation [12, 13], their incorporation in the ideation phase, where the form exploration happens through sketching, can be beneficial for the designers. The ubiquitousness of smartphone-based and easily deployable machine learning (ML) codes (https://www.tensorflow.org/lite/models) allowed the development of this pilot project to aid novice designers in the process of developing sketched representations for emotive forms. Since the tool is built on the digital platform, it can potentially reduce the need for the constant physical presence of a mentor and can perhaps enhance the assessment due to its agile nature [14].

2 Methodology Attributes of a product are determined by its form (to a large extent), and hence, exploration in the early design phase is necessary for the effective incorporation of an emotion (which is described by the attributes). Sketching iteratively during the ideation helps the designer to accomplish this feat. The initial ideation happens on the profile of the form which sets the shape in a 2D format. This shape is again composed of many line elements that have their characteristic features, which aggregates to an overall attribute of the shape. So, for the present study, an image classifier was built by retraining an existing CNN model MobileNets [15], on our generated dataset that contained line elements. The lines had different geometrical characteristics that mapped to specific attributes ,which were representative of the underlying emotions. Two mutually exclusive attributes were selected to characterise the line segments, which were then used

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Fig. 1 Dataset preparation for the image classification model

to train the model. The retrained model was further tested against the form profiles for the preliminary validation. After that, it was deployed as a smartphone-based application to be used by the novice designers to get feedback on their ideation sketches (see Fig. 3).

2.1 Preparation of the Dataset The data set for the model was obtained through images sampled from the video that captured the line elements drawn on a paper. For sampling, the ‘FFmpeg’ application was used, which gave more than 2000 images from the video [16]. The images were further processed on an image manipulation software, GIMP to minimise the background noise and improve the contrast. They were then resized to 256 px by 256 px size using a batch processing plugin (‘BIMP’) [17] (see Fig. 1). After this preprocessing, the images were manually categorised into two mutually exclusive attributes (labels)—‘cute’ and ‘rugged’, for training the classification model. Any inconsistencies and errors in the processed images were manually removed.

2.2 Training and Testing of the Model The image classifier was developed by retraining the CNN model MobileNets [15] using a Python script from the TensorFlow [18] library on the dataset containing the line elements categorised into two attributes (2.1). The data was split into training and testing set, and the model was trained with a step size of 500. The cross-entropy loss was used as a metric for classification accuracy.

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In order to obtain the attribute of the form profile, the image was split, and the attribute scores for every part were predicted using the image classifier. The splitting was performed using the image slicer library in Python [19], which split the image into four parts of equal pixel size. The scores were averaged for the categories ‘cute’ and ‘rugged’, then the attribute with the higher average score was assigned to the image. The attribute assigned using this method was then compared to the one assigned by the experienced designer for the sketches to test this approach.

2.3 Developing the Smartphone Application An Android-based smartphone application has been developed for implementing the trained classification model for the novice designers, which would act as a continuous feedback provider for their works during product form generation. The interface for the application has been developed by considering the prime functions and task flows undertaken by the app users (i.e. novice designers and learners in this case). This application is focused on the early design phases of iterative product development. In the present case, the application has manually drawn sketches or scanned sketches as input, but in the future, it can be integrated into digital sketching software platforms (Fig. 2).

Fig. 2 The proposed solution—A mobile application SketchmoE

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Fig. 3 Methodology for the development of an AI-based pedagogical tool

3 Results and Discussion 3.1 Model Accuracy During the training of the MobileNets model, the cross-entropy decreased over time (see Fig. 4). Since this is a cost function, its minimisation implied better association between the class labels and the image features. The training and the test accuracies were 100% and 79.8%, respectively.

3.2 Workflow The application takes concept sketches of forms (made by a designer during the exploration phase) as input (through a camera). The image is then processed (size, resolution and colour correction) to make it suitable for the classifier which outputs the attributes and their respective scores. This feedback can then be taken up by the designer to strengthen the desired attribute in the form (see Fig. 5).

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Fig. 4 Cross-entropy loss during training (initial value of 0.087 is omitted)

Fig. 5 Workflow of the smartphone application

3.3 Preliminary Validation of the Model The attribute scores for the eleven different sketches obtained by the model are compared against the attributes assigned by the designers. These sketches represent a closed form (of approximately the same proportions) with no intersecting line elements. The attributes predicted by the model (through maximum average score) are the same when compared to the attributes assigned by an experienced designer for each image. The results are summarised in Fig. 6.

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Fig. 6 A figure depicting the score of the rugged attribute and the cute attribute of different forms sketched out for preliminary validation as indicated by the circular labels with their sizes indicating the score

3.4 Discussion Though the model correctly predicted attributes for the above cases, it has few limitations in its current form. Limitations can range from incorporating a fixed number of attributes in the model to its applicability to a certain kind of sketches (those with intersecting line elements are currently not supported). Moreover, factors such as attributes emerging from the sketch, as opposed to the combined attribute of the parts of the sketch, are not captured by the present model. Also preparing a highquality dataset with non-mutually exclusive attributes is vital for the development of a reliable system of this kind. This tool can be used by the designers while sketching during the product ideation phase. The mobile application will allow the user to capture the image of the sketch and provide scores for the attributes in real time. Apart from presenting the current attribute for a sketch, the app will also provide suggestive feedback to the user to improve upon the sketch to ‘enhance’ an attribute. The inbuilt library feature will provide more information to the app user regarding the selected attribute. The systemic diagram (see Fig. 7) represents the positioning of the AI-based learning tool in the broader context of the ‘design thinking’ process. The proposed pedagogic tool is useful not only in the preliminary ideation stage but also in the idea refinement and consolidation stage, leading to the finalisation of the concept. The systemic diagram also elucidates briefly about the influence of the pedagogic tool

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Fig. 7 A systemic diagram representing the proposed pedagogic tool in a broader context

in the student’s learning process. The continual feedback from the mobile application helps the users in decision making while ideating on product forms (by sketching), through suggestions on the desired attributes and possible improvement in the features of the sketched forms. Over time, this can have a potential effect on the learner’s perception to create desirable emotive forms. The diagram also indicates the role of the human mentor (who is usually an experienced human designer) in the overall learning process of the novice designer. The mentor can provide extensive feedback or information and evaluate the progress of a learner in periods of a longer time interval as opposed to the continual involvement of the learner with the pedagogic tool (the mobile application in our case). Moreover, the human mentor can play an active part in the development of the AI-based pedagogic tool based on the changing design trends and the performances of the pedagogic tool users over longer periods of time.

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4 Conclusion The proposed smartphone application based on the classification model is analogous to a mentor who is continuously providing feedback to the learner at every instance of his conceptual design ideation phase. The solution leverages the power of ML to provide insights from a large set of data (that maps product forms to emotions) during the form exploration. At present, the feedbacks are on the shape features (like angularity or curvature) which influences the desired attribute in the product form. The designer can then factor in the recommendations to make the form more aligned towards the attribute. The solution also removes the need for the mentor or expert to be physically present while providing feedback to the learners. Since AI is making its mark in almost every domain of human interest, product design and design education is no exception; and the proposed solution effectively generates and communicates the feedback on the learner’s work, which aids in continuously training them to make them more seasoned in their profession. In the future, a real-time AI-based application based on digital sketching platforms could be derived from the present mobile application. Further, after reinforcement learning by the model over a period (with the same designer), the application may be useful for suggesting design ideas in terms of emotive form exploration in the iterative design exercise. This application can be an integral part of the digital sketching software platforms providing real-time continuous feedback, which can also learn from the designers’ concept generation workflow history. Moreover, the solution provides a roadmap to data-driven form design, which can be a useful technique for sustainable product development.

References 1. R.A. Calvo, S. Mac Kim, Emotions in text: dimensional and categorical models. Comput. Intell. 29, 527–543 (2013) 2. R. Plutchik, The Emotions. (University Press of America, 1991) 3. J.A. Russell, A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161 (1980) 4. S.M. Mohammad, P.D. Turney, Nrc Emotion Lexicon (National Research Council, Canada, 2013) 5. P.P.J. Mothersill, The Form of Emotive Design (2014) 6. G.L. Collier, Affective synesthesia: Extracting emotion space from simple perceptual stimuli. Motiv. Emot. 20, 1–32 (1996) 7. M. Anft, This is your brain on art. https://magazine.jhu.edu/2010/03/06/this-is-your-brain-onart/ 8. R. Bhaumik, A. Bhatt, M.C. Kumari, S.R. Menon, A. Chakrabarti, A gamified model of design thinking for fostering learning in children, in Research into Design for a Connected World (Springer, 2019), pp. 1023–1036. 9. K. Eissen, R. Steur, Sketching (5th printing): Drawing Techniques for Product Designers (Art Des. Ser, Bis, 2008) 10. G. Goldschmidt, The dialectics of sketching. Creat. Res. J. 4, 123–143 (1991)

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11. S.J. Westerman, P.H. Gardner, E.J. Sutherland, T. White, K. Jordan, D. Watts, S. Wells, Product design: preference for rounded versus angular design elements. Psychol. Mark. 29, 595–605 (2012) 12. S. Chaudhuri, E. Kalogerakis, S. Giguere, T. Funkhouser, Attribit: content creation with semantic attributes, in Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (2013), pp. 193–202 13. M.E. Yumer, S. Chaudhuri, J.K. Hodgins, L.B. Kara, Semantic shape editing using deformation handles. ACM Trans. Graph. 34, 1–12 (2015) 14. A.N. Bhatt, R. Bhaumik, K.M. Chandran, A. Chakrabarti, IISC DBox: a game for children to learn design thinking, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2019), p. V003T04A010 15. A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv Prepr. arXiv1704.04861 (2017) 16. FFmpeg, https://ffmpeg.org/ 17. A. Francesconi, BIMP, https://alessandrofrancesconi.it/projects/bimp/ 18. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv:1603.04467 (2016) 19. image-slicer 0.3.0, https://pypi.org/project/image-slicer/

Social Interaction-Enabled Industrial Internet of Things for Predictive Maintenance M. S. Roopa, B. Pallavi, Rajkumar Buyya, K. R. Venugopal, S. S. Iyengar, and L. M. Patnaik

Abstract As Internet of Things (IoT) advances from interacting objects to smart objects. Further, adopting social networking concepts into IoT leads to social objects. Following this conception, the Social Internet of Things (SIoT) comprising the amalgamation of social aspects into IoT was incised. In the recent past, the use of IoT technologies in the industrial sectors and applications has given birth to a new research area termed as the Industrial Internet of Things (IIoT) which has attained notable attention extensively. In machinery-based industries, elements such as bearings, axles, brackets, hangers, etc., are the essential machine element that serves a variety of purposes in the production environment. Thus, any severity related to it causing failure is unfavorable. This paper introduces the SIoT concept into the manufacturing industry for predicting the failure of the machine element before it could occur. This work proposes the construction of the ontological model that assists in the implementation of the prognostics and health management to predict the remaining useful life (RUL) of machine element, which is essential in minimizing maintenance cost and machine downtime, and further limiting disasters. Keywords Bearing failure · Industrial Internet of Things · Ontological model · Predictive maintenance · RUL prediction · Social Internet of Things

M. S. Roopa (B) · B. Pallavi IoT Lab, University Visvesvaraya College of Engineering, Bengaluru, India e-mail: [email protected] R. Buyya University of Melbourne, Melbourne, Australia K. R. Venugopal Bangalore University, Bengaluru, India S. S. Iyengar Florida International University, Miami, USA L. M. Patnaik National Institute of Advanced Studies, Bengaluru, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_64

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1 Introduction The Industrial Internet of Things (IIoT) is an industrial framework where machines or devices are linked and synchronized utilizing software development tools and third platform technologies in a machine-to-machine (M2M) and Internet of Things (IoT) context. IIoT applies unified protocols and architectures for the development of devices that has better control, seamless communication, and improved interoperability between the machines. It exhibits quality control, asset tracking, sustainable and green practices, supply chain effectiveness, and supply chain traceability. All this leads to predictive management in the industrial settings where data from embedded sensors and connected devices are gathered and exchanged intelligently. In industries with a high risk of human error, IIoT comes as a boon, where the precision level that is achieved through IIoT plays a prominent role in industrial predictive maintenance [1]. Predictive maintenance (PdM) is a strategy that deals with maintenance issues, given the increasing need to reduce the maintenance cost and machine downtime. Predictive maintenance relies mainly on sensors to monitor the machine and detect when a critical piece of machinery is close to failure. In machinery domain, machine elements plays a crucial role in the production environment, hence predicting its remaining useful life (RUL) to avoid the severity of failure benefits [2]. To predict the RUL of a machine element, it is still a significant challenge task, as it depends on several factors that cannot be measured and analyzed quantitatively. To enhance the accuracy in the prediction of machine elements, RUL and the factors that affect its failures become a critical issue and have attracted a large number of prognostic researchers [3]. The Social Internet of Things (SIoT) is a thriving research field that emerged as a result of the integration of social networking concepts with IoT. SIoT is an interaction paradigm where the objects establish social relations with various objects and users to achieve a common goal [4–6]. Integrating SIoT concept into the Industrial Internet of Things advances the machines with the formation of social relationships supporting the human–machine communications and machine–machine interactions. The idea of utilizing SIoT in industry is named as Social Internet of Industrial Things (SIoIT). SIoIT conceptualizes the application of social behavior to intelligent assets (refers to machines or machine parts, the term asset, machine, and resources that are used interchangeably) that are connected with actuators, sensors, or any identification technology [7]. In SIoIT, relationships are built among the industrial assets according to the rules set by the asset relationships.

1.1 Motivations Predictive maintenance is the recent research topic that determines the probability of failure of a machine before it could occur and allows industries to progress from

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a repair and replacement model to a predict and fix maintenance model. For a long time, we have been tending issues in the machinery when they crop up instead of thwarting them, which immensely affects the expenses and endeavors to fix the assets. If the prediction of these issues are made based on the historical data, there can be mitigation in the costs and finer allocation of resources related to the unplanned maintenance. With the introduction of the Industrial Internet of Things (IIoT), it enables us to enhance efficiency and production quality. The proposed PdM in SIoIT is based on the number of social relationships defined between assets and the actors (human operators). These relationships are used to predict the failure of the machine element and notify the prediction to the maintenance or repair personnel. Meanwhile, the sensor readings are examined over time to learn the relationship of the machine element.

1.2 Contributions The main contributions of this paper include the following: 1. PdM-SIoIT Architecture: An overview of predictive maintenance-based SIoIT architecture to detect the failure condition and predict the remaining life of the assets in industries. 2. Social Relationship Ontology for Predictive Maintenance: We construct an ontological model to describe various relationships in the prediction of multiple factors that might cause the failure of machine elements based on the obtained data points collected over time through different sensors. The RUL prediction and the health management of machine elements are based on continuous monitoring, and if any discrepancies are observed in the specified threshold, it sends alerts conforming to predefined rules.

1.3 Organization The rest of the paper is organized as follows. A brief summary of related works in Industrial IoT, Social IoT, and ontology model is discussed in Sect. 2. Predictive maintenance-based SIoIT architecture to detect the failure condition and predict the remaining life of the machine assets is explained in Sect. 3. The ontology model for predictive maintenance in SIoIT is discussed in Sect. 4. Section 5, emphasizes on the implementation of the proposed predictive maintenance-based SIoIT with the aid of a scenario, simulation setup, and results. The concluding remarks are presented in Sect. 6.

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2 Literature Survey In this section, we present the state-of-the-art research works in the field of predictive maintenance in IIoT, SIoT, and ontology-based fault diagnosis methods. Vianna et al. [8] have developed a predictive line maintenance optimization strategies subjected to multiple wear conditions in aeronautic systems. It utilizes a Kalman filter technique to calculate trends of degradation and wear values in future. A case study is carried out considering field prognostics data of various hydraulic systems for prediction. The limitation is that the states and variances of the model cannot be combined and shared among different models. Wu et al. [9] have presented hidden Markov model based on cluster K-PdM for RUL prediction and estimation of machinery deterioration supported by multiple key performance indicators (KPIs). An experimental approach is applied to exhibit its application on the PHM08 dataset. Yan et al. [10] have proposed a framework to predict the RUL of key components in a CNC machine. It exploits single and multiple source data models and employs a vibration signal to estimate the performance and forecast the remaining life. The limitation is deep learning techniques to examine big industrial data that were not adopted. Wang et al. [11] have explored a cloud-based model for predictive maintenance using a mobile agent approach. It enables appropriate information utilization, distribution, and acquisition for improved accuracy and reliability in the prediction of remaining service life, fault diagnosis, and maintenance scheduling of various induction motors. Swamy et al. [12] have developed a health-centric tourism recommender system. It is developed on an ontology framework, which can advise the availability of food based on climate attributes using user’s personal favorite and nutritive value. Semantic ontologies connect the difference between various user-profile information. The real-time IoT based healthcare support system is used to evaluate different food recommendations. Cao et al. [13] have proposed an approach that combines fuzzy clustering and semantic technologies to understand the significance of a machine’s failure based on the obtained classical data. A case study on a real-world industrial dataset is developed to estimate the suggested method’s utility and effectiveness. Wen et al. [14] have provided an ontology model for fault in the mechanical diagnosis system. It is based on the resource space model (RSM). Protégé 4 is used to make an ontological model for diagnosing AC motor defects. However, for mechanical fault diagnosis, this ontological model cannot be used. Xu et al. [15] have proposed an ontology-based failure diagnosis technique that overcomes the complexity of learning a sophisticated fault diagnosis information. It additionally provides a comprehensive procedure for fault diagnosis of loaders. The ontology model is highly expansible; because the components of the model like classes, individuals, and properties in the failure diagnosis can be greatly enhanced and updated.

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Jung et al. [16] have proposed a model that captures the spatio-temporal attributes along with assorted co-usage data varieties to predict the relationship between different objects. It explains the entropy and distance-based social strength computations among objects. It portrays the structure of the SIoT model to IoT that offers navigation of networks, which includes adaptation and effective objects discovery and services like human-centric social networks. However, it does not include how it has to deal with the strengthening of social connections as well as time-related deterioration. Further, numerous domains’ large datasets can also be used.

3 SIoIT Architecture for Predictive Maintenance (PdM-SIoIT) In this section, we present the SIoIT architecture for predictive maintenance and design an RUL predictor engine, which can be applied to a production environment in the industries to examine the assets existing condition and predict its remaining life before entering into practical failure. The proposed PdM-SIoIT architecture follows the three-layer system constructed from the base layer, network layer, and application layer, as shown in Fig. 1. The base layer includes manufacturing assets embedded with identification tags, sensors, and actuators. The second layer is the network layer, which is used for connectivity to cellular networks, GPS, WLANs, or Internet as required. The application layer consists of two sub-layers: interface sub-layer and SIoIT component sub-layer. The primary constituents of PdM-SIoIT is positioned in the SIoIT component sub-layer of the application layer. It includes RUL predictor engine, social agents,

Fig. 1 PdM-SIoIT architecture

Application Layer Interface Sub-Layer Machine Interfaces

Industrial Applications

Service APIs

SIoIT Component Sub-Layer Relationship Management

Social Agents

Profile Management

Context Management

Performance Management

Data/Metadata Management

RUL Predictor Engine

Ontologies

Network Layer Cellular Networks

WLAN

ZigBee

IEEE 802.11G

……

Internet

……

Actuators

Base Layer Manufacturing Assets

Identification Tags

Sensors

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Provides

Machine Element

Allows

Threshold value monitoring

PredicƟve Techniques

Feed

feed

Remaining use PredicƟon

Deep Learning

Data analysis

PdM Allows

models

Predicted o failure

Allows

Monitors Maintenance

Allows

Health management

Fig. 2 Taxonomy of remaining useful life (RUL) predictor

relationship management, profile management, security and access control, performance management, semantic engines, ontologies, and data/metadata repositories. The interface sub-layer of the application layer consists of industrial applications, machine interface, human interface, and service APIs to access the IIoT applications using different devices. The RUL predictor engine is an essential component of PdM-SIoIT architecture that predicts the remaining life of the machine assets in industries. The taxonomy of RUL predictor is illustrated in Fig. 2. The operation of the assets varies for different materials that provide preventive action and its threshold limit to predict the remaining useful life. The predictive methods are applied to collect and analyze the data from sensors embedded on the machine elements. The data is collected based on different parameters such as temperature, vibration, load, and lubrication level. When the parameters exceed the specified threshold limit, the machine’s failure is predicted in the mere future. Once a prediction is made, the necessary action will be taken to avoid failure in the future. The health management of the assets are performed when a failure is recognized; if the health state indicates a practical failure, then request for an operation-based maintenance (OBM) is scheduled. Ultimately, when the maintenance interruption is finished, new data obtained in such interruption is added or updated in the assets data and in the parameters of the predictive techniques to proceed with the prognostics health management cycle.

4 Ontology model for PdM-SIoIT The ontological model is a structure that defines relationships between classes and subclasses existing in a domain. It analyzes data to transfer information for granting decisions regarding the RUL of a mechanical element, before failure occurs. Each asset in the industry is associated with software agents that fulfill several computing essentials of the machine elements, such as managing relationships among the assets and the human operators [17]. We define various relationships that predict the failure of the machine and notify the prediction to the actors like maintenance or repair

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personnel who interact with the devices. Various relationships can be established among the assets and actors, which are enumerated as follows. 1. Coercion object relationship (COR) enables machines to intimate the maintenance personnel that machine element has crossed its specified fatigue limit, which might cause fatigue failure. 2. Torrid object relationship (TOR) is established between a temperature sensor and the maintenance team to take immediate action when the temperature exceeds its maximum limit, to avoid overheating of machine elements. 3. Corrosion object relationship (CROR): When the machine elements are exposed to corrosive fluids or corrosive atmosphere, it forms a CROR with saddle-type eddy current sensor, and then the information is transferred to the technician. 4. False Brinell object relationship (FBOR): When excessive external vibration occurs, a false Brinell object relationship is established between the vibration sensor and the maintenance team to avoid false brinelling on the machine elements. 5. True Brinell object relationship (TBOR): When the load exceeds the elastic limit of the ring material, a true Brinell object relationship is established between the vibration sensor and the maintenance team to avoid true brinelling on the machine elements. 6. Spalling object relationship (SOR) is formed between the UV sensor and technician when there is a marked increase in the vibrations generated by the machine elements. 7. Back freight object relationship (BFOR): When the load is induced in the opposite direction, a BFOR is established between an infrared sensor and maintenance personnel to avoid early failure of machine elements. 8. Withered object relationship (WOR) is established between smart oil sensors and lubrication experts when the required lubrication is not supplied to the machine element, which might cause excessive wear of balls and cages. 9. Defile object relationship (DOR): When there is contamination in the operating area, a DOR is formed between the revolutions per minute (RPM) sensor and the maintenance team to avoid machine element dents. The ontological model for predictive maintenance and its components is as shown in Fig. 3. The components of the model includes machines, sensors, machine element, actors, analyzer, and RUL predictor. The model is constructed in the industrial domain, studying machine elements such as bearings, brackets, axles, hangers, etc. The RUL predictor utilizes the information from other components and thereby predicts the remaining life of key machine element much ahead the actual failure occurs. In the ontology model, the object properties represent the links between components; and the data properties describe the values of the components. The ontology elements such as object and data properties for predictive maintenance is detailed in Table 1.

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Data

Specification

Mode

Effect Measurement

:hasRelationship

Actors Relationships COR T FBOR TBOR

Type Sensor

Alert Message

Minimum Value

isEmbeddedWith

Machine

Machine Element

Maximum Value

RUL Predictor

Measurement unit

Threshold Value

Analyzer

:hasRelationship

Analyzed Data

Fig. 3 Ontology model for predictive maintenance

5 Implementation of PdM-SIoIT In this section, we present an overview of the feasible implementation of the designed PdM-SIoIT architecture by an illustration under multiple wear conditions of assets and the simulation setup.

5.1 A Scenario A bearing is a machine component that minimizes friction among moving parts, forcing relative motion to merely the desired motion. Bearing facilitates free rotation around the fixed axis of the moving parts or a free linear movement. The bearing usually does a pretty excellent job of managing the moving parts; however, when a bearing fails, the outcomes can be catastrophic [18]. Bearing fails unexpectedly and untimely, despite planning and maintenance. The primary reasons of damage and unexpected bearing failure for all bearing types are overheating, overloading, improper lubrication, excessive external vibration, contamination, etc. [19], and they are discussed as follows: 1. Overloading: When the bearing is at a speed of 2200 rpm, the maximum load it can accept is approximately 47,620 N; if the load exceeds, there will be the formation of irregular spiderweb-like cracks and craters in the overlay surface which causes bearing failure.

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Table 1 Object and data properties for ontology model Object properties

Data properties

Properties

Domain

hasMode

Machine

Description Represents the mode of machinery operation

hasEffect

Machine

Identifies the effect of failure

hasMeasurement

Sensor

Measures the threshold values

hasCause

Machine element failure

Determines the cause of an element failure

hasType

Sensor

Represents different types of sensors used

isPartOf

Machine/machine element

Identifies the part that might get affected

isIdentifiedWith

Symptoms/effects

Rectifies the variations in normal routine operations of a machine

hasSpecification

Machine

Provides the machine specifications

hasAnalyzedData

Analyzer

Analyzes the data obtained from sensor

hasData

Actor

Represents the available predicted data

hasTeam

Actor

Represents the different types of actors

isEmbeddedWith

Machine

Represents that the machines are embedded with different types of sensors

hasRelationship

Machine, actors, and analyser

Represents the relationships between actors and machine elements

dependsOn

Machine

Machine depends on predictor for RUL prediction

monitors

RUL predictor

Monitors the threshold value for RUL prediction

Minimalvalue

Machine element

The minimum value under which an element can operate normally

MaximumValue

Machine element

Represents the maximum value which causes failure of a machine element

MeasurementUnit

Machine element

Every measurement has different units

AlertMessage

Sensor

Alerts when specific threshold is reached

FatigueLimit

Machine element

The load an element can accept

MaximumTemperature

Machine element

Maximum temperature condition an element can reach

VibrationValue

Machine element

The speed at which an element can operate

ContaminationLevel

Machine element

The limit of contamination which does not affect an element operation

LubricationLevel

Machine element

The minimum lubrication level an element should maintain

Loadtype

Machine element

The type of load on machine element

BrinellEffect

Machine element

The effect of different loads on an element is measured

Threshold value

RUL predictor

Life span of a machine element

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2. Excessive external vibration: When bearings in a machine experiences vibration above 3200 rpm, the bearing might face true and false Brinel, which might cause bearing failure due to excessive vibrations. 3. Contamination: A bearing that is typically contaminated beyond 1141 MPa causes denting of the bearing, and balls emerged from high vibration and wore, leading to bearing damage. 4. Overheating: Temperature above 400 ◦ F anneals the ring and ball materials of bearings. Excessive heat can cause serious problems, resulting in machine failure and hours of downtime. 5. Improper lubrication: Minimum lubrication level must be maintained at 1.97 µm, and the bearing is serviced either according to a schedule or when the lubricant goes below the specified minimum level. 6. Life span: The life of a bearing depends on the load and the speed at which it operates. Most of the bearings last approximately 8–12 years.

5.2 Simulation Setup and Results As IoT enabled industrial setting has not been extensively implemented to date, and the experiment of such an ecosystem is deployed on Node-RED [20], an opensource simulation, and modeling tool. Table 2 presents the complete information of the varieties of sensors deployed along with the threshold limit exerted for each of the bearing failure types. The performance of the bearing is monitored continuously; and if any discrepancy is observed in the specified threshold, the system sends alerts to the dedicated maintenance personnel regarding the fundamental specification of action. Figure 4 depicts the example of alerts generated in the event of any exceptional behavior with the help of NodeRED simulator. Simultaneously, besides creating alerts, the system also regularly predicts the RUL of the bearing, so that the maintenance can be planned in advance. The dataset used in this experiment is from prognostics and health management (PHM08) [21], which includes time series data. The dataset is separated into training and test datasets. It comprises twenty-one sensor measurements and three operational settings of an

Table 2 Sensor description for bearing threshold monitoring Sensor Description Unit Temperature sensor Pressure senors Smart oil sensor Vibration sensor

Temperature value Fatigue limit Lubrication level External vibration

RPM sensor

Contamination

Fahrenheit (◦ F) Newton (N) Micrometer (µm) Revolutions per minute (rpm) MegaPascal (MPa)

Threshold limit 400 47,620 1.97 (min) 3600 1141

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Fig. 4 Alerts generated using NodeRED simulation tool Table 3 Results Model Deep learning model Template model

Precision

Recall

F1-Score

Accuracy

0.960000

0.96

0.960000

0.978495

0.952381

0.80

0.869565

0.940000

engine with varying degrees of initial wear and manufacturing variation. We develop a data-driven method to predict the RUL of a machine that deteriorates from an unknown primary state to failure. The proposed method uses the data from the training set, and then, the performance of the RUL prediction using testing data is evaluated. The solution employs an advanced, recurrent neural network to improve the prediction accuracy of the bearings remaining life. To reliably evaluate the fault diagnosis of a bearing, along with accuracy, we compare the other metrics such as precision, recall, and F1-Score as given in Table 3. We see that the deep learning model results are better than the template when analyzing the above test results in the predictive maintenance template. It should be noted that the dataset used here is minimal, and deep learning models are known to offer excellent results with large datasets, so more massive datasets should be used for a more fair comparison.

6 Conclusions In this paper, the inclusion of social interactions into the industry settings for predictive maintenance is envisioned. A novel architecture for predictive maintenance in SIoIT systems is introduced to monitor future failure by keeping the companies prepared and allows them to plan even before the failure occurs. An ontological model is constructed to describe various relationships in the prediction of multiple factors that might cause an asset failure. To implement the proposed PdM-SIoIT

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architecture, the failure condition of an essential machine element, i.e., a bearing is discussed as an example. The simulation model is developed to detect the failure condition and predict the remaining life of a bearing. Further, we intend to implement the recommended scheme in the actual manufacturing settings and exploit the other deep learning techniques to improve prediction accuracy.

References 1. F. Civerchia, S. Bocchino, C. Salvadori, E. Rossi, L. Maggiani, M. Petracca, Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. J. Ind. Inf. Integr. 7, 4–12 (2017) 2. K. Zhu, T. Liu, Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans. Ind. Inform. 14(1), 69–78 (2017) 3. Y. Wang, Y. Peng, Y. Zi, X. Jin, K.L. Tsui, A two-stage data-driven-based prognostic approach for bearing degradation problem. IEEE Trans. Ind. Inform. 12(3), 924–932 (2016) 4. M.S. Roopa, S. Pattar, R. Buyya, K.R. Venugopal, S.S. Iyengar, L.M. Patnaik, Social Internet of Things (SIoT): foundations, thrust areas, systematic review and future directions. Comput. Commun. 139, 32–57 (2019) 5. M.S. Roopa, D. Valla, R. Buyya, K.R. Venugopal, S.S. Iyengar, L.M. Patnaik, SSSSS: search for social similar smart objects in SIoT, in 2018 Fourteenth International Conference on Information Processing (ICINPRO) (2018), pp. 1–6 6. M.S. Roopa, A. Siddiq, R. Buyya, K.R. Venugopal, S.S. Iyengar, L.M. Patnaik, Dynamic management of traffic signals through social IoT. Procedia Comput. Sci. 171, 1908–1916 (2020) 7. H. Li, A. Parlikad, Social Internet of Industrial Things for Industrial and Manufacturing Assets (2016) 8. W.O.L. Vianna, T. Yoneyama, Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles. IEEE Syst. J. 12(2), 1170–1181 (2017) 9. Z. Wu, H. Luo, Y. Yang, P. Lv, X. Zhu, Y. Ji, B. Wu, K-PdM: KPI-oriented machinery deterioration estimation framework for predictive maintenance using cluster-based hidden Markov model. IEEE Access 6, 41676–41687 (2018) 10. J. Yan, Y. Meng, L. Lu, L. Li, Industrial big data in an industry 4.0 environment: challenges, schemes, and applications for predictive maintenance. IEEE Access 5, 23484–23491 (2017) 11. J. Wang, L. Zhang, L. Duan, R.X. Gao, A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. J. Intell. Manuf. 28(5), 1125–1137 (2017) 12. V. Subramaniyaswamy, G. Manogaran, R. Logesh, V. Vijayakumar, N. Chilamkurti, D. Malathi, N. Senthilselvan, An ontology-driven personalized food recommendation in IoT-based healthcare system. J. Supercomput. 75(6), 3184–3216 (2019) 13. Q. Cao, A. Samet, C. Zanni-Merk, F.D.B. de Beuvron, C. Reich, An ontology-based approach for failure classification in predictive maintenance using fuzzy C-means and SWRL rules. Procedia Comput. Sci. 159, 630–639 (2019) 14. H. Wen, Y. Zhen, H. Zhang, A. Chen, D. Liu, An ontology modeling method of mechanical fault diagnosis system based on RSM, in Proceedings of the 2009 Fifth International Conference on Semantics, Knowledge and Grid (2009), pp. 408–409 15. F. Xu, X. Liu, W. Chen, C. Zhou, B. Cao, Ontology-based method for fault diagnosis of loaders. Sensors 18(3), 729 (2018) 16. J. Jung, S. Chun, X. Jin, K.H. Lee, Quantitative computation of social strength in Social Internet of Things. IEEE Internet Things J. 5(5), 4066–4075 (2018) 17. N. Gulati, P.D. Kaur, Towards socially enabled internet of industrial things: architecture, semantic model and relationship management. Ad Hoc Netw. 91, 101869 (2019)

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18. A. Harnoy, Bearing Design in Machinery: Engineering Tribology and Lubrication (CRC Press, 2002) 19. F.T. Barwell, D. Scott, Paper 10: the investigation of unusual bearing failures. Proc. Inst. Mech. Eng. 180(11), 277–317 (1965) 20. A. Javed, Complex flows: node-RED, in Building Arduino Projects for the Internet of Things, Apress, Berkeley, CA (2016), pp. 51–73 21. F.O. Heimes, Recurrent neural networks for remaining useful life estimation, in Proceedings of the 2008 International Conference on Prognostics and Health Management (IEEE, 2008), pp. 1–6

Emerging Trends in the Marketing of Financially Engineered Insurance Products Sunitha Ratnakaram, Venkamaraju Chakravaram, Nitin Simha Vihari, and G. Vidyasagar Rao

Abstract Insurance plays a fundamental role in the economies of nations worldwide. Protecting the future earnings of individuals, companies from uncertainties, and risks fastens a nation’s gross domestic product (GDP) [1]. The second decade (2D) of the current century (2D/twenty-first century) has witnessed a revolutionary disruptive technological advancement, innovative strategies, and financially engineered models in the marketing of insurance products across the insurance sector worldwide. Some of these technologies and advancements are named as “FinTech, InsurTech, Blockchain Technology, Cryptocurrency, Robotics, Cloud Technologies, Data Science, Data Analytics, Big Data, Financial Engineering, ICTs (Information and Communication Technology), IoT (Internet of Things), AI (Artificial Intelligence), Machine Learning, Mobile Phones, Social Media sites, and Drown Technologies” [2]. To list and to explain these major disruptive technologies of the sector is the main purpose of this research work. The main focus is to study the specially designed financial engineering models in the process of insurance marketing. To conclude, the detailed role of financial engineering applications in the design, development, and execution of marketing models in the insurance sector is discussed. Descriptive-cum-exploratory research methodology is used. Keywords Emerging trends · FinTech · Financial engineering · Insurance · InsurTech) S. Ratnakaram (B) · V. Chakravaram Jindal Global Business School, O. P. Jindal Global University, Haryana 131001, India e-mail: [email protected] V. Chakravaram e-mail: [email protected] N. S. Vihari BITS Pilani, Dubai Campus, Dubai, UAE e-mail: [email protected] G. Vidyasagar Rao UCCBM, Osmania University, Hyderabad, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_65

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1 Introduction Technologies are driving most of the sectors all over the world through their innovative apps, models, and online portals. Particularly, in banking and insurance sectors, technologies are disrupting for a decade. Every year, new things and new and innovative style of operations are taking place in these sectors in the form of FinTech and InsurTech.

2 Financial Engineering Financial engineering involves the design, the development and the implementation of innovative financial instruments and processes, and the formulation of creative solutions to problems in finance.–John Finnerty

Financial engineering (FE) is a process of creating innovative financial models and products or solving the existing financial problems of the sectors by using tools and formulas from econometrics, economics, financial mathematics, statistics, and computer sciences. The insurance sector is using FE applications in the creation of innovative products and models. The given Image 1 is showing the same combination of the listed sciences which is financial engineering. This sector is also using financial engineering applications in the process of marketing of insurance policies. Particularly in the designing process of customized insurance policies, customized riders with innovative features like short time coverages, specific event or specific purpose coverages etc. [3].

Image 1 Ref: https://sofe.iiqf.org/pages/what-fe.html

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3 Objectives and Hypotheses of Research 1. What are the emerging trends in the marketing of financially engineered insurance products? 2. What are evidence exists on the benefits of using financial engineering instruments as tools for the quick and satisfactory services rendered to the customers The following hypothesis is proposed: • H0: There are positive emerging trends in the marketing of financially engineered products in the insurance sector. Image 2 shows various steps in the designing and development process of financially engineering products. In this process, instruments or statistics, financial mathematics, econometrics, financial management models, computer sciences, information and communication technologies (ICTs), big data, blockchain technology, data analytics, data science, Internet of things (IoTs,), robotics, automation technologies, and drones are used. Image 3 shows the first generation of the financially engineering process in the insurance business. This image explains how the premium is being distributed among various portfolios to maximize the financial benefits to the customers. This typical

Financial Engineering Process (1)

Process Identification of need (2) Design of Insurance Product (3) Complex Model of Building Exercise (4)

Testing of Insurance Product (5) A perfect product based on the entire Exercise (6)

Pricing of the Product (7) Restructuring of the insurance product (8) Test Marketing (9) Launching of Financially Engineered Insurance Product (10)

Image 2 Financial engineering process in the design, development, and launching of insurance products

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Image 3 Financial engineering in life insurance business in 1950

image or financial engineering process in the life insurance business belongs to the period between 1950 and 2000. Image 4 shows the financial engineering process in the life insurance business after the privatization of the insurance sector in India. We can take an image for the first decade (2000-2010) of the current century. Insurance companies started participating in the share markets. The major portion shifting to the marketing investments in the

Image 4 Financial engineering in life insurance business in India–LPG era (2000)

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Image 5 Financial engineering in life insurance business in India–ULIPs era (2010)

form of various securities, bonds, and NAVs has been started during this period only. So many customers who invested in Unit Linked Insurance Policies (ULIPs) incurred heavy losses since the returns depend on the share market conditions. Image 5 represents the present era of the financial engineering process in the insurance business. See in the picture once, how the paid premium by the customer is being divided, shifted among multiple portfolios, investment zones, risk coverage premiums, and as rider premiums.

4 Emerging Trends in the Marketing of Insurance Products As per the recent Accenture report, 86% of insurance companies worldwide are focusing on the use of technologies in every stage of the insurance business. They believe that only they can be in a competitive edge in this technology world [4].

4.1 Artificial Intelligence (AI) AI technologies are using in the marketing of financially engineered products while issuing the policies to the customers. They are using AI technologies for facial recognition which will help the insurers in their underwriting process of the newly issuing policies. This AI-enabled app is also helping the insurers in the underwriting process to estimate the life span of the prospect applicant by detecting his/her habits using gamification techniques. Hence, they can easily calculate the risk levels and

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threats of the prospect to determine his/her required premium payable to the company (Pragya Saxena 2019).

4.2 Robotic Process Automation (RPA) Particularly, RPA is happening in the claim settlement process of insurance business and data management of the insurance companies. When the companies are achieving the better claim settlement ratios during business by using RPA automation technologies, goodwill of the company will increase and indirectly helps the marketing team to market their products to the prospect customers. RPA also helps insurers by simplifying their data management processes at high-speed computer servers and systems (Pragya Saxena 2019).

4.3 Machine Learning Underwriting is one of the important activities in the marketing process of insurance. To give exact risk coverage, riders, etc., consider the prospect’s profiles. Machine learning technologies have improved this underwriting process in the companies to generate an exact quotation of premium by considering the detailed profile of the prospects. These quotations include premium to be paid, duration of risk coverage, sum assurance value, maturity benefits, additional interim benefits, interim and maturity bonuses etc. Machine learning technologies also help insurers to list the lapsed policies and give strategies in renewing the process of that policies. Hence, in the prediction of premiums, risk coverages, and the process of claim settlement processes, machine learning technologies are playing a greater role in the sector (Pragya Saxena 2019).

4.4 Augmented Reality/Virtual Reality (AR/VR) These technologies are helping both clients and customers in their claims settling process. Particularly, when the risk/event occurred in a remote village or place, these AR/VR technologies will help both the stakeholders. By reconstruction of the scene of accident using the augmented reality. Hence, this kind of live video clips will help the underwriters to estimate the loss to be stele to the beneficiary (Pragya Saxena 2019).

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4.5 Big Data Predictive Analytics (BDPA) Analytical insights, risk chances, and risk coverage productions are the outcome of predictive analytics. Big data predictive analytics helps insurers for their smooth and effective marketing decisions. BDPA helps the marketing team to understand and to analyze the consumer behaviors also to identify their patterns. BDPA also helps the underwriters and claim management team (Pragya Saxena 2019).

4.6 Blockchain Technology (BCT) Insurance companies started using this BCT while analyzing the clients profile to decide his/her risk factors, insurance coverage to be allowed, risk riders to be allowed, etc. With the effective use of BCT, insurers can easily stop the fraudulent practices of some fraudulent customers and organizations particularly in the claims management process. Since the data is available in the decentralized ledger systems (BCT) about the customer, no need to submit the common documents again and again to avoid duplication and to minimize the repetition while applying for a new policy or while applying for surrender or the policy or while submitting application for the claim settlement process (Betsy McLeod 2019).

5 Other Emerging Technologies Using by the Marketing Team The marketing teams always try to present differently about their products and companies to grab the attention of prospects. They differentiate the business comparing with other service providers to make him/her accept their proposals. Teams mostly follow or use the following techniques (Betsy McLeod 2019). Communication of brand through videos: Marketing professionals are keeping the video testimonials of the existing customers. Companies also try to convey their commitment through these videos to customers and introduce their company Web site(s) or blogs to the customers [5]. They are habituating the prospects to visit the Web sites, to download their required information. Insurers are investing in search engine optimization (SEO) with mobile-friendly features and also with mail-friendly features [6]. Content should be in a quality manner, and it should be in crisp and easy to the user in understandable simple language and simple sentences. Companies are keeping contact information in all the folders of the Web site and pages of the Web site. Insurers are explaining the path and their action plan by explaining every step clearly in case if customers wish to submit their online applications then and there. Insurers are keeping chat boxes to guide the online prospects in their application

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submissions process or their decision-making process. Companies also use sensible language based on the age, gender, and educational levels of the customers [7]. Some insurers are using colors and images to convey the information while they are communicating with the customers in the chat boxes. Marketing teams of insurers are creating blogs, explaining the past performance of those policies, with FAQs, other analytical statements showing the yields and financial benefits that they are going to offer their prospects [8]. Marketing professionals are keeping reviews of the Web site visitors and customers also on the Web site. This will help those who wish to see the reviews of the company. Here, professionals are using statistical tools and analytical tools to display their reviews in different variations (Betsy McLeod 2019).

6 Social Media Marketing professionals are using effectively various social media platforms to introduce their products and features. Particular special marketing teams are disseminating information in the form of brochures and success stories. Also announcing their special offers, special plans through their benefit analysis statements using social media platforms. Some of them are WhatsApp, Facebook, Twitter apps, etc. Using these social media platforms, marketing professionals are easily doing new business with their existing customers [9]. Also, they are using these platforms to remind their renewal date, maturity date, claims procedure, etc., which also causes the health premium turnover (Betsy McLeod 2019).

7 Discussion Based on the above discussion, it is proved that the following listed technologies are playing a major role in the emerging trends in the marketing of financially engineered insurance products. These are trendsetters and associated with the financial engineering process, and it is proved that there are many instruments from the listed sciences that are involved in the design, development, and launching (marketing) process of financially engineered products in the insurance business. Hence, it has been declared that there is a significant role of these instruments in financial engineering. All these instruments are from statistics, financial mathematics, econometrics, Internet of things (IoT), Information and communication technologies (ICTs). Also from FinTech, InsurTech, Blockchain technology, Data sciences, Data analytics, Big data, Automotive technologies, Robots, Drones etc.

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8 Limitations and Scope for Further Research Since the research study used is descriptive-cum-exploratory research methodology, we could not get time and space to go for the collection of primary data from the key stakeholders of the topic. All the articles and research papers reviewed are up to 100, and we took 15 most appropriate references to compile this research output. There is a huge scope in this research field. Since technologies are playing a greater role in the banking and insurance sectors, particularly while designing the innovative and creative financially engineered financial and insurance models, companies are using these latest technologies. Upcoming researchers can study the role of the following technologies into their consideration. Role of blockchain technologies in the financial engineering process of banking and insurance sector products. Role of cryptography in the financial engineering process of the same sectors. Role of social media sights and their analysis in the marketing of financially engineered products. Role of consumer feedback sights on the marketing process of financially engineered insurance products.

9 Conclusion Based on the above discussions and the listed facts, researchers are concluding that emerging trends in the marketing of financially engineered insurance products are doing business progressively when compared with traditional business or marketing activities. These emerging trends are laid the strong research path to the upcoming researchers as we listed above. Hence, this research study has been concluded with this above, findings, discussions, suggestions, and scope for further research.

References 1. CII Report, Competing in a New Age of Insurance: How India is Adopting Emerging Technologies (June 2019) 2. Insurance Industry; 12 trends (May 2019). https://www.oneincsystems.com/blog/insurance-ind ustry-12-trends-for-2019 3. School of financial engineering, what is financial engineering, https://sofe.iiqf.org/pages/whatfe.html. Detailed and minor references available on request 4. Marek Seretny, Sustainable marketing-a new era in the responsible marketing development (January 2012) 5. Recent trends in insurance sector, https://nios.ac.in/media/documents/vocinsservices/m1-1f.pdf 6. Scale Focus, 9 insurance industry technology trends in 2020 [Updated] (October 2018). https:// www.scalefocus.com 7. Insurance business overview and the move towards the digital era by digital ventures (November 2018). http://www.dv.co.th/blog-en/digitizing-ins-view/ 8. S. K. Chaudhury, S.K. Das, Trends in marketing of new insurance schemes and distribution: an empirical study on Indian life insurance sector (December 2014)

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9. Where do insurers stand as they enter 2019? 2019 insurance outlook: growing economy bolsters insurers, but longer-term trends may require transformation. https://www2.deloitte.com/us/env/ pages/financial-services/articles/insurance-industry-outlook.html

Attitude of the International Students Towards Integrating ICT in Foreign Language Learning: A Case Study Khushboo Kuddus

and Nafis Mahmud Khan

Abstract The study investigated the attitude and the awareness of the foreign students about the role of Information and Communication Technology (ICT) in learning English and in overcoming the language barriers. The purpose of the study was to explore the use of ICT in enhancing the English language skills among the international students at a university of India. In order to collect the data, a survey was conducted and questions based on the objectives of the study were asked to the international students. The findings of the study suggest that students integrate both synchronous and asynchronous mediums like mobile applications, watching movies with subtitles, games, and YouTube to enhance their language proficiency. Moreover, the students prefer learning the language passively at their own pace and space without being actively involved in the learning process. Further, the international students at the university have positive attitude towards the use of technology to overcome foreign language barrier. Keywords Information and communication technology · Technology-enhanced language learning · Social networking sites · Mobile-assisted language learning

1 Introduction The remarkable growth of Information and Communication Technology (ICT) has made English, a global language. English is the language of technology and the language of the Internet. The excessive use of the Internet in almost all the fields has expanded the demand for English manifolds and at the same time has also made the native English language easily accessible across the world. It plays a significant role in being a lingua franca and a link language for the people sitting at the different K. Kuddus (B) · N. M. Khan School of Humanities (English), KIIT Deemed to Be University, Bhubaneswar 751024, Odisha, India e-mail: [email protected] N. M. Khan Division of Research, Daffodil International University, Dhaka, Bangladesh © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_66

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corners of the world. Further, with the growth and access of the Internet, people have become more exposed to the real, authentic and native English language which earlier was quite difficult. A significant integration of ICT in education and specifically in language teaching and learning has also been observed in the recent years. According to the studies, it has been found that the integration of technology increases learners’ motivation and at the same time engages the learners in the learning process. It provides the language learners with an authentic learning environment and exposes the learners to the authentic native English language. It encourages autonomous learning which supports learning at one’s own pace and space. Technology-enhanced language learning can provide learners a rich source of information and excellent materials to develop language proficiency. Though technology has a significant impact on language learning, does it suffice to overcome the foreign language learning barriers of the non-native speakers of English? As per the best possible information of authors, no work has been recorded in open literature about the attitude of international students towards the use of ICT in enhancing the foreign language skills. This motivates us to study the awareness of ICT among international students of the university and to integrate it in overcoming the foreign language barriers. It has been observed that the foreign students from the Middle East, Ethiopia, Syria, Nepal, and others who come to study in India, find it difficult to communicate with the other native and foreign students due to the lack of proficiency in English. Observing the benefits of integrating technology in language learning and the difficulties faced by the international students, the paper attempts to examine the attitude of the international students towards using ICT for overcoming the language barrier and its integration in enhancing language skills.

2 Literature Review Technology is playing a significant role in language learning across the world at different levels. Undoubtedly, the exponential expansion of Information and Communication Technology (ICT) has made English language learning easier and autonomous. The incorporation of technology in language learning makes the teaching and learning of foreign language easy, active and interesting [1]. In this context, Warschauer asserts that one significant advantage of incorporating technology is to increase the learners’ motivation as they are likely to spend more time on tasks when doing it on computer [2]. He further states that Information and Communication Technology provides the language learners with an authentic learning environment and fosters the enhancement of language skills like listening, reading, writing and speaking at the same time [3]. Undoubtedly, the tech savvy generation of today is certainly aligned towards technology enhanced language learning as they find it quite interesting and motivating. The technology-enhanced language learning (TELL) and mobile-assisted

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language learning (MALL) foster active learning in the learners, and they also enjoy the liberty of learning at their own time, pace and space [4–6]. Clark [7] states that using social networks can generate meaningful output and stimulate learners’ interest through the utmost encouragement. In addition to this, interaction through Internetmediated intercultural foreign language education tools such as tandem learning, pen-pal communication, collaboration, excessively gets a tremendous response from the language learner [8]. Hence, using social networking sites (SNS), learners can develop their language proficiency by interacting with multi-communities, through socialization process [9]. Moreover, using social networking sites in English language learning, students get more exposed to the target language and its native speakers, learn the authentic language collaboratively, become more motivated, autonomous and more accustomed to native speakers’ accent and pronunciation as they learn the target language in real and authentic situation [10]. Mobile-assisted language learning (MALL)—an emerging advanced educational technology that provides personal and learner-centred learning opportunities through flexible practices—has become an important trend in EFL learning [11]. For developing foreign language speaking skills, mobile phone application with automatic speech recognition could be the best possible way as it helps the learners to improve pronunciation skills through practice [12]. It has become the most popular and easily affordable device for learning the English language as it is widely used not only among younger generation but by individuals of all age groups and of all financial backgrounds. There are a number of mobile applications for learning English vocabulary, grammar and downloadable dictionaries like Merriam Webster with embedded pronunciation [10]. In addition, online games can support and improve various vocabulary fields and also give valuable feedback [13]. Yang 2013 adds that students increase their language awareness by using on-site games and by discussion in different social and cultural contexts [14]. Gamification-game element to non-game context language learning is a standout platform for learners’ preferences among other mobile language learning applications [15]. Telegram apps and movie clips invariably play a decisive role in the enhancement of foreign language learning [16]. The computer-assisted language learning (CALL) program offers students with a variety of items for learning the English language such as grammar, vocabulary, pronunciation, etc. Designed teaching and learning practice videos incorporated with pedagogical contour are another great wonder of technology that urges learners to be diligent in using materials that are authentic, memorable, and realistic [15]. Videobased instruction on YouTube is also an outstanding platform for language learning. A study shows that YouTube enhances students’ ability to engage themselves in learning and getting contentment in higher education [17, 18] A study was done on “Exploring the Effects of Telegram Messenger and Movie Clips on Idiom learning of Iranian EFL learners” where 59 students were assigned to two experimental and one control group. Finally, the results revealed that both the telegram and movie clips groups showed outstanding performances and they can be considered as effective educational tools for learning idioms [19]. Another

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study on “Social networking for language learners: Creating meaningful output with Web 2.0 tools” shows that how technology-based social networking sites provide language learners with vast resources of authentic written, audio, and video materials to supplement lessons and how the advanced technology learners of a language can easily interact with their peers through a meaningful practice that foster language acquisition and motivation [20]. A study on students’ attitudes towards use of technology in language teaching and learning in the UK was conducted and found that students were highly interested in using email and internet for study purposes [21]. Investigation on “Students’ Critical Thinking Skills, Attitude to ICT and Perception of ICT Classroom Learning Environments Under the ICT School Pilot Project in Thailand” showed that many students improved their critical thinking capability and developed the degree of attitude towards technology [22]. From the above review, it is believed that the study is appropriate enough with the time and the context as it focuses on the attitude of international students of 15 different countries toward technology and their awareness of the effectiveness of using technology to overcome the foreign language barrier and to develop language proficiency.

3 Methodology A questionnaire was designed after conducting a comprehensive review of the related literature. A survey of the international students at a university of India was conducted to examine the awareness of using technology in language learning among the foreign students from 15 different countries, viz. Ethiopia, Yemen, Colombia, Somalia, Iran, Syria, Nepal, Bhutan, Afghanistan, Togo, Chad, Maldives, Vietnam, Ghana and Saudi Arabia.

3.1 Participants For the purpose of the study, 100 foreign students from the academic years 2017– 2018, 2018–2019 and 2019–2020 were invited to participate in the survey. Total 88 responses were obtained out of which 58 were B. Tech., 15 PG and 15 Ph.D. students.

3.2 Researching Tools This study applies a quantitative approach with close-ended questionnaire. For sections II and III, a five-point Likert scale was designed for foreign students to find out their attitude, opinions and the integration of technology in learning English

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language. The questionnaire consisted of 20 items that showed the extent of agreement of students, from 1. strongly disagree to 5. strongly agree. To elicit data for the study, the questionnaire was divided into three sections: (a) participants’ background, (b) attitude to technology and (c) functional language learning experience using technology.

3.3 Procedure This study was conducted by survey method. The invitation clearly explained the objectives of the research study and also guaranteed the anonymity of the participants. The participants had the right to not participate or withdraw from the study at any time. The survey was conducted offline and so the questionnaires were given in person to the respondents. The questionnaire was administered among 100 international students of the university’s international boys hostel. Out of 100 samples for the study in hand, 88 responses were obtained. The data of the responses were quantified and were analysed by SPSS 20 program. Data calculated with the SPSS program were expected to determine the mean and standard deviation which were used to interpret the data and to draw conclusion.

4 Findings The obtained data from the questionnaire was examined in order to investigate the awareness of use of technology in English language learning. Since the young learners are not only tech savvy, but they are also using technology for education and for language learning in particular; therefore, it was considered important to analyse the awareness and the attitude of the foreign students about the incorporation and impact of technology in language learning.

4.1 Students Attitude Towards Technology in Language Learning Table 1 represents the “Attitude of the international students towards using technology in learning English language”. It is interesting to find from the table that 91% of the students have positive attitude towards the use of technology in learning English language, whereas 4.5% of the students are neutral, and the rest 4.5% did not use technology to learn the English language. The score obtained from item 3 indicates that most of the students believe that technology can improve English language skills. Hence, the table below shows that 80–90% of the students have

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Table 1 Distribution and frequency of attitude of international students towards technology in language learning Attitude to technology Sl. No.

Items

Strongly agree (%)

Agree (%)

1

I enjoy using ICT for language learning

47

33

4

53.4

37.5

4.5

2

3

4

5

Neutral (%)

Disagree (%)

Strongly disagree (%)

Mean

Standard deviations

1

3

4.36

0.899

1.1

3.4

4.22

0.823

4.18

0.917

4.19

0.814

4.14

1.008

I prefer all 38 the medium 43.2 of instruction in English

34

13

3

38.6

14.8

3.4

I believe that using technology can improve my English language speaking skills

37

37

9

3

2

42

42

10.2

3.4

2.3

I believe 32 that 36.4 knowing and utilizing technology properly for language learning is more useful

46

7

1

2

52.3

8.0

1.1

2.3

I like to use 39 technology 44.3 whenever face any language barrier

32

9

6

2

36.4

10.2

6.8

2.3

shown positive attitude towards usage of technology and have also agreed that usage of the technology helps in building their English language skills. Only a few students who range from only 3–9% have shown their disagreement towards the usefulness of technology in learning which is again a very minimal percentage of the sample. The conclusive finding of Table 1 has been illustrated in Fig. 1.

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Fig. 1 Attitude to technology

4.2 Students Attitude to Functional Language Learning Experience Using Technology Table 2 exhibits that majority of the international participants showed their awareness about use of technology in language learning. Out of the total population surveyed, 76.1% agreed to the use of mobile phones to improve language proficiency and 67% international students showed their interest in learning English language through use of various media of technology like chat, email or call over phones. The score obtained for item 7 clearly shows that 56.8% students watched English movies with subtitles to enhance their language skills (speaking, listening and reading). Further, by observing item 5 and 8, it can be understood that, students like to learn language in a passive way like reading e-books and games. From the above discussion, it can be inferred that around 60–75% of the students agree that they use various technological tools such as YouTube, mobile phones and apps to improve their language skills and prefer using English language while chatting, emailing or even talking with friends. Moreover, watching English movies with subtitles helps develop language proficiency.

5 Discussion From the study, it can be concluded that technology bears a great testimony for learning English language. A great percentage of students are showing positive response for using technology in overcoming English language barrier. Table 1 clearly exhibits that the international students are aware of the fact that technology plays a significant role in improving English language proficiency skills and they believe that if they utilize technology properly they can overcome language barrier easily. Most of the students agreed with using mobile phone and different apps to enrich their vocabulary skills and developing proficiency skills. They also use chat, email and text to friends in English. International students show their fondness for watching English movies with subtitles for developing their language proficiency skills.

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Table 2 Distribution and frequency of functional language learning experience using technology Distribution and frequency of functional language learning experience using technology Sl. No.

Items

1

2

3

4

5

Strongly agree (%)

Agree (%)

Neutral (%)

Disagree (%)

Strongly disagree (%)

Mean

Standard deviations

I use Youtube 16 to develop 18.2 my speaking

39

22

8

3

3.75

0.861

44.7

25.0

9.1

3.3

I use mobile phone to improve my language proficiency

20

47

14

6

1

3.90

0.827

22.7

53.4

15.9

6.8

1.11

I use different apps to enrich my vocabulary and proficiency skills

23

35

21

7

2

3.80

0.996

26.1

39.8

23.9

8.0

2.3

3.83

1.074

3.35

1.135

2.35

1.155

3.53

1.203

3.16

1.160

I use English 28 language 31.8 when I chat, email, or talk to friends over phone

31

17

10

2

35.2

19.3

11.4

2.3

I use e-books 14

29

25

14

6

33.0

28.4

15.9

6.8

15.9 6

7

8

I record my video conversation when I make dialogue in English

6

8

19

33

22

6.8

9.1

21.6

37.5

25.0

I like watching English movie with subtitle

22

28

18

15

5

25.0

31.8

20.5

17.0

5.7

I like to play language related games

10

28

23

18

8

11.4

31.8

26.1

20.5

9.1

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Fig. 2 Functional language learning experience using technology

Regarding the use of technology for language learning, students give their utmost positive response which is shown in Fig. 2, where more than 55% agreed to learning through technological tools for overcoming language barrier and 23% of the participants were neutral. So from this analysis, it can easily be understood that students are very much aware of using technology for language learning.

6 Limitations The generalizability of the study has the following limitations. Firstly, the sample consisted of only male international students as the data was collected from the international boys’ hostel of the university. Secondly, the sample for the survey was selected from only one university of India, and hence, further research can be done considering the international students from different educational institutes as well. Finally, with a small number of participants completing the survey, the likelihood of generalizing the findings of the study is limited. Therefore, investigation with a larger sample size is necessary.

7 Conclusion The study shows that the students are aware of the benefits of Information and Communication Technology in overcoming language barrier and also about its significant role in enhancing their proficiency of English language. But in some cases, we see that students learn the language skills passively by reading ebooks and playing games. Although most of the international students showed positive attitude towards the use of technology in overcoming language barrier purposes, there are some students who either have a neutral view or disagree with the point. It is because they might be hesitant in using ICT for language learning or might not be very tech savvy. In such cases, several trainings or workshops can be provided to the students so that they can be exposed to the benefits of integrating technology in learning the English language. Moreover, teachers can also suggest the students some available

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websites which are useful for developing students English language proficiency skills to overcome language barrier.

References 1. B. Skinner, R. Austin, Computer conferencing: does it motivate EFL students? ELT J. 53(4), 270–279 (1999) 2. M. Warschauer, Of digital divides and social multipliers: combining language and technology for human development. in Information and Communication Technologies in the Teaching and Learning of Foreign Languages: State of the Art, Needs and Perspectives (UNESCO Institute for Information Technologies in Education, Moscow, 2004), pp. 46–52 3. M. Warschauer, Computer-assisted language learning: an introduction, in Multimedia Language Teaching, ed. by S. Fotos (Logos International, Tokyo, 1996), pp. 3–20 4. A. Dash, K. Kuddus, Leveraging the benefits of ICT usage in teaching of english language and literature, in Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol. 160, ed. by S. Satapathy, V. Bhateja, J. Mohanty, S. Udgata (Springer, Singapore, 2020) 5. K. Kuddus, Emerging technologies and the evolving roles of language teachers: an overview. Lang. India 18(6), 81–86 (2018) 6. B. Chatterjee, K. Kuddus, Mass media approach to second language acquisition. J. Engl. Stud. 10(1), 10–16 (2015) 7. C. Clark, P. Gruba, The use of social networking sites for foreign language learning: an autoethnographic study of Livemocha, in Proceedings Ascilite Sydney (2010), pp. 164–173 8. S.L. Thorne, The intercultural turn and language learning in the crucible of new media, in Telecollaboration 2.0 for Language and Intercultural Learning, ed. by F. Helm, S. Guth (Peter Lang, Bern, 2010), pp. 139–164 9. T.Y. Ahn, S.M. Lee, User experience of a mobile speaking application with automatic speech recognition for EFL learning. Br. J. Edu. Technol. 47, 778–786 (2016) 10. B. Chatterjee, K. Kuddus, Second language acquisition through technology: a need for underdeveloped regions like Jharkhand. Res. Sch. Int. Ref. E-J. Literacy Explor. 2(2), 252–259 (2014) 11. H. Kellam, C.J. MacDonald, D. Archibald, D. Puddester, Designing digital video to support learner outcomes: a study in an online learning resource for healthcare professionals and students. Int. J. Online Pedagogy Course Des. (IJOPCD) 2, 45–66 (2012) 12. H. Hamzeh, The effects of two technological tools on idiom learning of Iranian EFL learners: a MALL perspective. MLSJ 4(1), 1–12 (2017) 13. M. Prensky, Digital Game-Based Learning (McGraw-Hill, Tradeberg, New York, 2002) 14. Y.F. Yang, Exploring students’ language awareness through intercultural communication in computer-supported collaborative learning. Edu. Technol. Soc. 16(2), 325–342 (2013) 15. B. More, A. Nicole, An examination of undergraduate students’ perceptions and predilections of the use of YouTube in the teaching and learning process. Interdisc. J. e-Skills Lifelong Learn. 10, 17–32 (2014) 16. The media of social media, http://tristantreadwell.wordpress.com/tag/grahl/ 17. B.G. ˙Ilter, How does Technology affect language learning process at an early age? Procedia Soc. Behav. Sci. 199, 311–316 (2015) 18. K. Kuddus, Web 2.0 technology in teaching and learning english as a second language. Int. J. Engl. Lang. Lit. 1(4), 292–302 (2013) 19. R. Chartrand, Social networking for language learners: creating meaningful output with Web 2.0 tools. Knowl. Manag. E-learn. Int. J. 4, 97–101 (2012) 20. D.A. Castaneda, M.H. Cho, Use of a game-like application on a mobile device to improve accuracy in conjugating Spanish verbs. Comput. Assist. Lang. Learn. 29, 1195–1204 (2016)

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21. G. Gay, S. Mahon, D. Devonish, P. Alleyne, P. Alleyne, Perceptions of information and communication technology among undergraduate management students in Barbados. Int. J. Edu. Dev. ICT 2, 6–17 (2006) 22. M.W. Rumpagaporn, Student’s Critical Thinking Skills, Attitudes to ICT and Perceptions of ICT Classroom Learning Environment Under the ICT Schools Pilot Project in Thailand (School of Education, University of Adelaide, Bangkok, 2007)

Limiting Check Algorithm: Primality Test Gunjan Sethi and Harsh

Abstract This algorithm is designed to check the primality of a large number in a very different manner by combining the research of Manindra Agrawal, Neeraj Kayal, Nitin Saxena [1] and Greek mathematics. The time complexity of the algorithm is shown in graphical and tabular way by implementing it in various ranges of numbers. Keywords Prime · Semi-prime · Euclid elements · Complexity · Prime ideals · Primality testing · Matplotlib

1 Introduction Mathematics, the actual science behind the daily life, performs an important role in our day-to-day decisions and activities and nothing wrong to say that even all the decisions in our day-to-day life requires some mathematics behind them either directly or indirectly. Today, all the industries, education institutes, our household applications are running because of mathematics. If there would be no mathematics, there would not be electricity and there would not be any kind of machines. Even the topic of today’s generation, machine learning and artificial intelligence, which made the machines learn easily and let them make decisions by themselves with lesser or even negligible assistance by a human being, is totally grounded on mathematics. Concerning the prime numbers, they are one of the major parts of mathematics. As stated in Wikipedia [2], prime numbers are those numbers which yields to reminder 0 only by dividing them with 1 or itself. Prime numbers, due to their uniqueness, perform a vital role in various cryptography techniques [3]. With the evolution of usage of prime numbers in various situations, a new jargon came up, i.e., semi-prime numbers [4] which are also derived from prime numbers to provide more flexibility G. Sethi (B) Delhi Technical Campus, Greater Noida, Uttar Pradesh, India e-mail: [email protected] Harsh Cult Skills, Jalandhar, Punjab, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_67

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to the usage of these numbers. Prime numbers are topic of research since Greek mathematicians in 200 BC and there after a long break the next research came up in seventeenth centuries. Also, Euclid’s elements enlighten the subject in a very convenient way than someone else does that.

2 Base Algorithm The very first algorithm to check the primality of a number were designed by Greek Eratosthenes in 200 BC as stated in [5]. This algorithm was designed in such a way that it just declare every multiple of a prime number as unprimed. Nowadays, the algorithm taught in theory to check whether a number is prime or not is that the number itself is divided with all the numbers from 2 to the number preceding the actual number and their remainders are checked. If all the numbers failed to lead the reminder of 0, the number is considered as a prime number. But if any number in 2 to number preceding the actual number leads to a reminder of 0, then the number is not a prime number, it is a composite number. Then, multiple algorithms came up and some of them were found to be failed and some others to be successful but when they got implemented in the real-world machines they took ample of time to execute that algorithm. Then a new research of Manindra Agrawal, Neeraj Kayal, Nitin Saxena of IIT Kanpur came up cited here [5] that says that every composite number must have a factor equals to or less than its square root.

3 Limiting Check Algorithm In our limiting check algorithm, we have combined the results of both the research cited above, i.e., Sieve of Eratosthenes [4] and the research by IIT Kanpur, Primes is in P [5]. Here in this algorithm number is checked for its primality only with the prime numbers lesser than or equal to its square root. If the number has no prime divisor before or equal to its square root, then the number is prime number else the number is a composite number. This is the conclusion of two researches as mentioned previously—one saying a number (say n)√needs to have at least one factor less than the square root of the original number ( n). This incorporates the usage of Greek algorithm that for every composite number at least one divisor must be prime and the other algorithm which says that for every composite number it must have a divisor before its square root. In this algorithm, the number is checked before its square root and only by all the prime numbers within that square root which would reduce the complexity by a factor of more than square root of the number.

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4 Results The result of this algorithm can be well understood by graphical representations of numbers with different orders and the time taken by the algorithm to implement that check on those numbers. It is of no use to design algorithm to check whether ‘13’ is a prime number or not or ‘23’ is a prime number or not. The algorithm must give correct results for these numbers, but the main focus is to check the numbers of higher orders which cannot be checked so quickly in the casual algorithm. For 2, 3 or 4 digits, numbers any algorithm will lead to almost similar kind of results. We have here plots for our algorithm for various 6-digit numbers, 8-digit numbers, 10-digit numbers and 12-digit numbers. Timing patterns may also vary with language and IDE used for the execution of the algorithm. The algorithm can be well implemented in any of the programming language, with any possible machine and with any supporting development environment. All the graphs are implemented in Matplotlib library of Python using PyCharm IDE and the machine which leads to this time patterns is made up of i5-3320 2.60 GHz processor, 4 GB RAM and hard disk of 5400 rpm. The timing patterns may vary in different machines as per the configurations. The reason for using python here is just to make the code crisp and short and easily readable to anyone with little or no knowledge of programming. The X—axis of the graph denotes the numbers, and the Y —axis is the time taken (in milliseconds) by the algorithm to test the number. The blue dots on the graph signify the relation between the number, and the time it has taken to accomplish the test on that number. The numbers on the X—axis is shortened in some cases by Matplotlib in the form of logarithm and is mentioned below with ‘le’ notation (Fig. 1).

Fig. 1 Limiting check algorithm of 6-digit numbers against time taken (in milli-seconds)

700 Table 1 Performance comparison on 6-digit number

G. Sethi and Harsh Prime number

Limiting check algorithm

SymPy algorithm

235,919

5.722e–05

8.296e–05

441,461

5.865e–05

7.748e–05

767,857

5.197e–05

8.630e–05

Figure 1 is the plot of 10 numbers of 6-digits along with the time taken by the algorithm to test their primality. As we can see, there are three different slabs 0, 1 and 2 ms (Table 1). Table 1 describes the results of numbers of 6 digits with the time taken by the algorithm to test their primality using SymPy algorithm (existing algorithm) and limiting check algorithm. It can be clearly analyzed that using limiting check algorithm in all the cases, the results are better that means the proposed algorithm consumes less amount time for doing primality test (Fig. 2). Figure 2 is the plot of 10 numbers of 8 digits along with the time taken by the algorithm to test their primality. As we can see, there is a variety of time ranging from around 6 to 25 ms (Table 2).

Fig. 2 Limiting check algorithm of 8-digit numbers against time taken (in milli-seconds)

Table 2 Performance comparison on 8-digit number

Prime number

Limiting check algorithm

SymPy algorithm

49,390,927

4.250e–03

9.525e–03

10,000,931

2.024e–03

7.202e–03

43,112,609

4.523e–03

1.630e–02

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Fig. 3 Limiting check algorithm of 10-digit numbers against time taken (in milli-seconds)

Table 2 describes the results of numbers of 8 digits with the time taken by the algorithm to test their primality using SymPy algorithm (existing algorithm) and limiting check algorithm. It can be clearly analyzed that using limiting check algorithm in all the cases, the results are better that means the proposed algorithm consumes less amount time for doing primality test (Fig. 3) Figure 3 is the plot of 10 numbers of 10-digits along with the time taken by the algorithm to test their primality. As we can see, there is a hierarchy of time ranging from around 130 to 420 ms (approx.), but there are two clusters one around 200–250 ms and one around 280–320 ms (Table 3). Table 3 describes the results of numbers of 10 digits with the time taken by the algorithm to test their primality using SymPy algorithm (existing algorithm) and limiting check algorithm. It can be clearly analyzed that using limiting check algorithm in all the cases the results are better that means the proposed algorithm consumes less amount time for doing primality test (Fig. 4). Figure 4 is the plot of ten numbers of 12 digits along with the time taken by the algorithm to test their primality. As we can see there is a hierarchy of time ranging from around 2000 to 12,000 ms. The line above is a straight line, and clustering of the points is almost negligible. Now if we consider the algorithm which check Table 3 Performance comparison on 10 digit number

Prime number

Limiting check algorithm

SymPy algorithm

1,694,343,422

4.148e–01

7.409e–01

1,000,000,007

3.623e–01

1.671e–00

2,147,483,647

6.119e–01

2.235e–00

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Fig. 4 Limiting check algorithm of 12-digit numbers against time taken (in milli-seconds)

the number until its half that would take immense amount of time to achieve the results of test and it is very hectic to work with this algorithm in cryptographies and various other applications where getting the results in time is a big necessity. The graphs of number and time taken by older stated algorithm to test the number are also mentioned here (Fig. 5). Figure 5 is the plot of ten numbers of 6 digits along with the time taken by the algorithm to test their primality. As we can see there is a variety of time ranging

Fig. 5 Plot of 6-digit numbers against time taken (in milli-seconds)

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Fig. 6 Plot of 8-digit numbers against time taken (in milli-seconds)

from around 2 to 110 ms (approximately). Now if we compare both the plots for 6-digit numbers, then we will find that on one hand this algorithm is taking up to 100 ms, but limiting check algorithm would cost you only 2 ms at maximum for the same number in the same machine. The difference is of the order of 50 or even larger (Fig. 6). The graph here is costing us ranging from 0 to 8000 ms, whereas the limiting check algorithm was taking only up to 25 ms. The difference is of the order of 320 times (Fig. 7). The graph here is costing us ranging from 0 to 1,500,000 ms (approx.), whereas the limiting check algorithm was taking only up to 500 ms at its hard. This shows the significant difference in both the algorithms when we consider a big integer. The difference is of the order of 3000 times. We have also tried this algorithm to test the primality on a 12 digit number, but it would not be able to test even a single number in 15 h of undisturbed CPU utilization. But on the other hand, limiting check algorithm can execute the primality of number in 12,000 ms, i.e., 12 s. We can wait for more time, but it is of no use to run a test of primality and wait for more than 15 h be to executed.

5 Discussion The graphical and tabular representations explain the time complexity of the algorithm against various numbers. If we test for 13 to be prime or not limiting check algorithm will run until base of square root of 13, i.e., 3 and eventually it has started

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Fig. 7 Plot of 10-digit numbers against time taken (in milli-seconds)

from 3, so the number of comparisons would be 1 only and in single comparison that can make a decision, but in the regular theoretical checking algorithm the number of comparisons would be 3. Now considering 31 as the number to check, then limiting check algorithm would conclude the result only in three comparisons, but in the regular theoretical checking algorithm the number of comparisons would be 14. These numbers are not that different but when we try to implement the limiting check algorithm for a big number like 999,999,000,001, then the casual algorithm would end up doing 499,999,499,999 but limiting check algorithm would only check for 664,579 comparisons only. Hence, the number of comparisons of limiting check algorithm is decent good than the other discussed methods. Now in graph as well we can see the algorithmic time complexity will increase by the size of number so higher the number, greater will be the time taken to test that number.

6 Future Works This algorithm is doing way better than some other algorithm, but this can also be improved by doing some amendments. We can improve the time taken by the algorithm by making the list of prime numbers up to square root in a recursive way. This list can be created by calling a recursive function which will create a list of all the primes in the range. Recursion can help us to check for primality of a factor before considering it a valid divisor. For example, if we want to check the primality of 101, then firstly base of its square root will be calculated which is 10 that confirms that at least one factor needs to be between 10 (greater than 1), and other research says that factor needs to be a prime number so we can check with recursiveness and

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make a list of all valid divisors under 10 which will be [2, 3, 5–7] and this can be done in a recursive manner. It is also possible to completely remove the list and check the primality on the go without storing numbers in any list. This would reduce the space complexity of the algorithm.

References 1. M. Agrawal, N. Kayal, N. Saxena, Primes is in P, in Annals of Mathematics (2004) 2. J.I. Ahmad, R. Din, M. Ahmad, Analysis review on public key cryptography algorithms. Indonesian J. Electr. Eng. Comput. Sci. 12(2), 447–454 (2018) 3. E.W. Weisstein, Semiprime, http://mathworld.wolfram.com/semiprime.html 4. Prime numbers, http://www-groups.dcs.st-and.ac.uk/history/HistTopics/Prime_numbers.html 5. Math. Comput. 64(209), 397–405 (1995). Published by: American Mathematical Society, Article Stable URL: http://www.jstor.org/stable/2153343 6. A.R.C. De Vas Gunasekara, A.A.C.A. Jayathilake, A.A.I. Perera, Survey on prime numbers. Elixir Appl. Math. 88, 36296–36301 (2015) 7. K.R. Srinath, Python–the fastest growing programming language. Int. Res. J. Eng. Technol. 4 (2017)

Factors Affecting the Local Governance Ngo Sy Trung, Do Huu Hai, Pham Van Tuan, and Nguyen Tien Long

Abstract This research focuses on an analysis of factors affecting the local governance, namely: (1) Intellectual level, (2) Participation level of citizens and social organizations, (3) Responsibility for reporting and accountability, (4) Capacity of officials and civil servants, (5) Information system. The authors survey practically influence levels of these factors on the local governance of local government levels in Vietnam (surveyed in 04 provinces representing three regions in Vietnam: Hanoi, Thanh Hoa, Quang Nam, Ho Chi Minh City). Research results show that the capacity of officials and civil servants has the greatest impact on the local governance, followed by Responsibility for reporting and accountability, Information system, Participation level of citizens, social organizations, and finally, Intellectual level. Keyword Local governance

1 Overview of Factors Affecting the Local Governance The term of “the local governance” has been studied in the world for a specific period of time, has been associated with decentralization in many countries since the 1960s. The connotation of the concept of the local governance, mentioned in many studies, includes entities: local governments, social organizations and citizens, in which the interactive relationship of these entities is the issue analyzed thoroughly such as: “Local governments manage via legal instruments and policies; social organizations N. S. Trung (B) Hanoi University of Home Affairs, Hanoi, Vietnam e-mail: [email protected] D. H. Hai Ho Chi Minh City University of Food Industry, Ho Chi Minh, Vietnam P. Van Tuan National Economics University, Hanoi, Vietnam N. T. Long TNU—University of Economics and Business Administration, Thai Nguyen, Vietnam © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_68

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and citizens actively participate in policy decision-making” [1] or “The local governance implies the adoption and implementation of regulations and procedures of organizations and institutions that allow citizens to express their concerns, exercise their participation right in the local development” [2] or” The local governance is the governance at the local level not only belongs to the government, but also belongs to the community in general and the interactions between the community and local authorities” [3], etc. Thus, it is possible to identify the factors that affect the local governance from its entities. For the government, it is the capacity of officials and civil servants (the Capacity of officials and social organizations of officials, the practical professional capacity of civil servants), Responsibility for reporting and accountability, and several of other factors of material facilities such as information system, techniques, etc. For citizens, it is the intellectual level, self-consciousness level of their participation right. For citizens and social organizations, it is their intellectual level, participation level into the management process of the government.

1.1 Intellectual Level and Self-consciousness Level of Citizenship (IL) The intellectual level is the knowledge of citizens, expressed through the knowledge about the fundamental issues of contemporary society they acquire such as: Economy, culture, law, science, technology, etc., and even the issues of history or the predictive issues of the future. Besides the scientific knowledge of the society, the intellectual level is also understood as the understanding and awareness of citizens about their citizen rights and responsibilities as well as the desire to exercise those rights and responsibilities. Some basic content reflects the intellectual level: Legal knowledge (TDDT1); Cultural-social knowledge (TDDT2); Economic and technical knowledge (TDDT3); Knowledge of science and technology (TDDT4); Awareness of citizenship (TDDT5); Awareness of civic responsibility (TDDT6). This is one of the basic and important criteria to assess the development level of society, accordingly, the more developed society is, the more improved the intellectual level is. In the local governance, when the intellectual level develops, citizens in whom participate promptly grasp the provisions of law relating to management fields of the government and give the profound social criticism. Citizens are better aware of their rights and responsibilities and will actively participate actively in checking and supervising the implementation of grassroots democracy. This has a strong impact on the local governance. Therefore, in the local governance, the authorities should pay attention to the participation forms and means of citizens in accordance with the intellectual level, in order to create a positive and effective interaction between the authorities and citizens.

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1.2 Participation Level of Citizens and Social Organizations (SO) Citizens and social organizations that are living and working in the area, understand the specific conditions of the locality. Therefore, their participation will have an impact on the management and administration of the government during the implementation of the local governance tasks. However, the important issue is why and how the participation level of citizens and these social organizations relate to the policy mechanisms and the political views of government leaders. Some basic content reflects the Participation level of citizens and social organizations: Positive attitude towards election activities (TCXH1); Positive attitude towards government policy decisions (TCXH2); Attention and criticism of government policies (TCXH3); Abide by the law (TCXH4); Proactively propose ideas and policies to the government (TCXH5); Join the opinion of citizens (TCXH6). Some impacts from the involvement of citizens and social organizations on the local governance are: • Firstly, the active participation of citizens and social organizations will give the government the opportunity to mobilize more resources (finance, human resources, information) from the community to archive easily the target of socio-economic development of the locality. • Secondly, the active participation of citizens and social organizations will reduce barriers between government agencies and community to receive easily feedback from the community on policies, public services, and avoid the negative consequences of the lack of referral information of service beneficiaries. • Thirdly, the active participation of citizens and social organizations will form the partnership in the local governance so that there can be joint initiatives between the government and the community. This has important significance, establishing a mutually beneficial interaction between the government and the community through a monitoring mechanism that prevents manipulation and group interests; At the same time, the restrictions of the governance and coercion type of traditional state management are resolved.

1.3 Responsibility for Reporting and Accountability (RA) Responsibility for reporting and accountability is the working modes clearly mentioned in the legal system of many countries, whereby state agencies are required to report and account the results of their assigned tasks to the higher level agencies, citizens’ elected bodies at the same level and the citizens. In that sense, Responsibility for reporting and accountability is also identified as one of the mechanisms for controlling the performance of public authorities, particularly through Responsibility for reporting and accountability, the superior government controls the activities of subordinate government, the citizens and social organizations control the operation of local government, etc. in a convenient way on the basis of compliance with the law;

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At the same time, officials and civil servants will be more aware of their obligations and responsibilities in the course of performing their duties and bear the consequences when they fail to fulfill their responsibilities. Some basic content reflects the Responsibility for reporting and accountability: Disclosure of legal information and policy information (BCGT1); Publication of administrative procedures (BCGT2); Publicizing the process of work settlement (BCGT3); Extensive policy consultations (BCGT4); Respect and listen to the people’s opinions (BCGT5); Responsible for work performance results (BCGT6). With interactions among the local governance’s entities, we can see that the local governance is dominated by responsibility for reporting and accountability of officials and civil servants. Namely: • The good implementation of responsibility for reporting and accountability will create a mechanism to control the activities of the local government from the citizens and social organizations. In addition, a mechanism of interaction between the government and the community is established, thus forming the political culture in the local governance. • The good implementation of responsibility for reporting and accountability will contribute to reduce many negative issues such as: corruption, arbitrariness, sectarianism, etc., through the deep self-awareness of the duty performance of officials and civil servants, as well as the consequences that officials and civil servants have to bear when they fail to fulfill their responsibilities in the course of performing their duties..

1.4 Capacity of Officials and Civil Servants (OCS) • Regarding the capacity of officials, it is the knowledge and the management skills of leaders of local authorities. So far, there have been many researches on the leadership, the leadership capacity as well as the leadership results, the impact of leadership capacity on the leadership results of businesses/organizations. Researchers have approached from a variety of perspectives and addressed various research objectives, and the results of this study are abundant and admitted. Cavazotte et al. [4]; Strohhecker and GroBler [5] and Thun (Thesis) [6], in the studies show that the knowledge, the awareness, the thinking capacity, the management skills are the basic factors forming the Capacity of officials and social organizations of the leaders, having the directly influences on the performance of leadership. Similarly, Northouse [7], through his study work, he also find out that the thinking capacity of managers (intelligence, initiative, task understanding, cognitive abilities) positively affects the leadership results of businesses/organizations. Quite consistent with the above-mentioned views, Hoa [8]; Su [9] also argues that the leader’s thinking capacity (knowledge, strategic vision) and the management and executive skills have a great influence on the performance

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of management. Therefore, for the local authorities, when leaders have the good knowledge, the thinking capacity and the management skills, they will mobilize the participation and support of the social forces in the area through the motivationmaking, trust-giving, encouragement, inspirational effort, the ability to propagate the strategy, the ability to motivate and attract citizens to support the strategy and the ability to link the system, etc., from that, helps them achieve the desired results in the local governance. On the other hand, this is the factor which has a great influence on the leadership and management results of the leaders of the authorities in the local governance. • Regarding the capacity of civil servants, that is the knowledge, skills of professional practice and the attitude of serving the people who directly perform the tasks of the local governance. Local civil servants are the people who regularly meet and address the needs of citizens and organizations. The level of satisfaction of citizens and organizations is an important criterion for assessing the working capacity of such civil servants. Therefore, civil servants with the knowledge, the professional practice skills and the good service attitude will be the factors that have a great impact on the performance of key tasks of the local governance, meeting the expectations and requirements of the people about a government serving in the context of the administrative reform. Some basic content reflects the Capacity of officials and civil servants: Knowledge and management skills of local government leaders (CBCC1); Knowledge and practice skills of civil servants (CBCC2); Attitude to serve people of civil servants (CBCC3). Along with the development of society, as the intellectual level is improved, as the science, technique and technology are widely used in all of social activities, it has set new demands on the capacity of local officials and civil servants. This requires the local officials and civil servants to constantly learn to accumulate the knowledge, the professional practice skills and the management skills, along with having a good attitude to serve the people in the best way. These are also regular requirements and tasks of officials and civil servants in the process of carrying out the tasks of local governance.

1.5 Information System (IS) Information system is a necessary and important support of the local governance. Building a good information system will create a premise for citizens’ active participation and social organizations, create the favorable condition for implementing the responsibility for accountability and public administration. Some basic content reflects the Information system: Legal and policy documents are widely and systematically (HTTT1); Management and operating information is public (HTTT2); The feedback mechanism of citizens is built and implement regularly and seriously (HTTT3).

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2 Study Model In the above study model, the independent variables have the influence on the dependent variable (the local governance: Decentralized work management (QTDP1); Attracting the participation of people and social organizations (QTDP2); Controlling people’s participation and social organization (QTDP3)) at different levels. This is the task that the research team focuses to clarify the relationship between these variables. Hypotheses: H1: The intellectual level has a positive relationship with the elements of the local governance. H2: The participation level of citizens and social organizations has a negative relationship with the elements of the local governance. H3: The responsibility for reporting and accountability have a positive relationship with the elements of the local governance. H4: The capacity of officials and social organizations is have a positive relationship with the elements of the local governance. H5: The information system has a positive relationship with the elements of the local governance.

3 Research Methods The study was conducted in two steps: the preliminary investigation and the formal investigation. With the preliminary investigation, the authors used qualitative research, conducted in-depth personal interviews and focused group interviews with a number of experts and civil servants who participated in the local governance to modify the research scale and complete the suitable questionnaire for Vietnam’s context. Note that the questionnaire is based on the overview of the research, and the scales which are checked by previous studies, so the authors only inherited and adapted the words. The components in the model are measured with a 5-level Likert scale, in which, 1 is strongly disagree and 5 is strongly agree. The last section is demographic information, including questions about gender, age, intellectual level, income and the number of years involved in the local governance, using the identifier scale. After that, the completed questionnaire was applied in the official quantitative survey in 04 places representing for three regions of Vietnam, including Hanoi, Thanh Hoa, Quang Nam and Ho Chi Minh City; the survey period is from 15th January 2020 to 31st Mar 2020. The research overall are the people who have participated in the local governance local, have the management experience; they are of all ages, living places, incomes, different levels. Sample subjects are those over 18 who have had the participation experience in the local governance. The measurement model consists of 27 observation variables and, according to the principle of at least 5 elements per

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measurement variable [10]; therefore, the minimum sample size required is 27 * 5 = 135. However, in this research, the authors selected a sample size of 200, larger than the minimal sample, ensuring the science of sample selection. Questionnaires are sent directly to respondents via the Internet with random sampling method and convenient sampling method. The results were 177 satisfactory, with a response rate of 88.5%. The characteristics of the sample in Table 1 are 78% for females, 65.5% for age from 18 to 36 and 66.7% for males and 36.5% for years involved in the local governance less than 2 years. Data processing method is done through the following steps: (1) checking the scale and the reliability of measurement variables by Cronbach’s alpha coefficient and the value by the Exploratory Factor Analysis (EFA); (2) analyzing the multiple linear regression for model and hypothesis testing. Table 1 Characteristics of research samples Sample size (n = 177) Gender Age

Average income per month

Academic level

Frequency

Ratio (%)

Male

39

22.0

Female

138

78.0

From 18 to 25

49

27.7

From 26 to 30

26

14.7

From 31 to 36

41

23.2

Over 36

61

34.5

Less than 3 million

36

20.3

Between 3 and 6 million

81

45.8

Between 6 and 10 million

45

25.4

Over 10 million

15

8.5

High school graduation

11

6.2

Graduated college/professional high school

20

11.3

Graduate

83

46.9

Post graduate

59

33.3

4

2.3

Less than 1 year

67

37.9

From 1 to 2 years

51

28.8

Over 2 years

59

33.3

Other Years involved in the local governance

Source The author’s reseach

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4 Study Findings 4.1 Scale Testing Using the SPSS 23.0 software, the author drew the results of the Cronbach’s alpha and the Exploratory Factor Analysis (EFA), suggested to remove and merger observation variables to help the scale to assess the concepts accurately. The first step is scale testing by the Cronbach’s alpha reliability coefficients. This step is used to exclude garbage items avoiding the case of garbage items make up the dummy factor when analyzing the EFA [10]. Testing standard is Cronbach’s alpha coefficient which must be greater than 0.6 and the correlation coefficient of each scale must be greater than 0.3 [10]. The analysis results in Table 2 show that all measures of the factors are satisfactory. Thus, all scales of factors are both reliable and are used to analyze the next factor. After examining Cronbach’s alpha, the next step is Exploratory Factor Analysis (EFA) for a preliminary evaluation of the uniqueness, convergence value, and discriminant value of the scale. The EFA was performed using the Principal Component Analysis method and the Varimax rotation method to group the factors. With a sample size of 177, the load factor coefficient of observed variables must be greater than 0.5; The variables converge on the same factor and distinguish it from other factors [10]. In addition, the KMO coefficient must be between 0.5 and 1. The analysis results in Table 3 show that the load factor coefficient of all the observed variables are greater than 0.5; Bartlett test with significance level Sig. Table 2 Summary of reliability and relative minimum variables of scales Scale

Number of observation variables

Reliability coefficient (Cronbach’s alpha)

The correlation coefficient variation smallest total

Intellectual level (IS)

6

0.843

0.605

Participation level of citizens and social organizations (SO)

6

0.842

0.659

Responsibility for reporting and accountability (RA)

6

0.824

0.664

Capacity of officials and civil servants (OCS)

3

0.801

0.571

Information system (IS)

3

0.778

0.579

Local governance (LG)

3

0.753

0.569

Source The author’s reseach

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Table 3 EFA factor analysis of the factors affecting the local governance Observed variables

Factors 1

Intellectual level (IL)

Participation level of citizens and social organizations (SO)

Responsibility for reporting and accountability (RA)

Capacity of officials and civil servants (OCS)

Information system (IS)

TDDT2

0.852

TDDT3

0.806

TDDT5

0.726

TDDT1

0.710

TDDT4

0.685

TDDT6

0.684

2

TCXH5

0.783

TCXH3

0.781

TCXH2

0.745

TCXH4

0.743

TCXH1

0.710

TCXH6

0.709

3

BCGT4

0.773

BCGT3

0.759

BCGT2

0.754

BCGT6

0.737

BCGT5

0.676

BCGT1

0.655

4

CBCC3

0.876

CBCC2

0.859

CBCC1

0.763

5

HTTT2

0.863

HTTT3

0.828

HTTT1

0.787

Source The author’s reseach

= 0.000 with KMO coefficient = 0.797. All 24 variables after EFA analysis are divided into 5 factors with eigenvalues greater than 1 and deviation more than 50%. The research model changes into 5 independent variables, and 1 dependent variable, used for the linear regression analysis and next hypothesis testing.

4.2 Regression Analysis and Hypothesis Testing First, using Pearson correlation analysis analyzes the correlation between quantitative variables. At a significance level of 5%, the correlation coefficients show that the relationship between the dependent variable and the independent variables is

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statistically significant (Sig value is less than 0.05). The magnitude of the correlation coefficients ensures that there are no hyperbolic phenomena. Therefore, it is possible to use other statistics to test the relationship between variables. Next, analyzing the multiple linear regression about the relationship between factors belonging to the local governance affects the local governance. According to Table 4, the R2 coefficient is 0.722, which shows that the multiple linear regression models were constructed in accordance with the data set of 72.2%. The results of the regression analysis showed that the 5 independent variables were IL, SO, RA, OCS, and IS (Table 3) with non-normalized Beta coefficients of −0.095; 0.316; 0.363; 0.049; 0.062 with significance level less than 0.05. Thus, the hypotheses H1, H2, H3, H4, H5 are accepted. Based on B coefficient (Table 4), it can be concluded that Responsibility for reporting and accountability has the strongest influence on the local governance; followed by the Participation level of citizens and social organizations; Information Table 4 Regression results of model of factors affecting the local governance Model

Non-normalized regression coefficient B

Normalized Value t regression coefficient

Significance Collinear tatistics (Sig.)

Standard Beta error

Tolerance VIF

(Constant)

1.471

0.015

Intellectual level (IL)

−0.095

0.015

−0.162

100.268 0.000 −6.466 0.000

1.000

1.000

Participation level of citizens and social organizations (SO)

0.316

0.015

0.540

21.522 0.000

1.000

1.000

Responsibility for reporting and accountability (RA)

0.363

0.015

0.621

24.754 0.000

1.000

1.000

Capacity of officials and social organizations (OCS)

0.049

0.015

0.083

3.304 0.001

1.000

1.000

Information system (IS)

0.062

0.015

0.105

4.190 0.000

1.000

1.000

Dependent variable: Local governance (LG) R2 adjusted = 0.722 Source The author’s reseach

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Fig. 1 Study model. Source The authors offer

system; Capacity of officials and social organizations. The intellectual level is the least influential factor in the local governance. The linear regression model obtained is as follows: LG = 1.471−0.095 ∗ IL + 0.316 ∗ SO + 0.363 ∗ RA + 0.049 ∗ OCS + 0.062 ∗ IS + e

4.3 Testing of the Research Model The SEM theoretical model results (Fig. 1) are shown in Fig. 2: Chi-square/df = 2,152; GFI = 0.900; TLI = 0.905; CFI = 0.916; RMSEA = 0.051, indicating a theoretical model suitable for market data. However, the estimation results (normalized) of the relationship between the concept: SO → LG is not statistically significant at 90% .

4.4 Testing of Research Hypotheses Using other methods in the study by software Amos 23 gave test results of formal theory with 5 hypotheses: H1, H2, H3, H4, H5. The estimation results show that the concept of Intellectual level, Responsibility for reporting and accountability, Capacity of officials and social organizations and Information systems have the same impact on the local governance and are statistically significant (P ≤ 0.05); Only the Participation level of citizens and social organizations has the opposite effect on the local governance (P > 0.05), indicating that:

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Fig. 2 CFA results of factor affecting the local governance (normalized). Source The author’s reseach

• The intellectual level has a positive relationship with the elements of the local governance. • The participation level of citizens and social organizations has a negative relationship with the elements of the local governance. • The responsibility for reporting and accountability have a positive relationship with the elements of the local governance. • The capacity of officials and social organizations is have a positive relationship with the elements of the local governance. • The information system has a positive relationship with the elements of the local governance. It means that the hypotheses: H1, H2, H3, H4, H5 are accepted. Only hypothesis H2 is different from the result using SPSS software for analysis and evaluation, the difference is appropriate because the greater the participation level of citizens and social organizations is, the less affected the local governance is.

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5 Conclusions and Recommendations Research results indicate that Capacity of officials and civil servants has the greatest impact on the local governance, followed by Responsibility for reporting and accountability, Information system, Participation level of citizens and social organizations, and finally Intellectual level. Based on the above research findings, some of following solutions are recommended: • In order to improve the effectiveness of the local governance, the capacity of officials and civil servants (knowledge, management skills of leaders; the knowledge, professional practice skills and the service attitude of civil servants) plays an important role in the local governance. However, in reality, some officials and civil servants, especially localities, do not have the necessary capacity to meet the requirements of work. Therefore, the improvement of the capacity of officials and civil servants of government agencies should be paid attention. • Forming a board of Responsibility for reporting and accountability that adapts to the conditions of each locality should be considered and discussed. In areas, there is no public investment, it is not necessary to set up a Responsibility for reporting and accountability board or the citizens’ Inspection Board can fulfill the tasks of the Responsibility for reporting and accountability board. • Information system must be considered properly. Besides previous propaganda forms such as the mass media, posters, propaganda and propaganda slogans, contests of election law, etc. should be held. Election-related materials may be sent to every person, everyone can read whenever they have free time to promote citizens’ mastery. • Improving the intellectual level of citizens to be more aware of their participation role in the activities of government should be focused. In addition, raising citizens’ awareness of the importance of implementing and promoting democracy in order to promote their real ownership is very necessary. Because of understanding the meaning and role of promoting democracy, citizens will actively use democratic rights and fight for the protection of democratic rights. When citizens take the initiative, implement the law on democracy, actively use and promote democratic rights, then they have real control, overcome the state of formal democracy and interests. Thank to this, the benefits of the state, the rights and interests of the new citizens are guaranteed. • Encouraging and creating favorable conditions for social organizations to actively participate in the local governance such as: Establishing mechanisms for interaction among the authorities with citizens and social organizations; supporting the development of the governance capacity, etc., to both promote the democracy and mobilize the resources from the community.

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References 1. Le rapport national sur le développement humain (Bénin, 2000) 2. M. Dêdêgnon, Gouvernance locale (Local governance) (2013), http://www.awepa.org 3. United States Agency for International Development—USAID, Decentralization and democratic the local governance programming handbook (2000) 4. Flavia Cavazotte, Valter Moreno, Mateus Hickmann, Effect of leader intelligence, personality and emotional intelligence on transformational leadership and managerial performance. Leadersh. Quart. 23(2012), 443–455 (2012) 5. J. Strohhecker, A. GroBler, Do personal traits influence inventory management performance? the case of intelligence, personality, interest and knowledge. Int. J. Prod. Econ. 142, 37–50 (2013) 6. N.B. Thun, Character strengths in leadership, Thesis, Doctor of Philosophyin Business Administration, The Departement of Management, Saint Mary’ s University, Halifax, Nova Scotia, Canada, 2009 7. P.G. Northouse, Leadership—Theory and Practice, 5th ed. (Western Michigan University, 2004) 8. T.T. Van Hoa, Leadership Enhancement of Vietnamese Business Executives in the Process of International Economic Integration (Ministerial Research Project, National Economics University, 2011) 9. D.N. Su, Leadership Competencies of Small and Medium Enterprises in Vietnam, PhD thesis, CIEM Institute for Economic Research, 2011 10. D.H. Hai, Corporate Culture—Peak of Knowledge (Transportation Publishing House, Hanoi, Monograph, 2016)

A Multi-factorial Code Coverage Based Test Case Selection and Prioritization for Object Oriented Programs Prashant Vats, Zunaid Aalam, Satnam Kaur, Amritpal Kaur, and Saravjit Kaur

Abstract The selection and prioritization of test case generates a series of test cases for Object Oriented Programs in a way that the selected test cases would have the maximum possibility of fault detection by ensuring the maximum code coverage. In this research paper we have put forward an algorithm for the test case selection and their prioritization in such a way which is based on the Multi Factorial code coverage for object oriented programs. The multiple code coverage factors would be chosen on the account of their probability of finding errors and the code complexity which has been introduced by these factors. For analysis and validation of our put forward test case selection and prioritization algorithm we have considered few test case studies using the Java Object Oriented Code. As a result of our case studies we have found that the put forward algorithm provides minimized efforts for testing the given Object Oriented Code with reduced time and cost. Keywords Test case selection (TCS) · Test case prioritization (TCP) · Object oriented programs (OOP) · Code coverage analysis · Multi factorial code coverage

P. Vats (B) Banasthali Vidyapith, Vanasthali, Rajasthan, India e-mail: [email protected] Z. Aalam · S. Kaur · A. Kaur S.G.T. University, Haryana, India e-mail: [email protected] S. Kaur e-mail: [email protected] A. Kaur e-mail: [email protected] S. Kaur C. T. Institute of Engineering and Technology, I.K.G. Punjab Technical University, Kapurthala, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_69

721

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1 Introduction Testing OOP is an important part of software development process. Without software testing Software can’t be successfully completed. It includes process of affirmation and substantiating the software during the testing of a OOP. Thus the main objective of OOP testing is to deliver and maintain the quality of Software product to while installing the OOP at the client end. But a OOP testing involves an over budgeting of testing cost in the form of efforts and timing. Due to timing and budgeting constraints sometimes the exhaustive testing of OOP is not possible while executing the all set of test cases. To reduce the efforts and costing of OOP testing up to some extent, sometimes the prioritization of the selected test cases becomes very necessary, so that while executing theses selected more error prone software faults can be revealed while executing these OOP. The selection of test cases and their prioritization becomes crucial as for fault detection it increases the efficiency of testing of OOP. The conventional TCP techniques which are being used for testing in OOP were not adequate as they basically concern of either class coverage or coverage of object oriented class method function calls. In our put forward work, an algorithm is being put forward for the TCS and TCP which is based on multi-factorial code coverage based approach for the test case selection and prioritization for OOP. This put forward algorithm uses nine factors for the TCS and TCP for OOP by to quantifying these factors on the basis of a test case selection and prioritization formula, a well optimized and prioritized set of test suite would be obtained for testing the OOP. The put forward algorithm for testing of OOP functions on the basis of the these factors which includes internal input files, native method, method, external output files, input variable, conditional statement, output variable, import packages and Line of Codes. For the affirmation of the put forward algorithm for testing of OOP it has been considered by applying on two testing case studies of OOP. The result shows the efficacy of the put forward technique.

2 Related Work on Testing of OOP Sultan et al. [1] have presented a regression TCP for OOP based on the craving graph model of affected test code using genetic algorithm (GA). ESDG (Extended System Dependency Graph) was put forward to find the class statement based level changes in the source code. The identified object oriented changes are stored in a file named changed and coverage information for each test case is generated from source code. Then the GA is used to selected test cases are determined for their order of preference. Panigrahi and Mall [2] put forward a Class model based testing approach to prioritize the regression test cases for OOP. They presented EOSDG (Extended OOS Dependence Graph) to represent all closely connected OO features like inheritance, association and aggregation of OO Classes, polymorphism among them. For the

A Multi-factorial Code Coverage Based Test Case Selection …

723

TCP they put forward M-RTP (Model based regression TCP). A forward test slice of EOSDG is constructed w.r.t. each modified model elements to identify the main model elements that are indirectly or directly affected. Ashraf et al. [3] have put forward value based test case prioritization technique. An algorithm which uses six factors: changes in requirement, customer priority, implementation complexity, execution time, requirement traceability, and fault impact of requirement have been used to determine the priority of the system test cases. Shahid and Ibrahim [4] put forward a new object code test case based TCP technique. They put forward a code coverage based test case prioritization algorithm. The test case which covers the largest portion of code is put at higher priority. Beena and Sarala [5] put forward coverage based TCS and TCP. They clustered the test cases into three groups outdated, required and surplus. Then by using these clusters test case selection algorithm (TCS) is put forward. Then the output obtained from TCS is given as an input to test case prioritization (TCP). Vedpal et al. [6] put forward a hierarchical TCP technique for OO software. Their put forward an algorithm which works at two stages. The classes are prioritized at the first stage and then test cases of prioritized classed are ordered at the second stage. Kavitha and Kumar [7] put forward a TCP for multivariate analysis based on severity. They put forward a TCP technique based on rate of fault detection and fault impact. This algorithm finds severer fault at the early stage of testing process.

3 Proposed Work The put forward test case selection and prioritization algorithm prioritizes the selected suite of test cases by keeping in consideration the nine code coverage testing factors. The test cases would be selected and will be put in ordered on the basis of these fault detection coverage of inclusive of these factors. The considered code coverage testing factors along weight assigned are as shown in Table 1 with having value between 0 and 1. Every code coverage testing factor would be designated with a weighing factor value which would denote the ability of the testing factor to detect the presence of error in the OOP. The ability of a test suit to cover the maximum number of the testing factors will show the number test cases being covered with ensuring the maximum coverage of codes of the given OOP and will have the ability to diagnose with detecting the maximum number of errors in an OOP. After execution of these the Code Coverage Testing factors the selected test cases among a test suite would be ordered and prioritized on the basis of the Test Code Coverage Value (TCCV) metric. More the value of the TCCV, highest would be the prioritized order of a test case to detect the probability of an error in OOP. The value for the TCCV for test case would be given by the following Eq. 1, n  i=0

TCCV = lim

⎧ n ⎨

n→∞⎩

j=1

Test Code Coverge Valuei j

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Table 1 Code coverage testing factors with assigned weights

∗

n 

S. No.

Code coverage testing factors name

Assigned weights

1

Internal input logical file (ILF)

0.2

2

External output interface files (EOIF)

0.2

3

Native input methods (NIF)

0.5

4

Import package files (IPF)

0.1

5

Input methods (IM)

0.5

6

Conditional logical statements (CLS)

0.4

7

Line of code (LOC)

0.1

8

Class input variable (CIV)

0.3

9

Class output variable (COV)

0.2

 Weight Assigned to the Code Coevrge Testing Factor

(1)

k=1

The algorithm for the put forward test case selection and prioritization technique is as shown below: For a given set of unprioritized test suite T Set T- > empty, Repeat until T’ = empty. Begin, Determine the Code Coverage Testing factors covered by the test suit Find value of TCCV for the test case. Recursively call TestCase_Select (); Arrange test cases in prioritized manner as per the code coverage. End

4 Experimental Results and Their Analysis For the experimental analysis and the validation of the put forward algorithm, it is being explained using the two case studies. The here mentioned case studies and results were being implemented in the Java language.

A Multi-factorial Code Coverage Based Test Case Selection …

725

4.1 Case Study 1 In the first case study for the put forward algorithm a code in Java is being implemented for a Class Student with its attributes like Student_Name, Student_Roll_number and as follows in Fig. 1. In the first case study a program written in Java is implemented as the OOP to be tested using the put forward algorithm. This program written in Java feeds in the information of student like Student name, Student Enrolment number, and their marks in various subjects. Then the program finds the total of marks for a student in his various subjects. Further it calculates the average marks for the individual students. Table 2 shows the Code Coverage Testing Factors Covered by Test Cases for OOP Fault Detection in Case Study 1. For the Case Study 1 finding the TCCV for the test cases for the given OOP by applying the Eq. 1 values are given in Table 3, the errors found in the OOP in the Case Study 1 by the test cases is given in Table 4. The selected and prioritized order for the test cases for OOP in Case Study 1 is given by applying the put forward algorithm is as TC3, TC5, TC2, TC1, TC4.

Fig. 1 To show the pseudo code for case study 1

Table 2 Code coverage testing factors covered by test cases for fault detection S. No.

Factors

TCI

TC2

TC3

TC4

1

ILF

0

2

EOIF

0

3

NIM

4 5

TC5

0

0

0

0

0

0

0

0

0

0

0

0

0

IPF

0

0

0

0

0

IM

3

10

10

2

11

6

CLS

0

1

0

0

0

7

LOC

7

19

17

1

1

8

CIV

0

1

1

0

0

9

COV

1

1

0

0

0

726

P. Vats et al.

Table 3 Values of test case code coverage value TCCV for the given test cases S. No.

Factors

TCI

TC2

TC3

TC4

TC5

1

ILF

0

0.4

0

0

0

2

EOIF

0.5

0.5

0.5

0.5

0.5

3

NIM

0.6

2

2

0.4

2.2

4

IPF

0

0.1

0.1

0

0

5

IM

2.1

5.7

5.1

1.8

7.5

6

CLS

3.2

8.7

7.7

2.7

10.2

7

LOC

0

0.1

0.1

0

0

8

CIV

0.5

0.5

0.5

0.5

0.5

9

COV

0.6

2

2

0.4

2.2

7.5

20

18

6.3

23.1

TCCV

Table 4 Errors detected by test case for case study 1 S. No.

TC1

El

*

E2 E3 E4

*

TC2

TC3

*

*

TC4

TC5 *

* *

E6

*

E7

*

*

E8

*

E9

*

*

E10

*

*

For Case Study 1, The efficacy of the put forward algorithm for the fault detection along with test case selection and prioritization is adjudicated by computing and then comparing the value of Average Percentage of Fault Detected (APFD) and using APFD graph drawn for the non-prioritized and prioritized test case suite is given in Table 5 and using Fig. 2. Table 5 Value for APFD for case study 1 for OOP Test case selection and prioritization for case study 1

APFD metric value (%)

Random test case selection and prioritization approach

46

Proposed test case selection and prioritization approach

64

A Multi-factorial Code Coverage Based Test Case Selection …

727

Fig. 2 To show the put forward and random approach for TCS and P for case study 1

4.2 Case Study 2 In the case study 2 we have considered a program written in Java for various kinds of banking related daily routines. This OOP written in mainly in Java for a Banking organization maintains the banking customer related data like Costumer name, his banking account number and type of Bank account as in Fig. 3. There would be different object oriented classes that would deal with different type of accounts like current account, Loan account, saving account etc. The said OOP code would consists of various functions related to different types banking operations like for depositing the money into a specific account, withdrawal of money from an account and displaying account related to different types banking operations like for depositing the money into a specific account, withdrawal of money from an account and displaying account related information for different account holders and many more. The pseudo code for the OOP for the Case Study 2 is shown in the Fig. 3. Table 6 shows the Code Coverage Testing Factors Covered by Test Cases for OOP Fault Detection in Case Study 2. For the Case Study 2finding the TCCV for the test cases for the given OOP by applying the Eq. 1 values are given in Table 7, the errors found in the OOP in the Case Study 2 by the test cases is given in Table 8. The selected and prioritized order for the test cases for OOP in Case Study 2 is given by applying the put forward algorithm is as TC8, TC4, TC5, TC7, TC3, TC2, TC1, TC6. For Case Study 2, The efficacy of the put forward algorithm for the fault detection along with test case selection and prioritization is adjudicated by computing and then comparing the value of Average Percentage of Fault Detected (APFD) and using APFD graph drawn for the non-prioritized and prioritized test case suite is given in Table 9 and using Fig. 4.

728

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Fig. 3 To show the pseudo code details for the code for OOP for case study 2

5 Conclusion In this research paper we have put forward a new multi-factorial based test case selection and prioritization technique for testing the OOP based on the code coverage of these testing which improvises the rate of fault detection during the early phases of the Object Oriented Testing. We have also performed an investigative two case studies for the put forward testing algorithm for OOP. After carrying out the experimental study for both the case studies the put forward algorithm has provided us a much improvised results in terms of the rate of error detection at the early phases of testing in OOP. For the validation of the proposed technique approach we have applied it on the two codes which were written in Java and using the APFD metric we have found that the experimental results has shown the effectiveness of the put forward testing

A Multi-factorial Code Coverage Based Test Case Selection …

729

Table 6 Code coverage testing factors covered by test cases for OOP fault detection in case study 2 S. No.

Factors

TC1

TC2

TC3

TC4

1

ILF

0

2

EOIF

0

0

0

0

2

1

2

3

NIM

0

0

0

0

4 5

IPF

1

0

1

IM

0

1

0

6

CLS

2

2

7

LOC

36

8

CIV

1

9

COV

2

TC5

TC6

TC7

TC8

0

0

0

0

0

2

1

1

0

0

0

0

1

1

0

1

0

0

0

2

0

0

2

3

2

2

2

3

42

31

36

34

48

31

36

1

0

0

1

1

0

0

2

2

3

2

2

2

3

T6

T7

T8

Table 7 TCCV values of test cases for test cases in case study 2 S. No.

Factors

TC1

TC2

TC3

TC4

1

ILF

1.2

0.4

0.2

0.4

0.1

0.9

0.2

0.3

2

EOIF

1.3

2.2

1.1

2.3

0.3

2.7

1.4

1.4

3

NIM

1.2

0.3

0.2

0.4

0.4

0.5

0.1

0.3

4

IPF

1.0

0.8

1.4

1.4

1.2

0.4

1.2

0.4

5

IM

0.2

1.3

0.3

0.3

0.1

2.3

0.2

0.2

6

CLS

2.3

2.2

2.4

3.2

2.2

2.2

2.6

3.3

7

LOC

3.6

4.2

3.1

3.6

3.4

4.8

3.1

3.6

8

CIV

1.9

1.9

0.5

0.9

1.1

1.2

0.6

0.9

9

COV

2.0

2.7

2.4

3.1

2.2

2.4

2.4

3.2

14.7

16

11.6

15.6

11

17.4

11.8

13.6

TCCV

TC5

Table 8 Errors detected by test case for case study 2 S. No.

TC1

E1

*

E2 E3

*

TC2

TC3

*

*

*

*

E4

*

E5

*

E6

E9 E10

TC5

T6

T7

T8

*

*

*

*

*

*

*

*

* *

* *

* *

E7 E8

TC4

*

*

*

*

*

* *

*

*

*

*

*

*

*

* *

*

*

* *

*

730

P. Vats et al.

Table 9 Value of APFD for case study 2 for OOP Test case selection and prioritization for case study 2

APFD metric value (%)

Random test case selection and prioritization approach

57.5

Proposed test case selection and prioritization approach

71.2

Fig. 4 To show the put forward and random approach for test case selection and prioritization for case study 2

algorithm for error detection in OOP. As future work the proposed algorithm can be applied for large chunks of codes and can be implemented as an automated tool to find its application into the software industry.

References 1. A.B.M. Sultan, A.A.A. Ghani, S. Baharom, S. Musa, An evolutionary regression test case prioritization based on dependence graph and genetic algorithm for object oriented programs, in 2nd International conference on emerging trends in engineering and technology, May, 30–31, London, UK (2014) 2. C.R. Panigrahi, R. Mall, Test case prioritization of object oriented programs. SETL abs Briefings 9(4), 31–40 (2011) 3. E. Ashraf, A. Rauf, K. Mahmood, Value based regression test case prioritization, in Proceedings of World Congress on Engineering and Computer Science, October, vol. 1, San Francisco, USA, pp. 24–26 4. M. Shahid, S. Ibrahim, A new code based test case prioritization technique. Int. J. Softw. Eng. Appl. 8(6), 31–38 (2014)

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5. R. Beena, S. Sarala, Code coverage based test case selection and prioritization. Int. J. Softw. Eng. Appl. 4(6), 39–49 (2013) 6. Vedpal, N. Chauhan, H. Kumar, A hierarchical test case prioritization for object oriented software, in International Conference on Contemporary Computing and Informatics (IC3I) (2014) 7. R. Kavitha, N.S. Kumar, Test case prioritization for regression testing based on severity of fault. Int. J. Comput. Sci. Eng. 2(5), 1462–1466 (2010)

New Horizons for the Application of Microalgae in the National Economy L. N. Medvedeva

and O. Roiss

Abstract The abstract should summarize the contents of the paper in short terms, i.e., 150–250 words. The research shows that green technologies, microalgae potential in particular, will probably become mandatory to the development of youth entrepreneurship. It is still important to learn about young students’ intentions with opening their own business. It is important to remember about the transfer of scientific discoveries about using microalgae in healthy food production and in rehabilitation of the environment. The hypothesis of scientific research has defined a toolkit, methods and techniques in two different directions: an assessment of capacity for the development of the youth entrepreneurship in the region and the inclusion of the microalgae into the strategic asset of the national economy. Keywords The global ecosystem · Imperatives of the national economy · Green technologies · Potential of microalgae · Regional cluster · Entrepreneurial potential

1 Introduction Innovative solutions based on environmentally friendly behaviour are united under the generic name of «green technologies» [1]. There are such major applications of the green technologies as environmental management, use of renewable energy sources, reduction of harmful emissions into the atmosphere, development of environmentally sustainable landscapes, search for new sources of nutrition and drinking water [2]. According to one of the predictions, by the 2050 the population of the Earth will rise to 9 billion people, which will require the intensification of entrepreneurship and search for new sources [3]. The question is about the forming of humans economical consumer behaviour and the search of intensives to develop innovative L. N. Medvedeva (B) Volgograd University, Volgograd 400005, Russian Federation e-mail: [email protected] O. Roiss GEO Roehren—Und Pumpenwerk Bauer GmbH, A 8570 Voitsberg, Austria © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_70

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Fig. 1 Smart technologies which make it possible for people to monitor their health and balanced nutrition

youth entrepreneurship. Today’s generation of young people is also known as millennials or Generation Z. Since they were born they have lived in the digital world, none of them can imagine their life without the Internet, a computer, a smartphone. The hyperactivity disorder created by digital technologies makes it possible for young people to interact with a tremendous amount of information and set up international collectives. According to the Microsoft researches, Generation Z closely monitors the world brands including personalization of food supplies and responsible consumer behaviour. Smart devices allow them to programme healthy lifestyle, monitor the UF radiation, measure the acid–base balance of cutaneous integument and determine the level of physical activity and the number of burnt calories (Fig. 1) [4]. Wellness industry (deliberate healthy lifestyle) is one of the world top ones now; the emerging technologies create new opportunities for balanced nutrition. Thus, the popularity of the category “beauty is inside” is increasing all around the world, consumers take internally products made of microalgae in addition to the external use of cosmetics. At the present day, it is granted to eat the following species: Arthrospira platensis, Arthrospira maxima, Chlorella vulgaris, Chlorella pyrenoidosa, Chlorella sorokineana, Dunaliella salina, Nostoc pruniforme, Aphanizomenon flos-aquae, Spirulina [5]. The hypothesis of the scientific research resolves itself into the rationale of using microalgae in economy on basis of youth entrepreneurship development. Descriptive and theoretical methods were applied to the research, among them are techniques for sociological researches (surveys, predictions modelling), economic–mathematical modelling; these methods have made it possible to describe the entrepreneurial approach in the microalgae potential rise. The novelty of the study is that it is important for youth entrepreneurship to search for new natural resources that can be transformed into innovations and establish their own production under their own brand in various sectors of the national or world economy.

2 Results and Discussion The researchers conducted by Volzhskiy Polytechnic Institute branch VSTU (2015– 2019) resulted in interviewing more than two thousand students and young people of Volzhskiy (Volgograd region, Russia) and separating out groups of youngsters, developing business in the basis of studying their intentions. As it is known, intentions are the initial phase of becoming an entrepreneur. It often happens so that

New Horizons for the Application of Microalgae …

735

the time of intention—action gap—is quite long. The theory of planned behaviour lets us distinguish the factors which contribute to one becoming an entrepreneur. Among them are parents or relatives with their own business, educational attainment, condition of supporting small-sized and medium-sized business infrastructure in the region. According to the theory of planned behaviour, the motivator of a young person’s behaviour is their personal attitude to the entrepreneurship [6]. The research has shown that young people who intend to connect their lives with entrepreneurship proceed with implementation of their intentions when they are still in the university. They actively participate in scientific groups and speak at conferences. Alongside with family and institute contexts, some external factors affect students’ forming of entrepreneurship intentions: institutional environment (availability of SMB supporting programmes) and mentality in the existing society. The question about how many people intend to move into small business stays important for the local authorities. The studying produced isolation of three age groups with diverse impact of external factors on their entrepreneurial intentions (see Table 1). In Russian Federation, SMB support is carried out in five programmers: “The improvement of business climate”, “The increase of SMB subjects’ access to financial resources”, “Acceleration of SMB subjects”, “Popularization of entrepreneurship”, “Creation of “farmers support and rural cooperation” system”. Entrepreneurial potential of the region can be assessed with the help of the following mathematical model: E entrepreneurtial = ρpersonal + ρlogistical + ρinvestment + ρinnovative

(1)

in which the components of entrepreneurial potential are calculated by the following formulas: Table 1 Descriptive statistics on participation of diverse youth age groups in entrepreneurship Age group/percent of a sample The first from 18 to 23 years/19%

The second from 23 to 27 years/45%

The third from 27 to 35 years/36%

Mean value/Standard deviation Actual shift from intention to action

0.7/0.1

3.2/1.4

4.4/1.9

Entrepreneurial intentions

4.6/1.62

5.1/1.7

4.8/1.5

Presence of family business

1.2/0.9

3.2/2.0

5.4/2.1

Mentality of a university

2.1/0.4

1.0/0.3

0.5/0.02

Regional business supporting institutes

0.8/0.1

6.8/2.1

5.1/2.11

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L. N. Medvedeva and O. Roiss

ρpersonal = W

Lx n

(2)

where W the coefficient of validity based on expert assessments; L x the elements forming personal entrepreneurial potential; n the amount of elements.

ρlogistical = W

Mx , n

(3)

where W the coefficient of validity based on expert assessments; M x the elements forming personal entrepreneurial potential; n the amount of elements.

ρinvestment = W

Ix , n

(4)

where W the weighting coefficient based on expert assessments; I x the elements that form the investment and business potential; n number of elements.

ρinvestment = W

In x , n

(5)

where W the weighting coefficient based on expert assessments; Inx elements that form innovative business potential; n number of elements. The motivational component of an entrepreneur is calculated using the formula: ρmotivational = where



Wi

(L x + Mx + Ix + Inx ) , ni

(6)

New Horizons for the Application of Microalgae …

737

800 700 600 500 400 300 200 100 0 2017

2018

2019

2020

2021

2022

2023

2024

2025

Fig. 2 Dynamics of the global biotechnology market, billion dollars. Source Global market insight

W the weighting coefficient based on expert assessments; ni number of components of internal motives and incentives. The capacity of the entrepreneurial potential of SMBs in the city of Volzhskiy in the Volgograd region was calculated based on expert assessments and data from sociological surveys on computer programs—11.87%. This indicates that the promotion of entrepreneurship needs to be strengthened, especially among young people. It is also important to help young people find business ideas. The increasing demand for products produced by biotechnologies offers new possibilities for small businesses. The global biotechnology market is expected to total $ 742 billion by 2025 (growth, in comparison with 2019, by two times) (Fig. 2). There is the potential of the microalgae Chlorella vulgaris among the green technologies. A microscopic monocellular organism, measuring from 2 to 10 microns in diameter, is able to assimilate solar energy (the cell consists of chloroplasts) and form organic substances and disengage oxygen [7]. Dry biomass of Chlorella vulgaris contains 45% protein, including essential amino acids, 35% carbohydrates, 10% omega-3 fatty acids and microelements (iron, iodine, potassium, selenium, etc.) [8]. Chlorella vulgaris has entered the ethnic cuisine of Japan, Malaysia and the Philippines: it is used for making smoothies, cocktails, and added to bread products. There is a huge industry of dietary supplements based on microalgae, mainly from Chlorella vulgaris and Spirulina [9]. Scientists at the Harvard T.H. Chan School of Public Health (USA) have developed a “healthy food Plate” in which proteins occupy—one-fourth of the plate, complex carbohydrates—one-fourth of the plate, (vegetables + fruits)—half of the plate. Scientists at Volgograd medical University are using healthy food products to conduct research on metabolic correction in obese

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Fig. 3 Structure of a “healthy food plate” with microalgae products included: dry powder and suspension of Chlorella vulgaris

patients [10]. Inclusion of microalgae products in medical nutrition is only a matter of time (Fig. 3) [11, 12]. Research conducted among students on the inclusion of microalgae products in the “Healthy food Plate” showed that 12% of the respondents are ready to try microalgae products (38% of the respondents preferred smoothies from Chlorella vulgaris); only 1.2% of the respondents are ready to eat regularly. Most students do not have strong ideas about microalgae; they include kelp (algae). The most famous microalgae were named: Chlorella vulgaris and Spirulina platensis (86% of the respondents). Ideas about microalgae have entered the information space of modern youth, but they are far from being actually included in the diet. One of the reasons is the lack of Russian production of microalgae products and their promotion. Since 2006, scientists OF FGBNU VNIIOZ (Volgograd) in cooperation with scientific and commercial organizations, agricultural and fish-breeding farms have been creating an evidence base for the legality of the use of the strain of Chlorella vulgaris IFR no. C-111 in the national economy. The laboratory of the Institute conducts research on the use of the strain of Chlorella vulgaris IGF# C-111 in animal husbandry, aviculture, and pond fish farming and for cleaning reservoirs [13, 14]. The BAUER GmbH group of companies (Austria), realizing that the environment is very sensitive to climate change, aims to take these factors into account when developing new machines and making decisions [15]. The company has been working for more than 80 years in the field of irrigation technologies. Today, 2.5 million hectares around the world are watered by irrigation machines produced by the hands and skills of 675 employees, irrigated by hand and skills, among which 64%. Chlorella vulgaris can be used to restore soil fertility. Using the technology for irrigation and fertilizing made by the BAUER Group, it is applied three times per season to the soil of agricultural fields (Fig. 4). Research on the use of Chlorella vulgaris in the economy is conducted in Germany, the USA, Japan and China [16]. Designed by Pierre Calleja, street lights generate the ability of microalgae to release energy during photosynthesis, while simultaneously absorbing carbon dioxide and saturating urban air with oxygen. The “BIQ House” project is a building with energy from microalgae, constructed by the design firm

New Horizons for the Application of Microalgae …

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Fig. 4 Watering and fertilizer application, manufactured by the BAUER Group, allows the introduction of Chlorella vulgaris chlorella into the soil of agricultural fields

Fig. 5 Use of Chlorella vulgaris in economic sectors: street biolights, big house wall biopanels «BIQ House», biofuel for cars

“Arup” in collaboration with the German company SSC Strategic Science Consultants and the Austrian architectural Studio Splitterwerk. Research is underway in Germany to obtain automobile fuel from Chlorella vulgaris (Fig. 5) [17].

3 Conclusion Our research aims to expand the understanding of the intentions and conditions of youth participation in business. No less important is the study of consumer behaviour of a person, his desire to lead a healthy lifestyle and use environmentally friendly products. The introduction of green technologies in the national economy is associated with the assessment of the entrepreneurial potential of territories and the search for new natural resources. The demand for products obtained using biotechnologies, including microalgae, is becoming a global trend. Research conducted at the FGBNU VNIIOZ (Russia) reveals to young people—future entrepreneurs—the possibilities of microalgae Chlorella vulgaris in food production and environmental health.

References 1. V.V. Melikhov, L.N. Medvedeva, A.A. Novikov, O.P. Komarova, Green technologies: the basis for integration and clustering of subjects at the regional level of economy, in Integration Clustering Sustain. Economic Growth (2017), pp. 365–82

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2. M.K. Starovoytov, L.N. Medvedeva, K.Y. Kozenko, Y.M. Starovoytova, G.I. Lukyanov, M.A. Timoshenko, Rational environmental management: the platform for integration and optimization of resources, in Contributions to Economics (2017), pp. 347–63 3. Human development report 2011. Sustainable development and equal opportunities are a better future for all 2011. Retrieved from: http://hdr.undp.org/sites/default/files/hdr_2011_ru_summ ary.pdf. Last accessed 2020/05/12 4. K. Bogatyreva, G. Shirokova, From entrepreneurial aspirations to founding a business: the case of Russian students. Foresight STI Gov. 11(3), 25–36 (2017) 5. N.I. Bogdanov, Environmental dimension aspects of planktonic Chlorella use, in Materials of the II International Scientific Conference on Global Environmental Problems: Local Solution (Pero, Moscow, 2019), pp. 8–11 6. I. Ajzen, The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991) 7. H. Tang, M. Chen, K.Y. Simon, S.O. Salley, Continuous microalgae cultivation in a photobioreactor. Biotechnol. Bioeng. 109(10), 2468–2474 (2012) 8. T. Granata, Dependency of microalgal production on biomass and the relationship to yield and bioreactor scale-up for biofuels: a statistical analysis of 60+ years of algal bioreactor data. BioEnerg. Res. 10(1), 267–87 (2017) 9. J.C. Bommer, Z.J. Sveida, B.F. Burnham, Further studies on the relationship between tetrapyrrole structure and usefulness as photosensitizers, in Proceedings of 1st International Conference on Clinical Applications of Photosensitization for Diagnosis and Treatment (1986), p. 129 10. C.C. Shalaeva, Possibilities of metabolic correction of obesity patients in outpatient practice, in Materials of the VI Congress of Therapists of Siberia (Novosibirsk) (2018), pp. 80–89 11. V. Minina, M. Ivanova, E. Ganskau, Healthy nutrition in everyday life of Russians J. Sociol. Soc. Anthropol. 21(4), 182–202 (2018) 12. A.L. Tumanova, Experimental studies on the effect of food suspension of microalgae Chlorella vulgaris on the human body. Int. J. Appl. Basic Res. 9, 85–88 (2011) 13. L.N. Medvedeva, M.V. Frolova, M.V. Moskovets, A.V. Medvedev, Introduction of naturesaving technologies is an environmental imperative in the development of regions. J. Volgograd State Univ. Econ. 21(4), 126–140 (2019) 14. A.E. Novikov, V.V. Melikhov, M.I. Filimonov, M.I. Lamskova, A.G. Bolotin, A.A. Novikov, T. Karengina, Pat. of the Russian Federation No 2015136412 appl. 27.08.2015, publ. 10.01.2017 15. BAUER GmbH. Retrieved from: https://www.bauer-at.com/ru/products/irrigation. Last accessed 2020/05/02 16. L.N. Medvedeva, K.Y. Kozenko, O.P. Komarova, Environment quality management in green cities. Eur. Res. Stud. J. 19(2), 34–45 (2016) 17. Working materials for the strategy of development of the biotechnological industry until 2020 year (project). Retrieved from: https://www.biorosinfo.ru/papers-society/Strategy_Bio industry.pdf. Last accessed 2020/02/12

Context of the Ghanaian Government: Social Media and Access to Information Adasa Nkrumah Kofi Frimpong, Ping Li, Samuel Adu-Gyamfi, and Sandra Chukwudumebi Obiora

Abstract Often overlooked–especially since Africa and technology are not usually encountered together–such innovative thinking concerning the political use of social media is rare in all its circumstances. In the middle of the nineteenth century, Conrad’s famous description of the European colonial invasion still lingers in the minds of the West. The paper investigates the extent of social media use by the Ghanaian government and how it compares to other OECD countries. By using data from Twitter, the results show that the Ghanaian government’s social media accounts are openly accessible. The government leverages the power of Twitter as an important informational and participatory medium in increasing people’s awareness and engagement in the government. The extent of the government’s social media use is quite comparable to other countries in the OECD region. Social media may not have enormous social and economic value creation impact; developing countries like Ghana with limited financial resources can make good use of the platform. Keywords Government information access · Social media · Ghana · Information and communications technology · E-government

1 Introduction Mergel [1] seems to be the probable forerunner in a theoretical study of the role of social media (SM) in government. Her research includes innovation and innovativeness of SM in public circles. She has equally outlined a multistage procedure for adopting SM in the contemporary government [2]. SM and the public ecosystem have A. N. Kofi Frimpong (B) · P. Li · S. C. Obiora School of Management and Economics–Center for West African Studies, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China e-mail: [email protected] S. Adu-Gyamfi Department of Information Technology Education, University of Education, 1277 Kumasi, Winneba, Ghana © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_71

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learned about participating and collaborating in government for transparency vis-àvis the interconnected world. Many researchers like [3] elaborate on the essential definitions of SM and tools in contemporary governance and providing an understanding of their usefulness [3, 4, 1]. As such, we review the current state of the art on SM in governance with regard to information access In Ghana. The country has earned enviable respect in democracy following her successful elections in 1992, 1996, 2000, 2004, 2008, 2012, and 2016. All these elections, mainly the 2012 and 2016, saw extensive use of SM [4]. Given the apparent role it plays, growing numbers of studies have sought to examine the influence of SM in the political sphere. For example, the research of [5] shows that political media, rationality, soundbites, and emotion have a positive effect on voter behavior and governance. In fact, [4] studied the dynamics of Ghanaian youth on SM toward political participation, knowledge, and internal efficacy. Imperatively, [4] showed that SM is promoting political discourse and enhancing civic involvement. [6] established that Web sites and SM platforms (Facebook and Twitter) have served as a contemporary form of information dissemination between local government, including metropolitan, municipal, and district assemblies (MMDAs) and citizens. The paper presents Ghana’s access to information, and the government’s social media use context, based on documental and secondary data analysis. The paper presents the extent of political and social media use in the Ghanaian context. We discuss the involvement of the media and how to help to disseminate information to ordinary citizens on social media. The paper highlights how the interplay of the government, media, and citizens on social media can contribute to sustainable governance. By using data from Twitter, the paper analyzes the extent of the political use of social media and how the usage leads to overturning governmental decisions toward good governance. The studies also show the influence of Ghanaian politicians on Twitter. The analysis shows that the government leverages the power of Twitter as an important informational and participatory medium in increasing people’s awareness and engagement in democracy. We believe that even though social media may not have enormous social and economic value creation impact, developing countries like Ghana with limited financial resources can make good use of Twitter.

2 Media Ghana has a robust media. The Ghana National Communications Authority [7] is the official licensing and regulator of the Ghanaian media industry. NCA is responsible for making sure that the media industry operates in a transparent manner that promotes peace for national and sustained development. There are 128 certified TV stations in Ghana [8]. Ghana Today Television (GTV) is the central station owned by the Ghana government, which is operated by the Ghana Broadcasting Corporation (GBC). As an agency of the Ghana Ministry of Information, GBC is the leading information hub of the country. It is constitutionally mandated to ensure information

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broadcast [9]. Compared to TV, there are more radio stations in Ghana, of which 505 are authorized and 392 in full use [10]. There are 31 government radio stations, five foreign, 81 community-based radio, 22 campus-based, and 366 fully private and commercial stations. The traditional newspaper readership is massive in Ghana with about 15 newspapers. The government-owned newspaper has an enormous audience of about 1.5 million [11]. Contemporary Internet publishing is also prevalent in Ghana. There is an annual award scheme for blogging and SM [12]. The media is a crucial disseminator of government information. Their activities are standardized by the National Media Commission constitutionally tasked to ensure the highest standard of media reportage is maintained. They contribute massively to the Ghanaian political discussion. Ghana has relative freedom of the media with no restriction whatsoever [13].

2.1 Government’s Access to Information The African Charter of Human and Peoples’ Rights by the Organization of African Unity upholds freedom of information to a very high degree through its declaration of Principles on Freedom of Expression in Africa [14]. The charter mandates all member countries to enforce all legislative instruments to ensure by protecting people’s rights with regard to accessing government information. To make the charter fully functioning, there exist specific standards serving as member countries benchmarks, including the African Principles of people’s Freedom of Expression and the Model Law on Access to Public Information. These are all adopted by the African Commission on Human and Peoples’ Rights. In Ghana, Article 21(1)(f) of Ghana’s 1992 Constitution gives full freedom to people and the media, including freedom of information [15]. The act provides the Ghanaian people the constitutional right to information held by the government and arms of government. Equally, as a democratic country, the law gives full exemptions that are necessary and consistent with the protection of matters that are of public interest. In 2015, Ghana, through the United Nations, committed to fully adopt and implement access to information laws in her quest to achieving the UN’s Sustainable Development Goals [16]. All government SM accounts are openly accessible. There are available downloadable and exportable files in CSV, Xls, pdfs, and videos to make information easily accessible. Interactivity on government Web sites cannot be said to be the best, but one can customize some form of data (see [17]). The GSS maintains and updates country statistics for all to access. Statistics, publications, databases, projects via the National Data Archive [17] can all be accessed. One can also make a formal data request either online or by post from several databases to compare, analyze, or diagnose their unique situation.

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2.2 Social Media and Elections in Ghana The involvement of social media in disseminating information to the general public by governments is on the ascendency of which Ghana is no exception. SM use in Ghana has been reported by [18, 19, 12]. The Ghanaian government uses different SM platforms to engage citizens in governmental activities, including the politicians in opposition [19]. The government uses SM as an information hub to keep citizens abreast of government activities. In this way, people can monitor and participate in these activities. The Golden Jubilee House comprises the official seat and office of the Government of Ghana, responsible for communicating all regulatory actions and government information to the citizenry. Following, the data from Twitter, one of the main communication instruments from Ghana’s office of the president, is analyzed.

2.3 Social Media Use by Ghanaian Politicians In the following review, we dwell on data in its natural setting to do our analysis. The data comes from the popular SM platform: Twitter. We considered only verified Twitter accounts. Verified Twitter accounts are those that have been sealed by Twitter as authentic and usually of public interest [20]. Ghana is host to about 10 million Internet users from a population of about 30 million [21]. About 5.6 millions (19% of Ghana’s population) are active SM users [22]. The most preferred platform is WhatsApp, with an estimated 30% SM users trailed by Facebook (28%). We used the NCapture extension in NVivo 12 to collect the SM data on Feb. 01, 2019. The program (NVivo 12) is the primary content analytical tool using queries and comparison diagrams. Some of the information was exported and analyzed in SPSS. Starting from the year 2013, the government leveraged on the power of Twitter as an important informational and participatory medium in increasing people’s awareness and engagement in the government. The figure shows the timeline of tweets and retweets emanating from the presidency. The analysis shows that from the period beginning 2013, there were 89% of tweets compared to 11% retweets. The Ghanaian government uses Twitter to disseminate government projects, services, both local and foreign activities, laws and by-laws, different forms of governmental alerts, and updates on diverse issues ranging from a currently passed to taxpayer notices. All these take place in different transmission formats such as video, text, animations, audio, and graphics. There are easy-to-see navigation buttons that link the official Twitter account to the Ghana government Web sites. People can equally communicate with the government or make service requests by tweeting to Twitter handler. We share the opinion of [23] that developing countries like Ghana with limited financial resources can make good use of Twitter. Indeed, the activities of the Ghana information service through the Ministry of Information, the official public relations bureau of the Government of Ghana, have changed. Unlike the traditional

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paper delivery of information on vans, the department has a vast SM presence with verified Facebook, Twitter, and YouTube accounts. They are mandated by the Ghanaian constitution as the department responsible for two-way government– citizen communication to let people know about ongoing and future policies, activities, and programs, with a broader vision to promote Ghana as a brand in the international market. The Ghana statistical service (GSS), the overseer of the Ghana National Data Archive, Country Statistics, Open Data Initiative, and the National Reporting Platform, also has significant social media presence. GSS was established in 1985 by the Ghana statistical service law (PNDC Law 135) with an official mandate to conduct surveys/censuses by collecting, compiling, analyzing, abstracting, and publishing Ghanaian statistical information [24]. Similar to other government portals, GSS has data visualizations of industry statistics related to Ghana. There are yearon-year inflation trends, GDP, and other fact sheets. All these have ensured massive people participation in the Ghanaian government. For example, on Jan. 31, 2019, The Ghanaian president made a statement on a campaign platform that people who are complaining about his lack of achievement as president are either deaf or blind [25]. This statement attracted so many social media comments, including the Ghana Federation of Disability, that the president tended to disdain blind and deaf people as socially unimportant. Finally, feedback came from the Ghanaian president through an apology on his social media platforms. People seem active in policymaking. They contribute to the general decision-making and service delivery processes on social media. Khan [23] explains that comments and feedback are vital in this process. The main opposition leader in Ghana equally has a vast social media presence. With verified Twitter, Facebook, and other accounts, he uses these platforms tremendously. As shown in Table 1, he has more followers than the president. The implication is that he subscribes more to other people’s tweets and updates (tweets) more. Again, he receives more direct messages (username references) than the president. However, it shows that people usually mention and repost (retweet) the Ghanaian president’s messages more than the main opposition leader. From which we consider, he is more popular and influential. The factors that dwell on this need further exploration. From our discussion so far, we agree with the notion [26] that a governments’ use of SM can create sustainable governance and political change (through voting realignment) in two main categories: information and interaction. Governments can use SM to deliver public services better and publicize political details to reach out to citizens from all walks of life. This results in enhanced transparency in government. Conversely, governments can use SM to improve their interaction with internal and external stakeholders. To achieve, “we-government” requires improved communication/collaboration and public participation in governmental decision-making processes as enshrined in “citizen coproduction” encompassed by “citizen sourcing.” Several examples of information and government interactivity exist in Ghana. A notable example is Afrobarometer [27], a watchdog organization. Through coproduction (citizen sourcing), the Afrobarometer has provided a citizen-based evaluation system where Ghanaians periodically evaluate the government.

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Table 1 Influence Ghanaian politicians on Twitter Country/role

Twitter account

Owner

Ghana’s president

@NAkufoAddo

Nana Akufo-Addo

Number of followers

Ghana’s opposition leader

@JDMahama

John Dramani Mahama 1,163,733

Ghana

@GhanaPresidency

Office of the president of the Republic of Ghana

47,027

884,910

Estonia

@EstonianGovt

Estonian government

13,659

Finland

@FinGovernment

Finnish government

16,522

Malta

@MaltaGov

Government of Malta

16,965

Israel

@Israel

Israeli government

South Korea

@TheBlueHouseENG

The Office of president

543,651

Japan

@JapanGov

The Government of Japan

265,594

Turkey

@trpresidency

Presidency of the Republic of Turkey

1,051,346

22,056

As shown in Figs. 3 and 4 (and others), information from such systems has helped to reduce information asymmetries and addressed the principal–agent problem. For example, the most trusted institution in Ghana is the Ghana Armed Forces, while the Ghana Police Service is the most corrupt [29]. The government can also monitor the pattern of citizens’ behavior on such platforms. For example, Ghana’s parliament dropped their decision to build a new parliamentary chamber after public outrage following a video that went viral on SM (Table 2) on the arrests of three protesters after they screamed: “drop that chamber” from the public gallery of the Ghana parliament in protest against the decision [30]. Table 2 shows the extent of Twitter engagement from the sampled stations after the announcement of the arrests in Jul. 07, 2019. There are around 53 TV stations in Ghana. The radio stations are more numerate, of which 505 are authorized, and 392 are in full use [10]. Table 2 suggests that, indeed, Ghanaians have opportunities to participate in policy refinement and that crowdsourcing is a vital component of SG. This shows the structural configuration and iterative nature of SG at recognizing and treating vulnerabilities while establishing resilience. To further our understanding of Table 2, we compare it to other famous political arrests around the world, as shown in Table 3. The result is quite remarkable. We, therefore, infer that mining of the collective knowledge could produce ideas and expertise that can be used to help improve the quality of governmental decision making in Ghana. Stamati et al. [31] describe this participation as one of the three principles of openness in SG. Openness is achieved when a government discloses information in a manner that is easily accessible, coupled with widespread citizen engagement through “frank and honest discussion about processes, policies, and decisions” [31]. This requires three main principles: participation, collaboration,

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and transparency [32]. Collaboration shows the extent to which governments establish partnerships with different stakeholder groups, including public agencies at all governmental levels, private institutions, and individuals geared toward improving government effectiveness. The extent of collaboration by the Ghanaian president, and the opposition leader, seems significant (refer to Figs. 1 and 2). They collaborate with public agencies at all levels (the electoral commission of Ghana), private institutions (EAA Foundation), and individuals. Indeed, collaboration with Ghanaian political leaders on SM for SG is achievable. Transparency refers to the continuous distribution or flow of reliable information (economic, social, and political) emanating from the government to all relevant stakeholders in a timely fashion [33]. Transparency is a pivotal condition of government accountability, and the two concepts go hand-in-hand, which, in combination, contribute to sustainable governance [34]. Citizens must have the right to know what the government is doing and why primarily to address the principal–agent problem. As the principals (citizens), they have the right to know about the government’s (agent’s) behavior to achieve inclusiveness. SM has proven to be a vital infomediary platform that translates and increases the meaning of government information, providing accountability, in turn, leading to good governance [34]. Furthermore, the effectiveness of government information disclosure (transparency) increases with active SM. Increased transparency can lead to improved governance outcomes by considering the activities and actors that facilitate this transformation, especially on SM. Table 2 contains examples of how government information has led to the timely reversal of government decisions in Ghana. In this Table 2 Twitter engagement following the broadcast of a viral video showing the arrests of three protesters in parliament [30] TV station

Twitter Handle

Comments

Retweets

Likes

Tv3 Network

@tv3_ghana

184

542

19,000

GHOne TV

@GHOneTV

369

12,000

42,000

United TV

@utvghana

386

79

16,000

Table 3 Twitter engagement following the broadcast of famous political arrests around the world TV station

Twitter Handle

Comments

CNN Africa

@CNNAfrica

10

Retweets 88

Likes

Caption

105

Dec. 10, 2019—Nigeria is facing backlash over the rearrest and detention of journalist and activist Omoyele Sowore 309

296

Dec. 06, 2019—Wanzhou Meng, the CFO and deputy chairwoman of Huawei, has been arrested in Vancouver and is facing potential extradition to the U.S. Al Jazeera English

@AJEnglish

39

545

330

October 7, 2018—Breaking news: Saudi journalist Jamal Khashoggi killed inside the Saudi consulate in Istanbul, says Turkish police

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Fig. 1 Frequency of social media use (Twitter) by Ghana’s political leadership in January 2019 8000 6798

7000 6000 5000 4000 3000 2000

2109 1304

1191

1000

195 78

294 41

Popular Hastag Ref

Popular @MenƟons

1048 54

0 Username References

President

Tweets

Retweets

OpposiƟon Leader

Fig. 2 Frequency of social media (Twitter) of Ghana’s leadership in February 2019

way, transparency holds the government to account for their actions. SM is pivotal to making sure that the powers invested by citizens in the state are not abused or misused. Transparency is vital for increasing the trust and democratic participation of citizenship for sustainable governance [31]. We believe that there is a perfect relationship between political discussion on SM and sustainable governance through voting realignment. The above mechanisms (ideologies, national policies, leaders, systems, and structures within the political

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Fig. 3 Level of corruption in Ghana 2012-2019 (Source [28]). Question In your opinion, over the past year, has the level of corruption in this country increased, decreased, or stayed the same?

Fig. 4 Ghana’s economic condition 2002–2019 (Source [28]). Question How well or poorly would you say the current government is handling the fight against corruption?

system) are vital for providing Ghanaian voters with the information they need to decide whether to vote for one political party over another.

3 Conclusion The paper presents the extent of political and social media use in the Ghanaian context. We discuss the involvement of social media in disseminating information to the general public by governments. We dwell on data in its natural setting to analyze the extent of the political use of social media. The data comes from the popular social media platform: Twitter to show the influence of Ghanaian politicians on Twitter. The analysis shows that the government leverages the power of Twitter as an important informational and participatory medium in increasing people’s awareness and engagement in democracy (refer to Figs. 1 and 2). We believe that even though social media may not have enormous social and economic value creation impact, developing countries like Ghana with limited financial resources can make good use of Twitter.

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20. Twitter, “Verified accounts,” Twitter Help Center, 2019. https://help.twitter.com/en/managingyour-account/about-twitter-verified-accounts (accessed Feb. 08, 2019) 21. Internetworldstats, “Africa Internet Users, 2019 Population and Facebook Statistics,” Internet Users Statistics for Africa, 2019. https://www.internetworldstats.com/stats1.htm (accessed Apr. 20, 2019) 22. K. Zurek, “Over 10 million Ghanaians use the internet - Report - Graphic Online,” Graphic Online, 2018. https://www.graphic.com.gh/news/general-news/over-10-million-gha naians-using-the-internet-report.html (accessed Apr. 20, 2019) 23. G. F. Khan, Social Media for Government. A Practical Guide to Understanding, Implementing, and Managing Social Media Tools in the Public Sphere. Singapore: Springer Singapore, 2017 24. GSS, “Ghana Statistical Service Legal Mandate,” Ghana Statistical Service, 2017. http://sta tsghana.gov.gh/aboutgss.php?category=MjkwMzA1NjI0LjE0MTU=/webstats/oq43q9p651 (accessed Jan. 30, 2019) 25. Starrfmonline.com, “Akufo-Addo apologizes to disabled people over campaign comment,” Ghaha Web, 2019. https://www.ghanaweb.com/GhanaHomePage/NewsArchive/Akufo-Addoapologises-to-disabled-people-over-campaign-comment-720478# (accessed Feb. 07, 2019) 26. L. Zheng, T. Zheng, Innovation through social media in the public sector: Information and interactions. Gov. Inf. Q. 31, S106–S117 (2014). https://doi.org/10.1016/j.giq.2014.01.011 27. Afrobarometer, “Afrobarometer - Ghana,” Afrobarometer - Center for Democratic Development (CDD-Ghana), 2019 28. J. Appiah-Nyamekye Sanny and S. Adusei Baaye, “Ghanaians voice dissatisfaction with living conditions, government economic performance,” Accra, 332, 2019 29. MyJoyOnline, Fight against corruption: government scores damning marks in the latest report, Joy Online (2019) 30. E. Appiah, Video: 3 protesters arrested after screaming ‘drop that chamber’, Joy Online (2019) 31. T. Stamati, T. Papadopoulos, D. Anagnostopoulos, Social media for openness and accountability in the public sector: cases in the Greek context. Gov. Inf. Q. 32(1), 12–29 (2015). https://doi.org/10.1016/j.giq.2014.11.004 32. S.A. Chun, L.F.L. Reyes, Social media in government. Gov. Inf. Q. 29(4), 441–445 (2012). https://doi.org/10.1016/j.giq.2012.07.003 33. D. Kaufmann, A. Bellver, Transparenting transparency: initial empirics and policy applications. SSRN Electron. J. (2005). https://doi.org/10.2139/ssrn.808664 34. J. Fox, The uncertain relationship between transparency and accountability. Dev. Pract. 17(4–5), 663–671 (2007). https://doi.org/10.1080/09614520701469955

Convolutional Neural Network and Data Augmentation for Behavioral-Based Biometric User Identification Sakorn Mekruksavanich and Anuchit Jitpattanakul

Abstract One classification problem that is especially challenging is biometric identification, which links cybersecurity to the analysis of human behavior. Biometric data can be collected through the use of wearable devices, especially smartphones that incorporate a variety of sensors, during the performance of activities by the users. In recent research, numerous identification systems using machine learning classification algorithms have been proposed to provide solutions to this classification problem. However, their ability to perform identification is limited to only suitable selected features of time-series data from the biometric raw data. Therefore, in this study, an architecture for the biometric user identification using walking patterns that employs a convolutional neural network is proposed. Validation of the proposed framework for biometric identification was accomplished through the generation of synthetic data from datasets of samples from public walking. As a result, the framework described in this study provides more effective outcomes in terms of the accuracy of the model and additional metrics than the conventional machine learning utilized for biometric user identification. Keywords Biometric identification · Convolutional neural network · Data augmentation

S. Mekruksavanich (B) Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand e-mail: [email protected] A. Jitpattanakul Intelligent and Nonlinear Dynamic Innovations Research Center, Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_72

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1 Introduction The development of wearable technology has been continuous and a new type of human–computer interaction has evolved with the accelerated development of communication and information technologies. Many practical applications, such as business transactions, fitness tracking, healthcare monitoring, and smart home monitoring, can be effectively conducted at any time through the use of wearable devices, in particular smartphones that incorporate a broad range of sensors that are connected to the Internet [2]. With regard to connectivity, the collection of biometric data, including pulse rate, electrocardiogram (ECG), electromyography (EMG), movement signals, etc., is enabled by wearable devices. Thus, a diverse range of new personalized applications that can be employed as wearable sensors for the recording of biometric information can be provided by these wearable devices [4–6]. Presently, biometrics user identification that connects cybersecurity with human behavior analysis is one of the most challenging classification problems. User identification systems are now based on something that you are (biometrics) rather than something that you possess (cards or keys) as cards, keys, or passwords can be discovered, copied, lost, or stolen. As a result, the collection of biometric information by wearable devices is focused on a person’s intrinsic characteristics that requires their physical presence, in which the measurement of one or more biometric traits can be applied to either authenticate or identify a subject [3]. In this way, the advantage of identification systems utilizing physiological features, for example, ECG, EMG, and other activity signals, is that the mobility of a user and a higher level of certainty of the individual liveness are assured, as the pattern of walking activity is especially useful for user identification. Therefore, many researchers use walking patterns collected by wearable devices for biometric identification, as they provide features that are unique to each person. In a large number of identification systems that have been recently proposed, the decision mechanism that is used involves machine learning (ML) classification algorithms. The performance of an identification system is measured by the effectiveness of its classification results. User studies in which participants are selected, multiple measures of observables are conducted, and the datasets are constructed from the measurements for a classification algorithm are often conducted to validate these systems. However, human-crafted feature extraction and selection is a problem is difficult for ML algorithms. In this study, the research focus is on biometric user identification that employs walking patterns, and the results can primarily contribute the following benefits: 1. The proposal of an architecture with convolutional neural networks for the purpose of biometric user identification utilizing walking patterns. 2. Various data augmentation techniques that enhance the performance of the proposed biometric user identification are studied. 3. That the proposed framework for the biometric user identification provides better performance than the baseline performances is demonstrated; hence, the establishment of a UIWADS dataset is completed.

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The rest of the paper is organized as following. Section 2 presents the related works of the biometric user identification. Section 3 describes the proposed framework of data augmentation techniques for the biometric user identification. Section 4 shows the experimental setting, dataset description, and results. Finally, Sect. 5 concludes the results.

2 Biometric User Identification Using Sensor Data Recently, many research studies have investigated the problems of biometric user identification [11]. Biometric methods are divided into two types, which are behavioral biometrics and physiological biometrics. The extraction of behavioral characteristics from user actions, for example, handwaving, keystrokes, voice and walking patterns, are the basis of behavioral biometrics. Physiological biometric methods employ physiological traits, namely fingerprints, the iris, or facial features. In this research, the focus is on behavioral biometric methods, as this work is concerned with the data of walking patterns. Wearable devices such as smartwatches and smartphones can be used to measure many of these user actions, and walking is the activity that is most commonly examined in behavioral biometrics. The use of accelerometers has provided fairly good performance for gait identification methods in the past. In Pan et al. [7], five different accelerometers were attached to the subjects, and it was shown that a combination of all of the data can improve gait recognition. Although this approach provides highly accurate results for a dataset with the multiple recording of 30 subjects, the need for an extensive setup for the data collection diminishes the usefulness of the results. Similarly, several sensors, one at the hip and one on the ankle, were attached to each participant by Gafurov et al. [1] in order to collect the results for a dataset containing 50 subjects. For the purpose of examining the ways that the gait pattern can be impacted, the subjects were requested to walk normally both while carrying a backpack as well as without it. Rong et al. [9] used a single sensor attached at the waist to obtain a dataset with 21 subjects. The results from these methods are promising; however, special accelerometer sensors built for this purpose must be used for the recording of the data from the sample. Although the size of these sensors is generally quite small, it is necessary to attach the sensor to the subject, usually with their knowledge.

3 Proposed Methodology The biometric user identification framework, which is proposed in this work, enables the walking data captured from accelerometer sensor to identify the user performing the walking activity. Figure 1 shows the proposed methodology to achieve the research objective in this study.

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Fig. 1 Proposed biometric user identification framework of this study

In this identification framework, raw activity data from the UIWADS dataset are used as raw data. The 30% of raw data are spitted and keep separately as test data for evaluating trained models. In the step involving data augmentation employing four different techniques (e.g., jittering, scaling, rotation, and time warping), the remaining data are used [10]. The purpose of these data augmentation methods is the generation of synthetic training data. Following this, the augmented training data are combined with the raw training data, which are then employed for the training of the CNN model. Finally, the trained CNN model’s performance is evaluated by the test data.

3.1 Description of the Dataset For the evaluation of the data augmentation techniques that are proposed, a commonly used benchmark dataset for user identification from walking activity known as the walking activity dataset (UIWADS) was considered due to this dataset being required to meet the criteria as follows: unique identifiers for each of the participants and a sample size of over 20 participants. Initially, the UIWADS dataset was collected for the performance of walking-based authentication with a two-staged approach, in which the dataset authors used the data to infer the posture, such as standing or walking, in the first stage, and then, a one-class classification to ensure that the posture readings are matched to the authorized user was performed in the second stage. Android smartphones located in the shirt pockets of the 22 participants provided the data collected for the dataset. The triaxial accelerometer was captured with frequency of 27 Hz. Time-step values range from 0 s to about 650 s depending on participant walking. This walking dataset has only four features of real type, which are represented by: time-step, x-acceleration, y-acceleration, and z-acceleration. A short sample from data is visualized in Fig. 2.

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Fig. 2 A short sample from time-series data from the triaxial accelerometer sensor

3.2 Time-Series Data Augmentation Synthetically generated time-series data that provides new data samples that can be obtained using data augmentation techniques in cases where the classification performances would be improved is explained in this section. The class labels of the samples used for the augmentation are assigned to these samples of synthetic time-series data. Overfitting can be prevented by the augmented data, which can also provide improvement of the generalization ability of deep learning [8]. Mirroring, scaling, cropping, rotating, etc., are permissible augmentation techniques for image recognition applications because minor modifications resulting from these techniques do not change the image’s label as could occur in real-world observation. However, for the time-series sensory data, these transformations that preserve the labels are not able to be intuitively recognized. Variability can be introduced without modification of the labels of the time-series data with a number of factors that include sensor placements, random noise, and the temporal properties of the activities. Four techniques for data augmentation were selected for the time-series data in this study as follows: • Jittering is applied as a method that simulates additive sensor noise. The mechanical noise of each of the sensors is different. The training data’s robustness against different sensor types and their additive and multiplicative noises is increased by simulating random sensor noise. In this research, white Gaussian noise was used to add jittering to the raw training data. • Scaling is another method that is employed in data augmentation, which alters the raw data’s magnitude while preserving the labels. This variation is adopted in

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cases when there is a change in the dimensions of the equipment that the sensor is attached to, for example, modifications in the excavator boom’s length, etc. The implementation of scaling involves the multiplication of the raw training data using a random scalar. • Rotation is implemented for the introduction of the label-invariant variability of the IMU sensor data in cases where the sensors are installed in the equipment with various orientations. • Time warping is a method that generates synthetic training data for a range of temporal properties of the tools used for a specific task, such as a loading activity that can be conducted using an excavator with a range of operating speeds. In order to compensate for this variability, each activity was warped (i.e., shortened or stretched) with different warping ratios.

3.3 Training CNN Model A convolutional neural network (CNN) model works well for identifying simple patterns within given data which will then be used to form more complex patterns within higher layers. A one-dimensional convolutional neural network (1D-CNN) is very effective when we expect to derive interesting features from fixed-length segments of the overall dataset and where the location of the feature within the segment is not of high relevance. Typically, the 1D-CNN is often applied to timeseries data, the convolutional output of which is one-dimensional. Structure of 1DCNN consists of three main layers that are an input layer, hidden layers, and an output layer as shown in Fig. 3.

Fig. 3 Structure of 1D-convolutional neural network

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4 Experiments and Results 4.1 Experiment Setup In this study, Python’s Scikit-learn and Keras were used to conduct the experimental implementation for each scenario tests consisted of the same 1D-CNN model. All tests were run on Google Colab platform. We set up different five experiments as following scenarios: 1. 2. 3. 4. 5.

Scenario I—raw training data without any augmentation, Scenario II—raw training data with augmented data using the jittering technique, Scenario III—raw training data with augmented data using the scaling technique, Scenario IV—raw training data with augmented data using the rotation technique, Scenario V—raw training data with augmented data using the time-warping technique.

4.2 Experimental Results To evaluate performance of biometric user identification, we performed five experiments on different scenarios as describe in Sect. 4.1 with the convolution neural network model. Concerning FAR and FRR values, the scenario II had FAR metric with lowest value than other scenarios, while the scenario V had FRR metric with lowest value than others. Other evaluated metrics of the user identification are present in Table 1. Table 2 shows the experimental results for the different user identification scenarios considering the UIWADS dataset with 22 subjects. Subjects in scenario II, which uses raw training combined with augmented data by the jittering technique, had all performance metrics with highest values than any other scenario. Table 1 Values of mean FAR and FRR including standard deviation in each data augmentation scenario Performance metrics Scenario

FAR

Standard deviation

FRR

Standard deviation

Scenario 1

0.003958

0.003571

0.107760

0.114872

Scenario 2

0.003030

0.003211

0.093484

0.099674

Scenario 3

0.003696

0.003970

0.119180

0.125588

Scenario 4

0.003379

0.003970

0.110670

0.115304

Scenario 5

0.003221

0.003100

0.088058

0.082485

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Table 2 Values of mean accuracy, precision, recall, and F1-score in each data augmentation scenario Performance metrics Scenario

Accuracy (%)

Precision (%)

Recal (%)l

F1-score (%)

Scenario 1

91.90

92.00

91.90

91.80

Scenario 2

93.90

93.90

93.90

93.80

Scenario 3

92.50

92.60

92.50

92.40

Scenario 4

92.40

92.60

92.40

92.30

Scenario 5

93.40

93.50

93.40

93.40

5 Conclusion and Future Works This work presented a data augmentation framework to improve the baseline of performance metrics, which are accuracy, precision, recall, F1-score, FAR, and FRR, of biometric user identification. We used walking activity data of 22 subjects recorded by smartphone sensors from the UIWADS dataset. The user identification task is successfully tackled using a four-layer one-dimensional convolutional neural network. Without any data augmentation, the 4-layer 1D-CNN provided a high accuracy value of 91.90% and a low FAR value of 0.003958 as baseline performance metrics. After applying the proposed data augmentation framework, the combination of raw training data and augmented data using the jittering technique improves the baseline performance of accuracy metric from 91.90% to 93.90%. When concerning the FAR value, the same scenario improves the baseline performance of FAR from 0.003958 down to 0.003030. For future implementation, we are planning to implement the proposed data augmentation framework with another deep learning models such as long short-term memory recurrent neural network.

References 1. Gafurov, D., Snekkenes, E., Bours, P.: Gait authentication and identification using wearable accelerometer sensor. In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies, pp. 220-225 (2007) 2. Hnoohom, N., Mekruksavanich, S., Jitpattanakul, A.: Human activity recognition using triaxial acceleration data from smartphone and ensemble learning. In: 2017 13th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 408-412 (2017) 3. Laghari, A., Waheed-ur-Rehman, Memon, Z.: Biometric authentication technique using smartphone sensor. In: 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 381-384 (2016) 4. Mekruksavanich, S., Hnoohom, N., Jitpattanakul, A.: Smartwatch-based sitting detection with human activity recognition for office workers syndrome. In: 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), pp. 160-164 (2018) 5. Mekruksavanich, S., Jitpattanakul, A.: Classification of gait pattern with wearable sensing data. In: 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI

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Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), pp. 137-141 (2019) Mekruksavanich, S., Jitpattanakul, A.: Exercise activity recognition with surface electromyography sensor using machine learning approach. In: 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), pp. 75-78 (2020) G. Pan, Y. Zhang, Z. Wu, Accelerometer-based gait recognition via voting by signature points. Electron. Lett. 45(22), 1116–1118 (2009) K.M. Rashid, J. Louis, Times-series data augmentation and deep learning for construction equipment activity recognition. Adv. Eng. Inform. 42, 100944 (2019) Rong, L., Jianzhong, Z., Ming, L., Xiangfeng, H.: A wearable acceleration sensor system for gait recognition. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2654-2659 (2007) Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., Kuli´c, D.: Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, ICMI ‘17, pp. 216-220. Association for Computing Machinery, New York, NY, USA (2017) G.M. Weiss, K. Yoneda, T. Hayajneh, Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access 7, 133190–133202 (2019)

Cybersecurity Attacks: Analysis of “WannaCry” Attack and Proposing Methods for Reducing or Preventing Such Attacks in Future Sumaiah Algarni

Abstract The technological revolution in the last few years has erased the communicational barriers and created a global network of users and data. At the same time, they have also made it easy for malwares and other cybersecurity threats to spread easily among users through networks. The threat of ransomwares in particularly has been risen in the last few years, with 2017 witnessing one of the most destructive ransomware attacks in the history of Internet. This attack known as the “WannaCry” has infected computers across the globe and crippled both individuals and organizations. The essay provides an analysis of this attack including its definitions, methods of infections, its impact on the society as well as discussion some important detection and prevention techniques to avoid similar attacks in future. Keywords Ransomware · WannaCry · Encrypt · Decrypt · Malware

1 Introduction With globalization, technological advances, the advent of the digital era, and communication barriers between individuals and organizations have largely been erased. Information ranging from an individual’s details to an organization’s data can be found on the virtual network of interconnected computers called as “Internet” (Craig 2011). However, this has made communication and information access easier as well as has made it easier for malicious software to spread rapidly from one place to another through interconnected networks [1]. Indeed, one of the most important security concerns in recent years has been the growing threat of cyberattacks, specific from ransomwares [2–4]. According to Ledin [5], the perpetrators of such cybersecurity threats are becoming increasing sophisticated with using methods like mobile-based malware propagation, smart grid hackings, cloud-based attacks, and GPS signal interferences. The “WannaCry” ransomware that combine a traditional S. Algarni (B) M.Sc. in Information Systems, School of Computing Sciences, University of East Anglia, Norwich, UK e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_73

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ransomware with worm-like self-propagation capabilities and wreak havoc worldwide is an example of such attack that happened in 2017. This paper will provide a detailed analysis of the “WannaCry” ransomware attack including its origin and definition, modes of infection, its impact on the society, the motivation of perpetrators as well as give some important techniques to detect and prevent similar attacks in future.

2 Analysis of the “WannaCry” Ransomware Attack Before analyzing “WannaCry” ransomware attacks, it is important to understand what ransomware is and how it works. Ransomware is any piece of malicious software or “malware” that blocks the victim’s access to his or her files by encrypting it and the only way to regain access can be through paying a “ransom” [6, 7]. The first ransomware attack was in 1989, known as the “AIDS Trojan” attack or “PC Cyborg,” and was spread through floppy disk [8, 9]. Since then, hackers have developed many more sophisticated ransomware programs that cripple user’s computers and force them to pay money to regain control over their data. These ransomware programs have two categories [10]. The first one is the “encrypting” types or the “cryptoransomwares” that include advanced encryption algorithms designed to block users’ files through encryption and the only way to unblock it through a decryption key provide after paying a ransom [11]. The second one is the “locker” types that lock the users out of their operating systems until payment is made [12]. CryptoLocker, Locky, and CrytpoWall are examples of the former while Winlocker is an example of the latter [1]. The “encrypting” types of ransomwares are more destructive and difficult to break because files cannot be decrypted without the key and it can change the extension of various files to create confusion [1, 12]. In addition, they spread through easily accessible ways such as phishing emails, suspicious software programs, unverified downloads, malicious Web sites, or infected advertisements, and this makes it extremely dangerous [9]. The losses caused by ransomwares attacks reach to several billions of dollars every year and attackers becoming more and more global and ambitious in their approach [3, 13].

2.1 Origins of “WannaCry” The “WannaCry” ransomware attack happened on 12th May 2017 with an estimated 200 k + affected endpoints in organizations around the world (Ehrenfeld [14]. The perpetrators requested from victims to pay $300 in bitcoins (Fox-Brewster 2017). It reported that the amounts of money paid during the period reach to $50,000 and can reach to 4 billion USD if all users paid ransom [15]. One of the most important reasons for the rapidly spread of this ransomware was that unlike traditional

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malwares that spread usually through emails, the “WannaCry” exploited the vulnerability in Microsoft’s implementation of Server Message Block (SMB) protocol called “EternalBlue” [16]. The first attacks were reported in the UK. However, it then spread rapidly to other countries in Europe as well as USA, India, China, Taiwan, Ukraine, and Russia [15]. The attacks not only affected individual users but also businesses from health, travel, telecom, security services, manufacturing, and several other sectors [17].

2.2 Anatomy of “WannaCry” In order to understand the vulnerabilities that lead to this rapid propagation of the “WannaCry” ransomware globally, it is important to understand the anatomy of the attack. Actually, the ransomware searches and encrypts more than 200,000 computers in 150 countries [16, 18]. It can infect computer in two ways. The first way is when a hacker perform an Internet search for IP addresses that contain the “EternalBlue” vulnerability, preys upon unpatched computers and attack them remotely through the execution of arbitrary code [10, 17, 19]. The second one is the “worm method” in which the ransomware spreads to other machines in the infected network through selfpropagation (ibid). Both methods have the same consequence of encrypting the files of the victim’s computer and locking them out of it. The reason “WannaCry” spread so rapidly was that it exploited the vulnerability to combine a traditional ransomware with worm-like propagation capabilities [1, 20]. This is accomplished by following a multi-step propagation pattern. In the first step, vulnerable systems with unpatched port 445 endpoints are identified. Then, a backdoor payload named “DoublePulsar” is uploaded that allow the “EternalBlue” exploit to control and inject code remotely into the system. An additional payload is also injected into the system through the backdoor, which in case of “WannaCry” is a ransomware. A communication-anonymizing software called “Tor-client” is installed on the affected system in order to prevent the communication from being tracked. Finally, the system files are encrypted using an AES algorithm that can only be decrypted using an RSA 2048 key, shadow copies are deleted and the ransom message is display. The payload also include code to search for additional systems within the network with open 445 ports. These systems will be exploited with “EternalBlue” and the process repeats, thus creating an attack chain (ibid). The overall process can be visualized as shown in Fig. 1 [1, p. 1939].

2.3 The Effects of Cyberattacks on Society Apart from affecting individuals and organizations, cyberattacks can have a large impact on the society. Factors such as identity theft and data loss are becoming very common due to technological increase [21]. It is predicated that about 7.1 million identity thefts occurred in the last eight years [3]. This issue is not only affecting

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Fig. 1 Propagation of “WannaCry” [1, p. 1939]

individuals, but can also harm the safety and security of the whole society because cybercriminals can use data and identities to commit crimes [20]. Some attacks can also impact the political climate and cause global unrest. As a result of these attacks, the security costs and financial losses are increasing across the world, and that affects the economy of the countries [22]. In addition to that, the human society as a whole is becoming extremely dependent on digital technologies and as a result “ It is becoming harder to identify the nodes and connection points whose protection must be prioritized” [23, p. 6]. Indeed, the Internet and the digital technologies could be great tools for hackers and cybercriminals because of digital anonymity and that make it easy for committing crimes [21]. This issue is compounded by the widespread use of new and sophisticated mobile, cloud and social media technologies. Cliff [24] points out that while these technologies can make life easier, any failure can cause “domino-effect chain reactions” leading to collapses. In fact, the “WannaCry” ransomware attack is a case in point that demonstrates the dangers of increasing dependence on technologies as whole.

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2.4 The Motivation of Hackers Given social, economical, and political risks related to cyberattacks, the need to understand the motivation of hackers is important. While the financial motivation behind attacks such as the WannaCry ransomware seem obvious, it is argued that the motivation is far from straightforward as in many cases and the real motivation is hidden [25]. However, in order to understand the motivation of the hackers, researchers have divided them into two categories “insiders” and “outsiders” [26, 27]. On the one hand, the “insiders” include aggrieved employees or family members who motivated by revenge or financial gains [28]. It also includes those who facilitate cybercrimes unintentionally. The “outsiders” on the other hand are classified into three subcategories: (a) organized attackers, (b) hackers, and (c) amateurs (ibid). Organized attackers may be motivated by political, religious, or ideological agendas who may want to inflict damages on their targets. Organized attackers may also be organized criminals who motivated by power and financial gains. Hackers may be motivated by the financial or antisocial reasons, or simply by the challenge of breaking into systems. Amateurs’ motivations are generally benign like improve their skills or curiosity (ibid). Regardless, their actions often lead to damage. Dealing with these various groups and understand their motivations is an extremely complex issue and should be given high priorities by countries.

2.5 Prevention of “WannaCry” and Other Ransomware Attacks in Future The once the computer is infected with the “WannaCry” attacks, it is possible to spread to other computers in the same network using “wrom” method. Dealing with such attacks could be extremely complex with two basic possibilities [29]. The first one is to take responsive procedures after the computer is infected, and the second one is to take preventive procedures before infection (ibid). In general, the postattack procedures are very limited especially if the ransomware is unknown and the data cannot be decrypted. Indeed, this was the case that happened with the “WannaCry” recently (Symantec Security Response 2017). While security experts have released many tools like “WannaKey” and “WannaKiwi” to help users fight against it, and these tools have very limited abilities and may not be able to recover files in most scenarios (Roettgers 2017). Indeed, either paying the ransom or relying on the backups are the only choices users may have (ibid). According to Levy [9], even if the users pay the ransom, there is no guarantee that the files will be recovered. Given this scenario, the best method to deal with such attacks would be to take preattack preventative security procedures that can prevent such attacks. These procedures could be taken at two levels, organizational and individual. At an organizational level, the steps that can be taken include the following:

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1. Backup important data frequently and effectively in order to decrease the negative impact if an attack occurs [9]. 2. Ensure that all Windows systems are patched with the latest security and operating system updates, especially for SMB or the Server Message Block [1]. 3. Block all vulnerable SMB ports from access by external hosts, especially port 139 and port 445, because these are the main ports that are open to exploits [30]. 4. Update the antivirus and antimalware signatures regularly on systems and network endpoints [9]. 5. Use the sandboxes and virtual machines as these make it easier to contain malwares from affecting the operating system [31, 32]. 6. At an individual level, attacks from “WannaCry” can be prevented by exercising caution when opening emails from unknown senders especially if they contain attachments, because these are the most common methods of infection [9]. Similar to organizational systems, personal computers can also be protected from attacks by ensuring that security and operating system patches, antivirus and antimalware signatures are up to date [9, 1]. Finally, users can back up their files in order to protect their data (ibid). The above-mentioned procedures could help to protect individuals and organizations from future attacks.

3 Conclusion Based on the “WannaCry” ransomware attack and other similar attacks in the recent years, it can be deduced that perpetuators of ransomwares are becoming increasingly sophisticated and ambitious in their approach. The emergence of the anonymous cryptocurrencies like bitcoins makes it very difficult for experts and governments to bring the perpetuators to justice. The “WannaCry” attack was especially vicious due to the combination of a ransomware and worm-like self-propagation capabilities. It is evident that the success of “WannaCry” was largely due to the lack of preventative measures by both users and organizations. Indeed, since there are currently no mechanisms to decrypt the files encrypted by this ransomware, the only way to minimize the damage is to take precautionary measures by both users and organizations to protect their computers and data. Three years on, ransomware attacks are still a concern that threaten millions of people worldwide and further study and investigation are recommended in this regard.

References 1. S. Mohurle, M. Patil, A brief study of wannacry threat: ransomware attack 2017. Int. J. Adv. Res. Comput. Sci. 8(5), 1938–1940 (2017)

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2. L.Y. Connolly, D.S. Wall, The rise of crypto-ransomware in a changing cybercrime landscape: taxonomising countermeasures. Comput. Secur. 87, 101568 (2019) 3. Internet Security Threat Report: 2017 https://digitalhubshare.symantec.com/content/dam/ Atlantis/campaigns-and-launches/FY17/Threat%20Protection/ISTR22_Main-FINAL-JUN8. pdf?aid=elq_ 4. S. Mansfield-Devine, Ransomware: taking businesses hostage. Netw. Secur. 2016(10), 8–17 (2016) 5. G. Ledin, The Growing Harm of Not Teaching Malware. Commun. ACM 54(2), 32–34 (2011) 6. R. Brewer, Ransomware attacks: detection, prevention and cure. Netw. Secur. 2016(9), 5–9 (2016) 7. A. Liska, T. Gallo, Ransomware: Defending Against Digital Extortion (O’Reilly Media Inc., New York, 2016) 8. J.Greene, Ransomware (Salem Press Encyclopedia of Science, New Jersey, 2017) 9. A. Levy, Avoiding the Ransom: Cybersecurity for Business Owners and Managers (Magnet Solutions Group Press, Austin, 2016) 10. J. Allen, Surviving ransomware. Am. J. Fam. Law 31(2), 65–68 (2017) 11. M. Wecksten, J. Frick, A. Sjostrom, E. Jarpe, A novel method for recovery from Crypto Ransomware infections. In 2016 the 2nd IEEE International Conference On Computer And Communications (ICCC), (2016 October) 12. I. Bica, R. Reyhanitabar, Innovative security solutions for information technology and communications. In: 2016 the 9th International Conference (SECITC) (2016, June) 13. C. Rozas, H. Khosravi, D.K. Sundar, Y. Bulygin, Enhanced Detection of Malware. Intel Technology Journal 13(2), 6–15 (2009) 14. J.M. Ehrenfeld, WannaCry, cybersecurity and health information technology: a time to act. J. Med. Syst. 41(7), 104 (2017) 15. J. Stevenson, The WannaCry ransomware attack (2017). Strategic Comments 23(4), vii–ix (2017) 16. W. Ashford, Ransomware attack could be wake-up call: a failure by many organisations to take cyber security seriously has long been blamed on the lack of a single significant event to shake things up. Does WannaCry fit the bill? Comput. Wkly. 4–5 (2017) 17. R. Hackett, Why a global cyber crisis stalled-this time. Time 189(20), 7–9 (2017) 18. A.L. Young, M. Yung, Privacy and security cryptovirology: the birth, neglect, and explosion of ransomware. Commun. ACM 60(7), 24–26 (2017) 19. B. Brian, WannaCry Exposes Defense Supply Chain Vulnerabilities. National Defence 20–21 (2017) 20. A. Azad, Ransomware: a research and a personal case study of dealing with this Nasty Malware. Informing Sci. Inf. Technol. 14, 087–099 (2017) 21. R. Upadhyaya, A. Jain, Cyber ethics and cyber crime: a deep dwelved study into legality, ransomware, underground web and bitcoin wallet. In: 2016 International Conference on Computing, Communication and Automation (ICCCA) (2016, April) 22. J. Thornton, Don’t click bait from cyber criminals. Public Finance. 11, 43 (2015) 23. D. Clemente, Cyber Security and Global Interdependence: What Is Critical? (2013) https:// www.chathamhouse.org/sites/files/chathamhouse/public/Research/International%20Security/ 0213pr_cyber.pdf 24. D. Cliff, Failures in Ultra Large Scale Complex Systems (The Chartered Insurance Institute, London, 2012) 25. P. Shakarian, J. Shakarian, A. Ruef, Introduction to Cyber-Warfare: A Multidisciplinary Approach (Elsevier, Waltham, 2013) 26. C. Han, R. Dongre, What motivates cyber-attackers? Technol. Innov. Manag. Rev. 4(10), 40–42 (2014) 27. D. Russell, G.T. Gangemi, Computer Security Basics (O’Reilly & Associates, California, 1993) 28. S.W. Brenner, Cybercrime: Criminal Threats from Cyberspace (Praeger, Santa Barbara, 2010) 29. R. Richardson, North, M: Ransomware: Evolution, Mitigation and Prevention. International. Manag. Rev. 13(1), 10–21 (2017)

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30. Microsoft Website: How to enable and disable SMBv1, SMBv2, and SMBv3 in Windows and Windows Server. https://support.microsoft.com/en-us/help/2696547/how-to-enable-anddisable-smbv1-smbv2-and-smbv3-in-windows-and-windows (2017) 31. K. Kendall, Practical Malware Analysis. In: Black Hat Conference, USA (2007) 32. G. Radhamani, G.S. Rao, Web Services Security and e-business (Idea Group Inc., PA, 2007) 33. T. Craig, Internet: Technology, People, Process (Hodder Wayland, London, 2002) 34. T. Brewster, WannaCry Hackers Are Using This Swiss Company To Launder $142,000 Bitcoin Ransoms. (2017). https://www.forbes.com/sites/thomasbrewster/2017/08/03/wannacry-hac kers-use-shapeshift-to-launder-bitcoin/#4638139f3d0d 35. G. Slabodkin, WannaCry had wide impact: recent ransomware attack hit much harder than initial estimates; NHS facilities, two U.S. healthcare systems are among its victims. Health Data Manag. 25(3), 6 (2017)

Enhancing Trust and Immutability in Cloud Forensics Pranay Chauhan and Pratosh Bansal

Abstract We are living in a digital era where most of us have an online presence with all their data on cloud. “Easy access of data from anywhere” is the biggest advantage of cloud but this same point becomes the cause of cloud-based crimes. There has been a drastic increase in cloud-based crime in the recent past. The immoral from any geolocation can target the victim of any geolocation, even cross-borders. Moreover, hunting down the immorals/suspects in these cross-border attacks is more difficult as suspects are easily able to coat themselves. In the cloud, data is stored at multiple geolocations, and this cross-border cloud data storage is the biggest challenge for law enforcement agencies. When the crime is committed from a different country this geographically sparse data becomes a major hindrance for the forensics investigation. LEA had to request the cloud service providers of all the involved locations (including the ones which are outside the country) to provide data for fetching out the right pieces of evidence. In the current state, LEA performs these forensic investigation requests through web portals or through email conversations. These requests take days or weeks to process which in turn delays the investigation process, and sometimes victims are left out without justice. Also, these web portals/applications through which the LEA demands the pieces of evidence are also based on cloud and thus are highly insecure. These not so secure portals raise a big question on the authenticity of the data and evidence received on them. Evidence could be easily tailored or removed (partially or fully) from the cloud storage. Keywords Cloud security · Digital evidence · Cross-border · Data localization · Smart contract

P. Chauhan (B) · P. Bansal Department of Information Technology, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore, MP, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_74

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1 Introduction With the advancement of technology, computing services have also evolved drastically. Thanks to the “Cloud Computing” technology now even the robust software could be executed on low-performance devices. Cloud in simpler words refers to “the computers and storage devices that are kept over a remote location and are allowed to be accessed by people via the Internet are termed as cloud.” Since these are located and could be accessed from distant locations, they allow users to run applications without installing onto their devices [1]. These computing machines kept over a remote location not only helps people to run heavy softwares on low-end devices but also allows them to store, access, and process unlimited data. Cloud computing has achieved a compound annual growth rate of 19% from 2015 to 2020. Grown from $67B to 162B. Another interesting fact to add here, Vormetric stated that 85% of the total enterprise organizations keep their sensitive information on the cloud [2]. Each coin has two faces; sadly cloud computing crimes are also increasing at the alarming rate. RedLock stated in 2018 that 49% of cloud databases are not encrypted and they have predicted that by 2022, 95% of the cloud security issues would be caused because of consumers’ fault [2, 3]. Though, there are numerous reasons to believe cloud computing, but all these security issues become its bottleneck. These barriers not only question its performance but may compromise the trust of cloud users on all service providers. Security is the major worry of cloud consumers and cloud service providers, since risk and trust cannot be considered as weak activity. Table 1 above depicts various types of attacks and threats in cloud environments. Apart from these listed conventional attacks, there are other security concerns such as compliance, availability, data localisation [4]. While massive data storage at multiple geolocations/distributed across borders, dependencies on other networks, and data centers are major privacy concerns. Conservative security techniques for integrity, confidentiality, and authentication could not be effectively integrated on various levels of a cloud environment. Subsequently, cross-border data storage and public networks require additional concern. When the cloud infrastructure is distributed across different nations and not limited to the same nation (within the border) then the local law enforcement agencies (LEA) faces extra challenges while dealing with the cross-border storage and security laws. Travel of data across national borders is another major concern for cross-border jurisdictional [3]. LEA often requires access to the infrastructure resources, forensic evidence, data, system test details, logs, contact information, etc. Cloud service providers usually take time for sharing this data and information, even if they agree to share the information, the process takes too long, and the CSP’s keep delaying the requests [5].

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Table 1 Shows various cloud threats and attacks Cloud threats

Attacks on cloud

Insider threats

Malware can be used • XSS attacks • SQL i attacks

Data leakage

Multiple side channels methods can be used for data leakage Example: Have i Been pwned, firefox monitor, breach alarm and other tools can be used for breach data identification.

User access and physical disruption

Man in the cloud attacks

External attackers and threats

DDOS attacks

Data segregation

Various authentication attacks such as • Phishing attacks • Replay attacks • Key loggers • Brute force attacks

Mis configured server

Improper server configuration can lead to big attacks

Crypto jacking

Malicious scripts are being used for mining of crypto currencies Example: Process explorer can be used to identify the malicious process running in network and cloud

2 Existing System for Investigation In the current state, LEA performs these forensic investigation requests through CSP’s web portals or through email conversations and through mutual legal assistance treaty (MLAT). These requirements take extensive time to process. This delays the forensics investigation process, and at times victims go away without justice served. These web portals and other web applications through which the LEAs demand the pieces of evidence are also based on the cloud and thus are highly insecure. Due to the lack of security, the evidence can also be tailored or removed from the cloud storage. These not so secure portals raise a big question on the authenticity of the data and evidence received on them [6]. Evidence could be easily tailored or removed (partially or fully) from the cloud storage.

3 Evidence Collections Methods Email communications is one of the most common ways for exchanging critical information about the evidence [7]. The investigation agencies directly demand information from the cloud service providers (CSP’s) or concerned authority. These authorities might take weeks to respond to these emails and sometimes might even ignore

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them. Most of the email services are insecure and are easily hackable [4]. Even if the CSP responds with the correct information, the evidence could be malformed by the suspect without knowledge of the victim or the investigation agencies. Phishing, spear phishing, spoofing, and zero days attack are few common and easy ways to hijack email accounts. According to Hostingbrunal, 1.5 million phishing web pages are formed every month, and 92% of the malwares are delivered through email only [8].

4 Cloud Service Providers’ Web Portals To deal with the messiness of the emails, the CSPs or concerned authorities released their own web portals through which LEAs/investigation agencies can request for specific evidence. Many CSPs host web portals on their own platform so anyone could easily authenticate themselves and could request for the critical information of the suspect. These portals usually ask the user (LEA/Investigator) to fill and submit the form along with the supporting documents such as court orders, search warrants, and other necessary documents too. [9]. The CSP or concerned authorities then analyses the received form and checks for the authenticity of the submitted documents, and thereafter, they provide the requested information [5]. The CSP’s or concerned authorities take an extensive amount of time to respond to such portal requests since these requests are overhead for them. In some cases, they might not be actively monitoring these portals, and so requests on these portals might lay unattended for weeks. Thus, the victim has to wait for a long time and would end up losing hope for justice. Also, these insecure web portals are hackable too. This again questions the authenticity of the evidence received [10].

5 Mutual Legal Assistance Treaty (MLAT) MLAT is an agreement among two or more countries which helps them to exchange critical information, data, and evidence while dealing with the cross-border cybercrimes. This treaty establishes a single point of contact between different countries through which these nations can exchange the pieces of evidence. Each involved country has to follow a three-level internal communication process before sending the request draft across the border. This process ensures that all the protocols are being followed while requesting the information [11, 12]. Below are the steps which are followed: • The local city authorities first send the request draft to the state-level authorities. • State-level authorities forward these request drafts to the central authority of the country.

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• The central authority checks the request draft for all the protocols and some additional grounds. • Post request draft examination central authority then forward this draft to law enforcement agencies. • LEAs or the investigators then collect all the evidence and revert back to the central authorities in admissible form. • The central authority then reverts these pieces of evidence to the state authority in admissible form. • The state authority reverts it back to the local city level authorities in admissible form. One can easily see how tedious and lengthy the aforementioned process is. Evidence can be easily tempered before reaching the right hands, and this hierarchical flow increases the processing time as well. “Time” is an essence of cross-border crimes; delay in request processing gives unfair advantage to the suspect [12].

6 Data Localization and Its Advantages Data localization/Data residency is a law that states that the people’s data must be stored and processed within the same country. Data must be sent out to other countries only after it meets all the data protection laws and local data privacy laws. According to this, the CSPs will have to establish their storage setups within the country and collect, store, and process within the same country. Data localization also helps in maintaining the privacy law for the citizens. This includes the protection of critical information including political judgments and other personal interests of an individual. With data localization, LEAs and/or investigators could easily get the case information and could save themselves from a lot of obstacles while dealing with the cross-border crimes. Data localization provides security for the citizens’ data and helps in protecting individual’s data from foreign surveillance. Also, it equips LEAs to have better monitoring over the data and its usage. The LEA and investigation agencies can get the evidence almost immediately without extended formalities and without relying on MLAT. a. Issues with Data Localization The most prominent issue with data localization is the CSPs’ denial to its adaptation. Many of the CSPs are opposing this privacy draft because this would require huge efforts from their side, and there are no tangible benefits for them. Storing data inside the country requires a lot of investment in terms of time, money, and resources. On top of that, some countries do not have well-developed facilities and infrastructure to maintain the security of the data. Without strict monitoring there is no guarantee that the CSPs will not store data outside the country [2, 13, 14]. They may continue to carry out such practices for their benefits.

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7 Proposed Solution Blockchain is the modern technology that could help in dealing with the cross-border crime issues by aiding in exchange of cross-border evidence. It is a data structure that allows the creation of a distributed digital ledger for storing and recording transactions (events/records) shared by all participating parties over a distributed network of computers [15]. Blockchain technology ensures data integrity and security and could overcome all the problems associated with time taking, not so secure conventional data sharing techniques such as emails and web portals. Blockchain maintains the hashes of the electronic data which are derived from the hashes of the previously inserted blocks. Using its consensus mechanism, it allows the insertions of new blocks only after the approval of all the present nodes in the network. Since blockchain is a distributed ledger, no single authority has the complete right or control over the data in the network [16, 17]. Law enforcement can employ blockchain-based solutions through which all the existing issues of evidence exchanging, evidence tampering, generating trust, maintaining logs, and metadata could be easily solved. Chain of all the evidence along with their metadata could be tracked down with this technology. Government agencies, LEA, and CSPs can use smart contracts, i.e., Blockchain applications for exchanging evidence instead of conventional procedures. Smart contracts could ensure trust, prevent malicious activities, and provide easy ways for data sharing. These are cryptographically sealed and are updated near real time. Digital evidence could be converted into lines of codes using these smart contracts. This code could be deployed on a decentralized and distributed blockchain network. Code would control the execution of evidence and control read/write transactions in a traceable, immutable, and irreversible manner by managing the permissions. Code is law, and it could be deployed either on permissionless or permissioned blockchain platform. Permissioned blockchain platforms have more advantages over permissionless blockchain platforms [18]. Once government bodies, authorities, or concerned agencies deploy code/evidence on a permissioned blockchain platform and they could grant permissions to concerned parties for examination of evidence. Concerned parties here could be law enforcement agencies, prosecution, defense, forensic expert, investigation agencies, court of law, central authority, and any other involved agencies. All these concerned agencies could access the information using cryptography concept of public and private security keys. Since evidence is stored on the blockchain, concerned authorities could access these evidence globally too. Blockchain stores metadata of the transactions along with the timestamps, this could help in tracking the overall case discussions with the involved individuals and groups. Blockchain could solve all the issues of conventional data sharing systems/solutions and provide below list of benefits: a. Data Integrity—Each block of the blockchain is immutable ensuring the integrity of digital evidence as data could not be tampered in between by anyone. b. Data Transparency—Each node of blockchain tracks the timestamp and metadata of every single change or transaction done. This could help in maintaining

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the transparency among the LEA and concerned authorities as all involved parties could see changes done by other involved parties. Decentralization—Blockchain is a distributed ledger, and the data is stored in the form of hashes, and so data is distributed across the multiple nodes present on the network. In this way, no individual has the complete authority over any block or data present in the network. This will eventually enhance the trust in the legal system. Consensus mechanism—Blockchain uses consensus mechanism which states that every node has an equal right over the digital evidence/data in the network. Hence, there would be no monopoly among the users of the system, i.e., among concerned authorities. Data authorization and authenticity—Permissioned blockchain allows you to control the data authorization of involved parties by setting appropriate read/write permissions. This will ensure only allowed parties could view the evidence/data. Blockchain uses public and private keys for allowing users/agencies access to the nodes. This secure key mechanism ensures authenticity and keeps the information secure from the outer world by preventing unauthorized data access. Time efficiency—This smart contract way is time efficient and faster compared to all conventional data sharing systems —Emails and Web Portals No third party dependency—Blockchain technology provides direct access of the required information to all the authorized personnel and thus eliminates the dependency on third parties for evidence requests and approvals. Trust in the legal system—Blockchain would provide correct untampered information/evidence to the right hands without unnecessary processes and delay which will speed up the entire investigation and would eventually enhance the trust in the legal system [19].

8 Conclusion Conventional data/evidence sharing techniques, such as emails, web portals, etc., have their own flaws. Data integrity and security are few of the major concerns with these traditional ways of evidence collection. Security concerns with these methods raises doubts over the integrity of evidence and also weakens trust in the cloud forensics. Using blockchain and converting all digital evidence to smart contracts would not only solve all these security problems but it also has its other set of advantages like decentralization, data transparency, and time efficiency. All these advantages of blockchain will strengthen and build the trust in the overall cloud forensics system.

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References 1. L. Jianf, et al., An IoT—oriented data storage framework in cloud computing platform. IEEE Trans. Ind. Inf. 10(2) 2. Cloud computing market and security issues available at https://www.gartner.com/en/inform ation-technology/glossary/cagr-compound-annual-growth-rate. Accessed on 16 Apr 2020 3. Cloud computing security and privacy issues available at https://www.nist.gov/system/files/doc uments/itl/cloud/NIST_SP-500-291_Version-2_2013_June18_FINAL.pdf. Accessed on Apr 2020 4. S.O. Kuyoro, F. Ibikunle, O. Awodele, Cloud computing security issues and challenges. Int. J. Comput. Netw. (IJCN) 3(5), 247–255 (2011) 5. A. Alenerzi, N.H. Zulkipli, H.F. Atlam, R.J. Walters, G.B. Wills, The impact of cloud Forensics readiness on security, in Proceedings of closer 2017, pp. 511–517 6. C. Modi, D. Patel, B. Borisaniya, A. Patel, M. Rajarajan, A survey on security issues and solutions at different layers of Cloud computing. J. Supercomput. 63(2), 561–592 (2012) 7. M.E. Alex, R. Kishore, Forensics framework for cloud computing. J. Comput. Electr. Eng. 60, 193–205 (2017) 8. Cloud based email attacks available at https://www.cloudsecuretech.com/types-of-email-att acks-and-the-damage-they-can-cause/. Accessed on 23 Apr 2020 9. State of cloud adoption and security. (2017). https://www.forbes.com/sites/louiscolumbus/ 2017/04/23/2017-state-of-cloud-adoption-mand-security/. Retrieved 25 May 2017 10. N.L.S., Forsenca, Boutaba, Cloud services networking and management in cloud services (2015), pp. 153–159 11. Mutual assessment legal treaty available at https://mea.gov.in/mlatcriminal.htm. Accessed on 19 Mar 2020 12. Mutual assessment legal treaty format description cross boarder available http://www.cbi.gov. in/interpol/mlats.php. Accessed on 26 Mar 2020 13. Cross boarder data storage issues available at https://gcn.com/articles/2019/04/09/cloud-actinternational-data.aspx. Accessed on 2 Apr 2020 14. Cross boarder data flows available at https://itif.org/publications/2017/05/01/cross-borderdata-flows-where-are-barriers-and-what-do-they-cost. Accessed on 4 Apr 2020 15. Guardtime cloud assurance with Blockchain (2017). Online available at https://www.guradt ime.com/solutions/cloud 16. S. Nakamoto, Bitcoin: a peer to peer electronic cash system (2008). Online available at http:// bitcoin.org/bitcin.pdf 17. Distributed ledger technology: beyond Blockchain (2016). Online available at www.gov.uk/ government. Accessded on Mar 2020 18. N. Szabo, The idea of smart contract (1997). Online available at www.fon.hum.uva.nl/pdf. Accssed on Apr 2020 19. Ethereum platform online available at www.ethereum.org. Accessed on Apr 2020

A Fuzzy Time Series Prediction Model of the COVID-19 Epidemic Mohammad Minhazul Alam, S. M. Shahadat Hossain, Md. Romman Riyadh Shishir, Sadman Hasan, Eumna Huda, Sabrina Yeasmin, Abdul Motaleb, and Rashedur M. Rahman

Abstract In our study, a number of time series analysis techniques are applied to the COVID-19 epidemic data. The result of the analysis is used to develop three single variable fuzzy time series models from the dataset of day to day newly confirmed cases of the virus in certain countries. Those developed models are later applied on infected cases of the countries that are not used before in model development. This helps in appropriate model selection for prediction of cases for unseen countries. The forecasted results are then compared and analyzed in detail with different performance metric. The time series analysis process described in this article can be used to better understand the epidemic trend of a particular country and help the government plan better intervention policies.

M. M. Alam (B) · S. M. S. Hossain · Md. R. R. Shishir · S. Hasan · E. Huda · S. Yeasmin · R. M. Rahman North South University, 1229 Dhaka, Bangladesh e-mail: [email protected] S. M. S. Hossain e-mail: [email protected] Md. R. R. Shishir e-mail: [email protected] S. Hasan e-mail: [email protected] E. Huda e-mail: [email protected] S. Yeasmin e-mail: [email protected] R. M. Rahman e-mail: [email protected] A. Motaleb University of Information Technology and Sciences, 1212 Dhaka, Bangladesh e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_75

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Keywords COVID-19 epidemic · Fuzzy time series analysis · Fuzzy prediction model · HOFTS · WHOFTS · PWFTS · Intervention planning

1 Introduction Time series data is a sequence of change of event that spreads over a specific length of time. In time series analysis, a dataset is analyzed and learned with some mathematical or machine learning models to understand the trend and make necessary prediction. Some of the statistical analysis tools used for the time series analysis are Simple Moving Average, Exponential Smoothing, Seasonal Autoregressive Integrated Moving Average (SARIMA), etc. Machine learning algorithms like LSTM which is a variant of recurrent neural network is also used for analysis and prediction of time series data.

1.1 Time Series Analysis Using Fuzzy Logic The fuzzy logic approach for time series analysis starts by dividing the universe of discourse into some discrete fuzzy sets. Each point of the time series is then assigned to a fuzzy set depending on the membership function value. Afterward, “if-then” fuzzy inference rules are extracted from the time series pattern and recorded as transmission from one fuzzy set to another. These rule sets are later used to compute new values for prediction. The key variables that affect the accuracy of a Fuzzy Time Series (FTS) model are: number of partitions (fuzzy sets), partition type (grid, entropy, or cluster), choice of membership function (triangular, trapezoidal, Gaussian, etc.), and order of the function being learned (lag). As the trained model records the time series as a transition from one set to another, use of too few partition numbers will not learn the training data well. On the other hand, use of too many partitions will overfit the training data and will not generalize well. The fuzzy algorithms used to learn a time series can be weighted (gives different weights to the past values in the series) or non-weighted. And again, a time series prediction can be made simply from the past values of the target variable (endogenous variable), or it can be predicted from a combination of multiple variables (exogenous variables).

1.2 Using FTS Models for the Prediction of COVID-19 The new corona virus, first detected in December 2019 in Wuhan, China is a fast spreading strain which has almost shut down all the international flights all over the world. As of writing this article, many countries have implemented strict home lock down in their major cities which are seriously affecting the economic condition of

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the overall population. Apart from the home lock down, governments around the world are taking new measures to decrease the number of casualties. In this regard, policymakers need to analyze the region wise infection spread time series data to understand the effectiveness of the measures and to prepare better for the future. In this study, we use univariate models of three FTS algorithms, namely—High Order Fuzzy Time Series (HOFTS), Weighted HOFTS, and Probabilistic Fuzzy Time Series (PWOFTS) to analyze and model the COVID-19 epidemic data [1]. An FTS model learns the value of the time series data E(t) at time t, based on a number of previous values (lag) of the time series (Eq. 1). E(t) = f (E(t − 1), E(t − 2), E(t − 3), . . . , E(t − n)), lag = n

(1)

The weighted version of HOFTS assigns weights to the fuzzy inference rules with the same precedent to improve the prediction accuracy. The PWOFTS is a probabilistic model, which assigns weight not only to the precedent of the function, but also to the consequent. As a result, PWOFTS would work better in complex environments with large number of training data. In this work, we have analyzed the dataset of day to day COVID-19 infection spread along with a mobility trend dataset. Several statistical tools are applied to the datasets to find correlations and patterns. We develop FTS prediction models using the infection dataset and describe a model selection procedure for prediction of the unseen data. Finally, we have also proposed how this study can be extended by developing a prediction model of the maximum infection size of a particular region which along with our current models may make better and more effective prediction.

2 Related Works T. Ganyani et al. estimated the generation time of the COVID-19 disease based on the onset data of its symptoms [2]. Their study conducted on the data of Tianjin, China, and Singapore found out that the COVID-19 has a mean incubation period of 5.2 days, and its standard deviation is 2.8 days. The pre-symptomatic transmission from one host to another was found to be 48% for Singapore and 62% for Tianjin, China. This is a very important study on the incubation nature of COVID-19 which explains the high susceptibility of dense population areas from the virus. In another study, Wei Xia et al. described that the longer than usual incubation period of the new COVID-19 virus may lead to a quarantine nightmare [3]. They collected data of 124 confirmed cases from Wuhan, China and analyzed their demography, exposure history, and symptom onset data. Their study finds that the mean incubation period is 4.9 days with 73% of the second-generation patients getting infected before the symptoms were present in the first-generation cases. These studies show the reason of COVID-19 being so infectious comparative to other diseases. From these works, we get a primary estimation that the spread of the infection at some particular day may depend on the number of infections about

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three to seven days back when most of the presently confirmed patients were moving around in their community or family. Xia Jiang et al. developed a dynamic transmission model of COVID-19 by dividing the population into six chambers—un-infected, infected without symptom, infected and highly infectious but not quarantined, diagnosed and quarantined, potential victims, and cured [4]. They tested the model with the actual data of COVID-19 infection in Hubei province, China. The work was done at the beginning of the epidemic and could utilize only a limited amount of data available then. However, at the early stage of an epidemic, when less than enough data is available, such models can be useful for short-term prediction and intervention planning. In another study, Fong et al. described a model for forecasting the COVID-19 with a group of statistical and machine learning algorithms utilizing a small dataset [5]. The dataset they used comprised of the first fourteen days infection data of Wuhan, China. They used statistical tools like ARIMA, SARIMA, Holt-Winters Addictive; along machine learning algorithms like Linear Regression, Decision Tree learning, SVM, and Neural Network-based algorithm Polynomial Neural Network with Corrective Feedback (PNN+cf). Their result showed that PNN+cf achieves the best result with RMSE value of 136.547. Al-qaness et al. [6] used a modified flower pollination algorithm that uses Salp Swarm Algorithm (SSA) to improve the performance of an Adaptive Neuro Fuzzy Inference System (ANFIS)-based COVID-19 prediction model. As another early research in the domain, the work had little data to work with, but the novel approach they proposed outperformed several other neural network algorithms including standalone ANFIS implementations. Apart from the COVID-19 outbreak data, the algorithm is also applied to two dataset of weekly influenza cases in US and China and was shown to be performed well. Kraemer et al. analyzed the human mobility and government measures, and those factors may affect the spread of COVID-19 outbreak in Wuhan, China using realtime mobility data and travel history of specific cases [7]. In their study, they found out that the initial human mobility data helped spread the virus spatially. However, after the control measures were taken, the correlation between mobility and virus spread dropped significantly. At this point, the virus spreads rather locally than over spatially separated areas. Their report suggests that zoning and controlling mobility of a large area of dense population during the early outbreak of the virus is the most effective measure the governments can adopt. Once the virus is spread into different areas, it will find ways to transmit locally and will pose a formidable task to control it. Sunhwa Choi et al. attempted to estimate the possible outbreak size of COVID-19 using Stochastic Epidemic SEIHR model for two regions of Korea [8]. The model was developed using the outbreak data of daily new confirmed cases in Daegu and NGP, which were the key outbreak regions in Korea. However, when modeling the values, they faced challenges like, varying delays between symptom onset to diagnosis reasoned by changing government measures and individual preferences. In addition to that they did not take into considerations the number of possible infected patients incoming from abroad which, according to their statement, could seriously

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Fig. 1 COVID-19 infection spread data—Italy (January 22nd–June 1st, 2020)

affect the model. They suggested that in order to reduce the epidemic quick testing and isolation of possible patients is the key factor.

3 Dataset Overview Two datasets that are explored in our study are as follows.

3.1 Johns Hopkins University COVID-19 Dataset In our study, we have used the time series data of COVID-19 infection made available by the John Hopkins University Center for Systems Science and Engineering (JHU CSSE) on GitHub [9]. The dataset contains day-wise cumulative number of confirmed cases, as well as the number of recovered cases and casualty for different countries and regions (or states). The dataset is updated daily from sources of World Health Organization, European Center for Disease Prevention and Control (ECDC), US CDC, and many other government sources of the corresponding countries. At the time of writing this article, the dataset contained information starting from January 22, 2020 to June 1, 2020. As an example, the normalized data of cumulative confirmed cases and newly confirmed cases per day is shown in Fig. 1 from the dataset of Italy. The number of newly confirmed cases at the ith day is determined by subtracting the number of total confirmed cases of day i − 1 from the total confirmed cases at day i. The data shows that the highest number of infections per day for Italy was March 11, 2020 after which it has seen a steadily declining trend.

3.2 Apple Mobility Reports In an ongoing effort to help the researchers fighting COVID-19, Apple Inc. has published its mobility trend report on the web [10]. The dataset reflects the aggregated

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Fig. 2 Day wise Apple mobility trend for Brazil (January 13th–June 2nd, 2020)

number of requests for direction in the Apple Map application by the people of a specific region/country. From this dataset, we can estimate the mobility of people of a particular region on a specific day. The dataset is updated daily and contains day to day data starting from January 13, 2020. At the time of writing this article, the dataset contains data up to June 2020 with the exception of 11–12 of May. We have used linear interpolation for estimating the value of these two days. In Fig. 2, we have shown a sample of this mobility dataset. The country data we presented here is of Brazil, where the COVID-19 infection spread is one of the highest in the world.

4 Methodology 4.1 Development Environment and Libraries Used In our experiment with the FTS models, we use the PyFTS library of the MINDS research lab which is a set of fuzzy logic libraries for Python [1]. The plots of this study are drawn using the Matplotlib library of Python. All the codes are compiled and executed in Anaconda Python which is a free software suitable for this work.

4.2 Dataset Exploration and Parameter Values Estimation Determining Lag from the Infection Time Series Data. In order to determine lag in the infection spread data, we have used the Autocorrelation Function (ACF) which is a standard statistical method for this purpose. As several previously discussed studies on COVID-19 suggests that the virus has an incubation period of two to eight days (95% Confidence Interval), and 73% of the second-generation patients gets infected before any onset of symptoms in the first-generation patients it is assumed that the lag will be around three to seven [1, 2].

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We have used the daily new number of confirmed cases for three different countries (Brazil, Italy, and India) and plot the ACF values for up to fourteen days in Fig. 3. From figure, we deduce that an estimated lag of eight to ten days should be enough for learning the models to predict a reasonably good result. A lag value too high usually makes the learning process slow. Hence, in our experiment, we have used a lag value of eight to generate the FTS models. Optimal Partitioning Determination. For determining the preferable number of partitioning, we make experiment with the day to day newly confirmed infection number of Brazil, India, and Italy and train the data using HOFTS to check the RMSE output of the training error. However, as discussed earlier that the total number of confirmed infections varies from country to country and depends on extraneous situations; as such, we first normalize the data of each country to one to find the best result. This way, we are focusing on the trend of the data rather than the actual number of infected cases. Figure 4 shows the RMSE value for different partition

Fig. 3 Autocorrelation value of the daily new confirmed cases for Brazil, India, and Bangladesh (January 22nd–June 1st, 2020)

Fig. 4 The effect of the number of partitioning on the training accuracy (RMSE) using HOFTS

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numbers when the model is trained using HOFTS with order value of 5. As too many partitions result in over-fitting of the training data, we chose the number of partitions to be 35. Choice of Membership Function. We have experimented with three different membership functions—Triangular, Trapezoidal, and Gaussian and obtained training error result for the data of several countries. As the results for these three different membership functions are found to be similar, we used the simple one—Triangular membership function, for the experimental models. Correlation Analysis with the Mobility Trends Data. We have calculated the Pearson correlation value of the Apple mobility data with the number of new cases per day for an incubation period of 2–8 days and found negative correlation value for all the three countries tested (Brazil, Italy, and India). The mobility dataset presents the daily trend of location search queries made through the Apple maps application interface by the users of a particular region. This trend reflects how much a particular population desires to travel to some distant unknown locations. The analysis of this dataset signifies that, as the infection spreads over time, people become less inclined to travel to far and unknown locations. However, it is unlikely that people will use a map application to visit their known business areas, job places, or markets. As a result, even though the mobility trend data shows significant decrease following the COVID-19 outbreak, the virus may still spread among people through their necessary daily interactions. And the perceived change in mobility trend would not affect the overall aggregated value of infection spread. Figure 5 shows the normalized value of mobility and confirmed number of infections change with eight days lag for Brazil. Finding the negative correlation value between mobility trends and infection spread, we choose not to use the mobility trend as an exogenous variable and opt for the mono-variable fuzzy time series models instead.

Fig. 5 Day-wise mobility trend versus newly infected cases with seven days lag for Brazil. Infection data—January 22nd–June 1st, 2020. Mobility data—January 14th–May 24th, 2020

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4.3 Development and Selection of Fuzzy Time Series Models As we draw the day-wise number of confirmed cases for four countries Brazil, India, Belgium, and Italy, we find the graph of Fig. 6. It can be seen that Italy and Belgium have successfully checked the infection with number of new infections decreasing significantly, which means, their data has a full cycle which we can use for training and testing purpose of similar other countries. On the other hand, countries like India and Brazil are still in the upward exponential slope. So the challenge now is finding the most suitable training model for the prediction of a particular country. As such, we distributed the dataset of 188 total countries randomly into a training set of 151 countries (80%) and testing set of 37 countries (20%). After that we have used the three FTS algorithms to train with the training set and generate models for each of the countries separately. Because different countries have different environment variables and have different infection spread rate, the challenge is to find the time series model that can predict the data of a particular country best. The COVID-19 dataset contains day to day infection spread data of countries from January 22nd to June 1st. So, firstly, we used all the trained models of all the 151 countries to generate prediction of the trend of May 19th–May 25th (seven days) of a test country. The model that can predict the value of these seven day period best is picked and then used for the prediction of the trend (three days window) of the next seven days (May 26th–June 1st). This is a reasonable procedure because if we had not known the value of these last seven days (May 26th–June 1st), we would have picked the model that can best predict the data of the previous seven days that we know of (May 19th–May 25th). We have repeated this procedure for all the countries in the test set for all three algorithms and calculated the Mean, Standard Deviation, and Percentiles of RMSE. The result of our analysis is presented in the following section.

Fig. 6 Trend of newly confirmed cases—Brazil, Italy, India, Belgium (January 22nd–June 1st, 2020)

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Table 1 Training and test accuracy of the prediction models Algorithm

Mean RMSE (in %)

STD of RMSE (in %)

25th and 75th percentile (RMSE)

HOFTS

11.25

12.85

2.5–12.64

WHOFTS

21.71

23.46

2.4–38.70

PWFTS

25.67

26.91

2.61–47.06

Fig. 7 Day-wise new infection case prediction result of Algeria for HOFTS, WHOFTS, and PWFTS (May 25th–June 1st)

5 Results The test accuracies of HOFTS, WHOFTS, and PWFTS for our test scenario are presented in Table 1. It is seen that HOFTS performed better than both WHOFTS and PWFTS. As the number of training data is one cycle only and the data differs from country to country, we see that the more complex models overfit the training data and fail to predict the test data satisfactorily. As a result, simpler algorithms like HOFTS generalize well and perform better in the test environment scenario. The original day-wise trend for one country (Algeria) along with the predicted value of the three aforementioned algorithms for May 25th–June 1 are shown in Fig. 7.

6 Conclusion In this study, we have demonstrated the use of different statistical and fuzzy time series techniques to develop a simple yet powerful FTS prediction model selection

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process for the ongoing COVID-19 epidemic. Three FTS algorithms used in this study are tested, and results are compared with the original values for a seven day look ahead prediction accuracy. The study will hopefully help the policymakers to better prepare for the pandemic and find more suitable prevention measures using the outcome. As the pandemic is still going on and there is no seasonality in the data yet, more complex algorithms like WHOFTS and PWFTS could not perform better than HOFTS in this study. Moreover, predicting the infection size is a challenging topic still now as it depends on many outside variables, like swiftness of testing and isolation, government measures, individual precaution, etc. A suitable prediction model may be developed to predict the maximum infection size (number of infected people) of a particular country or region based on the aforementioned variables. We can then use that model to first predict the maximum infection of a country that has not reached infection peek yet. Afterward, we have to normalize that particular country’s current infection trend with the peak value and then predict its infection trend with the trained model of normalized dataset from another country that has seen a full cycle of the infection. Used in this way, the fuzzy prediction models may be able to make long term prediction with better accuracy.

References 1. P.C.L. Silva et al., pyFTS: Fuzzy Time Series for Python (Belo Horizonte, 2018). http://doi.org/ 10.5281/zenodo.597359 2. T. Ganyani, C. Kremer, D. Chen, C. Torneri, C. Faes, J. Wallinga, N. Hens, Estimating the generation interval for COVID-19 based on symptom onset data. medRxiv 2020.03.05.20031815 [Preprint]. March 08, 2020. https://doi.org/10.1101/2020.03.05.20031815. Cited 15 June 2020 3. W. Xia, J. Liao, C. Li et al., Transmission of corona virus disease 2019 during the incubation period may lead to a quarantine loophole. medRxiv 2020.03.06.20031955 [Preprint]. March 08, 2020. https://doi.org/10.1101/2020.03.06.20031955. Cited 15 June 2020 4. X. Jiang, B. Zhao, J. Cao, Statistical analysis on COVID-19. Biomed. J. Sci. Technol. Res. https://doi.org/10.26717/bjstr.2020.26.004310 5. R.G. Crespo, E. Herrera-Viedma, N. Dey, S.J. Fong, G. Li, Finding an accurate early forecasting model from small dataset: a case of 2019—nCoV novel coronavirus outbreak. IJIMAI. https:// doi.org/10.9781/ijimai.2020.02.002 6. M.A.A. Al-qaness, A.A. Ewees, H. Fan, M. Abd El Aziz, Optimization method for forecasting confirmed cases of COVID-19 in China. J. Clin. Med. 9, 674 (2020) 7. M.U.G. Kraemer, C.H. Yang et al., The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 493–497 (2020) 8. S. Choi, M. Ki, Estimating the reproductive number and the outbreak size of COVID-19 in Korea. Epidemiol. Health 42 (2020). https://doi.org/10.4178/epih.e2020011 9. COVID-19, Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. https://github.com/CSSEGISandData/COVID-19. Accessed 22 June 2020 10. Apple Mobility Reports. https://www.apple.com/covid19/mobility. Accessed 22 June 2020

Modelling of Generation and Utilization Control for Isolated Hybrid Microgrid for Rural Electrification Kuldip Singh, Satyasis Mishra, Davinder Singh Rathee, Demissie Jobir Gemecha, and R. C. Mohanty

Abstract For ameliorating the villages, electrification Indian regime introduced many programmes under rural electrification scheme. Under these programmes’, regime is providing the electrical connection to each village in ON-Grid or OFFGrid mode. The rural sector is expected to play a key role for microgrid framework, because in future rural electrical demand will probably exceed more than 30% of the total yearly consumption in India. The implementation of microgrid for rural electrification differs on the type of dispersed generation facilities, accessibility of sources, distribution control, stability and reliability of system, economic analysis, and operating policy. Along with there are few issues in isolated microgrid authentic time electrical power management is the major challenges for isolated hybrid microgrid for rural electrification. In this study, we are discussing the basic modelling of generation, controlling for rural electrification with renewable sources, stability issue for isolated hybrid microgrid. Keywords Village electrification isolated hybrid microgrid · Generation · Utilization · Stability and reliability K. Singh Department of EEE, Centurion University of Technology and Management, R.Sitapur, Odisha, India e-mail: [email protected] S. Mishra (B) · D. S. Rathee · D. J. Gemecha Department of ECE, Signal and Image Processing SIG, Adama Science and Technology University, Adama, Ethiopia e-mail: [email protected] D. S. Rathee e-mail: [email protected] D. J. Gemecha e-mail: [email protected] R. C. Mohanty Department of Mechanical Engineering, Centurion University of Technology and Management, R.Sitapur, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_76

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1 Introduction Powertransmission and distribution is a challenging and arduous task in rural components of India. As per the Indian regime statistics, nearly about 30% of the villages are still in lack of electricity as there is no opportune communication for conveyance. In India, more than 73% population are predicated on villages in which the major population in villages depend on irrigation and diminutive business. In today’s life, electricity plays major role in human life for amending the socio-economic conditions, connectivity with outer world through TV/radio/phone, income amendment with incipient opportunities of minute household business, and it is additionally playing the vital role for edification amendment [1]. The electricity use reducing the traditional way of cooking and lighting, which will have impact on health conditions. As the traditional grid is utilized for supplying the electrical energy to rural and urban consumers that provide the supply through central high voltage transmission network with conventional sources predicated central generation units. As conventional sources are not reliable due to economic and environmental impact in India for rural electrification, so the scenario is transmuting from conventional sources to non-conventional energy sources like solar, biomass, wind energy, etc [2, 3]. Active efforts are afoot to analyses and providing the solution for village electrification through isolated hybrid microgrid with renewable sources. Albeit most of the work in the microgrid areas along with astute grid has been done throughout the world for rural electrification in ON-Grid mode. The literature surveyed unfolds the rural electrification and utilization scenario in India, different challenges for village electrification, implementation microgrid, demand side management, power quality and scope of isolated hybrid microgrid along with power management in local distribution and generation units. Wimmler et al. the renewable energy is used for islanding electrification, as renewable energy sources are natural sources. The smart grid technology is used to shift the loads from source to storage during off generation time. The demand side management is the major component of smart grid for communication between electrical load demand and electrical generation [4]. Goy et al. the study considers the large-scale building load management for thermal system for improved and optimized the energy consumption, control energy wastage, peak load demand and integration of renewable energy sources [5]. Alham et al. the wind power generation unit with ramping and mismatch between generation and demand is major issue in power system. Arefifar et al. gave the emphasis on the systematic and optimized approach implementation for evaluating the reliability indices and improving the supply security of the microgrid [6]. Ma et al. focused on operation of micro-energy grid with optimal dispatch strategies from multiple generation sources. The aim of the study is minimizing the daily operation cost with day ahead dynamic optimal generator model with mixed integer linear programming optimization based on demand response [7]. Chekried et al. described on energy flow management system for domestic loads with sola PV system. The solar PV system with stored system is implemented in the study to fulfil the daily load demand for domestic uses in each home. The energy management system is the communication

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and control system between the generation sources integration and load demand with different mode of operation [8]. These factors considered for system planning are load forecasting, inadequate correlation or power balancing, faster ramp rate and power quality. The demand side management is implemented for dynamic economic dispatch for load balance along with dynamic operation and control of power system. The demand side management system uses for “peak clipping” and load shifting at utilization to attain a load fluctuating objective by using time of use for changing the tariff during ultimate period [9]. The study considers the scope and methodology for implementation control for isolated hybrid microgrid in Sect. 2, Sect. 3 is planning and load forecasting for rural electrification, Sect. 4 is generation model for isolated hybrid microgrid, Sect. 5 is the results based on real-time study, and finally the paper is concluded.

2 The Scope and Methodology for Control Implementation The isolated hybrid microgrid has gained more attention for improvement of the stability and reliability of electrical system in rural area with “renewable energy” sources. In islanded hybrid microgrid, all the sources are natural resources to produce electrical generation. The generation is depending on the climate and natural condition and availability of sources. The load demand in rural area does not steady all the time, during peak demand is higher than off peak hours, due to sudden load change the reliability and quality of power generation is exceptionally low. At some time, the generation from solar PV power plant is excess compare to maximum load demand. The excess generating power is flowing to biomass generation plant and creating the transient stability problem, which interrupted the inverter generation from solar PV plant and biomass power plant. To overcome this problem, electrical power management system is required. It is the real-time power management system which acts as a bridge between the utility and generation sources, and it is also the planning, implementation and monitoring of electrical utility at each house in the village. As a result, it changes the time of utility based on priority and time pattern for control the peak load demand. The main objective of real-time power management system is to control the electricity utilization based on the generation during peak demand and off-peak demand in each household in the villages in rural area [10–12]. In largescale power system, the energy mismatch between electrical load demand and power generation is controlled by slack bus. However, in isolated hybrid microgrid, such power reserve is often absent. Thus, it is more interesting for development of new mechanism for isolated hybrid microgrid to enable the generation control, distribution control and load control with optimization control game theory. In this study, the methodology for isolated hybrid microgrid is implemented based on Time, priority of load, generation and integration with central control unit along with balance distribution system. The central control unit RPMS is implemented on non-cooperative game theory approach based on mixed Nash equilibrium optimization techniques.

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In isolated hybrid microgrid, for village electrification, the first challenge is planning and load forecasting in each household with local radial electrical distribution system. In the rural electrification, with renewable energy, stability and reliability of electrical distribution system are major challenges due to uncontrol load utilization and power generation from solar PV plant and biomass power plant. The study is carried out for 125 households in the village.

3 Planning and Load Forecasting for Rural Electrification In electrical generation and distribution system, the planning is major concern for assuring that the increasing demand for electricity in the village can be fulfilled by isolated hybrid microgrid in additions to technical adequate with economic feasibility. Even though considerable work has been done for technical adequate for the sources of generation in microgrid with systematic approach to electrical power generation and local distribution, its application of real-time distribution load and generation control for isolated hybrid microgrid planning is considered in the study, which has been unfortunately somewhat neglected. For future electrification, it needs a fast and economic development tool to evaluate the consequence of different proposed alternatives for village electrification and their impact on the lively hood, economical, reliable and safe electric demand for domestic system [13].

3.1 Load Demand Module A set of Pd = {1, 2, . . . , n} houses that share the load on solar power plant and biomass power plant. For each house η, let L ηh represent the total load demand with respect to appliance utilization based on time t ∈ τ = {1, 2, . . . , T }. For the integration based on load demand, the time slots are taken based on hourly total time T = 24. The total daily load demand for user n is   L n  L 1n , . . . , L nT

(1)

The total load demand for all users during a time t is L dt 



L tn

(2)

L dPeak = max

(3)

n∈N

Daily peak load demand t∈τ L dt

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Average Load demand L Davg =

1  L dt T t∈τ

(4)

Peak to average ratio of load demand is T max L dt Pdr ms =  t∈τ t∈τ L dt

(5)

We define τ be the set of operating time slot of user n ∈ N , and the user is restricting the minimum and maximum power consumption in different operating time slots. t max 0 ≤ Pdn ≤ Pdn ,∀t ∈ τ

(6)

Let Pd M = predetermine total daily load demand of connected load from each home n ∈ N . The energy balance equation is 

t Pdn = Pd M

(7)

t∈τ

In isolated hybrid microgrid, loads and sources are in distribution nature. We pretended to be equivalent injected power from microgrid or load flow analysis. Here (k)spec (k)spec Pn and Q n represent to specified active power and reactive power injection (k) in the bus. The Q (k) n and Pn represent to equivalent active power and reactive power (k)cal and Q (k)cal are calculated values of injected active power injection in bus. The Pn n and reactive power in the bus [13]. The resultant active power Pn(k) = Pn(k)spec − Pn(k)cal

(8)

(k)spec − Q (k)cal Q (k) n = Qn n

(9)

The reactive power

Here Pn(k)cal =

n     (k)  (k)  V V |Yni | cos θni + δ (k) − δ (k) n o i i

(10)

i=1

qn(k)cal = −

n     (k)  (k)  V V |Yni | cos θni + δ (k) − δ (k) n o i i i=1

(11)

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The maximum demand in the rural electrification is changing with respect to load scheduling and time. In this study isolated hybrid microgrid, the maximum demand is occurring during priority four (P4 ) [14]. In summer during priority, (P4 ) maximum connected load/home L Dmix P4 = 2.145 kW/home In winter, during priority (P4 ) maximum connected load/home L Dmix P4 = 2.005 kW/home The diversified power demand of the composite group of loads or unrelated loads over a specified period in each home. Diversified Demand/Home L DDiv = 0.750 kW. The non-coincident demand is the sum of group of loads without any restrictions over the interval of time. Non-coincident demand/home L DNC = 0.2 kW The connected loads the sum or maximum load consuming apparatus in each home Maximum connected load/home L maxCL = 2.245 kW The demand factor is the ratio of maximum demand of a local distribution system in microgrid. L DF 

268.125 Maximum Demand = = 0.953 Total Conneected Demand 281.25

(12)

The utilization factor is the ratio of maximum demand of a connected load to the rated capacity. L UF 

268.125 Maximum Demand = = 0.893 Rated System Capacity 300

(13)

The load factor is the ratio of the average load over a designated period of time to the peak load. In village, load factor can be calculated based on priority scheduling or time scheduling. L LF 

L AVHome Average Load 1.495 = = 0.665 = Peak Load L PLHome 2.245

(14)

The diversity factor is the sum of the maximum demand of individual home in the local distribution system to maximum load demand on isolated microgrid. L DIVF 

n LH L HD1 + L HD2 + L HD3 + . . . + L HDn 48.68 = 0.961 = i=1 Di = L CON D max L CON D max 50.62 (15)

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The load diversity is the difference between the sum of peak of more than one individual loads as the peak of the combine load. L LD 

n 

L HDi

− L maxCL = 50.62 − 48.68 = 1.94 kW

(16)

i=1

The coincidence factor is the ratio of maximum coincident total demand on to the sum of maximum power demand from each home at microgrid. L CD max 50.62 L CONF  n = =1 L 48.68 i=1 H Di

(17)

The contribution factor is define Ci “the contribution factor in isolated hybrid microgrid is depends on the priority scheduling of load w.r.t. maximum demand of number of houses in village. L CF

n Ci × L h i n  i=1 i=1 L h i

(18)

4 Generation and Controlling of Isolated Hybrid Microgrid The generation module of isolated hybrid microgrid is based on the solar power plant and biomass power plant. The generation is controlled by generation control unit with respect to time and priority of Loads. The Generation solar power plant is based on AC power flow solution for distribution system. The generation of solar PV plant depends on time and light intensity. Let t = generation from solar power plant ∀ t ∈ τ PPV t PPV max = maximum generation from solar power plant ∀ t ∈ τ t PPV min = minimum generation from solar power plant ∀ t ∈ τ

The generation of solar power plant with respect to light intensity λ   PPV gen = Pλ1 . . . Pλt . . . Pλτ

(19)

where Pλt = maximum power generation. The generation biomass power plant is based on AC power flow solution for distribution system. The generation of biomass power plant depends on time, load demand and availability of biomass. Let

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The generation of biomass power plant with respect to total biomass availability Bmt to fulfil the demand for 24 h.   PBM gen = PB1mt . . . PBt mt . . . PBτmt

(20)

where PBt mt = maximum power generation. At each time slot t, t t PPV gen + PBM gen ≥ 0

(21)

The total demand in isolated microgrid is shared by three different sources  t∈τ

t Pdη =



PPV gen + PBM gen

(22)

t∈τ

4.1 Non-cooperative Game Theory The load balancing for isolated hybrid microgrid with real-time optimization is set by threshold s to achieve the power load balancing. To measure the load demand in different houses, we define the electric load ratio in home n at time t as [15] rnt (s) =

E nt (s) + Bnt Cm

(23)

where E nt is the energy consumption of home n and it depends on the energy threshold s for all homes Bnt = background power load and Cm = capacity of source m. Let qit = Cm − Bnt maximum supply available to the load i at time t. Usually in each household use the electricity in inflexible patterns and the demand on microgrid can be inelastic. Base on the load ratio rit . The electric load index (ELI) across all locations and time slots as  2 rit (s) Ci (24) ELI  t∈τ i∈N

where ELI is motivated by the index measurement techniques used for feeder load balancing is isolated hybrid microgrid distribution system. ELI not only measures the overall load ratio but also gives different weights based on the capacities in different

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locations. The RPMS aims at balancing the load ratio rit (s) at all locations and all time slots by minimizing the ELI. Optimal response time: Any source has maximum response time at most twice the optimal response time. Let PV and biomass has maximum load PL max . Let load PL be assigned to PV and biomass  r1 (L 1 ) ≤ rs L s + d j ∀ s ∈ S

(25)

By Nash equilibrium, the response function is linear We have ri (L i ) = L i L1 ≤ Ls + d j ∀ s ∈ S 

m L1 ≤ L1 ≤

Ls + d j

s∈S

1  Ls m s∈S



(26) (27)

+ dj

(28)

At optimal allocation: Load PL is assigned to solar power plant and biomass power plant. So, opt ≥ d j . 

Ms =



dL

(29)

ρ L ∈PL

s∈S

Maximum loaded on solar power plant and biomass power plant at least average is =

1  1  dL = Ms m ρ ∈P m s∈S L

(30)

L

Total time or average time is =

 s∈S

Ms rs (L s )

(31)

5 Simulation Results The study is carried out with and without real-time power management (RPMS) control for isolated hybrid microgrid with solar power plant and biomass power plant DG Set. Figure 1 shows the load demand in 24 h without distribution control, and the demand on distribution system is unstable. Figure 2 shows the load demand control

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Fig. 1 Total load demand in microgrid without control

Fig. 2 Total load demand with RPMS control

with RPMS system with respect to time, which will improve the stability of isolated hybrid microgrid with load controlling on distribution system with distributed control in each home with respect to time and priority. Figure 3 shows the integration of biomass and solar power plant with respect to load demand without generation control and the excess reverse power flow to biomass DG set which changes the frequency and speed of DG set. The transient instability condition is occurring, which interrupts the solar power plant generation and damages the installation of biomass DG set. To overcome the transient stability problem generation control implemented with RPMS control unit for isolated hybrid microgrid, which integrate the solar power plant and biomass power plant w.r.t. load demand and control the reverse power flow by scaling the solar generation as shown in Fig. 4.

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Fig. 3 Reverse power flow without control

Fig. 4 Reverse power flow control with RPMS system

Figure 5 shows the frequency of distribution system with and without RPMS control unit, which is the major cause for poor power quality in local distribution system. Figure 6 shows the biomass DG set speed with and without RPMS generation control unit, which is controlling the voltage and frequency instability issue. The planning of distribution generation system for rural electrification has the important role for improvement of stability and reliability. The paper describes the different characteristics of load and distribution system planning in different sections. In the characteristics, the different factors for load planning, which are affecting the stability, reliability and quality of hybrid microgrid, have been considered. The

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Fig. 5 System frequency with and without RPMS control

Fig. 6 Speed of biomass alternator with and without RPMS control

characteristics of load planning describe the different parameters of load, maximum and minimum load demand on microgrid, planning of load based on the time, priority and session load demand. The RPMS control improves the stability of isolated hybrid microgrid by controlling the excess power generation as shown in Fig. 6. The realtime power management system is the effective solution for zero feeding in biomass generation unit for stabile operation for village electrification with renewable energy

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sources. The focus of study is planning the stabile, reliable and cost optimize system for village electrification system with renewable sources.

6 Conclusion The optimize scheduling of generation from renewable energy sources with respect to time and priority to fulfil the electrical load demand. The major challenge for isolated microgrid for village electrification is load scheduling in each home with respect to time and generation, which is directly related to stability and reliability of distribution generation system. In this study, real-time power management system (RPMS) is proposed for optimize generation scheduling by integration of different renewable energy sources with respect to time and load demand and distribution control for balancing the load distribution in radial distributed system.

References 1. D. Palit, A. Garimella, M. Shardul, S. Chaudbury, Analysis of the electrification programme in India using the ‘Energy plus’ framework and the key lessons. Final report 2015 GNESD, Teri.2015, pp. 1–83 2. A. Jain, S. Ray, K. Ganesh, M. Aklin, C. Cheng, J. Urpelainen, Access to clean cooking Energy and Electricity Survey of States. CEEW Report—2015, pp. 1–65 3. R. Bilolikar, R. Deshmukh, Rural Electrification in India—An Overview (National Power Training Institute, 2014), pp. 1–12 4. C. Wimmler, G. Hejazi, E. de Oliveira Fernandes, C. Moreira, S. Connors, Impact of load shifting on renewable energy integration. Elsevier Energ. Procedia 107, 248–252 (2017) 5. S. Goy, A. Ashouri, F. Marechal, D. Finn, Estimating the potential for thermal load management in buildings at a large scale: overcoming challenges towards a replicable methodology. Elsevier Energ. Procedia 111, 740–749 (2017) 6. S.A. Arefifar, Y.A.-R.I. Mohamed, T.H.M. EL-Fouly, Optimum microgrid design for enhancing reliability and supply security. IEEE Trans. Smart Grid 4(3), 1567–1575 (2013) 7. T. Ma, J. Wu, L. Hao, Energy flow modelling and optimal operation analysis of the micro energy grid based on energy hub. Elsevier Energ. Convers. Manag. 133, 292–306 (2017) 8. F. Chekired, Z. Smara, A. Mahrane, M. Chikh, S. Berkane, An Energy flow management algorithm for a photovoltaic solar home. Elsevier Energ. Procedia 111, 934–943 (2017) 9. M.H. Alham, M. Elshahed, D.K. Ibrahim, E.E.D.A. EI Zahab, Optimal operation of power system incorporating wind energy with demand side management. Elsevier J. Ain Shams Eng. J. 8, 1–7 (2017) 10. D. Salomonsson, A. Sannino, Low-voltage dc distribution system for commercial power systems with sensitive electronic loads. IEEE Trans. Pow. Del. 22(3), 1620–1627 (2007) 11. W. Lu, B.T. Ooi, Optimal acquisition and aggregation of offshore wind power by multiterminal voltage-source HVDC. IEEE Trans. Pow. Del. 18(1), 201–206 (2003) 12. A. Barbato, A. Capone, Optimization models and methods for demand side management of residential users: a survey. MDPI J. Energ. 7, 5787–5824 (2014) 13. K. Singh, M.N. Kumar, S. Mishra, Load flow study of isolated hybrid microgrid for village electrification. Int. J. Eng. Technol. 7(2), 232–234 (2018). ISSN 2227-524X. http://dx.doi.org/ 10.14419/ijet.v7i2.23.11925

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14. T. Gonen, Electrical Power Distribution Enegineering, 3rd edn. CRC Press, pp. 1–88. ISBN 978-1-1381-9802-9 15. C. Miao, Y. Zhang, Fully mixed nash equilibria for the load balancing game on uniform parallel machines. Int. Math. 33, 1607–1612 (2010)

Toyama de Walking: Practice of Location-Based Data Collection Using Augmented Reality Applications Yuya Ieiri, Hung-Ya Tsai, and Reiko Hishiyama

Abstract Location-based data is used in a wide variety of applications and can be collected using various methods. Besides these methods, it is expected that a method to collect location-based data by walking applications using augmented reality (AR) will be common in the future. AR has the nature that encourages users to collaborate with the real world, and it is supposed to help collect location-based data about the atmosphere and quality of the city. However, the features of the location-based data collected by walking applications using AR have not yet been revealed. Clarifying the features is important to grasp the position of the collection method using AR. Therefore, we developed an AR walking application “Toyama de Walking” for collecting location-based data and conducted a demonstration experiment using the application. As a result, the features of the location-based data collected by walking applications using AR were revealed. Keywords Location-based data · Augmented reality · User behavior analysis

1 Introduction Location-based data can be used in various situations. For example, there has been a study on the analysis of users’ behavior in outdoor activities using location information [1]. In addition, there has been an attempt to represent commercial and social activities in the city by using location-based data [2], and an analysis of a crowd of Y. Ieiri (B) · H.-Y. Tsai · R. Hishiyama Faculty of Science and Engineering, Waseda University, Tokyo, Japan e-mail: [email protected] H.-Y. Tsai e-mail: [email protected]; [email protected] R. Hishiyama e-mail: [email protected] H.-Y. Tsai Osense Technology Co., Ltd., Taipei City, Taiwan © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_77

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people in the city [3]. One of the reasons for the widespread use of location-based data is the use of devices, such as smart phones that are equipped with global positioning system (GPS) function. Such devices enable the collection of various types of location-based data such as that transmitted using social media websites such as Twitter,1 Facebook,2 and Foursquare3 via smart devices, and a huge volume of such data can be collected at present. Various researches have been conducted on the location-based data collected by the above methods. In particular, Twitter data with location information has been shown to be useful in investigating human movement patterns [4] and there are numerous ways to use it [3, 5]. In addition, as a part of the location-based social networks (LBSN), various studies have been conducted on location-based data collected using Foursquare [2, 6]. Besides these methods, it is expected that a method to collect location-based data by walking applications using augmented reality (AR) will be common in the future. AR has matured rapidly in recent years, showing great potential for practical applications [7]. In addition, AR has been used for various purposes [8], and walking applications are typical example of using AR. Walking applications using AR encourage users to collaborate with the real world, and the game aspects of the applications represented by “Pokemon Go4 ” encourage users to continue using it. In particular, the nature that encourages users to collaborate with the real world is supposed to help collect location-based data about the atmosphere and quality of the city that each person feels. Such location-based data is useful for clarifying the image of the city. Clarifying the image of the city is important in city management [9]. Tursi et al. [1] attempted to collect users’ location data by walking application using AR. However, the features of the location-based data collected by walking applications using AR have not yet been revealed. Clarifying the features is important to grasp the position of the collection method using AR in the future. The positions of the collection methods are necessary to judge what kind of method is useful in a situation. Therefore, the purpose of our study is to clarify the features of the locationbased data collected by walking applications using AR. In order to achieve this goal, we developed a walking application, called “Toyama de Walking”, using AR. In addition, we conducted an experiment near Waseda University, Japan, and analyzed the results of the experiment to reveal the features of the location-based data collected by walking applications using AR. The remainder of this paper is structured as follows: Sect. 2 contains the details of our “Toyama de Walking” application. Details of the conducted experiment and the results of the experiment are given in Sect. 3. Conclusions from this work and ideas for future work have been discussed in Sect. 4. 1 https://twitter.com. 2 https://www.facebook.com. 3 https://foursquare.com. 4 https://www.pokemongo.com/en-us/.

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2 Toyama de Walking “Toyama de Walking” is a native application using AR and the users of the application walk around using mobile devices such as smart phones. In this application, the users can send the location-based data, and the location-based data contains location information and text data, similar to Twitter. In addition, in order to investigate where the location-based data is transmitted, data on the characteristics of the transmission point can also be collected. Firstly, in “Toyama de Walking,” the user registration screen is displayed. On this screen, the user enters their user name and the password to register on the application. Following this, a destination selection screen, similar to the one shown on the left side of Fig. 1, is displayed. When the user selects a destination on this screen, the screen transitions to the walking screen similar to the one shown in the center in Fig. 1. In the walking screen, the destination is displayed as an AR-Point of Interest in the augmented reality space and the direction of the destination is displayed by a compass at the bottom of the screen. Ambiguous navigation using a compass is suitable for walks while feeling the surrounding atmosphere [10], and is compatible with walking applications using AR, which encourages the user to collaborate with the real world. The user can send out location-based data by pressing the tweet button at the bottom left of the walking screen if he or she gets any thoughts or feelings. When the user presses the tweet button, it transitions to the tweet screen, similar to the one shown on the right side of Fig. 1. On the tweet screen, the user’s thoughts and feelings are transmitted as text data along with the user’s location information. At the same time, the characteristics of the area are evaluated by a rating between one to four stars in three aspects: “How good is the atmosphere?”, “How lively is the

Fig. 1 User interface of the “Toyama de Walking” application

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Fig. 2 System architecture of the “Toyama de Walking” application

area?”, and “How surprised are the users are?” The number of stars indicates the degree, with four stars indicating the highest degree. When the user approaches a destination, AR-Point of Interest changes into a present. By tapping on this present, the user can earn points. In addition, the user can also earn points by sending out location-based data on the tweet screen. The system architecture of “Toyama de Walking” is shown in Fig. 2. In this application, mobile clients retrieve content data, such as destination data and other tweet data, from the backend system and display it as an AR object. When the user uses the application, the mobile client uploads the user’s username, location information, and data about the tweet to the database. In addition, the backend system stores the updated content data in the database and can check all the data in the database by querying.

3 Experiment and Results A demonstration experiment using the “Toyama de Walking” application was conducted in the neighborhood of Waseda University in order to clarify the features of the data that can be collected by the method to collect location-based data using walking applications based on AR. The target area of this experiment is shown in Fig. 3. The black circles labeled 1, 2, and 3 in the figure indicate the location of the destinations in this experiment. The experiment was conducted as an event. The event experiment was held for the Waseda University students and they could participate in a lottery by walking around the neighborhood and accumulate certain points using our application. The star in

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Fig. 3 Target area of the experiment

Fig. 3 indicates the starting point, at which the instructions of the application and the lottery were held. Therefore, it can be said that the experiment participants used the application freely to explore the neighboring areas and transmitted location-based data as one likes. In the experiment, 14 participants transmitted location-based data, and the total of 51 location-based data were collected. Then, we divide these 51 data into three groups based on the characteristics of the text part of the collected data. 8 out of 51 data (Group 1) are data about the application features such as “I’m going to reach 60 points,” and 26 out of 51 data (Group 2) are data about what users felt when they were walking such as “I’m starving! I want to go for lunch.” In addition, 17 out of 51 data (Group 3) are data about the atmosphere and quality of the city such as “There was a beautiful cherry blossom tree,” and “The curry is 470 yen.” Checking the data of Group 3, it was found that Street X in Fig. 3 is a street with cherry blossoms and has a good atmosphere, and Area Y is an area where many stores are crowded. As described above, the collected location-based data showed the characteristics of the target city. Figure 4 shows the 51 data points on the map corresponding to group shown above. Each pin in Fig. 4 indicates the location where the data for each group was transmitted. We discuss the surroundings of the three destinations 1, 2, and 3 in Fig. 4. It can be seen that a large amount of data was transmitted from around destinations 1 and 2, while that transmitted from around destination 3 was small. There were landmarks around destination 1 such as the starting point where the instruction and lottery for this event was held. There were many restaurants and supermarkets in the vicinity of destination 2. On the other hand, the area around destination 3 was a quiet area, the number of the stores in this area was small, and there were few people on this street. Therefore, it was speculated that a considerable amount of location-based data would be transmitted when there are objects of interest for the users around the destination.

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Fig. 4 Location-based data obtained from the experiment shown on the map corresponding to each group Table 1 Experimental results focusing on the four-level evaluation Score 1 2 (a) How good is the atmosphere? (b) How lively is the area? (c) How surprised are the users? Total

0 1 2 3

2 9 6 17

3

4

22 25 26 73

27 16 17 60

In the experiment, location-based data not only in the form of text data, but also in the form of the four-level evaluation in terms of three aspects, namely, “How good is the atmosphere?”, “How lively is the area?”, and “How surprised are the users?”, was collected using the “Toyama de Walking” application. The results of the four-level evaluation of the 51 data points are given in Table 1. Out of 51 data inputs received, the data corresponding to the scores of 1 or 2 are classified as red, while those corresponding to scores of 3 or 4 are classified as blue. The results of the classification corresponding to the three aspects are shown by the red and blue pins on the map in Fig. 5. From Table 1 and Fig. 5, it can be seen that the location-based data was frequently transmitted at a pleasant, lively and surprising area. Furthermore, we analyzed whether the characteristics of the collected data (Group 1, Group 2, Group 3) differ depending on the difference in the evaluation from these three aspects. The differences in the evaluation for each of the three aspects and the characteristics of the collected data are summarized in Table 2. The first column of these tables shows the evaluation value from each aspects, and the first row of these tables shows the characteristics of the data. Each value in the table indicates the number of data. Pearson’s Chi-square test was performed on each contingency table in order to confirm whether the characteristics of the collected data differ depending on the difference in the evaluation level from the three viewpoints. The results are shown in

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Fig. 5 Location-based data shown on the map focusing on the four-level evaluation. The pins in red indicate the location-based data corresponding to scores of 1 or 2 while the pins in blue correspond to scores of 3 or 4 Table 2 Contingency tables showing the relationship between the evaluation results of three aspects and the characteristics of the data Evaluation Group 1 Group 2 (feeling) Group 3 Total (application (atmosphere and features) quality) (a) How good is the atmosphere? 1 and 2 0 3 and 4 8 Total 8 (b) How lively is the area? 1 and 2 0 3 and 4 8 Total 8 (c) How surprised are the users? 1 and 2 0 3 and 4 8 Total 8

2 24 26

0 17 17

2 49 51

9 17 26

1 16 17

10 41 51

6 20 26

2 15 17

8 43 51

Table 3 The results of Pearson’s Chi-square test Aspect Chi-square value Degree of freedom (a) How good is the atmosphere? (b) How lively is the area? (c) How surprised are the users? ∗ p < 0.05

p-value

2.002

2

0.368

7.698

2

0.021*

2.760

2

0.252

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Table 3. From Table 3, it was confirmed that the difference in the evaluation level of “How lively is the area?” makes a difference in the characteristics of the collected data ( p < 0.05). The result shows that the lively of the city influences the characteristics of the collected data. Specifically, it was speculated that a lot of data about feelings would be sent out in calm places, and a lot of data about the atmosphere and quality of the city would be transmitted in lively places.

4 Conclusion We collected location-based data using “Toyama de Walking.” Analysis of the collected data revealed the following features of the location-based data collected by walking applications using AR: (i) Location-based data about the atmosphere and quality of the city can be collected during the walk. (ii) A lot of location-based data can be collected around attractive destinations. (iii) Location-based data was frequently transmitted at a pleasant, lively and surprising area, and the lively of the area influences the characteristics of the collected data. A future work is to try a city management by conducting a large-scale experiment using “Toyama de Walking” and collecting a large amount of location-based data. Furthermore, it is necessary to clarify the features by comparing the collection method using AR with other collection methods using Twitter, Facebook and Foursquare. Acknowledgements This research was supported by the Waseda University Grant for Special Research Projects and a Grant-in-Aid for JSPS Research Fellow (19J22365).

References 1. Tursi G, Deplano M, Ruffo G (2014) AIRCacher: virtual geocaching powered with augmented reality. In: Proceedings of 25th ACM conference on hypertext and social media, pp 330–332 2. T. Agryzkov, P. Martí, L. Tortosa, J.F. Vicent, Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain). Int J Geogr Inf Sci 31(1), 100–121 (2017) 3. Khalifa MB, Díaz Redondo RP, Vilas AF, Rodríguez SS (2017) Identifying urban crowds using geo-located social media data: a Twitter experiment in New York City. J Intell Inf Syst 48(2):287–308 4. Al-Jeri M (2019) Towards human mobility detection scheme for location-based social network. In: 2019 IEEE symposium on computers and communications (ISCC), pp 1–7 5. M.M. Mostafa, Mining and mapping halal food consumers: a geo-located Twitter opinion polarity analysis. J Food Prod Market 24(7), 858–879 (2018) 6. M. Li, R. Westerholt, A. Zipf, Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places. Geo-spatial Inf Sci 21(3), 159–172 (2018) 7. Diaz C, Walker M, Szafir DA, Szafir D (2017) Designing for depth perceptions in augmented reality. In: 2017 IEEE international symposium on mixed and augmented reality (ISMAR), pp 111–122

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8. Azuma RT (1997) A survey of augmented reality. Presence: Teleoper Virt Environ 6(4):355– 385 9. Lynch K (1960) The image of the city. MIT Press, p 11 10. Ieiri Y, Mizukami T, Nakajima Y, Ayaki R, Hishiyama R. (2017) Effect of first impressions in tourism by using walk rally application. In: Proceedings of the 2017 international conference on culture and computing (culture and computing), pp 1–6

Graphene-Based Wireless Power Transmission: Charge as You Drive P. C. Nissimagoudar, H. M. Gireesha, R. M. Shet, Nalini C. Iyer, Ajit Bijapur, and H. R. Aishwarya

Abstract Many automotive industries aim to limit their impact on the environment and transform the mobility of automobiles into a sustainable mode of transportation. Electric vehicles seem to be key milestones in this regard. This paper intends to introduce a “Charge as you drive”—a newly evolved concept that can be used to overcome limitations of existing electric vehicles and aid in better battery management. Wireless power involving a high-efficiency charging system that uses magnetic resonance coupling to generate electricity could revolutionize the highway transportation system. With this strategy, when using a fairly small battery pack, a vehicle effectively has a limitless electrical range, which can significantly reduce the consumption of oil. These coils are powered by graphene-based solar panels. As solar power is renewable, infinite, eco-friendly and also considered to be a form of energy that is much safer to use. Thus, by introducing the above-proposed solution in electric vehicles, the obstacles of charging range, time, and cost can be easily mitigated. Keywords Magnetic resonance coupling · Graphene-based solar panel · Wireless power transmission

1 Introduction According to the environmental protection agency, 75% of carbon monoxide comes from automobiles. These automotive vehicles emit a wide variety of gases and solid matter, causing changes in air and global climate. Burning large quantities of fossil fuels, such as gasoline and diesel used in motor cars, have caused global temperatures to increase by 0.6 °C or 1 °F. The pollutant emissions from these vehicles contribute to unhealthy, immediate, and long-term effects on the environment as well as human health. This has its effect on wildlife, sea levels, and natural landscapes as well. P. C. Nissimagoudar (B) · H. M. Gireesha · R. M. Shet · N. C. Iyer · A. Bijapur · H. R. Aishwarya School of Electronics and Communication Engineering, KLE Technological University, Hubli, Karnataka 580031, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_78

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With the world becoming more eco-conscious, automobile industries aim at limiting their impact on the fossil fuel-driven internal combustion engines, by introducing the vehicles powered by electric motor. Electric vehicles recognized as a future of mobility is an attempt to help reduce gas emissions and improve air quality. They offer a key path achieving in greater than 80% reduction in global warming, oil dependency, and smog formation. More the electric vehicles on the road, the lower the production of harmful gases and hence lower the pollution rate. Unlike the conventional fuel-driven automobiles, electric vehicles can be powered using batteries which acts like a “Gas tank.” These vehicles are fitted with onboard batteries which stores and uses the energy needed to power a set of electric motors, which ultimately gives the car the acceleration. The batteries used powers all the electronic devices in the vehicle. It can be either recycled or used elsewhere after a battery has worn out, as it will still have nearly 70–80% of its original energy capacity. The controller controls the amount of power the batteries get, so that the engine does not burnout [1, 2]. Because electric vehicles use an electric motor, the driver can take advantage of the momentum of the motor when applying pressure on the brakes. The forward momentum of the motor can be used to recharge the battery as well. When the vehicle is stationary, there is no electrical current being processed, so no energy is being used up. One of the electric vehicle’s main feature is that they can be plugged into off-board charging power sources. These vehicles are powered from the electricity by regenerative braking, as well. However, charging of electric vehicle battery through charger and wire is inconvenient, hazardous, and expensive. Although, these plug-in electric vehicles are burdened by the need for cable and plug charger, with this system user has to physically plug-in for charging purposes. In addition to these, batteries seem to be the limiting factors as recharging these batteries takes time. On a single charge, these vehicles can travel about only 100 miles on an average [3, 4]. In this paper, we propose an innovative idea of wireless power transfer using the principle of magnetic resonance coupling to overcome the limitations of existing electric vehicles. Dynamic charging will allow the transportation sector to supply a very large fraction of the energy and greatly reduce petroleum consumption. Transmitting coils embedded in the highway are powered by graphene-based solar panels. Solar cells require materials that are conductive and allow light to get through; graphene is used here due to its superb conductivity and optical transparency. A layer of graphene has an optical transparency of 97.7%. The proposed model would be onetime investment, revolutionizing the highway transportation system. This is a wonderful idea which can be implemented, or the government can undertake this as a huge project and completely change the Indian approach toward electrification for transportation The organization of the rest of the paper is as follows. Section 1.1 describes the basic principle of magnetic resonance coupling, Sect. 2 explains the proposed methodology, and experimental results for the proposed model are briefed in Sect. 3, and conclusions in Sect. 4.

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1.1 Magnetic Resonance Coupling The inductively coupled power system consists of two coils called L 1 and L 2 , respectively, the transmitter coil and the receiver coil, all of which come from a magnetically coupled inductor system. A magnetic field is created in the receiver coil by an alternating current in the transmitter coil that induces a voltage. For charging purposes, this voltage can be used [5]. The efficiency of the power transfer depends on the relation between the inductors and their quality. The link between the inducers (z) and D2 /D ratios is determined. Also defining the relation between the structure and the angle of the coils.

2 Methodology Electrification for transport was a major concern for energy, resources, the environment, and many other factors. Wireless power transfer technology plays an important role in this connection by eliminating all the problem with charging. A key difference is that a transformer/receiver is replaced by loosely coupled coil systems between a wireless power transmission and conventional gas-fueled vehicles. The series of coils connected to an electrical current would be embedded onto the highway, and the receiving coils would be attached to the vehicle bottom. On the road would be built up the series of coils connected to an electric current, and the receiving coils would be attached to the base of the vehicle. This echoes with the pace of the vehicle. Power can be transferred to the battery without significant energy losses, if the operating frequency of the battery is between 50 and 95% of the frequency. Figure 1 shows magnetic fields that continuously transmit the electricity to charge the battery. The solar panels are powered by graphene-based transmission coils. In order for the transmission coil to be powered by a primary compensation network, the DC power produced from solar panel is converted to high-frequency graphene, the thinnest material known to people with good conducting, optical clarity, and very Fig. 1 Magnetically coupled inductor

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high strength, and is used to improve the efficiency of sun cells. These solar graphic cells also can be used to extract electricity from raindrops. Unfair weather like rain and snow therefore has no effect on the load capacity [6] (Fig. 2). Figure 3 shows the vertical magnetic flux of the transmitter and receiver coil. From this magnetic flux path, where loops are generated and the receiving coil receives the vertical magnetic flux that is fitted at the bottom of the electric vehicle, a highfrequency power source is distributed through transmitting coil. The transmit and receive coil are 60 cm wide. The transmitting coil and the surface of the road have an air gap of 20 cm between them. To transfer 100 kw of power, five 20 kw pickup modular is to be installed in the electric vehicles.

Fig. 2 Wireless power transmission

Fig. 3 Design of power transmitter

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2.1 Design Process The magnetic induction coupling compresses of two stages which are as discussed below.

2.1.1

Wireless Power Transmission Between the Coils

A resonant wireless transmission network comprises magnetically coupled transmitting, receiving coils, and various electronic control circuits. The resistance of the coil essentially increases as the frequency changes. It is therefore important to design a low loss belt that helps improve the quality factor. The constant current source shown in figure shows the wireless power transfer system. (a) The simple passive elements shown in Fig. 4. (b) The transmitting and receiving coil inductances, where the LS and the LR are, may be designed. When a transmitter coil is driven by the source (M is a mutual inductance and k a magnetic coupling coefficient), the transmitting and receiving coils shall be magnetically coupled. For maximum power transference, the condensers CS and CR are connected in series with each coil in the model. The

Fig. 4 a WPT system configuration. b The analogous circuit model, load transfer power and efficiency of the actual transmitted power; the coil resonance frequencies are set at 20 kHz with the transmission power being 50 W at the resonant frequency

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internal resistances of each coil are RS and RR, and the load resistant is RL. The model can be used to calculate the loop impedance of the ZS transmitter and the ZR receiver loop impedance Z s = Rs +

1 + jωL s jωCs

Z R = R R + RL +

1 + jωL R jωC R

(1) (2)

where the source current is IS and IR, respectively, the load current. The PL transmitted power can be calculated with the real-power transfer efficiency η   ω2 M 2 . R L . Is . Is∗ PL = Re VL . I R∗ = Z R . Z ∗R   Re VL . I R∗   n= Re Vs . Is∗ n=

ω2 M 2 . R L  Re Z s Z R Z ∗R + ω2 M 2 . Z ∗R 

(3)

(4)

(5)

Figure 4b demonstrates both the efficiency of power transfer and the transmission of power from the illustration case. The power transfer efficiency is dependent on the parameters of the passive circuit and operating frequency, and the performance of the power transmission is typically maximized at the resonant frequency.

2.1.2

Generation of Power from Graphene-Based Solar Panels

The solar panel, based on graphene, converts sunlight into DC electricity, acting as a source for powering the connecting coils. Solar panels may be wired in series or in parallel, respectively, to increase voltage or current. A 12 V solar panel’s rated terminal voltage is typically around 17.0 V, but this voltage can be reduced to around 13–15 V as required for charging by the use of a solar regulator. Solar regulators or charge controllers have the purpose of regulating the current from the solar panels and ensuring the correct supply of the power to the inverter without damaging it. In order to drive the transmitter through an inverter network, the dc voltage is converted to a high-frequency alternative current. Reflected sunlight and different conditions of temperature raise the output current by 25% above a solar panel’s average output. The solar regulator must be sized in such a way that 125% of the rated short-circuit current of the solar panel can be handled. The solar panel based on graphene can also be used to obtain power from the impact of the raindrops. The gout is not pure water. They contain salts which dissociate themselves into ions. The positively charged ions will bind to the surface of graphene and get enriched in delocalized electrons. The

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Fig. 5 Transmitter circuit

consequence of this process is a double layer of electrons and positively charged ions. This difference in associated potential is sufficient to generate a voltage and the current needed to power coupling coils. So solar panels will operate in all climatic conditions with the aid of graphene [7]. Transmitter circuit The module of the transmitter is a direct power supply connected to an oscillator a coil. The transmitting coil generates an oscillating magnetic field from the AC current during this operation, which transforms the electrical supply into a high-frequency alternating current (Fig. 5). Receiver circuits This receiving module consists of a voltage regulator to provide a constant voltage and connecting voltage to the battery mounted in an electric vehicle [7]. It receives all its power in the magnetic field produced by the transmission coil of the receiver version of a wireless power transfer device (Fig. 6).

3 Experimental Results From Table 1 analysis, we get to know that the transformer output will give 11 V but expected is 12 V same way rectifier will give 11.5 V, and the oscillator will give 10 V.

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Fig. 6 Receiver circuit

Table 1 Output voltage measurement of the transmitter module

Parameter

Voltage type

Theoretical value (V)

Actual value (V)

Transformer output

AC

12

11

Rectifier output

DC

11.9

11

Oscillator output

AC

11.9

11.5

3.1 Critical Pointing Coupling between two coils increases with decreasing distances between two resonators this trench is roughly proportional to increases in device efficiency with shorter distances (Fig. 7).

Fig. 7 Matlab distance simulation versus frequency analysis and spiral to spiral

Graphene-Based Wireless Power Transmission: Charge as You Drive Table 2 Transfer efficiency test result

Distance (cm)

Transfer efficiency (%)

0

96

6

80

11

75

16

68

21

55

26

48

823

Power transfer system the full wireless power transfer system consists of two components: the transmitter (Tx) and the receiver (Rx). To optimize the transmission capacity, choose identical resonators for both transmitter and receiver. The wireless power transfer device is here developed as a linear series. From Table 2, we can get to know that as distance increases and frequency decreases and transfer efficiency decreases.

4 Conclusion Transportation based on fuel has caused enormous pollution problems. In the present case, the use of electric vehicles offers an eco-friendly alternative but suffers from a major downside when it comes to charging. So our proposed model—“Ride as U-Drive” is used to provide an beneficial alternative to wireless vehicle charging. The coupling coils are powered using graphene-based solar cells where graphene increases the solar cell’s performance. The analysis of radiations produced by the coupling coils does not affect the human health nor vehicles performance. For a wireless charging electric vehicle, a safe zone should always be established. When an automobile is usually made of steel, a very strong defensive material, the magnetic flow density must be assured to comply with the safety requirements at usual positions such as standing outside the car or inside the vehicle.

References 1. W. Khan-ngern, H. Zenkner, Wireless charging system for electric vehicle. Date of Conference 3–5 Oct 2016 2. M. Shidujaman, H. Samani, M. Arif, Wireless power transmission trends. Date of Conference (2014) 3. R. Pudur, V. Hanumante, S. Shukla, K. Kumar, Wireless power transmission: a survey. Date of Conference 9–11 May 2014 4. Z. Zhang, H. Pang, A. Georgiadis, C. Cecati, Wireless power transmission—overview. Date of Conference 10 May 2018 5. A. Kurs, A. Karalis, R. Moffatt, J.D. Joannopoulos, P. Fisher, M. Soljacic, Wireless power transfer via strongly coupled magnetic resonances. Science 317, 83–86 (2007)

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6. S.K. Chaganty, S. Siva Prasad, Contactless vehicle charging using magnetic resonance coupling method. Date of Conference, 20–23 Aug 2011 7. A. Karalis, J.D. Joannopoulos, M. Soljac, Efficient wireless nonradiative mid-range energy transfer. Ann. Phys. 323, 34–38 (2008)

Vision-Based Driver Authentication and Alertness Detection Using HOG Feature Descriptor P. C. Nissimagoudar, A. V. Nandi, H. M. Gireesha, R. M. Shet, and Nalini C. Iyer

Abstract Driver’s drowsiness is one of the main causes of the car collision, which raise fatality and wound count worldwide. This makes it necessary to develop a system that ensures the safety of the driver as well as co-passengers. Further, driver authentication plays an important role in preventing car robberies and fraudulent switching of designated drivers, thus ensuring the security of the vehicle and passengers. In this work, we propose an algorithm for real-time and continuous driver authentication and drowsiness detection. Driver authentication is implemented using face recognition with dlib’s face detector which is found to be robust compared to openCV face detector. The SMS regarding authentication of the driver is sent to the vehicle owner with details so that he can keep track of drivers. Further, if the driver is authenticated, then he is monitored continuously using the webcam to detect the early signs of drowsiness. Behavioral measures of the driver-like eyelid movements and yawning are considered for the detection. First, for face detection, we will apply facial landmark detection made using Histogram of Oriented Gradients (HOG) feature and then extract the eye and mouth regions using shape predictor with 68 salient points next to the eye aspect ratio and mouth aspect ratio, i.e., EAR and MAR, respectively, are computed in order to determine if these ratios indicate that the driver is drowsy, if so then he is alerted using speech message. Keywords Driver authentication · Driver drowsiness · Histogram of Oriented Gradients (HOG) · Behavioral measures

1 Introduction The driver’s status monitoring system includes the driver’s drowsiness and authentication of a driver to access the vehicle. In this work, we will discuss how to provide better security to a vehicle and to detect fatigue and drowsiness of the driver to avoid P. C. Nissimagoudar (B) · A. V. Nandi · H. M. Gireesha · R. M. Shet · N. C. Iyer KLE Technological University, Hubli, Karnataka 580031, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_79

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accidents. One of the major reasons for the accident is the driver’s not paying attention to the road. As of September 9, 2018, Global Road Safety statics showed that 1.47 lakh deaths caused due to road accidents in India, and annually, road accidents claim more than 1 lakh lives every year. The majority of road accident is due to the driver’s fault, this includes the driver’s distraction, over speeding, drunken driving. Road deaths due to the accident wipe out a city per year that is over 1.3 lakh per year. 60% of deaths are of young people in age of 18–35. Sadly, India loses 3% of its GDP due to road accidents. The vigilance of driver degrades due to lack of sleep, long continuous driving, or any other illness like brain disorders, etc. According to Central Road Research Institute (CRRI) study, 40% of critical accidents are caused due to fatigue. Driver drowsiness is a major factor in increasing road accidents. There is a requirement for a system to monitor the driver’s actions and reactions, check for fatigue, and on detection of fatigue or drowsiness. Addressing this issue requires monitor of driver’s alertness using non-invasive techniques using emerging technologies like computer vision [1], machine learning [2], and deep learning [3–5]. As this problem is claiming lives and losses to individuals and sometimes public property, it increased our interest to design a cost-effective warning system with authentication which enhances the safety of drivers, thus saves lives. To build an application that monitors the driver non-intrusively. The main objectives of the contribution are • To provide the security by authentication of driver and safety by the drowsiness detection system. • Design a system for authentication using face recognition techniques and drowsiness detection using the behavioral measures of the driver. • To make the system work, in despite of driver in glasses and in different illuminations.

1.1 Related Work Many works have been done to implement different algorithms and techniques for driver authentication and drowsiness detection, some of them are briefed below. Hajinoroozi et al. [6], this work is the investigation of deep learning algorithms for the identification of driver’s intellectual states (drowsy and alert) using EEG data. The proposed CCNN consists of many convolution layers as well fully connected layer but it uses a 1D filter for convolution along the channels. Back-propagation is the method used for training CCNN weights. Derman et al. [7], in this paper, they have proposed an approach based on deep neural networks for real-time and continuous authentication of vehicle drivers. For testing in realistic conditions, from 52 different subjects, they have collected 130 in car driving videos. For this scenario, they have first collected facial images of authorized drivers, trained a classifier based on those features, and have performed the real-time verification. For feature extraction, they have applied transfer learning

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and fine-tuning on a pre-trained deep neural network model, and for the classification purpose, the SVM and cosine similarity-based approaches were examined. Yashwanth et al. [8], this work proposes a framework for the detection of drowsiness and the rate of drowsiness of the driver. For the prediction of a driver as drowsy and alert, they have concluded the data set of nine subjects that Neural Network Classifiers (NNC) and decision tree have given better results compared to linear SVM and LDA. So, attributes such as eye blinks, eye closure are used for making decisions regarding drowsiness. The organization of the paper is as follows, Sect. 2 discusses the system design which provides detailed design. In the next Sect. 3 comprises of implementation details which describe the structural information of the design. In Sect. 4, the results and discussion section, we discuss the output and results of the proposed algorithm for driver authentication and drowsiness detection algorithm. In Sect. 5, the conclusion and future scope provides the information about the discussion on the outcome of our work, and further, the improvements and the advancements concerning our work are discussed in the future scope section.

2 System Design The proposed solution uses a behavioral measure based on the computer vision approach. This measure is more flexible to drivers and convenient for the implementation. In this approach, drowsy behavior is identified by a driver’s behavior like eye closure, yawning, and eye blinking. It may not work properly in low-lighting conditions; to overcome this limitation, we can make use of infrared camera or light. Figure 1 represents the block diagram of our proposed system. It provides a highlevel view of the system representing components involved and their relationship. Input component, the camera which is focused on driver captures video, then transmits it to a processor for processing to interpret the driver’s information sends the owner with driver access via SMS, and alerts the driver through voice output in case of asleep. The Dlib library [9], which has a pre-trained facial detector. Since facial

Fig. 1 Block diagram of vision-based driver authentication and alertness detection system

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detection is a kind of object detection, it detects 68 landmarks on the face to identify an object as a face. As soon driver ignites car, the camera takes the input video stream a Histogram of Oriented Gradients (HOG)-based detector in Dlib finds the location of face and encodes it into 128 numerical facial features. Then, it compares with the encoded database, the result of the comparison is sent to the owner via SMS alert regarding car usage and driver details, avoiding the fraudulent usage of the car.

3 Implementation Details In this section, we discuss the implementation details of the proposed solution. Thereby, meeting the aforesaid objectives specified. Implementation details provide the information of actual flow and things utilized in the work.

3.1 Algorithm An algorithm is set of rules or the process that must be followed in the manipulations or problem-solving operations. The problem can be solved step by step by making use of an algorithm. Technically, machines use algorithms to carry out an operation by listing the detailed instructions. The algorithm for our work is shown below: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

14.

Initialization of camera for an input video stream. Reading the frame from the video stream. Resizing and conversion of RGB image to gray-scale image Detection of the frontal face from the captured frame using HOG feature descriptor which is trained using SVM. If the face is not detected or there are too many faces in the captured frame and if the attempt counter is less than six go to step 2. Encoding the content of the captured frame. Comparison of the encoded image with an array of encodings created in step 4. Send the details along with authentication status to the vehicle owner, if the person is authorized else send the notification to vehicle owner. Initialization of EAR and MAR thresholds Read next frame for detection of drowsiness Detect the frontal face from the frame using the HOG feature descriptor. If the face is not detected, then alert the driver and go back to step 10. Extraction of facial features using shape predictors such as Eye Aspect Ratio (EAR). Mouth Aspect Ratio (MAR). If the EAR is less than the threshold or MAR is greater than the threshold, consecutively for the defined number of frames, then the person is considered as drowsy. So alert the driver. Display “Drowsiness Alert!!!” on the frame.

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15. Go back to step 10. 16. The drowsiness detection process stops when the user turns off the vehicle.

3.2 Mathematical Model Shape Predictor After detecting a face in the image, the basic idea is to find 68 facial landmarks including inner and outer edge of eyebrows, points of nose, lips, and eyes. The further center position of the face is obtained through manipulations like rotation, increase as shown in Fig. 2b. 128D Vector Conversion (Encoding) In our algorithm of face recognition, we are encoding the contents of image into 128-dimensional vector that quantifies face in an image. Based on this encoding, we can measure similarities between two face images for further classification. According to research, it is found that encoding into a 128D vector gives a higher accuracy compared to other dimensions. Hence, from this analysis, we choose a 128-dimensional byte vector for compactly representing each face, and it is optimal for applications like recognition and clustering [8]. Eye Aspect Ratio (EAR) EAR is computed using Eq. (1). The numerator of this equation calculates the distance amid the vertical eye landmarks while the denominator calculates the distance amid horizontal eye landmarks, weighting the denominator appropriately since there is only one group of horizontal points but two groups of vertical points. EAR =

P2 − P6  + P3 − P5  2P1 − P4 

(1)

where P1 , P2 , P3 , P4 , P5 , P6 are 2D facial landmark locations of eye, which is shown in Fig. 2a. Eye blink count eye blink rate is calculated by taking average of eye aspect ratios of both eye as shown below:

Fig. 2 a 2D facial landmark locations of eye. b 68 points of reference to identify object as face. c 2D facial landmark locations of the mouth

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EYEblink detect =

EARleft eye + EARright eye 2

(2)

Mouth Aspect Ratio (MAR) MAR is computed as shown below. The numerator of this equation calculates the distance amid the vertical mouth landmarks while the denominator calculates the distance amid horizontal mouth landmarks, weighting the denominator appropriately since there is only one group of horizontal points but two groups of vertical points. MAR =

P2 − P8  + P3 − P7  + P4 − P6  2P1 − P5 

(3)

where P1 , P2 , P3 , P4 , …, P8 are 2D facial landmark locations of the mouth, which is shown in Fig. 2c. Euclidean Distance The distance between the vertical landmarks and the horizontal landmarks of facial features considered for the calculation of EAR and MAR ratios is the Euclidean distance which is the ordinary straight-line distance between two points. Suppose, if we consider two points x = (x1, x2, …, x n ) and y = (y1 , y2 , …, yn ) in the Euclidean space, then Euclidean distance between x and y is given by,  d(x, y) = d(y, x) = (y1 − x1 )2 + (y2 − x2 )2 + . . . + (yn − xn )2   n  =  (yn − xn )2

(4)

i=1

where, d(x, y) = d(y, x) = Euclidean distance between 2 points x and y. (x 1 , x 2 , x 3 , …, x n ) = Coordinates of the point x. (y1 , y2 , y3 , …, yn ) = Coordinates of the point y.

3.3 System Specifications Defining the system parameters with the right values plays a vital role in an accurate execution of the software, which are shown in Table 1. After evaluating the different test conditions, we have arrived at the mentioned thresholds. We have taken the images with the resolution of 500 * 500 for processing, and the frame rate of the video stream is 30 fps.

Vision-Based Driver Authentication and Alertness Detection … Table 1 System specifications

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S. no.

Parameter

Threshold

1

Eye aspect ratio

0.2

2

Mouth aspect ratio

0.58

3

Eye aspect ratio frame count

15

4

Mouth aspect ratio frame count

5

5

Resolution of processing image

500 * 5000 pixels

6

Video frame rate

30 fps

4 Results and Discussion The proposed model for the driver authentication and drowsiness detection is tested for various test conditions as explained in the below section, Accuracy is one of the metrics for evaluating classification models. Initially, for the accuracy analysis of authentication algorithm images of ten people were considered. Among the predictions obtained, we found that nine were right and one was wrong. Therefore, from this analysis, it is clear that the accuracy of the proposed model for driver authentication is 90%. Next, for testing drowsiness detection algorithm, images of 50 people containing different test cases were considered; among the results obtained, it was found that 41 predictions were right and the other 9 were wrong. Hence, the accuracy of the proposed drowsiness detection algorithm is 82%. Finally, the overall accuracy of our proposed algorithm for the “driver authentication and drowsiness detection” system can be expressed as the average of these two, i.e., 85%.

4.1 Unauthorized and Authorized User Entry The algorithm was tested by considering the unauthorized person as a driver. Five attempts are made to check if the person is authorized; if his image is not present in the data set provided, then he is considered as unauthorized. Considering one of the people from the data set to test the proposed algorithm gives the following output, which is shown in Fig. 3a, and the real-time captured image of the person is compared with data set, and he will be considered as the authorized driver since the captured image matches with one of the images of data set, which is shown in Fig. 3b, then the algorithm proceeds further for the continuous real-time drowsiness detection of driver.

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Fig. 3 a Result of unauthorized entry. b Result of authorized entry

4.2 Drowsiness Detection After the authentication of the driver is completed and he is found to be authorized driver, then the real-time stream of video with the driver’s face is considered for drowsiness detection. Here, we have considered two factors, i.e., EAR and MAR for classification of the driver as drowsy or alert. We have considered a few test cases, and they are described below: Case 1—Normal Condition: The first case, where the driver is normal condition with no signs of drowsiness; hence, there is no alert as shown in Fig. 4a. Case 2—Person is wearing spectacles: There is the possibility that the driver may wear spectacles, so it is mandatory to consider this case as well and the proposed algorithm should work for such cases also. Here, is the case is shown in Fig. 4b.

Fig. 4 Drowsiness not detected while person is a normal, b with spectacles, c low light condition, d eyes closed, e yawning, f yawning and eyes closed, g obstacle in front of face, h driver distraction

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Case 3—Driving in low light: As per our objective, the proposed algorithm should work fine even if there is low light, e.g., while driving at night. So here is the case is shown in Fig. 4c, where even the algorithm works fine with the detection of eyes and facial features. Case 4—Eyes closed condition: Eyelid movement or eye blinking rate is one of the important behavioral measures which tells about the drowsiness of a human being. EAR ratio varies as the eyelid movement changes, so if the ratio is found to be lower than the predefined threshold continuously for the prescribed number of frames, which means he has closed his eyes for a longer time; then the driver is declared as drowsy as shown in Fig. 4d. A speech message is used to alert the drowsy driver. Case 5—The person is yawning: Yawning is considered as one of the significant sign of drowsiness. So once the face recognition and prediction of mouth region are done, we are computing the MAR ratio, and if it is found to be more than the predefined threshold, then it indicates that a person is yawning, i.e., he is drowsy as shown in Fig. 4e, and hence, he is alerted with a voice message. Case 6—Eyes closed and person is yawning: When the driver is yawning as well as his eyes are closed, the driver is considered to be drowsy. So in such cases also the driver will get an alert from the alerting system as represented in Fig. 4f. Case 7—If there is an obstacle in front of the face: The proposed framework is robust enough to recognize the face and detect the eyes and mouth; from Fig. 4g, we can see even when we have a hand in front of the face as an obstacle, eyes, and mouth are detected, and the driver is alerted as he is drowsy since eyes are closed as well the person is yawning. Case 8—Driver distraction: Even though we have considered the cases of drowsiness to alert the user, there may be a situation where the driver may be distracted due to some other reasons. Hence, it’s necessary to consider the case when the driver’s face is tilted which is a sign of losing concentration. There is a need to alert the driver in such situations as shown in Fig. 4h. The defined algorithm was tested for different test cases and driving conditions, and we found that our objectives are achieved.

5 Conclusion and Future Scope This work provides the software solution for the real-time authentication and drowsiness detection of the driver based on face recognition and feature extraction (shape predictor) algorithm. This work will help the vehicle owners to keep track of their drivers, ensuring the security of vehicles as well aids drivers to stay alert while driving, in turn, helps in reduction in car accidents due to drowsiness as well provide transportation safety. The proposed framework provides information to owners about driver authentication through SMS alert. Drowsiness detection is achieved by taking into consideration the eyelid movement and yawning of the driver. Finally, we conclude saying that the proposed algorithm for driver authentication and drowsiness

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detection offers a non-invasive approach without any annoyance and interference for low cost, continuous and real-time verification, and detection. Although the proposed solution for authentication and drowsiness detection provided accurate results, then there is scope for improvement. A more robust system can be built which will not only verify the driver and alert the owner, but also will not allow the driver to turn on the ignition if he is not an authorized person, and further, algorithm can be improved to send an image of a person attempting to start the vehicle to the vehicle owner.

References 1. F. Schroff, D. Kalenichenko, J. Philbin, Facet: a unified embedding for face recognition and clustering, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 815–823 2. P.C. Nissimagoudar, A.V. Nandi, Precision enhancement of driver assistant system using EEG based driver consciousness analysis & classification, in Computational Network Application Tools for Performance Management(2020), pp. 247–257. https://doi.org/10.1007/978-981-329585-8_21 3. P.C. Nissimagoudar, A.V. Nandi, H.M. Gireesha, EEG signal analysis using wavelet transform for driver status detection, in Innovations in Bio-inspired Computing and Applications, IBICA 2018. Advances in Intelligent Systems and Computing, vol. 939. (Springer, Cham, 2019). https:// doi.org/10.1007/978-3-030-16681-6_6 4. P.C. Nissimagoudar, A.V. Nandi, H.M. Gireesha, EEG based feature extraction and classification for driver status detection, in Innovations in Bio-inspired Computing and Applications, IBICA 2018. Advances in Intelligent Systems and Computing, vol. 939 (Springer, Cham, 2019). https:// doi.org/10.1007/978-3-030-16681-6_15 5. A.M. Kulkarni, A.V. Nandi, P.C. Nissimagoudar, Driver state analysis for ADAS using EEG signals, in 2019 2nd International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India (2019), pp. 26–30. https://doi.org/10.1109/icspc46172.2019.897 6799 6. M. Hajinoroozi, Z. Mao, Y. Huang, Prediction of driver’s drowsy and alert states from EEG signals with deep learning, in 2015 IEEE 6th International Workshop on Computational Advances in Multi-sensor Adaptive Processing (CAMSAP) (IEEE, 2015), pp. 493–496 7. E. Derman, A.A. Salah, Continuous real-time vehicle driver authentication using convolutional neural network-based face recognition, in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (IEEE, 2018), pp. 577–584 8. C. Yashwanth, J.S. Kirar, Driver’s drowsiness detection, in TENCON 2019–2019 IEEE Region 10 Conference (TENCON) (IEEE, 2019), pp. 1622–1625 9. Dlib Library. https://medium.com/@aanilkayy/extract-eyes-in-face-i%CC%87mages-withdlib-and-face-landmark-points-c45ef480c1

ICT4D in Channelling Welfare Services in India: Case Study of Haqdarshak Komal Ramdey and Hasnain Bokhari

Abstract In 2015 Digital India project was launched to improve governance by employing information and communication technologies (ICTs). The ICT-led reform espouses to transform India into a digitally empowered society where electronic public services are accessible via web and mobile. Despite outstanding achievements in the ICT sector there are over 200 million people that live below the poverty line in India. In past decades several welfare schemes have been initiated to promote equitable development. However, due to the prevalent information divide, these schemes suffer to reach those at the bottom of the pyramid. This paper attempts to contextualise ICT4D in India within the sustainable development perspective. It tries to examine the potential ICTs offer to the marginalised sections of India. The paper presents a case study of social enterprise Haqdarshak that uses artificial intelligence to channelise social welfare schemes. Keywords Digital india · ICT4D · Haqdarshak · Sustainable development · AI

1 Introduction In 2015 India launched its highly ambitious eGovernment programme titled “Digital India” aimed at reforming the government processes by employing modern information and communication technologies (ICTs) [1]. ICT-led reform promises to transform India into a digitally empowered society thereby ensuring G2C services via digital communication channels. Digital India programme by the Indian Ministry of Electronics and Information Technology (MeitY) stands on nine pillars that pledges to improve online infrastructure, Internet connectivity, government procedures as well as access to mobile connectivity [2]. The foundations of Digital India can be K. Ramdey (B) Willy Brandt School of Public Policy, University of Erfurt, Erfurt, Germany e-mail: [email protected] H. Bokhari University of Erfurt, Erfurt, Germany e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9_80

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traced back to 1990 and 2000s when the groundwork for digitisation was interpolated. A noteworthy regulation in this regard was the National Telecom Policy (1999) that paved the way for structural reform in the telecom sector in India [3]. The policy allowed companies to provide mobile services on a revenue share basis instead of fixed fees [4]. This helped in triggering a radical transformation in the ICT sector in India that led to low-cost internet bandwidth and affordable data plans. These initiatives have resulted in telephone subscribers to reach a 1.2 billion landmark by the end of 2019, with 514 million rural subscribers and 669 million in urban India. Similarly, the internet subscribers in India stand at 637 million, with 410 million residing in urban areas whereas 227 million come from rural parts of India [5]. The statistics from 2019 reveal an exponential growth in telephone and internet services that also assists in bridging the digital divide. Another crucial precursor to the initiation of the Digital India programme is also the National eGovernance Plan (NeGP) by the MeitY launched in May 2006 [6]. NeGP’s focus was to develop Mission Mode Projects (MMPs) to establish data centres, wide area networks and service delivery gateways. Amidst the euphoria about ICTs at the national level, the Indian government continued to show its commitment towards ICTs at international fora. MeitY represented Indian perspective at the UN ICT Task Force in 2001 where the digital divide received a global recognition [7]. Similarly, MeitY remained instrumental in the World Summit on Information Society (WSIS) in Geneva (2003) and Tunis (2005) [8]. MeitY’s presence at these platforms eventually provided India with an opportunity to host the third UN Internet Governance Forum in Hyderabad in 2008 [9]. Such a commitment to the ICT sector in and abroad has today resulted in achievements like unique digital ID (Aadhar card) and/or digital financial solutions to complement the challenge that Digital India programme has undertaken. At the heart of the Digital India programme lies the development of citizen-centric eServices. Two of the nine aspects of Digital India are decisive for the promise of digital empowerment and these include, Information for All and electronic public services (or e-Kranti) [10]. Both these pillars promise to be highly citizen-focused and aim to reform bureaucratic procedures to provide eServices over multiple digital channels. Nevertheless, despite these achievements in the ICT sector, the fact that 270 million people in India still live below the poverty line has been a matter of concern [11]. Several national and international indicators highlight, for instance, the challenge of access to proper education for children, adequate healthcare and the job prospects for youth [12]. In the past seven decades, several attempts have been made to address these issues through various economic and social welfare schemes. Considering India’s enormous size and diversity, the inclusion of the wider population in these welfare schemes has remained a major hurdle [13]. While the digital infrastructure in India is going through its prime phase, it’s the social strata that require a special emphasis particularly when it comes to bridging the information divide. It remains to be seen if these apprehensions elucidate some of the pertinent ICT4D challenges such as ‘can Digital India programme help to uplift the population at the bottom of the pyramid?’ With low (digital) illiteracy rates, how far will Digital India’s 6th pillar (information for all) be able to bridge the information divide? Hereinafter, this paper while acknowledging the tremendous growth in the ICT sector,

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aims to look at the potential that ICT could offer to the marginalised and poor population of India. In this regard, this paper explores the case of social welfare schemes within the context of Sustainable Development Goals (SDGs). More importantly, this paper discusses a case study of an Indian social enterprise “Haqdarshak” that intends to bridge the information divide by employing digital solutions to bring the social welfare schemes for the marginalised Indian population. This paper also explores the role of SDG 17 i.e. Global Partnerships for Sustainable Development and how it can help the objectives of the Digital India programme.

2 Contextualising ICT4D in Indian Context The past two decades have seen tremendous growth in the ICT sector worldwide. According to World Digital Competitiveness Ranking 2019, India advances four places to 44th position with the biggest improvement in the technology sub-factor level, holding the first position in telecom investment [14]. However, within the ICT4D context, Indian performance has met a mixed response. In United Nations eGovernment Survey, India’s position has fluctuated between 87 to 125 in the past 20 years [15]. Limited ICT infrastructure, payment gateways, digital divide and lack of public policy remained the key concerns. The UN’s Millennium Development Goals (MDGs) encouraged developing countries to employ ICTs as a parallel medium of development [16]. Between 2000 and 2010 a number of countries started ICT-led projects either at international motivation, at times initiated by donor organisations and sometimes out of national enthusiasm around ICTs. The response remained both celebratory as well as critical [17]. Celebratory because developing countries found an opportunity to extend their modern ICTs and digital infrastructure from urban to rural areas. Critical because several studies blamed the approach towards ICT4D as too centralised, top-down and disregarded indigenous knowledge [18]. Thus, using ICTs effectively to serve citizens online has remained a struggle because of the technological complexity, complicated organisational routines and technology acceptance – a reason that several ICT4D projects initially partially failed [19]. Nevertheless, within the ICT4D context Indian presence has also been acknowledged at the international scale. For instance, the government of Karnataka’s land registry system Bhoomi serves as the best practice within the developing country context [20]. Similarly, Gyandoot project by the State of Madhya Pradesh introduced the concept of rural information kiosk to provide a variety of G2C services. Gyandoot’s rural telecentre model has since then been replicated in several low-income countries to bridge the gap between digital haves and have-nots [21]. Andhra Pradesh’s eSeva project also opened doors to alternative thinking in eGovernment development [22]. Indian ICT4D initiatives challenged the web-only eService solutions and guided the international debate towards multi channels electronic service delivery [23]. The unique aspects of these projects are their focus on the realisation of local realities, indigenous knowledge and existing communication patterns. Such initiatives have

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also received critical acclaim from several academic quarters where Subhash Bhatnagar, M.P. Gupta and Rahul De stand as notable mentions. While stocktaking ICT4D initiatives in India it is imperative, as the case of Gyandoot, eSeva or Bhoomi has reminded us, to acknowledge the presence of unheard and focus on the voices from within as we proceed to more citizen-centric electronic public services. In 2015 UN MDG’s mandate came to an end and new set of targets for human development have been set out in the form of Sustainable Development Goals (SDGs). Incidentally, SDGs announcement also coincides with the commencement of Digital India programme. Both programmes espouse to improve the service sector to be accessible to the bottom of the pyramid. SDGs represent a holistic agenda that entails achieving progress across social, economic and environmental pillars. India with 17% of the world’s population, holds an important place in achieving the SDGs [24]. India has raised the levels of prosperity for its people in the recent past. Several programmes and welfare schemes have been launched at the state and national level in past decades. While targeting economic growth, infrastructure development and industrialisation, India’s fight against poverty has become fundamentally focused on social inclusion and empowerment of the poor [25]. Even though with such progressive ideas and social welfare schemes, the challenges remain in many areas including health, nutrition, basic infrastructure, quality of education among others. Currently, India faces a bigger challenge of information divide along with digital imbalance. The accessibility of welfare schemes to a wider public remains an issue [26]. Delivery of social welfare can accelerate the progress to achieve SDGs by relying on ICTs and eGovernment programmes. SDG 9 (Building resilient infrastructure) and SDG 17 (Partnerships for the Goals) present a unique opportunity in India to utilise the presence of civil society organisations and technology firms. They can not only complement the objectives of SDGs and the Digital India programme but also India’s commitment to the welfare of its low-income and marginalised groups. The following section explores the case of Pune based social enterprise called Haqdarshak where social innovation meets digital entrepreneurship and looks at such an ICT4D initiative how it can help meet several SDGs.

3 Case Study: Collaborative Partnerships for Development “Haqdarshak Empowerment Solutions” Since 1951, the Indian government struggled hard to promote equitable and economic development with a focus on disadvantaged and vulnerable groups. For the past few decades, central and state governments in India introduced social welfare schemes to provide a safety net to marginalised sections of society. Indian Ministry of Finance reported over 900 welfare schemes promulgated by the central and state governments. These schemes are built upon three tiers that provide cash-based, subsidy-oriented and services-based welfare benefits [27]. The rapid growth of ICTs has motivated Indian government to revamp its Public Distribution System (PDS) particularly now

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that every citizen has a unique ID to rely more on technology-based Direct Benefit Transfer (DBF). The welfare schemes, however, have been a matter of scrutiny by media and civil society alike [28]. The critique goes, as also pointed out by High Court of Kerala, that due to the information divide, many a time a majority at the bottom of the pyramid has not even heard of the existence of welfare benefits [29]. In 2016, a social impact organisation named Haqdarshak (“Haq” means rights and “Darshak” means “showing the path”) in Pune was established with an aim to pursue an ICT-based collaborative effort to bring welfare schemes to people’s doorstep [30]. The rationale behind Haqdarshak is that information is a fundamental right and every citizen deserves to know his/her entitlements. Haqdarshak realised that even though governments’ have launched many social welfare schemes, however, their outreach still remains a big challenge. Poor publicization, complex paperwork, illiteracy and above all information and digital divide are attributed as some of the foremost factors that result in welfare budget to remain unused [31]. The lack of awareness of welfare schemes inspired Haqdarshak to exploit the potential of social and digital innovation to bring efficient collaboration between the government and private enterprise. Haqdarshak launched an artificial intelligence-based mobile and a web platform for citizen-service delivery platform that aims at dissemination of information about social welfare schemes particularly those at the bottom of the pyramid and helps them avail their entitlements.

3.1 Haqdarshak Model Technology, collaboration and social entrepreneurship are at the heart of Haqdarshak model. Haqdarshak realised that there is a lack of single repository (a sort of onestop-shop) for the welfare schemes in India. Haqdarshak started building a searchable data bank for welfare schemes that could parse through citizens’ eligibility to various scheme(s) and also explain its procedure. It follows a two-pronged approach in which Haqdarshak trains community members in India’s rural hinterlands referred to as village entrepreneurs (VE) [32]. The VEs are taught to use Haqdarshak app in their local language. The VEs engage with their fellow community members by going door to door to inform about the possibility of their entitlements. The VEs’ incentive is a possibility to earn a livelihood in due process. Haqdarshak model relies on collaborations and its wishes to partner with civil society organisations, multinationals as well state governments. VEs receive their monetary incentive through these partner organisations or from beneficiaries for which they pay a monthly service fee to Haqdarshak.

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3.2 Technology and Impact Haqdarshak’s research team regularly builds welfare scheme databank in a searchable way to provide entitlement results for every Indian citizen. Haqdarshak’s mobile/web platform relies on artificial intelligence by using the rule-mapping system to screen citizens. Citizen is asked a number of questions based on personal, occupational and financial status before showing the eligibility for welfare schemes. Eligible schemes can be displayed in the local language to reduce the information gap between citizens and their entitlements. Haqdarshak app does not depend upon internet connectivity and can work as a stand-alone application but the app does have the possibility to synchronise with Haqdarshak server once connected to the internet. The VEs can use their own mobile phones or sometimes the ones provided by the partner organisation to screen the citizens for their eligibility to various welfare schemes. Since its beginning, Haqdarshak focussed on establishing partnerships and managed to form collaborations with organisations such as Tata Trust, Save the Children as well as Rajasthan government. Initially, its focus was limited to Maharashtra and Rajasthan but now the Haqdarshak’s network has spread across 20 Indian states where 6136 VEs have been trained. The initial results look promising as Haqdarshak has been able to reach out 226,717 (0.23 million) citizens, out of which 111,277(0.11 million) were rural and 8329 were urban. The Haqdarshak app has logged in 173,647 (0.17 million) applications through which 142,646 (0.14million) people received benefits from the the government’s welfare schemes through Haqdarshak, out of which 43.51% were male, 56.45% female and 0.04% were from the third gender. In monetary terms, Haqdarshak has been able to mobilise an amount of Rs. 587,241,806 (590 million) value of benefits that would have otherwise remained underutilised [33]. Haqdarshak’s socio-technological solution not only assisted the government in the right allocation of welfare money but it is also helping the Indian government reach its global commitments towards SDGs. As such Haqdarshak’s impact areas are aligned with six SDGs namely SDG 1, 3, 4, 5, 6 and 9 that range from gender equality to quality education, good wellbeing to no poverty and clean water to innovation and industry.

3.3 Analysis of Issues and Challenges Haqdarshak is a social enterprise and its business model is for-profit, therefore, it relies on funding which is one of its major issues faced. Currently their revenue model primarily relies mainly on private funders. Similarly, several welfare schemes were designed decades ago, and India has gone through major structural and fiscal changes. Thus, Haqdarshak requires data that would involve working directly with the community [34]. Similarly, a major challenge in partnerships domain is working with the government for scale-up and sustainability, however, government process tends to suffer from the red tape that hampers Haqdarshak’s prospects of quicker

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collaborations. Haqdarshak has been able to adopt the SDGs through its work and is able to create a strong impact in uplifting the communities that it has worked with. Even though Haqdarshak has sturdily used the power of partnerships to maximise the outreach which is also a contribution to SDG17 i.e. Partnerships for Sustainable Development Goals, there is no detailed evidence of the methodology used in the impact assessment. The data that represents the number of people who have benefitted from the government welfare schemes and the value of the benefits through Haqdarshak, doesn’t signify the individual sectoral impact or individual welfare scheme’s impact. Having said that, Haqdarshak has managed to socially mobilise people and enable the environment to make the social welfare schemes more accessible to the bottom of the pyramid through different partnerships and keeping people at the centre of solutions. Haqdarshak has so far been able to take over 25 high profile partners including Bill and Melinda Gates Foundation, UNDP, Aga Khan Foundation and Tata Trust. In terms of monetary benefits, Haqdarshak’s compound an annual growth rate of 114% increased from 2017 to 2019 with annual profit of Rs. 1.95 crore in profit after taxes [30].

4 Conclusion The case of Haqdarshak has shown as to how community informatics can be used to bridge the information divide that can also help reach development goals. Its AI-based solution targeted at the population at the bottom of the pyramid is a prime example of the potential that ICTs offer in sustainable human development. Haqdarshak’s model based on social innovation and collaboration also presents a good example for the Indian government’s Digital India programme. It promises to promote citizens’ access to ICT to encourage their participation. However, providing access to these ICT services still seems very limited. Citizens must be enabled to use these services for government-citizen interaction. In a country like India, where social welfare schemes are very important to bridge the inequalities and where technology is still not widespread, relying on ICT4D alone can result in the exclusion of many. Organisations and CSOs with direct outreach to these communities have a significant role to reach those in rural areas. As highlighted in SDG 17, Partnerships for the Goals, there is a huge potential for exploring partnerships with organisations like Haqdarshak and other CSOs, who have far-reaching impact in the communities. They could not only empower these communities with information but also bring the concerns and voices of the ones which have not been heard by the governments. As a future recommendation, these partnerships can also open a door for further developments for making the welfare schemes accessible. Similarly, the concept of open data can also help improve measurement of policies, better government efficiency, deeper analytical insights, greater citizen participation, and a boost to the local companies by way of products and services that use government data.

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Author Index

A Aalam, Zunaid, 721 Aditya, S., 39 Adlakha, Sushant, 395 Adnan, Md Nasim, 473 Adu-Gyamfi, Samuel, 741 Aiholli, Nagaraj R., 595 Aishwarya, H. R., 815 Alam, Mohammad Minhazul, 779 Algarni, Sumaiah, 763 Ananth, V. Vignaraj, 39 Apte, Tejaswini, 365 Ashish, Ravi, 439

B Badrinarayanan, M. K., 1 Bandyopadhyay, Saubhik, 535 Bangad, Divya Ashok, 245 Bansal, Divya, 553 Bansal, Pratosh, 771 Bhagvandas, Ashni Manish, 629 Bhagwat, Alok, 209 Bhandari, Suchandra, 535 Bhatia, Sajal, 463 Bhaumik, Rahul, 649 Bhavsar, Bansari, 19 Bhoyar, Pravin Kumar, 49 Bijapur, Ajit, 815 Biswas, Arghya, 29 Bokhari, Hasnain, 835 Borwankar, Antara, 29 Buyya, Rajkumar, 661

C Castanha, Jick, 317 Chafe, Shreya Shailendra, 245 Chakravaram, Venkamaraju, 675 Chandra, Theodore S., 279 Chatterjee, Punyasha, 535 Chauhan, Pranay, 771 Chauhan, Sudakar Singh, 29 Chin, Cheng Siong, 185 Chitrao, Pradnya Vishwas, 49, 209 Chopra, Akanksha Bansal, 341 Christian, Annan, 19

D Dalal, Vipul, 177 Deochake, Saurabh, 563 Deshmukh, Abhishek, 503 Deshmukh, Rashmi J., 621 Dinesan, Abhishek, 629 Divekar, Rajiv, 49 Dixit, Veer Sain, 341 Domb, Menachem, 165

E Edla, Damodar Reddy, 157

G Ganeshan, M., 383 Geetha, V., 585 Gemecha, Demissie Jobir, 791 Ghatkamble, Rajlakshmi, 289 Gireesha, H. M., 815, 825

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Tuba et al. (eds.), ICT Systems and Sustainability, Advances in Intelligent Systems and Computing 1270, https://doi.org/10.1007/978-981-15-8289-9

845

846 H Hai, Do Huu, 707 Hamid, Khaild, 639 Han, Shengnan, 297 Harsh, 697 Hasan, Sadman, 779 Hassan, Mohamed, 639 Hatture, Sanjeevakumar M., 573 Hegde, Vinayak, 417 Hiremath, P. S., 307 Hishiyama, Reiko, 805 Hossain, S. M. Shahadat, 779 Huda, Eumna, 779

I Ieiri, Yuya, 805 Indrawati, 317 Iyengar, S. S., 661 Iyer, Nalini C., 815, 825

J Jacob, Rohan Ninan, 11 Jadeja, Mahipal, 147 Jain, Anshul, 223 Jain, Ketan Kumar, 395 Jain, Nitesh, 223 Jaisingh, Kelavath, 77 Jayakumar, P., 447 Jayan, Abhijith, 447 Jegatha, M., 493 Jha, Ravi Shankar, 513 Jindal, Rajni, 357 Jitpattanakul, Anuchit, 753 John, Anice, 525

K Kabir, Moumita, 483 Kakulapati, V., 89 Kannadasan, R., 439 Kaur, Amandeep, 431 Kaur, Amritpal, 721 Kaur, Saravjit, 721 Kaur, Satnam, 721 Kaushik, Abhinesh, 269 Keerthiga, B., 39 Kesavan, S. Ragul, 39 Khan, Nafis Mahmud, 685 Khara, Satvik, 113 Khot, Shubhangi Tanaji, 621 Kofi Frimpong, Adasa Nkrumah, 741 Kothari, Komal, 113

Author Index Kranthi kumar, V., 89 Kuddus, Khushboo, 685 Kumar, Amith Kiran, 279 Kumari, Kamlesh, 233 Kumar, Navin, 101 Kumar, Tarun, 649

L Li, Ping, 741 Lobiyal, D. K., 269 Long, Nguyen Tien, 707

M Mahmud, Khan Raqib, 483 Mamud, Samiul, 535 Manjrekar, Amrita A., 621 Marathe, Nilesh, 503 Medvedeva, L. N., 733 Meghana, H. N., 417 Mehnaz, Laiba, 357 Mekruksavanich, Sakorn, 753 Mishra, Satyasis, 791 Mistry, Nilay, 19 Mohanty, R. C., 791 More, Abhilasha, 177 Motaleb, Abdul, 779 Mukesh, Rajeswari, 1 Mukhopadhyay, Debajyoti, 563

N Naik, Amrita, 157 Naik, Sapan, 63 Nandi, A. V., 825 Nasir, Afsana, 473 Nazli, Nursyafiqah Binte, 185 Nguyen, Duc Dinh, 123 Nguyen, Long, 123 Nissimagoudar, P. C., 815, 825

O Obiora, Sandra Chukwudumebi, 741

P Pai, Shashidhar, 453 Pallavi, B., 661 Pallavi, M. S., 417 Panchami, V., 77 Patil, Kanishk, 405 Patil, Prakashgoud, 307

Author Index

847

Patil, Ratna, 405 Patnaik, L. M., 661 Paul, Bijan, 473, 483 Pawar, Manjula K., 307 Pente, Yash, 503 Pillai, Subhash Kizhakanveatil Bhaskaran, 317 Pingili, Madhavi, 373 Pokala, Sai Surya Kiran, 77 Popy, Fatima Noor, 483 Prajapati, Himanshu, 113 Prajapati, Sunny Prakash, 649 Prakash Raj, G., 543 Prem Kumar, S., 543 Purohit, Hinddeep, 199

Shetgaonkar, Sima, 607 Shet, R. M., 815, 825 Shishir, Md. Romman Riyadh, 779 Singh, Alok, 607 Singh, Kuldip, 791 Singh, Manwinder, 639 Singh, Tanya, 223 Singh Tomar, Rudra Pratap, 585 Sinthia, Fatema Zaman, 473 Solanki, Manishkumar R., 257 Sonune, Harsha, 245 Spandana, R., 417 Sriram, Padmamala, 629 Srivastava, Sanjeev Kumar, 133 Sujanani, Anish, 453

R Rachh, Rashmi, 595 Rahman, Rashedur M., 779 Rajan, E. G., 373 Rajesh Khanna, M., 543 Rajeswari, P., 279 Ramanjinailu, R., 89 Ramdey, Komal, 835 Rana, Sanjeev, 233 Rashid, Mohammad Rifat Ahmmad, 473, 483 Rastogi, Shubhangi, 553 Rathee, Davinder Singh, 791 Ratnakaram, Sunitha, 675 Roiss, O., 733 Roopa, M. S., 661 Roy, Sohini, 329

T Tamane, Sharvari, 405 Trung, Ngo Sy, 707 Tsai, Hung-Ya, 805 Tuan Van, Pham, 707

S Sahoo, Priti Ranjan, 513 Saini, Gurdeep, 585 Saini, Jatinderkumar R., 365 Sai Sandeep, R., 89 Sait, Unais, 649 Salvi, Sanket, 585 Samuel, Subi George, 447 Sapraz, Mohamed, 297 Saxena, Rahul, 147 Sen, Arunabha, 329 Sethi, Gunjan, 697 Shah, Aagam, 113 Shah, Hinal, 63 Shankar Lingam, M., 373 Shankar, S. K., 1 Sharma, Aditya, 395 Sheik, Reshma, 525

V Vaghela, Ravirajsinh, 199 Varma, Datla Anurag, 439 Vats, Prashant, 721 Venkadeshan, R., 493 Venkataraman, Reethesh, 629 Venkata Sai Sandeep, V., 439 Venkatesh, B., 439 Venugopal, K. R., 661 Verma, Atul Kumar, 147 Verma, Vijay, 395 Vidyasagar Rao, G., 675 Vignesh Raaj, N. S., 543 Vigneshwaran, P., 383 Vihari, Nitin Simha, 675 Viswavardhan Reddy, K., 101

W Wali, Uday V., 595 Warang, Kshitij, 503 Wejinya, Gold, 463

Y Yadav, Naveen, 585 Yankati, Pallavi V., 573 Yaratapalli, Nitheesh Chandra, 629 Yeasmin, Sabrina, 779 Yedla, Sandeep Kumar, 77