Next Generation of Internet of Things: Proceedings of ICNGIoT 2021 (Lecture Notes in Networks and Systems, 201) 981160665X, 9789811606656

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Lecture Notes in Networks and Systems 201

Raghvendra Kumar Brojo Kishore Mishra Prasant Kumar Pattnaik   Editors

Next Generation of Internet of Things Proceedings of ICNGIoT 2021

Lecture Notes in Networks and Systems Volume 201

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering University of Alberta, Alberta, Canada; Systems Research Institute Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, 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/15179

Raghvendra Kumar · Brojo Kishore Mishra · Prasant Kumar Pattnaik Editors

Next Generation of Internet of Things Proceedings of ICNGIoT 2021

Editors Raghvendra Kumar Department of Computer Science and Engineering School of Engineering and Technology GIET University Gunupur, Odisha, India

Brojo Kishore Mishra Department of Computer Science and Engineering School of Engineering and Technology GIET University Gunupur, Odisha, India

Prasant Kumar Pattnaik School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar, Odisha, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-16-0665-6 ISBN 978-981-16-0666-3 (eBook) https://doi.org/10.1007/978-981-16-0666-3 © 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

International Conference on Next Generation of Internet of Things (ICNGIoT 2021) organized by the Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur, Odisha, India, is a multidisciplinary conference which was organized during February 05–06, 2021. There is an increase in the number of physical objects that are being connected to the Internet at an unprecedented rate realizing the idea of the next generation of Internet of Things (IoT). A recent report states that “IoT smart objects are expected to reach 324+ billion entities deployed globally by the end of 2025.” Similarly, while the number of connected devices already exceeds the number of humans on the planet by over two times, for most enterprises, simply connecting their systems and devices remains the first priority. A recent report states that “The overall Internet of Things market is projected to be worth more than one billion U.S. dollars annually from 2017 onwards.” As a result, data production at this stage will be 44 times greater than that in 2009, indicating a rapid increase in the volume, velocity and variety of data. However, there is highly useful information and so many potential values hidden in the huge volume of IoT-based sensor data. Next-generation IoT-based sensor data has gained much attention from researchers in health care, bioinformatics and information sciences and from policy- and decision-makers in governments and enterprises. This book includes new emerging work in this domain which will be beneficial to academicians, researchers, scientists and students who are working in this field. Moreover, this book provides insight on future developments of IoT field that can be used as a decision support for management. The book covers research work belonging to the next-generation IoT technologies and applications. Gunupur, India Gunupur, India Bhubaneswar, India

Raghvendra Kumar Brojo Kishore Mishra Prasant Kumar Pattnaik

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Contents

Gaming: Around the World Geography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sunita, Tamanna Jain, and Sudhir Kumar Sharma

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Smart Blind Stick for Blind People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urmila Pilania, Ashwani Kaushik, Yatharth Vohra, and Shikhar Jadaun

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How Societies and Businesses Will Technologically Evolve with COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Catarina Costa and Sara Paiva

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Identification of Premature Diagnosis for Detection of Brain Tumor Using Blockchain Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Artatrana Biswaprasan Dash, Biswaranjan Mishra, and Amar Nath Singh

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Cuckoo Search Applied Path Planning of Twin Robot in Multi-Robot Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bandita Sahu, Pradipta Kumar Das, and Manas Ranjan Kabat

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IoT-Malware Classification Model Using Byte Sequences and Supervised Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Om Prakash Samantray and Satya Narayan Tripathy

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Chatbots as a Game Changer in E-recruitment: An Analysis of Adaptation of Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. R. Swapna and D. Arpana

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Unsupervised Classification Approach for Anomaly Detection in Big Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ravi Kishan Surapaneni, Sailaja Nimmagadda, and K. Pragathi

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Non-functional Testing-Based Framework for Developing Reliable IoT Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. S. Jasmine

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Enhancement the ChaCha20 Encryption Algorithm Based on Chaotic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hussain H. Alyas and Alharith A. Abdullah

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Change Point Detection Technique for Weather Forecasting Using Bi-LSTM and 1D-CNN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 S. Selvi, V. Bala Nivetha, R. Divya, S. Gayathri, and G. S. Pavithra Attacking the Application of Three-Pass Protocol in Hill-Cipher . . . . . . . 121 Rifaat Z. Khalaf, Hamza B. Habib, and Sarkesh Khalid Aljaff Secure Data Sharing Based on Linear Congruetial Method in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Rana M. Zaki, Teaba W. A. Khairi, and Akbas Ezaldeen Ali Evaluating the Effects of Varying Model Parameter Values on the Characteristics of a Photovoltaic Module . . . . . . . . . . . . . . . . . . . . . . 141 Ameer Ali Kareem, Alaa Abdalhussain Mashkor, and Naseem K. Baqer Forwarding Information Base Design Techniques in Content-Centric Networking: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Mohammad Alhisnawi Implementing Cybersecurity in IoT Using IPAI Algorithm . . . . . . . . . . . . 175 A. V. Kalpana, D. Digvijay, R. Chenchaiah, and C. Sai Vignesh OCR-Based Automatic Toll Collection and Theft Vehicle Detection Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 A. V. Kalpana, K. Kavitharani, and M. Nandhini A Comprehensive Survey on Software-Defined Network Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Roaa Shubbar, Mohammad Alhisnawi, Aladdin Abdulhassan, and Mahmood Ahamdi Performance Evaluation of Nanorobot Drug Delivery Mechanism for Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 A. R. Venmathi and L. Vanitha A Correlation and Slope-Based Neighbor Selection Model for Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Jehan Kadhim Shareef Al-Safi and Cihan Kaleli Intrinsic and Simplified Complex Network Embedding Model . . . . . . . . . 269 Ahmad F. Al Musawi and Preetam Ghosh Classification of Sickle Cell Anemia Using Energy-Based KNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 M. Vadivel, V. Vijaya Baskar, V. G. Sivakumar, and S. P. Vimal

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Binary Image Encryption Based on Chaotic and DNA Encoding . . . . . . . 295 Sheimaa A. Hadi, Suhad A. Ali, and Majid Jabbar Jawad Analysis of Use of Social Networking Sites for the Education by Ministry of Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Boshra F. Zopon Al-Bayaty, Methaq Talib Gaata, and Yasmin Makki Mohialden Web-Based Car Workshop Management System—A Review . . . . . . . . . . 321 Mritha Ramalingam, Geetha Manoharan, and R. Puviarasi Recognition of the Researchers Faces in Images Using Convolutional Neural Networks (CNN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Ashwan A. Abdulmunem, Zinah Abdulridha Abutiheen, and Zahraa A. Harjan Intelligent Healthcare Monitoring System Using Cloud Computing . . . . 343 S. Srinivasan, S. Prasanna Bharathi, G. Chamundeeswari, and P. Muthu Kannan Enhancement of the U-net Architecture for MRI Brain Tumor Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Assef Raad Hmeed, Salah A. Aliesawi, and Wesam M. Jasim Designing and Improving NG-PON2-RoF with Inelastic Scattering and Nonlinear Impairments by Signal Processing Techniques . . . . . . . . . . 369 Mohammed Ahmed AbdulNabi, Wasan Kadhim Saad, and Bashar J. Hamza An Efficient Framework for Locating Stroke in Brain MRI Images Using Radon Transform and Convolutional Neural Networks . . . . . . . . . . 385 R. Rajagopal and S. Edwin Jose Securing of Software-Defined Networking (SDN) from DDoS Attack Using a Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Mehdi Ebady Manaa and Mohamed Salman Abedoun Wireless Sensor Network (WSN) Routing Optimization via the Implementation of Fuzzy Ant Colony (FACO) Algorithm: Towards Enhanced Energy Conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Ahmed J. Obaid Efficient Design for Square RFID Tag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Furkan Rabee and Daniah Mohammed Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm . . . . . . . 435 B. V. Santhosh Krishna, J. Jijin Godwin, S. Tharanee Shree, B. Sreenidhi, and T. Abinaya

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Prediction Communication Time and Data Size Based-Bluetooth in Mobile Crowdsensing for IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Abbas M. Ali Al-muqarm and Furkan Rabee JOJO—A Social Media Application with a Live Map Interface to Advance Social Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Abhishek Pughazhendhi, Sri Balaji Muruganandam, and Sridevi B. Investigation the Receiver Complexity of a Three-Dimensional OCDMA System Based on Different Codes . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Rasim Azeez Kadhim and Suhad Shakir Jaber Health Cloud—Health Care as a Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 D. Muthukumaran, K. Umapathy, and S. Omkumar Information Gain-Based Enhanced Classification Techniques . . . . . . . . . . 499 Enas Fadhil Abdullah, Alyaa Abdulhussein Lafta, and Suad A. Alasadi IOT Based V-Tank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 A. Sai Nandan, B. Sai Sumanth, R. Lilith Kumar, and M. P. Karthikeyan Water Body Detection of Haditha Dam Lake from Satellite Imagery Using Image Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 519 Rabah N. Farhan and Sawsan Abdulaali Arif Identity-Based Data Outsourcing with Comprehensive Auditing in Cloud-Based Healthcare Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 K. Rakesh, B. Krishna Teja, M. Venkata Akhil, and T. Ramesh Edge Technology Enabled IOT Blockchain-Based Health Monitoring for Chronically Sick Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Munisamy Shyamala Devi, P. Swathi, M. Nitesh Kumar Sah, Ayesha Jahangir, and Shubham Santhosh Upadhyay Computer Vision-Based Framework for Anomaly Detection . . . . . . . . . . . 549 Rashmi Chaudhary and Manoj Kumar Novel Method for Relative Automobile Maintenance Index for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Navin Sridhar and Kamalnath Venkateswaran Brand Quality Detection Based on a Comparative Analysis . . . . . . . . . . . . 571 C. G. Raji, A. Vinish, K. Amrutha, K. Drusya, A. Munawara, and Sarthaja Blockchain-Enabled IoT Security in Automotive Supply Chain . . . . . . . . 585 Sonali Patwe and Shraddha Phansalkar Imbalanced Classification for Botnet Detection in Internet of Things . . . 595 Deepa Krishnan and Preeja Babu

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Validation and Feasibility of Differentially Private Local Aggregation of Real-Time Data Streams from Resource-Constrained Healthcare IoT Edge Devices . . . . . . . . . . . . . 607 Niramay Vaidya, Srishti Shelke, Snehkumar Shahani, and Jibi Abraham Performance Analysis of Duobinary and CSRZ Modulation Formats on Self-Phase Modulation Effect in Optical Communication Network Using Fiber Bragg Grating (FBG) . . . . . . . . . . . 621 TusharKant Panda, A. B. Mukesh Kumar Behera, Guriya Kumari, and Swadhin Polei COVID-19 Patient Health Management System Using IoT . . . . . . . . . . . . 635 P. Ramchandar Rao, Ch. Rajendra Prasad, Sridevi Chitti, Shyamsunder Merugu, and J. Tarun Kumar Implementation of Artificial Neural Network for Image Recognition Using Chinese Traffic Sign Image Dataset . . . . . . . . . . . . . . . . 647 Manisha Vashisht and Brijesh Kumar Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Ajit Kumar Sahoo and Siba K. Udgata Design and Implementation of a Computerized Library Management System Using GUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Alaa Hussein Ali Al-Obaidi Deep Things-Net: A Novel Approach for Glitch Detection in IoT Devices Using Deep Iterative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Thirumurugan Shanmugam, Jamal Abdullah Al-Zahli, and S. Radha Rammohan IoT-Based Smart Security System on a Door Lock Application . . . . . . . . . 695 Debabrata Dansana, Brojo Kishore Mishra, K. Sindhuja, and Subhashree Sahoo Analyzing Multidimensional Communication Lattice with Combined Cut-Through and Store-and-Forward Switching Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Dmitry A. Zaitsev, Tatiana R. Shmeleva, and Roman N. Guliak Implications of E- Learning in Education: An Analysis . . . . . . . . . . . . . . . . 717 Deepanjali Mishra Behavior Analysis for Human by Facial Expression Recognition Using Deep Learning: A Cognitive Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725 Sudheer Babu Punuri, Sanjay Kumar Kuanar, and Tusar Kanti Mishra Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737

Editors and Contributors

About the Editors Dr. Raghvendra Kumar is working as Associate Professor in Computer Science and Engineering Department at GIET University, India. He received B. Tech., M.Tech., and Ph.D. in Computer Science and Engineering, India, and Postdoc Fellow from the Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He serves as Series Editor of Internet of Everything (IOE): Security and Privacy Paradigm, Green Engineering and Technology: Concepts and Applications, publishes by CRC Press, Taylor & Francis Group, USA, and Bio-medical Engineering: Techniques and Applications, publishes by Apple Academic Press, CRC Press, Taylor & Francis Group, USA. He also serves as Acquisition Editor for Computer Science by Apple Academic Press, CRC Press, Taylor & Francis Group, USA. He has published number of research papers in international journal (SCI/SCIE/ESCI/Scopus) and conferences including IEEE and Springer as well as serves as Organizing Chair (RICE-2019, 2020), Volume Editor (RICE-2018), Keynote Speaker, Session Chair, Co-chair, Publicity Chair, Publication Chair, advisory board, Technical program Committee Members in many international and national conferences and serves as Guest Editors in many special issues from reputed journals (Indexed By: Scopus, ESCI, SCI). He also published 13 chapters in edited book published by IGI Global, Springer, and Elsevier. His researches areas are computer networks, data mining, cloud computing and secure multiparty computations, theory of computer science, and design of algorithms. He authored and edited 23 computer science books in the field of Internet of things, data mining, biomedical engineering, big data, robotics, and IGI Global Publication, USA, IOS Press Netherland, Springer, Elsevier, CRC Press, USA. He is Managing Editor in International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242.

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Dr. Brojo Kishore Mishra is working as Professor in Computer Science and Engineering Department at the GIET University, Gunupur-765022, India. He received his Ph.D. degree in Computer Science from the Berhampur University in 2012. He has published more than 38 research papers in national and international conference proceedings, 28 research papers in peer-reviewed journals, and 32 book chapters and authored 02 books and edited 05 books. His research interests include data mining and big data analysis, machine learning, and security. Dr. Prasant Kumar Pattnaik Fellow IETE, Senior Member IEEE, is Professor at the School of Computer Engineering, KIIT Deemed University, Bhubaneswar. He has more than a decade of teaching research experience. Dr. Pattnaik has published numbers of research papers in peer-reviewed international journals and conferences. His researches areas are computer networks, data mining, cloud computing, and mobile computing. He authored many computer science books in the field of data mining, robotics, graph theory, Turing machine, cryptography, security solutions in cloud computing, mobile computing, and privacy preservation.

Contributors Aladdin Abdulhassan College of Information Technology, University of Babylon, Babylon, Iraq Alharith A. Abdullah College of Information Technology, University of Babylon, Babil, Iraq Enas Fadhil Abdullah Department of Computer, Faculty of Education for Girls, University of Kufa, Najaf, Iraq Ashwan A. Abdulmunem Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq Mohammed Ahmed AbdulNabi Engineering Technical College-Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq Mohamed Salman Abedoun Department of Information Networks, College of Information Technology, University of Babylon, Babylon, Iraq T. Abinaya Electronics and Communication Engineering, Velammal Institute of Technology, Chennai, India Jibi Abraham Department of CE and IT, College of Engineering Pune, Pune, Maharashtra, India Zinah Abdulridha Abutiheen Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq

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Mahmood Ahamdi College of Engineering, University of Razi, Kermanshah, Iran Ahmad F. Al Musawi Department of Information Technology, College of Computer Science and Mathematics, University of Thi-Qar, Thi Qar, Iraq Boshra F. Zopon Al-Bayaty Department of Computer Science, College of Science, Mustansiryiyah University Affiliation, Baghdad, Iraq Abbas M. Ali Al-muqarm Computer Science Department, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq Alaa Hussein Ali Al-Obaidi Babylon University-College of Fine Arts, Babylon, Iraq Jehan Kadhim Shareef Al-Safi Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkey; Digital Media Department, Thi-Qar University, Thi-Qar, Iraq Jamal Abdullah Al-Zahli Department of Information Technology, University of Technology and Applied Sciences, Suhar, Sultanate of Oman Suad A. Alasadi Department of Information Network, College of Information Technology, University of Babylon, Babil, Iraq Mohammad Alhisnawi College of Information Technology, University of Babylon, Babylon, Iraq Akbas Ezaldeen Ali Computer Science Department, University of Technology, Baghdad, Iraq Suhad A. Ali Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq Salah A. Aliesawi College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq Sarkesh Khalid Aljaff Department of Mathematics, College of Education for Applied Science, University of Karkuk, Karkuk, Iraq Hussain H. Alyas College of Information Technology, University of Babylon, Babil, Iraq K. Amrutha Department of Information Technology, MEA Engineering College, Perinthalmanna, Kerala, India Sawsan Abdulaali Arif Ministry of Water Resources, Baghdad, Iraq D. Arpana JAIN (Deemed to be University), Bangalore, India Preeja Babu Department of Information Technology, NMIMS Deemed-To-Be University, Mumbai, India Sudheer Babu Punuri GIET University, Gunupur, Odisha, India

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V. Bala Nivetha Department of Computer Science & Engineeering, R.M.K. Engineering College, Gummidipoondi, Tamil Nadu, India Naseem K. Baqer Department of Electrical Engineering, University of Kufa, Najaf, Iraq G. Chamundeeswari Department of ECE, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani, Chennai, India Rashmi Chaudhary University School of Information Communication and Technology, GGSIPU, Delhi, India R. Chenchaiah Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India Sridevi Chitti Department of ECE, School of Engineering, SR University, Warangal, Telangana, India Catarina Costa Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal Debabrata Dansana Department of Computer Science and Engineering, GIET University, Gunupur, India Pradipta Kumar Das Department of IT, VSSUT, Burla, India Artatrana Biswaprasan Dash DAV Autonomous College, Titilagarh, India D. Digvijay Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India R. Divya Department of Computer Science & Engineeering, R.M.K. Engineering College, Gummidipoondi, Tamil Nadu, India K. Drusya Department of Information Technology, MEA Engineering College, Perinthalmanna, Kerala, India Rabah N. Farhan Renewable Energy Research Center, University of Anbar, Ramadi, Iraq Methaq Talib Gaata Department of Computer Science, College of Science, Mustansiryiyah University Affiliation, Baghdad, Iraq S. Gayathri Department of Computer Science & Engineeering, R.M.K. Engineering College, Gummidipoondi, Tamil Nadu, India Preetam Ghosh Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA Roman N. Guliak Odessa State Environmental University, Odessa, Ukraine Hamza B. Habib Department of Mathematics, College of Science, University of Diyala, Diyala, Iraq

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Sheimaa A. Hadi Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq Bashar J. Hamza Engineering Technical College-Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq Zahraa A. Harjan Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq Assef Raad Hmeed Department of Students Affairs and Registration, University of Anbar, Ramadi, Iraq Suhad Shakir Jaber Computer Center, University of Babylon, Hilla, Iraq Shikhar Jadaun Computer Science and Technology, Manav Rachna University, Faridabad, India Ayesha Jahangir Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India Tamanna Jain Institute of Information Technology and Management, Janakpuri, New Delhi, India Wesam M. Jasim College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq K. S. Jasmine RV College of Engineering, Bengaluru, Karntaka, India Majid Jabbar Jawad Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq J. Jijin Godwin Electronics and Communication Engineering, Velammal Institute of Technology, Chennai, India S. Edwin Jose P.S.R Engineering College, Sivakasi, Tamil Nadu, India Manas Ranjan Kabat Department of CSE, VSSUT, Burla, India Rasim Azeez Kadhim College of Information Technology, University of Babylon, Hilla, Iraq Cihan Kaleli Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkey A. V. Kalpana Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India Kamalnath Venkateswaran Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India Ameer Ali Kareem Department of Electrical Engineering, University of Kufa, Najaf, Iraq

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M. P. Karthikeyan Department of CSE, R.M.K Engineering College, Chennai, India Ashwani Kaushik Computer Science and Technology, Manav Rachna University, Faridabad, India K. Kavitharani Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India Teaba W. A. Khairi Computer Science Department, University of Technology, Baghdad, Iraq Rifaat Z. Khalaf Department of Mathematics, College of Science, University of Diyala, Diyala, Iraq B. Krishna Teja Department of Computer Science and Engineering, RMK Engineering College, Chennai, India Deepa Krishnan Department of Computer Engineering, NMIMS Deemed-To-Be University, Mumbai, India Sanjay Kumar Kuanar GIET University, Gunupur, Odisha, India Brijesh Kumar Manav Rachna International Institute of Research and Studies, Faridabad, India Manoj Kumar Netaji Subhas University of Technology East Campus (Formerly AIACT&R Geeta Colony), Delhi, India Guriya Kumari Department of Electronics and Communication Engineering, GIET University, Gunupur, India Alyaa Abdulhussein Lafta Department of Communication, Engineering Technical College of Al-Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq R. Lilith Kumar Department of CSE, R.M.K Engineering College, Chennai, India Mehdi Ebady Manaa Department of Information Networks, College of Information Technology, University of Babylon, Babylon, Iraq Geetha Manoharan Faculty of Computing, College of Computing and Applied Sciences, University Malaysia Pahang, Kuantan, Malaysia Alaa Abdalhussain Mashkor Department of Electrical Engineering, University of Kufa, Najaf, Iraq Shyamsunder Merugu Department of ECE, Sumathi Reddy Institute of Technology for Women, Hasanparthy, Telangana, India Biswaranjan Mishra GIET University, Gunupur, India Brojo Kishore Mishra Department of Computer Science and Engineering, GIET University, Gunupur, India

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Deepanjali Mishra School of Humanities, KIIT University, Bhubaneswar, India Tusar Kanti Mishra Dept. of CSE, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India Daniah Mohammed Computer Science Department, Faculty of Computer Science and Mathematics, University of Kufa, Kufa, Iraq Yasmin Makki Mohialden Department of Computer Science, College of Science, Mustansiryiyah University Affiliation, Baghdad, Iraq A. B. Mukesh Kumar Behera Department of Electronics and Communication Engineering, GIET University, Gunupur, India A. Munawara Department of Information Technology, MEA Engineering College, Perinthalmanna, Kerala, India Sri Balaji Muruganandam Department of Electronics and Communication, Velammal Institute of Technology, Panchetti, Chennai, India P. Muthu Kannan Department of ECE, Saveetha School of Engineering, SIMATS, Thandalam, Chennai, India D. Muthukumaran Department of ECE, SCSVMV, Kanchipuram, India M. Nandhini Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India Navin Sridhar Department of Computer Science and Engineering, Venkateswara College of Engineering, Anna University, Chennai, India

Sri

Sailaja Nimmagadda VRSEC, Vijayawada, Andhra Pradesh, India M. Nitesh Kumar Sah Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India Ahmed J. Obaid Faculty of Computer Science and Mathematics, University of Kufa, City, Iraq S. Omkumar Department of ECE, SCSVMV, Kanchipuram, India Sara Paiva Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal TusharKant Panda Department of Electronics and Communication Engineering, GIET University, Gunupur, India Sonali Patwe Symbiosis International University, Lavale, Pune, India G. S. Pavithra Insicle, Chennai, Tamil Nadu, India Shraddha Phansalkar HoD, Computer Engineering, Symbiosis Institute of Technology, Lavale, Pune, India Urmila Pilania Computer Science and Technology, Manav Rachna University, Faridabad, India

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Swadhin Polei Department of Electronics and Communication Engineering, GIET University, Gunupur, India K. Pragathi VRSEC, Vijayawada, Andhra Pradesh, India S. Prasanna Bharathi Department of ECE, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani, Chennai, India Abhishek Pughazhendhi Department of Electronics and Velammal Institute of Technology, Panchetti, Chennai, India

Communication,

R. Puviarasi Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India Furkan Rabee Computer Science Department, Faculty of Computer Science and Mathematics, University of Kufa, Kufa, Iraq S. Radha Rammohan Dr. M.G.R Educational and Research Institute, Chennai, India R. Rajagopal P.S.R Engineering College, Sivakasi, Tamil Nadu, India Ch. Rajendra Prasad Department of ECE, School of Engineering, SR University, Warangal, Telangana, India C. G. Raji Department of Computer Science and Engineering, MEA Engineering College, Perinthalmanna, Kerala, India K. Rakesh Department of Computer Science and Engineering, RMK Engineering College, Chennai, India Mritha Ramalingam Faculty of Computing, College of Computing and Applied Sciences, University Malaysia Pahang, Kuantan, Malaysia P. Ramchandar Rao Department of ECE, Center for Embedded & IoT, SR University, Warangal, Telangana, India T. Ramesh Department of Computer Science and Engineering, RMK Engineering College, Chennai, India Wasan Kadhim Saad Engineering Technical College-Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq Ajit Kumar Sahoo School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, India Subhashree Sahoo Department of Computer Pondicherry University, Kalapet, India

Science

and

Engineering,

Bandita Sahu Department of CSE, VSSUT, Burla, India A. Sai Nandan Department of CSE, R.M.K Engineering College, Chennai, India B. Sai Sumanth Department of CSE, R.M.K Engineering College, Chennai, India

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C. Sai Vignesh Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India Om Prakash Samantray Department of Computer Science, Berhampur University, Berhampur, Odisha, India B. V. Santhosh Krishna Senior Assistant Professor, New Horizon College of Engineering, Bengaluru, India Sarthaja Department of Information Technology, MEA Engineering College, Perinthalmanna, Kerala, India S. Selvi Department of Computer Science & Engineeering, R.M.K. Engineering College, Gummidipoondi, Tamil Nadu, India Snehkumar Shahani Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India Thirumurugan Shanmugam Department of Information Technology, University of Technology and Applied Sciences, Suhar, Sultanate of Oman Sudhir Kumar Sharma Institute of Information Technology and Management, Janakpuri, New Delhi, India Srishti Shelke Department of CE and IT, College of Engineering Pune, Pune, Maharashtra, India Tatiana R. Shmeleva A.S. Popov Odessa National Academy of Telecommunications, Odessa, Ukraine Roaa Shubbar College of Information Technology, University of Babylon, Babylon, Iraq Munisamy Shyamala Devi Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India K. Sindhuja Department of Computer Science and Engineering, GIET University, Gunupur, India Amar Nath Singh Department of CSE, Amity University, Noida, Jharkhand, India V. G. Sivakumar Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, India B. Sreenidhi Electronics and Communication Engineering, Velammal Institute of Technology, Chennai, India Sridevi B. Department of Electronics and Communication, Velammal Institute of Technology, Panchetti, Chennai, India S. Srinivasan Department of Biomedical Engineering, Saveetha School of Engineering, SIMATS, Chennai, India

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Editors and Contributors

Sunita Institute of Information Technology and Management, Janakpuri, New Delhi, India Ravi Kishan Surapaneni VRSEC, Vijayawada, Andhra Pradesh, India H. R. Swapna JAIN (Deemed to be University), Bangalore, India P. Swathi Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India J. Tarun Kumar Department of ECE, School of Engineering, SR University, Warangal, Telangana, India S. Tharanee Shree Electronics and Communication Engineering, Velammal Institute of Technology, Chennai, India Satya Narayan Tripathy Department of Computer Science, Berhampur University, Berhampur, Odisha, India Siba K. Udgata School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, India K. Umapathy Department of ECE, SCSVMV, Kanchipuram, India Shubham Santhosh Upadhyay Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India M. Vadivel Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, India Niramay Vaidya Department of CE and IT, College of Engineering Pune, Pune, Maharashtra, India L. Vanitha Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, India Manisha Vashisht Manav Rachna International Institute of Research and Studies, Faridabad, India M. Venkata Akhil Department of Computer Science and Engineering, RMK Engineering College, Chennai, India A. R. Venmathi Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, India V. Vijaya Baskar School of EEE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India S. P. Vimal Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India A. Vinish Department of Information Technology, MEA Engineering College, Perinthalmanna, Kerala, India

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Yatharth Vohra Computer Science and Technology, Manav Rachna University, Faridabad, India Dmitry A. Zaitsev Odessa State Environmental University, Odessa, Ukraine Rana M. Zaki Computer Science Department, University of Technology, Baghdad, Iraq

Gaming: Around the World Geography Sunita, Tamanna Jain, and Sudhir Kumar Sharma

Abstract Video games have been popularized among youngsters. It has been observed since past few years that there is a sudden demand of video games in the gaming industry. It is proved as a boom in the gaming industry and has earned profit to the gaming industrialist. This research paper shows a brief description of gaming around the world. It contains history of gaming and its growth till now. This research paper is for new generation who wants to dive in the gaming industry. The Gaming industry has made huge profit since last three years. It attracts the youngsters from new upcoming products. Keywords Video games · Gaming geography · PlayStation · Nintendo · Sega Microsoft career

1 Introduction Video game can be described as a software that deals with media and entertainment. According to the net domestic product (NDP), about 91% of children between the ages of 2 and 20 play video games on various digital devices namely personal computers (PC), consoles, mobiles and tablets. The Entertainment Software Association (ESA) report for 2017 suggests that gamers are now more concerned with game graphics rather than other aspects that challenge game developers to make video games a reality. The gaming industry is growing day by day. According to the Playista.com, gaming industry will generate 2.43% year-on-year profit. The Companies such as Sony, Microsoft, Nintendo make various advances every year to run this race [1]. Video games were announced in 1958, and thereafter, its popularity increased rapidly over several decades. Above all, gamers feel that video games provide more value for their money than films and music. Games provide a range of interactive over cinematics and soundtracks. According to digital trends, games played on mobile Sunita · T. Jain · S. K. Sharma (B) Institute of Information Technology and Management, Janakpuri, New Delhi 110059, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_1

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devices also increased by about 32% when Android and IOS accounts have a lot of growth.1,2 Several theories have shown that besides having negative effects there are many positive effects on sports, mentally and psychologically. It totally depends on the game player and the type of game he is playing. Game content plays a big role in this. This research paper provides the information of gaming, types of video games, gaming industry and growth. The video game industry is an economic sector that involves the development, processing and monetization of video games. At a time when computers themselves were just entering the market, there was no predetermination. Today, we have provided graphics cards, sound cards and processes that are very efficient. Even the console videos of their video were of low definition because at the time games were very simple in graphics. Nowadays, we have well-designed video games that seems to be a reality, so they are looking for high quality plans. Future of gaming from the 1950s gaming industry is growing at a rapid pace. This research paper provides a brief description of gaming around the world. It contains history of gaming and its growth till now. This research paper is for new generation who wants to dive in the gaming industry. The Gaming industry has made huge profit since last three years. It attracts the youngsters from new upcoming products. This paper tracks the history, growth and aspirations of the gaming industry, provides insight into the future and discusses some of the most prominent types of video games. The paper is further divided into four sections. The second section describes detailed description of the paper. The third section is future of gaming. The conclusion is presented in the last section.

2 Detailed Description This section is further detailed into four sections namely history of gaming, types of gaming, gaming industry and growth and statistic of growth.

2.1 History of Gaming Playing is one of the most popular hobbies among teens, but it is not new. The Gaming was invented in the early 1950s by William Higginbotham. It was a simple tennis game, and later, in 1970, the first arcade marketing game “COMPUTER SPACE” was introduced by Nutting Associated [2]. 1 Google.scholars.in, 2 Nintendo.com,

Wikipedia.com. Sony.in.

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In 1977, Fairchild and Atari released their first video game marketing system which was the first game release program. The configuration of Atari’s video management system was as follows: • IMOS Microprocessor 6502 • Stella, a traditional chip of graphics that manages TV sync with other types of all video filtering functions • Uses RAM 128-bits • The 4-kilobyte ROM is based on game cartridges. The biggest change in the gaming industry came when Nintendo entered the market. In 1985, they introduced the Nintendo Entertainment system and everything changed. They introduced the Big Hardware to the gaming industry that used gameplay instead of starvation. A year later in 1994, Sony came up with its first PlayStation, and Microsoft introduced their Xbox in 2004. Both types of games are now in the game’s governing body [3]. There are different types of games including their size. The first 2D game developed in 1950. It was a tennis match. In 2D games, gamers can see one side of the game with its player. Only the size is visible on the screen, and the other one is hidden. Other popular 2D games were contra, Mario, etc. Some years later, the first 3D game came out of the market which was the “3D monster maze”. It was a kind of pseudo 3D game also known as two and a half features [4]. After several years, various companies came into this race such as Saga, Activation and Ubisoft. The game industry has grown a lot with a variety of different perspectives and ideas. Due to the technological enhancement, we can play games on smartphones which was not possible in old cellular phones. Gaming companies are rolling out various ideas for new games including 5D world, AR and VR games, etc., which will lead to a big change in the gaming world3,4 .

2.2 Types of Video Games Video games are the best way to relax your mind and spend time. There are different types of games we have in the country. Every game has its own style and style of play. Other types of video games are as follows: • 2D and 3D games • First person and third person shooter • Virtual reality (VR), augmented reality (AR) and mixed reality (MR) Games.

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2D and 3D Games

2D games can simply define them as a platform game in which a player can run, shoot, collect and jump on a platform. Axis and size play a major role in this. The size will be very difficult to develop. Simple 2D games have a player whose only side is visible while the other side is hidden. It applies to the X and Y axis of the graph. We cannot see the other side of the game that is over. 2D games were popular at the beginning of the gaming phase. Another game was a big hit like Mario, Contra, etc. But after a while, more and more 3D games were introduced, and now, our days are playing. 3D games was introduced in 1981 and changed the overall look of the game. 3D games were a completely different concept. In 3D games, developers have introduced various methods that has transformed games look logical. In 3D games, the gamer can move and look similar to a real person. In 3D games, the developers have given us a real 3D space to feel.

2.2.2

First-Time Shooters (FPS)/ Third-Party Shooters (TPS) Games

FPS stands for first-time shooters. In the field of video games after several years with development technology developers, they started to create camera control games where the user would be able to control the player and his camera to move and view in any direction. In that game, there was the idea of FPS and TPS coming. An FPS game is where the gamer only sees the player’s hand and weapons within the game. FPS is a simple game that represents gameplay from a character’s point of view. TPS stands for third-party shooters which means the gamer can see full player views and surroundings. On TPS, the user can see the player’s entire body and control it. TPS is a game where the camera is behind the character. Both FPS and TPS have their pros and cons. As with FPS, there are limited views of the surroundings, but at TPS, we have a great view of the surroundings. But at some point in the TPS character blocks, the foresight would be enemies.

2.2.3

AR/VR Games

AR represents Augment’s reality that adds a digital element to live viewing often uses a portable camera on a smartphone. In the game experience, one can feel the presence of objects and game characters near him. AR technology is used to display overlapping points in sports games that are broadcast on and out of 3D emails, photos or text messages on mobile devices. The industry leaders are also using AR to do amazing and transformative things with holograms and customized commands. VR represents virtual reality. Virtual reality is a new concept, as name implies virtual reality means, reality is introduced almost entirely. For a truly immersive experience, one needs VR glass. VR means assimilation experience that blocks the

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actual world. By using VR devices for instance the HTC, Oculus Rift and Google Cardboard, people can be carried to many actual world locations and imaginations. For virtual gamers who can play games using VR glasses that will give them a different experience. Gamer will be able to control the game from the motion of their hands and will be able to feel in games like the actual presence. The virtual reality is generally attained by using a headphone like the professional Facebooks accounts and is used importantly in two different methods. • Generate and develop VR for games, enjoyment and gaming (like video games and PC games or 3D videos like we see in movies how it uses graphic show). • Better real-life exercise by building virtual copy where people can apply before (like aviation for pilots). VR is possible in virtual reality modelling language encoding where can be employed to generate a number of images and identify types of connection that can be possible. Both of these technologies enable experiences that are often highly anticipated and sought for entertainment purposes. Whereas previously, it was seen as mere imagination of scientific fiction, a new world of consciousness came into being under the control of the user, and deep layers of interaction with the real world were realized. There is a new concept coming into the market called mixed reality (MR). Mixed reality is a concept where both AR and VR come together to make the digital world and the real world come together. This concept is currently being tested by Google on their “Google HOLO Lens”. This concept will make users feel that they are in an invisible world [5].

2.3 Gaming Industry and Growth The video game industry is an economic sector that involves the development, processing and monetization of video games. At a time when computers themselves were just entering the market, there was no predetermination. Today, we have provided graphics cards, sound cards and processes that are very efficient. Even the console videos of their video were of low definition because at the time games were very simple in graphics. Nowadays, we have a well-designed and well-designed video game that seems to be a reality, so they are looking for high quality plans. The Gaming industry has been started many years ago in 1950. The first video game company was Atari developed a simple tennis match, and it was a big disaster at the Brookhaven National Laboratory. After sometime in 1983, Nintendo had entered in the console world with the release of its first video game system NES. Nintendo quickly got a big market response thanks to its 8-bit conversion system. At that time, the NES was on sale for $ 249. The success of NES is a major setback for Atari due to Nintendo’s graphics experience and sound system.Since the release of NES managing in the market, Nintendo had forced Atari to ship. As a result, Atari gave up.

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In an attempt to keep it secure further credit, Atari stopped the production of their programs and their effort on publishing of creating video games. Infect Nintendo released Super Mario Bros game. It was the fastest growing console [6]. Instant NES types are as follows: • • • • • •

The CPU: Nintendo has 2AO3 8-bit processor running at this 1.79 MHZ Main RAM: 2 KB Palette: 48 colours and five colours in the base palette; red, green and blue. Sprite Dimensions: 8 × 8 and 8 × 16 pixels Maximum screen count of sixty four (64) Nintendo’s next big hit is the NES Wii and the NES GameCube.

After Nintendo, another opportunity came on the market, while SEGA came in. Sega has decided to introduce its first console in the market, and they are launching “Sega Genesis”. Sega genesis began to meet with replicable hardware and a dedicated graphics system. The market went straight to Sega, and they gained 92% of the market value after selling their millionth stock. Nintendo had a Super Mario as their trademark Sega needed a product, so they came up with the idea for SONIC. In 1994, the NES partnered with Tony Entertainment to develop a video game where Sony was about to launch its first console under the name Nintendo, but due to a licensing problem, Nintendo threw Tony into the project. But, the Sony do- not wanted to surrender, and therefore, Sony’s president, Norio Ogha, decided to start their own company listed as the Sony Computer Entertainment Division. The PlayStation would initially play both NES games and PS games, but neither Nintendo nor Sony agreed to allocate their hardware rights. Tony decided to focus on bringing the experience of the next 3D 32 bits games to market [7]. The Sony released their first concert on 2 Dec 1994 and made a huge profit. Tony had the upper hand from Sega because of its easy-to-use interface and easy gamer development [8]. Quick PS-1 types are as follows: • • • • •

The CPU has 32-bit R3000A RISC running at this 33.9 MHz RAM is 16 Mbits Range is 8 Mbits Palette is 16.7 million colours. The resolution is 256 × 224 minus 740 × 480.

After the PS1, Sony introduced one of its concerts such as the PS2, PS3, PS4, and most recently, the PS4 pro which supports 4 K games. In 2002, Microsoft decided to enter the gaming market and launch its first video game called XBOX. The Xbox is based on a personal computer and has an Intel Pentium 3 processor with an 8 GB hard drive. After a while, Xbox is no longer available in the market because its Xbox 360 successor is introduced with more powerful specifications. The other versions are Xbox One, Xbox One X and Xbox One S, and both support 4 K games2.

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Fig. 1 Graph of gaming elements

2.4 Statistic of Growth According to developer in gamers, nowadays, care most about the graphics of games and after that all other factors like story, gameplay, challenges, etc. The current percentage ratio of all the element of the gaming Industry are shown in Fig. 1.5 As depicted in Fig. 1, the quality of graphics plays the huge role in gamer’s life other than that price plays another role. Price of a game matters a lot because the game should justify its price. After that, other factors come like story plot and online capabilities. In Fig. 2 the sales of gaming console is shown, the Nintendo released the first gaming console with best graphic quality, but when Sony enters in market from that day in terms of gaming experience and graphic quality, Sony is ruling the market. Sony is giving best quality of products to their users. In same field, Microsoft is also producing best quality of products as Xbox, but still Sony’s PlayStations are performing best and in Demand. According to the Graph by Fortune.com, Nintendo’s demand suddenly raised and crushes other brands in competition. Where Sony was on second most seller, and Microsoft was at third.6

5 Statistics 6 Statistics

by Statista.com. by Statista.com.

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ALL-TIME HOME AND HANDHOLD CONSOLE SALES BY COMPANY Nintendo crushes the competitions for totl number of consoles sold when you combine home and handheld offerings NINTENDO SONY MICROSOFT MICROSOFT SEGA ATARI NEC COLECO BANDAI MAGNAVOX/PHILIPS MATTEL NOKIA

0

200 400 TOTAL SALES IN…

600

800

Fig. 2 Graph of gaming consoles

According to Fig. 3, there are different platforms on which we can play games such as PC, mobile phones, tablets, etc. Out of which, PC/MAC comes at top while on

The Most Important Gaming Plaƞorms in 2019 Mac AR Headsets VR Headsets XBOX One/X Smartphones/Tablets PS4/Pro Nintendo Switch PC

0%

10%

20%

Most interested in Fig. 3 Graph of gaming platforms

30%

40%

50%

Currently developing for

60%

70%

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second smartphones and tablets. The Demand of PC based games is very huge, and the reasons are as follows: 1. 2. 3. 4.

PCs can be upgraded to higher specification after years. PC can play any type of games. User can find free games as well as paid games for PC on platforms like Steam. Price and variety of games are more on PCs as compared to consoles.

The Smartphones are one of the new platforms of gaming. There are huge fans of smartphone gaming. Games like “Clash of Clans”, “Candy Crush”, “PubG”, etc., Smartphone is a portable device which is easy to carry anywhere so that is why smartphone gaming is also in demand. After smartphones, console gaming is on third where PS4 pro are on top and all other console are below them. The Gaming industry is a huge world, Creating a game is not an easy task. One person cannot do this, requires a whole team. There are various different departments in gaming including designing, animation, programming, testing, etc., all of these departments have their own work to do, and it takes several months or years to create a best video game. Most of the youngsters are nowadays looking for their career in gaming industry. Gaming industry has numerous options for those who wants to join gaming industry. One can be a programmer, designer, tester, etc., or if one loves to play game, then one can be a professional gamer. There are many professional players who participate in competitions and win prices and also get paid to play and review games. The Gamer can also be a YouTuber where he/she can do live stream and gameplays and can earn good amount from YouTube.

3 Future of Gaming From the 1950s, gaming industry is growing at a rapid pace. Today, gambling is a great platform for teens and is at that level where one can choose a sports game as a full-time job with it. Therefore, we can predict that the gaming industry will grow again in the future. Companies are introducing many new concepts for games and machines that have great potential and bring real-time gaming experiences. Many companies are already adapting the idea of AR and VR where there is a game concept to deliver future results. PlayStations also offer a dual shock controller through their browser that produces vibration effects in the hands of players when such events occur in the game [9]. . The gaming industry has been transformed into multiplayer platform in which multiple players can participate from different geographical locations in real-time. Many more games are needed and will be as time flies like Counter Strike, Player Anonymous Player, Resident Evil, Pubg, etc.

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Fig. 4 Graphs of game types

The different types of communication is gaming industry are shown in Fig. 4. it is up to us what kind of games we want to play like Horror, Shooters, Racing, Open World, etc.7,8,9 According to statement.com, shooting games are up from 2 years ago. Games such as Counter Strike and DOTA 2 are required among teens. So, when we talk about the future of the gaming industry, then somewhere, there are bright and high growth opportunities because of all the new things and ideas. AR and VR are the future of games.

3.1 Benefits of Gaming Gaming is not just a way to pass the time it plays a major role in one’s life. Sports games affect riders in psychological ways too. It totally depends on the game. Studies have shown that gambling improves the problem-solving skills of the user and also provides the ability to think differently to solve problems. Games not only work mentally but also help to advance knowledge in the IT industry. 7 Gamehub.com. 8 Nintendo.com, 9 Competitive

Sony.in. Gaming book, Sega.com.

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Gaming is also useful to the gamer because it is a medium way where the gamer can earn money by becoming a professional gamer or joining the game development industry. Therefore, one can choose gaming as a career. Some of the game’s benefits are as follows: • • • • • • •

Develop collaboration Improve brain speed Improve focus Develop problem-solving skills Develop multitasking skills Develop social skills Improve memory.

There are some bad points to play with such as violence, some adult content, but on the other hand, playing games has a huge amount of benefits. Therefore, the gaming industry is a good career choice.10,11

3.2 Tools and Techniques There are many tools and technologies for beginners and game developers, and it has never been easier to create play tools without programming language. But nowadays, game development tools are available online where engineers and beginners can create games easily and quickly. The best game developing tools for beginners in 2020 are Construct2, GameMaker: Studio, Unity, ClickTeamFusion2.5, GameFroot, Sploder, Godot Engine, Stencyl, Flowlab and GameSalad.12 Presently the following technologies are used for developing games: Unreal Game Engine, Unity Game Engine, SDK’s, Visual Studio, C ++, Kismet, Python, which support animation and rigging effects. There are many ways to create a tool like Agile project management, waterfall project management, live cafe, triage, an interactive meeting that combines information from management to developer management and back. The choice of technology for developing game is constrained by financial and human resource expert [10].

4 Conclusion Ultimately, we can conclude that the gaming industry is a big world. There are hundreds of companies, thousands of products and millions of customers, and this rate will increase significantly in the future. Developing a game is not an easy task, 10 Journal

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11 Gamehub.com. 12 blog.sagipl.com.

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and it is a company day and night effort. Is it a public duty to buy games and play them rather than downloading broken genres online? We support the developer and the company. From 1950 until now, the 2018 gaming industry has changed a lot in terms of graphics, gameplays, technologies and devices. More compositions will come in the future, and those ideas will transform gamers into a complete gaming experience and that time is not yet far from where we will be able to experience the gameplay as a real-world experience.

References 1. Dale G, Shawn Green C (2017) The changing face of video games and video gamers: future directions in the scientific study of video game play and cognitive performance. J Cog Enhance 1(3):280–294. https://doi.org/10.1007/s41465-017-0015-6 2. Murphy BJ (2019) Two-worlds theory: when offline and online worlds begin to blur. In: The Transhumanism Handbook (pp. 637-643). Springer, Cham 3. O’Donnell C (2010) The nintendo entertainment system and the 10NES chip: carving the video game industry in Silicon. Game Culture 6(1):83–100. https://doi.org/10.1177/155541201037 7319 4. Lin C, Lee S (2010) The competition game on hub network design. Trans Res Part B: Method 44(4):618–629. https://doi.org/10.1016/j.trb.2009.09.002 5. Hutzschenreuter T, Burger-Ringer C (2018) Impact of virtual, mixed, and augmented reality on industries. 6. Palomba A (2020) Gaming industry. The Rowman & Littlefield Handbook of Media Management and Business 2:285 7. Marchand A, Hennig-Thurau T (2013) Value creation in the video game industry: industry economics, consumer benefits, and research opportunities. J Inter Market 27(3):141–157 8. Kushner D (2006) Beating Sony at its own game [copyright protection]. IEEE Spectrum 43(2):57–59 9. Zackariasson P, Wilso TL (eds) (2012) The video game industry: formation, present state, and future. Routledge 10. Chover M, Marín C, Rebollo C, Remolar I (2020) A game engine designed to simplify 2D video game development. Multi Tool Appl 79(17–18):12307–12328. https://doi.org/10.1007/ s11042-019-08433-z

Smart Blind Stick for Blind People Urmila Pilania, Ashwani Kaushik, Yatharth Vohra, and Shikhar Jadaun

Abstract People who did not have the ability to see the world find difficulties in seeing the hurdles in front of them, which can make their life in danger. The smart blind stick will act as a way to identify the world around them. In this paper, we represented a smart blind stick with ultrasonic sensors to detect any hurdles and also use IR camera to identify that hurdle in front of the user with the help of Google API, within a range of 1 m. Speech warning messages will be given to the user if any hurdle is detected by the sensor. This blind stick uses a microcontroller called “Arduino Nano.” The stick has a feature of sending an SOS message to the guardian which was inserted in the system with the location of the “blind stick” holder with Google Map link. The stick can detect in the range of 1 m and give an alert message to the user which makes the blind to move 2 times faster of his usual speed. The smart stick is budget-friendly, fast responsive, consuming low power, portable and also a lightweight device. Keywords Smart blind stick · Arduino nano and ultrasonic sensor

1 Introduction Visually challenged people are the ones who cannot recognize the small details with their eyes. People having visuals accuracy of 6/60 or the horizontal extent of visual arena with their eyes open a smaller amount about 20°, and then these people are supposed to be blind [1]. Such people are in need of kindaa devices which can help them for fighting with these disabilities. As mentioned in [2], 10% of these people have unstable eyesight at all to help them move around independently and safely. Hence, special devices are intended to resolve this kind of issues. To get the information about the obstacle in front, active or passive devices can help such people. Passive sensor is able to just accept a signal [3]. It identifies the reproduced, released or communicated electromagnetic energy delivered by usual energy sources. Active U. Pilania (B) · A. Kaushik · Y. Vohra · S. Jadaun Computer Science and Technology, Manav Rachna University, Faridabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_2

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sensor releases a signal and accepts a biased version of the redirected signal. These sensors are able to identify reproduced replies from items exposed with artificially produced energy sources. These sensors are also able to sense and notice distant and close hurdles. Additionally, these sensors can find precise measurement of the distance among blind and hurdles [4, 5]. In smart blind stick, four active sensors were used: infrared, laser, ultrasonic and radar sensors. Therefore, before the user tries this device, he or she must be aware of its uses for this purpose training which is essential. With the help of training, user can properly understand its functioning [6]. User must be aware of the signals generated and able to handle real-time problems with the help of this device. But major problem is that such type of training is generally expensive than the device itself. So many of the users are not able to afford this training [7]. In the proposed paper, shortcoming of the existing papers is removed as listed below: • Designed a “smart blind stick” to identify hurdles and its ability to distinguish and communicate loudly the hurdles as shown in Fig. 1. • Training provided for understating the smart device is comparatively not as costly as for other smart sticks proposed in existing papers. • A complete description about smart stick is provided with its uses, how to handle hurdles, position of hurdles, etc. • Provided two services to communicate required information to the users. Integrated Google API for speech warning messages that preserving with its usual dimension to make it more user-friendly device [6]. • With the support of earphones, the speech cautioning messages are capable of expressing noticeably cautioning information to blind user instead of inconceivable sound and public awkwardness [8]. • Attained good feedback time of approximately 5 ms in average distance 1, and a, b, c, d be arbitrary integers then the following properties hold: (1) (2) (3) (4)

a ≡ a(modn). If a ≡ b(modn), then b ≡ a(modn). If a ≡ b(modn) and b ≡ c(modn), then a ≡ c(modn). If a ≡ b(modn) and c ≡ d(modn), then i. ii. iii.

(5)

If a ≡ b(modn), then i. ii.

(6)

a + c ≡ b + d(modn). a − c ≡ b − d(modn). ac ≡ bd(modn). a + c ≡ b + c(modn). ac ≡ bc(modn).

If a ≡ b(modn), then a k ≡ bk (modn) for any positive integer k.

Theorem 2 The linear congruence ax ≡ b(modn) has a solution if and only if d|b, where d = gcd(a, n). If d|b, then it has d mutually incongruent solutions modn.

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Corollary 1 If gcd(a, n) = 1, then the linear congruence a ≡ b(modn) has a unique solution modulo n. Theorem 3 The system of linear congruence below ax + by ≡ r (modn) cx + dy ≡ s(modn) has a unique solution modulo n whenever gcd(ad − bc, n) = 1.

3 Hill-Cipher Algorithm In cryptography, one of the applications of modular arithmetic is Hill-Cipher [9]. It uses linear congruence techniques to encrypt and decrypt data. The main idea of using linear congruence in Hill-Cipher is the multiplication of linear congruence times its inverse. As a result of the difficulty of applying the cryptanalysis techniques like frequency analysis on Hill-Cipher algorithm, then this algorithm is hard to break. Also, Hill-Cipher is hard to be breakable if the hacker has only the ciphertext; however, it will be easily broken if they have part of the plaintext. Moreover, both of the sender and receiver should generate the key for the encryption and decryption processes [4]. Also, gcd(ad − bc, n) = 1, and before the encryption, the plaintext is broken into block of size n. We will discuss the example below to illustrate Hill-Cipher algorithm. Example 1 Consider the congruence below. C1 ≡ 2P1 + 3P2 (mod26) C2 ≡ 5P1 + 8P2 (mod26). To encrypt the plaintext “RIFAAT,” then the first block is “RI” is numerically equivalent to R = 17 and I = 8. This is replaced by: C1 ≡ 2(17) + 3(8) ≡ 58 ≡ 6(mod26) C2 ≡ 5(17) + 8(8) ≡ 149 ≡ 19(mod26). Decoding requires solving the original system of congruence for P1 and P2 in the terms of C1 and C2 . The plaintext block P1 P2 can be recovered from the ciphertext block C1 C2 by the congruence. P1 ≡ 8C1 − 3C2 (mod26) · P1 ≡ 8(6) − 3(19) ≡ 17 P2 ≡ −5C1 + 2C2 (mod26) · P2 ≡ −5(6) + 2(19) ≡ 8.

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Fig. 1 Figure illustration of three-pass protocol

which is the same as the first block “RI.” The remaining plaintext can be restored in a similar way.

4 Three-Pass Protocol The three-pass protocol is a technique used by the sender to send messages securely to the receiver without the requirement of exchanging the keys in advance [6]. In this technique, the message is encrypted, using the sender encryption key, and sent by the sender to the receiver. The receiver encrypts the received encrypted message using his own encryption key; then sends it back to the sender. The sender decrypts the received encrypted message using his own decryption key (the inverse of his own encryption key); then sends it back to the receiver. Finally, the receiver decrypts the received decrypted message by his own decryption key (the inverse of his own encryption key) to get the original message [4]. Figure 1 illustrates the technique above.

5 Three-Pass Protocol in Hill-Cipher (TPP_HC) The standard Hill-Cipher algorithm converts the plaintext into ciphertext with first key then back to plaintext again with the second key. However, to apply three-pass protocol in Hill-Cipher algorithm (TPP-HC), then the original message will not be gotten from the converting of ciphertext directly because this converting will give a different order of the characters. Also, the standard Hill-Cipher algorithm can be used for any size of linear congruence, while applying three-pass protocol in Hill-Cipher requires n = 2. That is, the encryption formulas are C1 ≡ a P1 + b P2 (modn) C2 ≡ c P1 + d P2 (modn)

(1)

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Also, the decryption formulas are P1 ≡ aC1 − bC2 (mod26) P2 ≡ −5C1 + 2C2 (mod26)

(2)

6 Attack If the attack could get the transmitted messages between two parties and could know the ciphertext C1S , C2R , and C3S which are sender’s ciphertext, receiver’s ciphertext, and again sender’s ciphertext, respectively, then (TPP-HC) system will be broken. This system will be broken by depending on C3S and finding its inverse. After that, the sender’s encryption key K S will be calculated, even if the receiver’s encryption key K R was not known. Therefore, the plaintext will be found for whatever the size of the used matrix was in the encryption process. That is, C1S = M · K S

(3)

C2R = K R · C1S = K R · (M · K S )

(4)

C3S = C2R · K S−1 = K R · C1S · K S−1 = K R · (M · K S ) · K S−1 = K R · M

(5)

Then, K S is calculated as below −1 · C2R = (K R · M)−1 · K R · (M · K S ) C3S

= M −1 · K R−1 · K R · M · K S = KS Now, after fining the encryption key K S , then the plaintext can be found easily as below: C1S · K S−1 = M · K S · K S−1 = M To explain above, we will discuss the example below. Example that is M  =   “RIFA,”  1 For simplicity, suppose, we have the message  17 8 5 10 21 , and let the sender and receiver keys are K S = and K R = . 5 0 2 6 55 Also, to avoid the case of the determinant to have common factors with modulus, we use the modulus 29.

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Then, K S−1



18 28 = 23 15

 and

K R−1



 1 23 = . 28 12

Now, 

     17 8 5 10 14 15 C1S = M · K S = · . (mod29) = 5 0 2 6 25 21       21 14 15 24 22 · . C2R = K R · C1S = (mod29) = 55 25 21 21 6       24 22 18 28 10 16 −1 · C3S = C2R · K S = . (mod29) = 21 6 23 15 23 11 Then, calculating −1 C3S



23 14 = 2 13



Therefore, KS =

−1 C3S



· C2R

     23 14 24 22 5 10 = · . (mod29) = 2 13 21 26 2 6

Finding M = C1S · K S−1 =



     14 15 18 28 17 8 · . (mod29) = 25 21 23 15 5 0

7 Conclusion Attacking the ciphertext in the standard Hill-Cipher algorithm, which depends on distributing the keys between the sender and receiver, is very difficult to be done. The reason for the difficulty is if we have a matrix of a size N , then there exist 26 N ×N unique keys to decrypt the ciphertext and getting the plaintext. For example, when N = 3, then there exist approximately 269  5.43 × 10−12 keys to check all of these matrices and that is considered as an impossible case; it will take about 8 years for a normal personal computer to check all of cases, while attacking the threepass protocol in Hill-Cipher algorithm is easy by depending only on the ciphertexts between the sender and receiver and then finding the inverse matrix for them and

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that leads to find the plaintext. Also, if the alphabetical space is not a prime number (N = 26), then there is possibility that the plaintext will not be gotten because there is no inverse for the received ciphertexts.

References 1. Rahim R, Ikhwan A (2010) Study of three pass protocol on data security. Int J Sci Res (IJSR) 5(11):102–104 2. Gupta A, Walia NK (2014) Cryptography algorithms: a review. Int J Eng Dev Res (IJEDR) 2(2):1667–1672 3. Abdullah AA, Khalaf RZ, Habib HB (2019) Modified BB84 quantum key distribution protocol using legendre symbol. In: 2nd scientific conference of computer sciences (SCCS). University of Technology-Iraq, IEEE 4. Siahaan APU (2016) Three-pass protocol concept in Hill Cipher encryption technique. Int J Sci Res (IJSR) 5(7):1149–1152 5. Toorani M, Falahati A (2009) A secure variant of the Hill Cipher. In: Symposium on computers and communications. IEEE 6. Abdullah AA, Khalaf R, Riza M (2015) A realizable quantum three-pass protocol authentication based on Hill-Cipher algorithm. Math Probl Eng 2015:6, Article ID 481824. https://doi.org/10. 1155/2015/481824 7. Rahman M, Nordin A, Abidin AFA, Yusof MK, Usop NSM (2013) Cryptography: a new approach of Classical Hill Cipher. Int J Secur Appl 7(2):179–190 8. Rosen KH (2011) Elementary number theory and its applications, 6th edn. Addison-Wesley, Pearson 9. Shibiraj N, Tomba I (2018) Modified Hill Cipher: secure technique using latin square and magic square. Int J Comput Sci Eng 6(12):315–320

Secure Data Sharing Based on Linear Congruetial Method in Cloud Computing Rana M. Zaki, Teaba W. A. Khairi, and Akbas Ezaldeen Ali

Abstract Each day the need for cloud computing increases to provide flexible storing and cloud services and according to the users’ wishes. If a certain owner of data wishes to share this data with another user, the security of data will form a complicated problem. In the current research, a secure data sharing and storing system is suggested on the cloud, with offering data secrecy and access control authenticity for data. The suggested model reduces the load on the central management and offers encryption and decryption for the files shared by the users. The suggested system consists of three fundamental entities: cloud service (CS), certificate authority (CA), and users, where CA is the trusted entity to generate keys and distribute them on other entities and to encrypt and decrypt files. Keywords Data sharing · Cloud computing · CA · CS

1 Introduction Cloud computing is an Internet-based computer that shares information and resources with everyone. Cloud computing supports the infrastructure of services of multiple distributed fields and for multiple users [1, 2]. The organizations use high cloud services within its budget and do not invest in the infrastructure and maintenance of services offered by the cloud users, and this leads to a loss of control on the data and cloud computing which results in security problems for these organizations [3]. The research is organized as follows:

R. M. Zaki (B) · T. W. A. Khairi · A. E. Ali Computer Science Department, University of Technology, Baghdad, Iraq e-mail: [email protected] T. W. A. Khairi e-mail: [email protected] A. E. Ali e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_13

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The second section presents the related works achieved in terms of secure sharing with encryption techniques on the cloud. In the third section, the proposed model is presented to build a secure and safe system for sharing and storing data on the cloud, while the fifth section will illustrate the conclusions [4].

2 Related Work Many researches and studies have been conducted, and they are related to the current direction: Mazher et al. [3]: Sharing data within a group is about controlling the cloud access for the users where the security issue raised. This problem was solved by the researchers, by suggesting the methodology of SeDaCS by presenting an encryption/decryption server if a user wishes to upload the shared file on the cloud along with the private key that is used to encrypt the data. The suggested methodology presented secrecy and privacy for data and security for sharing data. Ching-Hung et al. [5]: The researchers presented a methodology to use the public key encryption PKE to share data for a group of users on the cloud, and this methodology included solving the problems of key distribution and secure data access through generating keys, distributing these keys on users, and data encryption. Uma et al. [6]: The researchers have presented a model to solve authenticity and security problems of data and access to these data by a group of users on the cloud using encryption and digital signature. MdMozammil et al. [7]: The cell phones are used to upload, download, and share data, but the storage capacity of these devices is limited. The suggested solution through this research is the owner of the data encrypts them using Blowfish algorithm.

3 Proposed System The system consists of three models they are certificate authority (CA) model, users (Owner and Clients) model, and cloud service (CS) model. Figure 1 illustrates the system architecture. The proposed system model for safe data sharing on cloud computing with intension is to provide data confidentiality and access control over shared data, and it also removes the burden of key management and files by users. The system also supports dynamic changes of membership and enables clients to reach the data they require even when the owner does not exist in the system. Key Generation The suggested security system demands the generation of three types of keys:

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Fig. 1 Architecture proposed system

1. 2. 3.

Public key (PK) Private key (PRK) Symmetric key (SK).

The key generation center is connected to the entity CA that distributes the public and private keys to other entities of the system, while it keeps the symmetric key. Figure 2 shows the generation of keys using LCM. The linear congruential method produces a sequence of integers (x 1 , x 2 , x 3 , …) between zero and (m − 1) according to the following recursive relationship: xn+1 = (axn + c) mod m n = 0, 1, 2, . . . where the initial value x0 is called the seed. a is called the constant multiplier c is the increment m is the modulus. The selection of the values for a, c, m, and x0 drastically affects the statistical properties and the cycle length. The random constant numbers D resulted from the LCM method are transformed into binary code to obtain a symmetrical key (SK). Prime pairs of numbers are selected (pi , qi ) equal to the number of entities in the system in order to find n i = pi qi , and then ei , qi are selected for each ni that achieves

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Fig. 2 Block diagram of keys generation

ei .di mod ϕ(ni ) = 1 where ϕ(ni ) is Euler function. Algorithm 1 Keys generation

Input: a, c, m and x o Output: public, private and symmetric keys Step 1

Using LCM method, a sequent set of constant numbers is obtained between (0 – m −1)

Step 2

The resulting sequent in the previous step is transformed into binary code to obtain the symmetric key (continued)

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(continued) Step 3

Arranged pairs of (pi , qi ) are chosen from prime numbers, number of these pairs is equal to number of system entities and belong to the interval [0, m−1]

Step 4

Find n i = pi qi , i represent the number of system entities

Step 5

For each ni , d i and ei are selected so that ei .di mod ϕ(n i ) = 1 is achieved

Step 6

(d i , ni ) represents the private key, while (ei , ni ) represents the public key

Notation Table 1 shows the list of notations with description, which we have in our proposed model: Encryption/Decryption CA is the entity that is responsible for encrypting files using symmetric key and stream cipher system. Encryption: F  = F ⊕ SK Decryption: F = F  ⊕ SK. The Digital Signature: Digital signature is achieved by the system entities through encrypting the messages with the use of private key. M 1 = File-id, owner-id M 1 = File-id, owner-id, client-id. Digital signatures are extracted using public keys. Table 1 Notation with description

Notation

Description

M1

Message = File-id, owner-id

M2

Message = File-id, owner-id, client-id

F

Plain text file

F

Cipher text file

CS

Cloud service

ACL ,

M1 M2

Access control list 

Cipher message

PKown , PRKown

Public and private keys of the owner

PKCA , PRKCA

Public and private keys of the CA

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Fig. 3 File upload

File Upload The owner sends a request to the CS includes a list that contains File-id, user-id, and access control for the users that wish to access the files indicated in the list, where the owner encrypts the list using his/her private key (PRKown ) and RSA algorithm: ACL = PRKown (ACL) CS extracts the information from AC L  list using the owner’s public key   ACL = PKown ACL The information’s are placed in a CS table. If the owner wishes to share the users some data in file (F), a request is sent by the owner to CA includes the file (F) and message (M 1 = file-id, owner id), and this message is encrypted with the owner’s private key (PRKown ) and RSA algorithm (Fig. 3)    M1 = PRKown M1

4 Experimental Results This item includes the results of applying the proposed model, and first keys are obtained by the entity CA using LCM method; Table 2 shows the generation of (50) random numbers of length (2).

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Table 2 50 random numbers

96 – 11 – 86 – 61 – 36 – 47 – 13 – 09 – 85 – 41 – 77 – 93 – 89 – 65 – 21 – 57 – 73 – 69 – 45 – 01 – 37 – 53 – 49 – 25 – 81 – 17 – 33 – 29 – 05 – 61 – 97 – 13 – 22 – 85 – 26 – 35 – 11 – 19 – 83 – 95 – 91 – 59 – 03 – 55 – 71 – 99 – 23 – 15 – 51 – 39

m = 100 x0 = 13 initial a = 5 c = 31 In order to test the randomness of the number listed in table (–) Chi-Square test and independence tests are used. Table (–) shows that N = 50 and the partial intervals number (n = 5), and it is found that the expected numbers in each partial interval is (i = 1, …0.5) Ei =

N = 10 n

and number of views Oi , hence: c=

5  (Oi − E i )2 = 1.8 Ei c=1

2 c = 1.8 < x0.05 (4) = 9.49.

The numbers are uniformity distribution, and in order to test the independency of number, a sequential of (−) and (+) signals is found for the numbers shown in table (—), and then the total numbers of users is found: Total number of users = 26, and E = 33, V = 8.6 Therefore: Q = −2.4 < Q t = 1.96 The random numbers are independent. The keys used in the system are found after testing the randomness and independence of the generated numbers in the suggested generator. The random numbers shown in table (–) are transformed into binary code to generate the symmetric key, which will be used by the entity CA. Prime numbers are found from table (–), and they are as shown in Table 3. Pairs of prime numbers are chosen from table (–) (pi , qi ), where (i = 1, 2, …, k) and (k) are the number of entities in the suggested system as shown in Table 4. For each entity, we determine:

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Table 3 Prime numbers 11

61

47

13

41

89

73

37

53

17

29

5

61

97

19

11

19

83

59

3

71

32

Table 4 Prime numbers (pi , qi ) (pi , qi )

(11, 61)

(47, 13)

(41, 89)

(73, 37)

(53, 17)

(29, 5)

(61, 97)

(13, 11)

(19, 83)

(59, 3)

(71, 23)

n i = pi ∗ qi Q i = ( pi − 1)(qi − 1) Then, public (PKi ) and private keys are found (PRKi ) so that: P K i ∗ P Rki mod Q i = 1 n 1 = n owner = p1 ∗ q1 = 11 ∗ 61 = 671 Q 1 = Q owner = 10 ∗ 60 = 600 n 2 = n C A = p2 ∗ q2 = 47 ∗ 13 = 611 Q 2 = Q C A = 46 ∗ 12 = 552 Table 5 shows public and private keys for both owner and CA and as so for other entities of the suggested system. Table 6a shows the ACL list that the owner wishes to send to CS which contains files to which users want to access. Also, Table 6b illustrates the ACL list after being sign by using RSA algorithm and the private key (PRK1 ). Figure 4 shows file (F 1 ) with the message (M 1 ) (file_id, owner_id) that the owner wishes to upload it on the cloud in order to share some data of that file with a number of users. Figure 5 shows file (F 1 ) with the message (M 1 ) after being signed using the RSA algorithm and the private key (PK1 ) and send it to CA. CA uses the public key of the owner (PK1 ) to extract the message (M 1 ), and then it uses the symmetric key (SK) to encrypt the file (F 1 ) through stream cipher encryption to obtain the encrypted file (F 1  ). This file is sent to CS with the message (M 1 ) after signing it with the use of RSA algorithm and its private key (PRK2 ) as shown in Fig. 6. After the CS receives the encrypted file (F 1  ) and it extracts the encrypted message (M 1  ) using RSA algorithm and the private key of CA (PRK2 ), both the encrypted file (F 1  ) and message (M 1  ) are stored in a table owned by the CS until they are called by other users.

Secure Data Sharing Based on Linear Congruetial Method … Table 5 Public and private keys of owner and CA

Table 6 ACL

Owner

137 CA

Public

Private

Public

Private

7

343

5

221

11

491

7

631

13

277

11

251

17

353

13

637

19

379

17

617

23

287

19

523

31

287

25

265

29

269

29

533

31

271

31

463

37

373

35

347

41

161

37

373

43

307

41

377

47

383

43

475

49

649

47

599

53 .. .

317 .. .

49 .. .

169 .. .

File ID

Client ID

File access control

(a) 5

50

Yes

17

35

Yes

18

33

No

19

47

Yes

125

127

Yes

137

87

Yes

150

56

No

163

122

Yes

(b)

5 Conclusions We suggested a methodology to share the security of information and digital signature among several users with certificate authority and cloud service. The suggested system presented a technique to generate three types of keys by the center of key generation that is related to the certificate authority entity, which manages public and private keys and distributes them to the entities of the system. While the symmetric

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File ID: 5 Owner ID: 9

Fig. 4 Plain file

File ID: 127 Owner ID: 125

Fig. 5 Plain file with signature

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File ID: 145 Owner ID: 97

Fig. 6 Cypher file with signature

key is saved to be used for encryption and decryption processes performed on the uploaded files that meant to be stored on the cloud computing device, the public and private keys are used for authenticity. The suggested system also presented a technique to share files that users wish to access, where the owners of these files are not available always on the Internet.

References 1. Sivarakthi T, Prabakaran N (2014) Applying digital signature with encryption algorithm of use authentication for data security in cloud computing. Int J Innov Res Comput Commun Eng 2(2):456–459 2. Xu P, Jiao T, Wu Q, Wang W, Jin H (2016) Conditional identity-based broadcast proxy reencryption and its application to cloud email. IEEE Trans Comput 65(1):66–79 3. Ali M, Dhamotharan R, Khan E, Khan SU, Vasilakos AV, Keqin L, Zomaya AY (2015) Secure data sharing in clouds. Syst J 99:1–10 4. Han J, Susilo W, Mu Y (2013) Identity-based data storage in cloud computing. Future Gen Comput Syst 29(3):673–681 5. Ching-Hung YA (2013) Secure shared group model of cloud storage. In: Advanced information networking and applications. Workshopsp., pp 663–667 6. Somam U, Lakhani K, Mundra M (2010) Implementing digital signature with RSA encryption algorithm to enhance the data security of cloud in cloud computing. In: Parallel distributed and grid computing, pp 211–216

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7. Alam MM, Hati S, De D, Chattopadhysy S (2014) Secure sharing of mobile device date using public cloud. In: Confluence the next generation information technology summit, pp149–154

Evaluating the Effects of Varying Model Parameter Values on the Characteristics of a Photovoltaic Module Ameer Ali Kareem, Alaa Abdalhussain Mashkor, and Naseem K. Baqer

Abstract This paper conducted a review of the most important features and parameters concerned with the modelling of a photovoltaic (PV) cell/module. The primary focus was on the photovoltaic cell, the essential component in the transformation of light into electric current. Furthermore, for the purposes of the present study, it has been depended on previous research to design a simulation model of a photovoltaic cell and module using MATLAB to represent the PV cell and the module. Additionally, based on existing models, the current–voltage and power–voltage characteristics of a photovoltaic module were calculated by varying the values of certain parameters, such as irradiance (G), temperature (T ), series resistance (Rs ), diode ideality factor (N) and diode reverse saturation current (I r ). The simulation results of the PV module were compared with each other across the range of parameter values. It has been found that the simulated output power of the photovoltaic module depended on a number of different parameters that were used to model the photovoltaic module. The impact of different parameters and their respective values was observed in the way the values of output power and voltage fluctuated. The MATLAB code provided the means to identify the maximum power point (MPP) as well as the voltage at MPP, for a PV module for different parameter values. In this paper, the respective MPP, voltage and current at different MPP values were plotted on P–V curves to illustrate the changes for each parameter. The simulation results show that the modelled parameters were influenced by the current–voltage and power–voltage characteristics of the photovoltaic module, with corresponding changes in the MPP value. For the purposes of proposed investigation, it has been used MATLAB to simulate the Solarex MSX-60 multi-crystalline photovoltaic module in order to observe the impact of changing the value of the various parameters and to verify the accuracy of PV model. By varying the values of particular model parameters, the best modelling A. A. Kareem (B) · A. A. Mashkor · N. K. Baqer Department of Electrical Engineering, University of Kufa, Najaf, Iraq A. A. Mashkor e-mail: [email protected] N. K. Baqer e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_14

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of the PV module that gives simulation data approximately equal to actual data in the datasheet was obtained. Keywords Photovoltaic (PV) · Maximum power point (MPP) · Current of maximum power point (I mpp ) · Series resistance (Rs ) · Voltage of maximum power point (V mpp ) · Diode reverse saturation current (I r ) · Irradiance (G) · Temperature (T ) · Diode ideality factor (N)

1 Introduction The “becquerel effect”, named after the French scientist Edmond becquerel, who discovered the photovoltaic (PV) effect, eventually led to the development of solar energy. Almost twenty years later, British scientists William Grylls Adams and Richard Evans day manufactured a solid PV cell from selenium and produced an electrical current with a conversion efficiency of 1–2% [1]. The first generation of silicon-based semiconductor photovoltaic cells was introduced in the year 1954, with an efficiency of 6% [2], and was employed in a number of fields. The continuing technological advances since the 1980s have paved way for the exponential growth of PV cell production, as well as significant reductions in costs. In the present era, studies of renewable energy have become an extremely serious matter, triggered by the increasing problems created by the energy crisis, leading to the worldwide search for and usage of modern alternative sources of energy, for example, solar energy, water, wind and geothermal. In this context, a type of green energy which is almost inexhaustible and environmentally friendly is of course solar energy, which is seen as one of the most promising alternatives to traditional energy sources. It therefore follows that photovoltaic solar energy acquired from sunlight has been increasingly utilised to generate electrical power [3]. In recent years, the demand for solar energy has increased. However, the drawback of a traditional PV system is the high cost compared with other energy sources. Therefore, there is a great deal of research in the solar energy field to secure greater efficiency from the photovoltaic module. In order to produce a photovoltaic system with higher efficiency, more elaborate modelling, analysis and simulations are needed. Inefficiencies in the quality control of power generation cause major headaches when linked to the electricity grid system. Choosing a suitable PV module will also require estimating the PV power needed because of the problems with the inefficiencies in the power generation process [4, 5]. Previous research into simulated mathematical models of a photovoltaic system has provided with an information database of their best features [6]. The modelling of photovoltaic modules includes an evaluation of their current– voltage and power–voltage characteristic curves. In practical terms, the single diode model is the simplest mathematical model with which to test the various PV modules proposed by previous studies. These studies limited themselves to three parameters,

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namely short circuit current (ISC), open circuit voltage (VOC) and diode ideality factor (n). However, it is not possible to obtain a precise relationship between MPP and open circuit voltage of the P–V characteristics of the module without the use of series resistance (Rs ) [7]. The impact of series Rs was proposed though it lacked accuracy when subjected to large changes in temperature. In the simplified single diode model, the inclusion of additional shunt resistance (RP ) improved temperature sensitivity [8]. The results of varying parameter values for a particular photovoltaic module were demonstrated, and the resulting changes in model parameter values were plotted on current–voltage and power–voltage curves [9]. Changing model parameters values led to variations in the values of MPP, the voltage at MPP (V mpp ), the current at MPP (I mpp ) and VOC for PV module. The evaluation of the performance of a model as a source of DC voltage using a polycrystalline photovoltaic array in MATLAB simulink set-up was demonstrated in [10]. Current–voltage and power–voltage curves of a photovoltaic array were analysed through various weather situations with variable irradiance and temperature values. It was found that several parameters had an impact on the MPP and V mpp as well as I mpp of a PV array [11]. This paper illustrated the electrical output of the physical system equivalent of a PV cell module through mathematical modelling. The computer simulation of the photovoltaic module was done by using MATLAB. The model that was developed utilised main parameters to draw current–voltage and power–voltage curves and then to compute values for MPP, V mpp as well as I mpp for the PV module. A comparison of the simulation results of the PV module was performed on the basis of variable model parameters such as g, t, IR, n and Rs . The effect of varying the model parameters was observed with the changing values of MPP, V mpp and I mpp . By using this comparison analysis, it was possible to achieve the best and most accurate modelling of the PV module with efficient results by inputting different values for the model parameters. The outcomes of the simulation revealed the impact of modifying the PV parameters on current–voltage as well as the power–voltage characteristics of the photovoltaic module. In this paper, a Solarex MSX-60 multi-crystalline photovoltaic module was used in a MATLAB simulation to demonstrate the impact of changing the various parameters as well as verifying the accuracy of the PV model.

2 Photovoltaic Cell Modelling Precise simulation of the mathematical PV model is an important aspect of the PV cell. Modelling of the PV cell requires an estimation of the current–voltage and power–voltage curves to simulate PV cells in different environmental situations. The most commonly used method utilises an electric circuit that primarily depends on a single diode [12]. The equivalent electrical circuit allows to model the characteristics of a photovoltaic cell. As mentioned earlier in this study, simulations have been done by means of MATLAB. In order to model the photovoltaic module, the same modelling technique was used [1]. Figure 1 represents page layout.

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Fig. 1 Equivalent circuit for solar cell

The solar cell, where d is the diode, RP is shunt resistance, and Rs is series resistance of a PV module. Output current, I, in Fig. 1 is determined by using Kirchhoff’s current law (KCL) as follows: I = I pv − Id −

V + I ∗ Rs Rp

(1)

The I pv is the light/photon generated current; V is the voltage of the PV cell, and I d is the diode current. Therefore, by using Eq. (1), I d equals:  Id = Ir



exp

q Vd +I ∗Rs NKT



 −1

(2)

where T K Q Vd

operating temperature of the of PV cell, Boltzmann constant (1.381 × 10–23 j/k), electron charge (1.602 × 10–19 c), voltage across the diode (d). And n: diode ideality factor (between 1 and 2). By substituting Eq. (2) into Eq. (1): I = I pv

    q V +I ∗Rs V + I ∗ Rs NKT −1 − − Ir exp Rp

(3)

where (I r ) is the reverse saturation current for the diode at reference temperature (TO). I r is obtained by using an open circuit (where i = 0) with no output current. Equation (3) [13] yields the solution for I r as follows:   Ir (To ) = I pv eq

Voc /kT

−1



(4)

For a good approximation, setting the short circuit condition (I pv = I sc ), I pv for any irradiance, g (w/m2 ), equals: I P V (G) = (G/G o ) ∗ I P V (G o )

(5)

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where I pv (Go ) is the light/photon generated current under standard test conditions, obtained from the datasheet [14]. G is irradiance in w/m2 measured by the PV cell, and Go is standard irradiance, equivalent to 1000 w/m2 at reference temperature (25 °C).

3 The Photovoltaic Module A single solar cell usually generates only about 0.5 V of electricity. A 24 v PV module consists of 72 cells connected in series [15]. The current in a PV module increases when several series photovoltaic cells are connected in parallel [16]. However, the power available from a standard module is not adequate to provide the required load, a common occurrence for the majority of PV system applications. In order to obtain the desired voltage or current and then the desired voltage output power, several PV modules are linked in series or in parallel or a combination of both to form a PV panel, called a PV array [17]. There are several types of commercially available PV modules based on industrial cells, such as mono-crystalline silicon (C-SI), polycrystalline silicon or multicrystalline silicon (POLY-SI or MC-SI), and thin film. MLT Mitsubishi photovoltaic solar module is a mono-crystalline type, and the Solarex MSX-60 photovoltaic solar module is a multi-crystalline type, while the shell st40 photovoltaic solar module is a thin film type [18].

4 Modelling a Photovoltaic Module The MSX-60 multi-crystalline PV module was selected for the MATLAB simulation. The module was created from 36 multi-crystalline silicon solar cells placed in series, which provided 60 w maximum power output [14] (Table 1). In order to obtain fairly accurate results from the PV module model of the equivalent electrical model, an electrical model with only slight modifications was used as indicated in Fig. 2. The model consists of a diode (D), series resistance (Rs ) and current source (I PV ). In a single diode module, the impact of RP is nominal and can thus be disregarded [19]. Let Rp = ∞ in Eq. (1) then:     q(V +I Rs ) I = I P V − Ir exp N K T −1 where T I

cell temperature in Kelvin (K), cell/module current,

(6)

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Table 1 Specifications of MSX-60 PV module

Electrical features

MSX-60 PV

Units

Maximum power (PMAX)

60

W

Short circuit current (ISC)

3.8

A

Open circuit voltage (VOC)

21.1

V

Current for PMAX (IMP)

3.5

A

Voltage for PMAX (VMP)

17.1

V

Temperature coefficient for VOC

−(80 ± 10)

MV/°C

Temperature coefficient for ISC

(0.065 ± 0.015)

%/°C

Temperature coefficient of power

−(0.5 ± 0.05)

%/°C

NOCT

47 ± 2

°C

https://www.solarelectricsupply.com/media/custom/upload/Sol arex-MSX64.pdf

Fig. 2 Equivalent circuit for solar cell used in MATLAB simulations

V

cell voltage, equal to module voltage/No. of cells in series. At reference temperature (T o ), reverse saturation current of diode (I r ) given by Eq. (4) with diode ideality factor is added: 

 Ir (To ) = I P V eq Voc /N kT − 1

(7)

Reverse saturation current (I r ) is temperature dependent, and I r at operating temperature (T ) is calculated by Eq. (8) [8]. To calculate the light/photon generated current (I pv ) at a given cell temperature (T ): I P V (T ) = I P V (To ) ∗ (1 + α(T − To ))

(8)

In the datasheet, the value of I pv (T o ) is given for standard test conditions (irradiance equivalent to 1000 W/m2 and a temperature of 25 °C). T o is the reference temperature of the photovoltaic cell in Kelvin (K), i.e., 298 K (25 °C), and a is the temperature coefficient for I sc . Rs heavily impacts on the slope of the current–voltage curve near V oc . Rs is determined by evaluating the slope dV /dI for current–voltage curve for V oc . Using Eq. (6), Rs becomes:

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nkT

Rs = −

dV q − dI Ir ∗ eq (V +I ∗Rs /nkT )

(9)

To calculate Eq. (10), let V be equal to V oc , and let I be equal to zero and then Eq. (10) becomes:   nkT dV q Rs = − − dI Voc Ir ∗ eq(Voc /nkT )

(10)

5 Results and Discussion MATLAB computer coding or simulation was utilised for the purpose of determining the maximum power point value and also the corresponding voltage and current values for the maximum power point of a photovoltaic module for a given set of PV parameter values. As part of this research, the MPP and V mpp values were tabulated in a database in which the parameters were changed one at a time. Subsequently, default values were selected for the different parameters. For example, irradiance was set at 1000 W/m2 which coincides with an I pv = 3.8 A (I pv = Ipvo × (G/Go )). Series resistance was set at 0.0012 . Diode reverse saturation current was set at 0.002906 mA. Diode ideality factor was set at 1.6. Circuit temperature was set at 25 °C. The effect which parameter variations had on the current–voltage and power– voltage characteristics of the photovoltaic module were noted as follows.

5.1 Impact of Irradiance Changes Figures 3 and 4 and Table 2 display changes in the current–voltage and power–voltage characteristics for different levels of irradiance. Parameters Rs , N, I r and T were set at the default values mentioned earlier. From Fig. 5 and Table 2, it can be observed that the irradiance value has a direct effect on I sc and also V oc ; however, I sc was significantly affected. It is evident that increasing the irradiance value brings about corresponding increases in both I sc and MPP.

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Fig. 3 Current–voltage curves for various values of solar irradiance

Fig. 4 Power–voltage curves for various values of solar irradiance Table 2 Impact of changing the value of solar irradiance

Irradiance (G) (W/m2 )

Maximum power point (MPP) (W)

Voltage at the MPP (V mpp ) (V)

200

10.41

18.59

400

22.21

19.52

600

34.53

20.21

800

47.15

20.68

1000

60

21.1

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Fig. 5 Current–voltage curves for various values of solar temperature

5.2 Impact of Temperature Changes Figures 5 and 6 and Table 3 display changes in the current–voltage and power– voltage characteristics for different solar temperatures. Parameters Rs , N, I r and T were given the default values mentioned previously. It may be observed from Fig. 6 and Table 3 that increasing the temperature negatively impacts on the power output of the solar cell and consequently MPP. Increasing temperature leads to a reduction in power.

Fig. 6 Power–voltage curves for various values of solar temperature

150 Table 3 Impact of changing the value of solar temperature

A. A. Kareem et al. Temperature (T ) (°C)

Maximum power point (MPP) (W)

Voltage at MPP (V mpp ) (V)

25

60

21.1

35

57.39

20.21

45

54.76

19.52

55

52.13

18.82

65

49.5

18.12

Fig. 7 Current–voltage curves for various values of series resistance

5.3 Impact of Series Resistance Changes Figures 7 and 8 and Table 4 display the changes in the current–voltage and power– voltage characteristics for different series resistance settings. Parameters Rs , N, I r and T were given the default values mentioned previously. It may be noted that the minimum value of Rs triggers the maximum power output. When the value of Rs increases, the MPP and V mpp correspondingly decrease, as shown in Fig. 8 and Table 4.

5.4 Impact of Diode Ideality Factor Changes Figures 9 and 10 and Table 5 display the changes in current–voltage and power– voltage characteristics for different values of diode ideality factor. Parameters Rs , N, I r and T were set at the default values mentioned previously. Based on the simulation results from Fig. 10 and Table 5, it is evident that the nearer the value of N to one, the greater the power output obtained from the PV

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Fig. 8 Power–voltage curves for various values of series resistance Table 4 Impact of changing the values of series resistance

Series resistance (Rs ) ()

Maximum power point (MPP) (W)

Voltage at MPP (V mpp ) (V)

0.0012

60.01

21.1

0.0036

58.96

20.06

0.006

57.9

19.91

0.0084

56.87

19.76

0.0108

55.83

19.61

Fig. 9 Current–voltage curves for various values of diode ideality factor

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Fig. 10 Power–voltage curves for various values of diode ideality factor

Table 5 Impact of changing the value of diode ideality factor

Diode ideality factor (N)

Maximum power point (MPP) (W)

Voltage at MPP (V mpp ) (V)

1.6

60.11

21.0194

1.7

59.59

21.0191

1.8

59.09

21.0188

1.9

58.63

21.0185

2

58.17

21.0182

module will be. It will also be noted that higher values of N correspond with higher values for the reverse saturation current and lower values for V oc .

5.5 Impact of Diode Reverse Saturation Current Changes Figures 11 and 12 and Table 6 display changes in current–voltage and power–voltage characteristics for different diode reverse saturation current values. Parameters Rs , N, I r and T were set at the default values previously mentioned. It may be noted from Fig. 12 and Table 6 that when the value of I r increases, both V oc and MPP for the PV module decrease. The results of the mathematical simulation clearly demonstrate the effect which model parameters such as G, T, Rs , I r and N have on the values of MPP, V mpp and I mpp of a PV module. Modifying the model parameters of the photovoltaic module is necessary in order to obtain an efficient model and to achieve PV characteristics that are as nearly equivalent as possible to the physical system characteristics found in the datasheet. For irradiance variation, it was noted that with an increase in irradiance,

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Fig. 11 Current–voltage curves for various values of diode reverse saturation current

Fig. 12 Power–voltage curves for various values of diode reverse saturation current Table 6 Impact of changing the values of diode reverse saturation current

Diode reverse saturation current (I r ) (A)

Maximum power point (MPP) (W)

Voltage at MPP (V mpp ) (V)

2.9060e−06

60

21.1

5.9060e−06

56.3

19.98

10.9060e−06

53.11

19.05

15.9060e−06

51.16

18.35

20.9060e−06

49.75

18.12

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the I sc value increased proportionally while small incremental increases in the V oc value led to an increase in the MPP value. For the temperature variation, it was noted that small incremental increases in the I sc value occurred as temperature increased, while a decrease in the V oc value led to a proportional decrease in the MPP value. When N was increased to 2, the MPP value decreased. When the series resistance Rs increased, there was a very slight reduction in the I sc value. Meanwhile, a significant decrease in the V oc value led to a low MPP value. Finally, increasing the diode reverse saturation current led to very slight decrease in the I sc value; whereas, when the value of V oc decreased, there was a corresponding noticeable decrease in the MPP value. The results of the simulation demonstrate that it is possible to achieve higher output power from a PV module by increasing G and decreasing each of T, Rs and I r , while sliding N’s value towards one. Therefore, it is possible to achieve the best modelling of a PV module with highly efficient results by applying different values for the various model parameters.

6 Conclusion This paper discussed a particular mathematical model for the electrical equivalent of a PV cell and also illustrated a PV module. Simulation of the photovoltaic module was achieved by using MATLAB. MATLAB coding facilitated the evaluation of MPP, voltage and current for the MPP of a photovoltaic module for different parameter values. The model was developed using main parameters which were then used to plot the current–voltage and power–voltage characteristics. MPP and corresponding current and voltage values for MPP were calculated for different values of the model parameters, for example, irradiance, diode ideality factor, temperature, diode reverse saturation current and series resistance. A comparison of MPP, V mpp and I mpp values of PV module was conducted by varying the values of the various model parameters. The impact of varying the model parameters was observed by how the current– voltage and power–voltage characteristic curves for the PV module changed. MPP, V mpp , as well as I mpp values were plotted after modifying the model parameters one by one. By comparing the results, it may be concluded that it is possible to achieve the modelling of a PV module with a sufficiently high degree of efficiency by inputting a particular set of model parameter values. The best modelling of the Solarex MSX-60 PV module that gives simulation data approximately equal to actual data found in the datasheet was achieved when the model parameters G, T, Rs , N and I r are equal to 1000 W/m2 , 25 °C, 0.0012 , 1.6 and 2.9060e−06, respectively. Accordingly, it would be reasonable to use the results of the simulation to develop a cost-effective and time-saving PV system as well as being useful with investigating the maximum power point tracker (MPPT) algorithm.

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References 1. http://www.a-ghadimi.com/files/Courses/Renewable%20Energy/REN_Book.pdf 2. Chapin DM, Fuller CS, Pearson GL (1954) A new silicon p–n junction photocell for converting solar radiation into electrical power. J Appl Phys 25(5):676–677 3. Jiang Y, Qahouq JAA, Orabi M (2011) Matlab/Pspice hybrid simulation modeling of solar PV cell/module. In: Conference proceedings—IEEE applied power electronics conference and exposition—APEC, pp 1244–1250 4. Hossain MZ, Rahim NA (2018) Recent progress and development on power DC–DC converter topology, control, design and applications: a review. Renew Sustain Energy Rev 81:205–230 5. Bendib B, Belmili H, Boulouma S (2018) Modeling and simulation of PV generator characteristics under LabVIEW. In: 2018 6th international renewable and sustainable energy conference, no c, pp 1–6 6. Dadkhah J, Niroomand M (2018) Real-time MPPT optimization of PV systems by means of DCD-RLS based identification, vol 3029(c), pp 1–9 7. Pendem SR, Mikkili S (2018) Modelling and performance assessment of PV array topologies under partial shading conditions to mitigate the mismatching power losses. Sol Energy 160:303– 321 8. Nguyen XH, Nguyen MP (2015) Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink. Environ Syst Res 4(1):1–3, 8–9, 11 9. Shongwe S, Hanif M (2015) Comparative analysis of different single-diode PV modeling methods, pp 1–9 10. Patel H, Gupta M, Bohre AK (2016) Mathematical modeling and performance analysis of MPPT based solar PV system, pp 157–162 11. Razak A, Irwan Y, Leow WZ, Irwanto M, Safwati I, Zhafarina M (2016) Investigation of the effect temperature on photovoltaic (PV) panel output performance. Int J Adv Sci Eng Inf Technol 6(5):682 12. Huynh DC, Dunnigan MW (2016) Development and comparison of an improved incremental conductance algorithm for tracking the MPP of a solar PV panel, vol 3029(c) 13. Putera P, Aulia Novita S, Laksmana I, Imran Hamid M, Syafii S (2016) Development and evaluation of solar-powered instrument for hydroponic system in Limapuluh Kota, Indonesia. Int J Adv Sci Eng Inf Technol 5(5):284 14. https://www.solarelectricsupply.com/media/custom/upload/Solarex-MSX64.pdf 15. www.energy.wsu.edu 16. EL Shahat A (2010) PV cell module modeling & ANN simulation for smart grid applications. J Theor Appl Inf Technol. www.jatit.org 17. SolarDirectTM (2016) Solar PV modules. SolarDirect.com, p 1 18. Yahya-Khotbehsara A, Shahhoseini A (2018) A fast modeling of the double-diode model for PV modules using combined analytical and numerical approach. Sol Energy 162:403–409 19. Ali ZH, Ahmed AK, Saeed AT (2018) Modeling solar modules performance under temperature and solar radiation of Western Iraq. Int J Power Electron Drive Syst 9(4):1842–1850

Forwarding Information Base Design Techniques in Content-Centric Networking: A Survey Mohammad Alhisnawi

Abstract An instance of information-centric networking (ICN) is Content-Centric Networking (CCN) which represents the upcoming generation of the Internet paradigm. CCN concentrates on content distribution by allowing it to become routable and addressable directly. Its fundamental objectives are to enhance the conventional network operations by allowing the network equipments to perform routing decisions depending on the content’s name instead of host address. Unlike IPv4/6, the CCN names are hierarchically structured and have long, changeable, and unlimited lengths, which makes the quick name lookup an issue that needs to be addressed. Despite the considerable preferences of CCN architecture, hardware challenges, including Forwarding Information Base (FIB) are remain existing in the implementation of CCN. Fast memory lookup and adequate storage requirements represent the most important issues within these challenges. This paper presents an up-to-date survey of FIB design techniques in content-centric networks. Its main purpose is to classify comprehensive FIB designing and implementation techniques in an abridged style. Then presenting their pros and cons and identifying the requisite necessities for preferable FIB enhancement in terms of scalability. Keywords Content-centric networking · Forwarding information base · Interest packet · Data packet

1 Introduction Mainly, the Internet was contagious to permit resource participation among endsystems [1]. The Internet architecture has been improved quickly and the services presented by the Internet have expanded dramatically. Several Internet applications have been emerged like: electronic mail, VoIP, video/audio streaming, content delivery, instant messaging, and so on. Recently, the most important interest of the users of the Internet is the recovering of information from the network [2]. The current M. Alhisnawi (B) University of Babylon, Babylon, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_15

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network connectivity that has IP based architecture ensures communications among end-hosts [3]. The destination IP is used as a fundamental feature to perform longest prefix match (LPM) with the routing table within the network equipment to forward an incoming packet. Recently, the most utilized approach to reach any information within the Internet is to request Universal Resource Locater (URL) of the required information [4]. These URLs cannot be treated in the IP-based architecture directly and a redirection process must be accomplished to address this issue. Domain Name System (DNS) is utilized to do that by mapping any requesting URL name into its equivalent IP addresses. Mainly, the current Internet relay greatly on middle-boxes and, hence, it is hard to manage and lower effective [5]. It can notice that there exists a trend on the Internet where content and services are higher significant than hosts or servers. Hence, another architecture is in demand to handle this development. Content-Centric Networking (CCN [6]) can be considered as one of such novel architectures where content is addressed by utilizing hierarchically structured names to substitute IP-based addressing scheme [7]. CCN [8, 9] employs two significant kinds of messages: interest and content object. Commonly, an interest message is utilized to requests a content object. The main challenge in CCN architecture is the message forwarding process to the services that are addressed by a confirmed interest message [10]. These services are addressed by utilizing URL like names that are commonly longer than conventional IP addresses [11, 12]. Interest (Ipkt) and Data (Dpkt) packets in CCN networks are utilized in the communication operation between content producers and content consumers [13]. Whenever a consumer sends an Ipkt for specific content, the Dpkt created by the content generator can be sent back by any node receiving the Ipkt and already saving the requested data. In another meaning, if any CCN node holds the requested Dpkt on the route between the consumer and the producer, the CCN node generates the content to the consumer [14]. Consequently, Dpkt is sent from the network equipment nearest to the consumer [15, 16]. To perform the packet forwarding process, a CCN router holds 3 main data structures: Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB) [17]. CS is utilized to holds, temporarily, Data packets that already retrieved [18, 19]. PIT is utilized to keep the necessary information about the incoming port (face) from which the Interest packet have arrived in order to remember from which face the retrieved Data packet must be transmitted back [20]. Just like the routing table in the existing Internet architecture, FIB table is utilized to forward the incoming Interest packet depending on the information registered in it [21]. In CCN, the name lookup depends on the longest prefix matching approach [22]. In comparison with IP lookup in the existing Internet architecture, name lookup in CCN router have extra issues related to memory usage, fast lookup, and update. CCN names are longer than IPs and they are unbounded which results in extra time consumption [23]. Also, FIB table must be large enough to hold all the necessary forwarding information. Consequently, without utilizing an appropriate data structure or compression method, FIB tables can exceed the capacity of current commodity equipment. A

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CCN router must manage recurrent updates generated by content creation and elimination which result in doing the update process more frequently than the existing Internet [24]. In this paper, we study and analyze several FIB design and implementation techniques with respect to CCN. Moreover, we mention the advantages and disadvantages of each one in order to offer a good reference to the authors that are interesting in designing FIB tables for CCN. The rest of the paper is structured as follows. Section 2 presents a comprehensive description of the CCN architecture. Section 3 classifies the current FIB design techniques. Section 4 evaluates the existing FIB design techniques. In Sect. 5, we described the current issues that should be considered in designing FIB table. Finally, we conclude in Sect. 6.

2 CCN Architecture CCN can be considered as an effective and unpretentious communication paradigm driven by subscribers who send Interest packets to demand for content by its CCN name, without any interesting about the IP addresses of the network equipment that holds this content. Whenever a consumer requests a content, it generates an Interest packet to its upstream node towards the providers which may be the node that owns this content or the node that storing it. Providers artlessly respond to the incoming Interest packet can be transmitting back the equivalent Data packet to the consumers [25, 26]. CCN architecture introduces two kinds of packets. The first one is the “Interest” packet, via sending these over the outgoing interfaces, a node announces the node’s demand for content that is named by the packet. It is simply broadcasted on the available interfaces in the hope to get the relevant data returned by the mechanisms of CCN architecture. Usually, by default, the Interest packets contain the desired content name. Apart from this, it is accompanied with selective information such as scope inside the network where the data must come from or specific filter information. While the second packet, namely “Data” packet that is used in response to an incoming Interest packet. Names in CCN are hierarchical with consequence. Hence, Data packet is considered to satisfy an Interest packet in that they keep a one-to-one relation, where Interest packet is consumed by data [27]. As explained earlier, forwarding and routing in CCN paradigm are name-oriented. These CCN names are URL-like names that have hierarchical structure. Depending on these CCN names, CCN routers forward incoming Interest packets by utilizing LPM lookup technique to examine the outgoing faces from the FIB table [28]. First, rather than utilizing constant-length addresses, CCN [29] forwarding lookup uses variable-length names that have hierarchical structure. To perform a forwarding process, the CCN name is compared versus number of data structures (discussed next). This comparison will lead to longest prefix or accurate match. As an example, consider a CCN Interest packet with the name /com/youtube/video1 that comprises three partitions separated by (/). There exist three possible matching situations can be found with the FIB table:

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/com/youtube/video1, /com/youtube, and /com. Second, CCN employs three various data structures [30, 31]: • Forward Information Base (FIB): it is utilized for storing information that is necessary in packet forwarding process. It is like a routing table in common IP router. FIB saves information on which face Interests must be forwarded upstream towards the source of the content of the question. Hereby the design enables more than one entry that may be needed to be queried in parallel, as forwarding is not just limited to one spanning tree. • Pending Interest Table (PIT): it records the arrival interfaces of Interest packets that have been forwarded. Nonetheless, are remain biding for an appropriate Data’s packet. This information is needed in order to bring data to their consumer. For increasing the PIT utilization, the PIT entries must be expired swiftly, somewhere about the packet RTT. Nevertheless, data will be dropped in case they are expired and it will be the responsibility of the consumer to generate their Interest packets. • Content Store (CS): it works as a cache (i.e., buffer) structure in a CCN router [32]. These store chunks for a very long time via applying for cache update policies. As content is self-authenticating as well as self-identifying, every one of the packets should be useful to certain potential participants in the network nearby. Ability for serving content directly rather than generating additional lookups reduces total bandwidth usage as well as latency [33]. A high-level view of the forwarding process in the CCN router can be found in Fig. 1. Whenever an Interest packet reaches a CCN router, the latter will lookup the required content in its CS (which stores temporarily the already fetched data). If the requested data exist in CS, the router will reply directly by sending back the Data packet. Else, the router will examine the incoming CCN name against PIT table. If this

Fig. 1 Packet forwarding process in CCN router

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name is previously registered in PIT then only the incoming face will be registered. Otherwise, a new entry will be inserted into the PIT that holds the arriving CCN name associated with its incoming face. Then this name will be examined against FIB table to forward the Interest packet to its upstream node. Whenever a Data packet reaches the router, the latter will cash (optionally) the data in its CS and will send back this Data packet through the entire interfaces that are listed in the PIT and then it will remove this PIT entry [34].

3 Classification of the Common Existing FIB Design Techniques The FIB may exhibit rigorous constraints in terms of scalability, speed, and memory usage. The demanding issue is the design and achievement of a scalable, quite quick FIB because it imposes per packet searching with long, numerous names. This requires adequate size of memory to hold all these CCN names which lead to memory consumption and make the lookup process to be an issue. To address the problem, several FIB design techniques have been proposed. Hence, in this section, we will present several popular techniques for designing FIB table in content-centric networks.

3.1 Name Component Encoding Technique (NCE) Prior to searching the FIB table, NCE technique [34] tries to increase the speed of the lookup by encoding components to codes. This technique utilizes Code Allocation Mechanism (CAM) in order to minimize the bytes which depict a code by contracting the overall number of codes. Next, they improve the State Transition Arrays mechanisms for trie structure to minimize storage utilization and quicken LPM process. Lastly, they suggested the techniques of handling the STA to meet the recurrent update in CCN names that are elucidated by NPT. As an instance, as presented in Fig. 2, the particular nine CCN names can be regulated as an NPT

Fig. 2 Explanation of CAM

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with fourteen nodes. Various partitions departing a specific node must be encoded variously.

3.2 Name Filter Technique (NF) A novel design and implementation for CCN name lookup in FIB table have been suggested in [35]. This design is based on employing a NameFilter, a two-stage Bloom filter (BF). Name prefixes are inserted into BFs depending on their lengths during the first stage. Also, in this stage, LPM of the CCN name is specified by querying these BFs. Name prefixes are classified depending on their associated face(s) into groups during the second stage and each of these groups is saved into BF. The outgoing face(s) identical to that specific LPM will be get by examining the second stage BFs. BFs in both stages have been improved depending on their veritable processes. In the first stage, they depend on using one memory access BF which minimizes the number of memory accesses in the query process from k (which represents the number of utilized hash functions) to solely one. Moreover, they utilize merged BF (MBF) during the second stage to minimize the number of memory accesses by a factor of N (which represents the number of faces). To minimize time complexity, the employed hash functions have been enhanced for character strings. They specify the same size for all Bloom filters and enforce the same collection of hashes to all these BFs because the number of CCN prefixes in the BF for every port is asymptotic to one another. The Merged BF integrates the N BFs bound to N outgoing faces, with N bits in the same position grouped into single bit string. They save this bit string in a memory inside a word or little adjacent words. Figure 3 depicts N BFs on the left that have been transformed into single MBF on the right. In MBF, every position saves a bit string with machine word aligned. From the most significant bit to the

Fig. 3 Combine and transpose the conventional BFs to MBF

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Fig. 4 STD of name search operation

least significant bit, the N-th bit save the congruous hash outcomes from the N-th BF and the residual unutilized bits are stuffed with zero. The 1st position of each BF in this figure is one (equivalent to the outcomes from the first hash), which transforms into a bit string of 111…1 in the first array of the MBF through combining and permutation.

3.3 Greedy Name Lookup Technique (GNL) To improve CCN name search achievement the authors of [24] suggested greedy name lookup mechanism to improve the lookup route of CCN name search and by improving string-oriented hash table to maximize lookup progress of the hash table and to decrease storage usage. Figure 4 depicts the greedy name search operation in the form of state transition diagram (STD). There exist 4 states: start state (state-0), end state (state-1), middle states (state-2 and 3). They suggest utilizing such kind of hash tables to minimize storage usage which saves the key’s signature in the entry rather than the key itself.

3.4 Binary Patricia Trie Based Technique (BPT) The authors of [36] suggested a Binary Patricia Trie (BPT) technique to perform CCN name lookup for packet processing. The suggested approach is based on the following notions: • FIB table can be greatly compressed by utilizing BPT to be suitable within modern high speed memory. • It is conceivable to proposing and enhance a trie data structure whose magnitude is contingent upon the size of the ruleset, instead of the length of the rules. Their choice for BPT comes from three causes. First, the binary explanation supplies the extreme chance to compress participated parts among various CCN prefixes. In the commonly utilized component technique, solely shared components

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can be compressed, in BPT, arbitrary shared bit series can be compressed. Second, handling CCN names as bit strings denotes that they do not suppose any feature of CCN names, hence their design can be enforced on any name approach. Third, while CCN names are represented as binary strings on the wire through forwarding process, they can immediately utilize the name’s wire styling in table search and forwarding without analyzing the name. The component technique, nevertheless, would require analyzing the wire format to generate out the component boundaries prior to table search, which can endure fundamental processing delay. Consequently, in spite of component technique is extra conjectural, BPT is great public and costs less in storage and processing.

3.5 Bloom Filter Pre-searching Technique (BFPS) The aim of the work suggested in [37] is to enhance the lookup achievement of a name prefix trie (NPT) by employing BFs. First, they suggest generating a hash table (HT) depending on NPT. The right part of Fig. 5 describes the hash table that has been generated depending on the information stored in the NPT presented on the left side of the same figure. Each node in NPT is saved into a hash table. In

Fig. 5 Hashing-based NPT

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Fig. 6 NPT with a BF

order to save level-i node, the i partitions starting from the root until level-i will be concatenated, and the resulted string is utilized as a hash value to get the index. Also, if the resulted string has inconstant length must be transformed into a constant-length string, because every entry of the HT has a constant width. Also, this string will be saved in the hash table in an entry that has an index equal to the hash value. In saving the Lth partition, where L is the content’s length, the port connected with the content name is saved too. The NPT-BF is depicted in Fig. 6. BF is examined prior to accessing the hash table and this access will minimize the number of unnecessary accesses to the HT by announcing false queries when there is no corresponding entry.

3.6 Radix Trie Based Name Component Encoding Technique (RaCE) The work presented in [38] suggested a scalable and storage effective radix trie based name component encoding technique, RaCE. This technique concentrates on encoding the CCN name partitions in 32-bit integer where congruous partitions use the exact number. In order to eliminate the impact of preserving superfluous information, this technique uses the Radix trie as its fundamental structure. To reduce the storage usage and increase the lookup speed, RaCE utilizes space-optimized

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Fig. 7 RaCE framework

ordered trie structure (RT). In this structure, all the nodes that have solely one child will be gathered with each other and for this reason , this structure is called pathcompressed trie. Figure 7 depicts the general description of RaCE technique which involves CT and ENT with single stack.

3.7 Fast Two-Dimensional Filter Based FIB Design Technique (FTDF) The work presented in [39] depends on utilizing the proposed query data structure (i.e., filter) named Fast Two-Dimensional Filter (FTDF) associated with chained, hierarchical HT. This filter has been saved in an on-chip memory and it has been utilized to minimize the amount of accesses to the off-chip memory (i.e., hash table). This technique includes two stages: pre-processing and search stage. During the first stage, all the CCN names (with their parts) are saved in both FTDF and HT with one hash index and without recurrence. During the second stage, the lookup is doing first within the FTDF before the hash table to decrease the superfluous accesses to the hash table. Figure 8 depicts the entire structure of this technique. The top side depicts the pre-processing stage whereas the bottom side depicts the search stage of this technique. The update engine in this figure is in charge of doing the update.

3.8 New Bloom Filter Architecture for FIB Lookup (NBFA) In [9], they suggest utilizing two-stage BF architecture to implement a level-priority trie (LPT). BFs have been utilized in several network applications to enhance the lookup achievements of routing tables due to their simple structure. The suggested two-stage BF can be stored on a tiny on-chip storages and accomplishes FIB search solely by reaching to these BFs for greater than 99.99% of incoming CCN names. The structure of this technique has been depicted in Fig. 9. Excepting the root node, l-BF is programmed for each node of the LPT and saves the level for every node. For instance, while node com has level 4, k cells belonging to the hash indexes produced by key com are put to value 4. Next, the p-BF is programmed for each CCN name

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Fig. 8 General architecture of the FTDF technique

Fig. 9 Proposed 2-phase BF architecture

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prefix in a FIB and saves the outgoing port information belonging to every prefix. For instance, k cells belonging to the hash indexes produced by CCN name prefix com/youtube/user/skyDoesMinecraft are placed to value 3 because the outgoing port of the prefix is 3.

4 Evaluation of the Existing FIB Design Techniques In this section, we will present our evaluation for the previously discussed techniques for designing FIB table in content-centric networks. The evaluation has been done by employing a dataset [40] that holds up to 30 million prefixes with various formats and sizes ndnSIM [41] has been employed in this evaluation which is an open-source package which accomplishes CCN protocol stack for NS-3 simulator.

4.1 Search Performance We compare search speed for all the discussed techniques on a group of up to 30 Million CCN name prefixes. The results of this experiment have been presented in Fig. 10. From this figure, it can be concluded that FTDF technique has the highest achievement and this is a result of utilizing their suggested approximate membership query data structure that minimizes noticeably the number of accessing to the hash table. Moreover, this technique makes use of solely one hash function that has been utilized for accessing both the filter and the hash table. NF technique gets the secondbest performance after FTDF. NF utilizes several Bloom filters (BFs) that can be accessed quickly. GNL comes next because it uses state transition diagram to increase the search performance within a string-oriented perfect hash table. Then BFPS comes next because it tries to decrease the search time by employing BF to minimize the number of times for accessing the hash table. NBFA also utilizes a Bloom filter to minimize the search time but it comes next to BFPS because it employs 2-stage BF architecture to implement a level-priority trie which consumes extra time than a hash table. RaCE uses radix trie based name component encoding technique which, in turn, needs extra time to perform the search process. BPT technique depends on utilizing a binary patricia trie data structure which needs extra search time to perform the search operation. Finally, NCE comes later with the worst achievement because the lookup accomplished sequentially beginning from the root node, and because the CCN name lengths can be extremely extended.

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4.2 Update Performance One of the most important factors to measure the performance of the employed FIB table is the update performance. Just like the routing table in the existing network architecture, new entries are added/deleted to/from FIB table depending on the routing protocols. The update processing rate for all the techniques has been presented in Fig. 11. It can be concluded that FTDF technique has higher achievement over the other seven techniques. This technique has the ability to make a quick insertion/deletion because of its simple structure. It utilizes solely one hash function that is employed to access both the utilized filter and the hash table. NF technique gets the second-best performance because of its underlying data structures (i.e., Bloom filters) that can perform insertion/deletion operations easily and quickly. BFPS technique comes next because it utilizes Bloom filter with a hash table that maximizes the speed of insertion/deletion operations. Then, NBFA technique comes next because its utilizing Bloom filter with level priority trie to increase the update processing rate. GNL technique has lower update performance than the previously mentioned techniques because of its utilizing to transition diagram along with the string-oriented

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hash table. RaCE technique depends on name component encoding which will affect the update performance. BPT technique comes before the last one because of its utilizing for binary patricia trie data structure that increases the time of updating process. Finally, NCE technique has the worst update processing rate among all the discussed techniques because of its sequential search which makes the operation of deleting an existing item or adding a new one to its proper position consume extra time.

4.3 Memory Cost The memory usage for all the eight techniques are depicted in Fig. 12. It can be noticed that when the number of prefixes increased, the memory consumption for all the techniques increased too. NF technique consumes the minimum memory comparing with the other seven techniques. NF technique utilizes only several Bloom filters that are compressed data structures without any further use for a trie or a hash table. Then, FTDF technique comes next because of their utilizing for a membership query data structure along with a hierarchical hash table. Their membership query

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data structure consumes quietly small memory. NBFA technique comes after FTDF technique. NBFA uses both BF and HT but BF consumes extra storage than the filter that has been used in FTDF technique. BPT comes next because it tries to compress the FIB table by employing binary patrictia trie. Then, GNL and NCE comes next. RaCE comes after GNL and NCE because of its utilization to radix trie structure which consumes extra memory. Finally, BFPS technique comes with highest memory consumption because of their extra utilization for data structures: Bloom filter, trie, and a hash table.

5 Existing Issues in FIB Design There exist several issues that should be considered when trying to design and implement FIB table for content-centric networks. These issues are: • Memory usage: unlike the current network architecture, content-centric networks make use of CCN names which are URL-like names that have hierarchical structure. Consequently, it needs to consume large memory to store these CCN names. Therefore, any attempt to design and implement FIB table for CCN should

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consider the using of a data structure that can store such kinds of names with a minimum storage requirement. • Lockup speed: FIB table in CCN holds many entries every one of them contains CCN name and, hence, it is necessary to make fast lookup when an Interest packet comes in order to meet wire-speed requirements. • Update speed: just like the routing table in the existing IP-based network paradigm, FIB table in CCN need to be updated periodically depending on the information gathered from neighbors CCN routers. Consequently, any design for FIB table should have the ability to perform the update process sufficiently quickly. • Scalability: the ability of FIB table to be able to accommodate the increasing amount of CCN names (i.e., entries) is an important aspect that should be considered when designing FIB table. These entries are placed on the FIB table as a result of exchanging the forwarding information among all the CCN devices in the same network and among the current network and their adjacent ones.

6 Conclusion A deep review of recent advances in the area of the FIB design techniques was presented in this paper. The design and implementation of FIB have a crucial role in the implementation of Content-Centric Networks because this component can impact the performance of these kinds of networks, since the FIB table is controlled on the forwarding of the incoming Interest packets that arrived at the CCN nodes. Moreover, this paper classified various techniques depending on the employed data structure, which are NCE, NF, GNL, BPTB, BFPS, RTBNCE, FTDF, and NBFA. Also, it explained their general aims and processes as well as how they design and implement each of these techniques inside CCN architecture, culminating with comparative reviews of the different design choices in each of these areas. Eventually, the conclusion is highlighting the differentiated strength and weaknesses in every technique, and also this study can state that FIB table is a promised and fertile research field for more investigation and underlined the requirement for improvements in terms of scalability.

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Implementing Cybersecurity in IoT Using IPAI Algorithm A. V. Kalpana, D. Digvijay, R. Chenchaiah, and C. Sai Vignesh

Abstract The Internet of Things (IoT) may be a set of interconnected electronic systems, mechanical and digital systems, items, and species. Or those with identifiers (UIDs) that are able to transmit data over a network without human-to-human or human-to-computer communication being needed. The Internet of Things is the fastest evolving example of the potential of individuals and organisations to transform physical activity. In various areas such as nursing, education and learning, resource management and information processing, the platform has found useful. To extend the cyber protection of networks against ID devices and DDoS attacks that use bandwidth on advanced Internet of Things (IoT) devices, preventive technology has been suggested. Such systems are decentralised and self-configurable and do not need current facilities and protection has become a major concern to be taken into account due to the absence of immense volatile node movements. The suggested solution focuses on the study of the bandwidth attacks inquiry, which primarily targets DDoS, which can be a very difficult task and difficult to track, which decreases network performance. The diagnostic procedures of IDS, which are called add-ons to the intrusion detection system, recognise intrusion security mechanisms in IoT systems to persistently secure and avoid intrusion. Keywords IoT · Cybersecurity · DDoS · IPAI · Malicious

1 Introduction Within the Internet-based data infrastructure, the Internet of Things may be an emerging worldwide trend that helps the worldwide supply chain management to exchange goods and services. It is a programme domain that integrates multiple innovations and social arenas. IoT has been described as “a chain of things, all integrated with different devices and linked across the wide web of the globe”. The basic goal is to ensure the different things that can be linked and run to determine A. V. Kalpana (B) · D. Digvijay · R. Chenchaiah · C. Sai Vignesh Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_16

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that they can communicate with themselves and users. It is a vibrant IT architecture that has the potential to self-configure interoperable connectivity protocols between physical and virtual identities of items through keen interfaces that facilitate bilateral continuous sharing of critical data and environmental information and according to real-life incidents, one of the major challenges facing. Life immediately triggers actions. Traditional fixed networks are far better than their wireless IoT counterparts, as we all know [1]. Conventional infrastructure networks allow traffic to pass through numerous routing machines, such as switches and gateways, which are also shielded by heavily designed firewalls and many other strategies for security management. As such, such networks are well equipped against any form of intrusion or denial of service attacks. On the other hand, the IoTs are also called peer networks, in essence mobile, and are innately responsible for different types of attacks [2–4]. The connectivity ties between nodes in the network are wireless in the ad-hoc context in which the node geometry sometimes varies, and there is no centralised control inside the network, conventional wired network protocols are not appropriate for implementation. So, to avoid any very attacks, it is important for any communication node to have some stable protection mechanism [5].

2 Background Work In the existing system, the early realisation of this technology is that there are many security and privacy issues that may be minimised to successfully implement largescale Io technology. Prevention solutions have been proposed to expand network cyber defence against ID devices and DDoS threats that use specialised Internet of Things bandwidth. These systems are autonomous and self-configurable and do not need pre-existing infrastructure and, owing to the lack of enormous unpredictable node movements, security has become a big concern to take into account. With the inclusion of several various points of attack, the severity of DDoS and the damage has increased and thereby created an acceptable atmosphere to damage the security and efficiency of IoT technology [6]. The main issues are: Security has become a serious concern to consider. With the addition of numerous different attack sources, the magnitude of DDoS and thus the vulnerability increased and created a sufficient atmosphere to harm the security and performance of IoT technologies. The IoT Cloud Platform is a server-based network which connects and manages an IoT product with web services which allow the user to interact on the Internet, as well as with other web applications. Recent developments in multimedia and mobile computing technologies require low power and high performance digital signal processing and VLSI schemes [1]. The Internet of Things is an ongoing global trend of the web-based information system that enables data and goods to be exchanged over a network of humanto-computer interaction. It is capable of revolutionising the relationship between individuals and organisations in the modern world. In certain fields, such as nursing, resource control, education, information processing, among many others, the IoT

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device can be recognised. A multitude of security and privacy concerns that need to be resolved for IoT’s effective implementation on a commercially viable broad scale are faced with the rational concept of IoT [7]. This paper analyses the security challenges associated with IoT networks through a review of recent scholarly literature to foster an insight into the safety requirements of IoT networks. The results of the study showed that security threats are one of every major with ever-growing IoT problem, and it is important to reduce them drastically for the effectiveness of this network.

3 System Design In the proposed system, highlights a possible security approach and proposes a convenient prevention scheme to use to DDoS vulnerable IoT networks supported by the fundamental structure and functions of the prevailing IDS, we set the case for the time-dependent mode within the proposed algo. The proposed remedial algo is administratively and technically compatible with various security requirements, and adjusted in line with the prevailing information, simultaneously updating the blacklist. Following this might be recommended for the Reaction Module and ensures network performance, security and survival within the event of an attack [2, 3]. • The proposed solution algorithm is administratively and technically compatible with various security requirements. • Adjusts to available information and automatically updates the blacklist. • Succeeding this might be recommended for the Reaction Module. • In the event of an attack, it guarantees network efficiency, stability and survival.

4 Attacks and Vulnerabilities’ in IoTs Vulnerability within the protection framework is taken into account as a defect. Any device is insecure if, without proper authentication, a person has licensed access to the data [8–11]. Because of their lack of central control, finite energy, restricted capacity, wireless networking media, node versatility, scalability, etc., IoTs are more vulnerable to such vulnerabilities. Spoofing, eavesdropping, replay, and tonnes of other threats are directly responsible for wireless connexions. It is apparent from the literature that the network does not have a direct line of defence. The network’s main networks shift effortlessly in either direction and then the network members enter the network; some of the nodes can be exploited by an enemy inside the network to perform a destructive activity [4]. Any influencing factor within the IoT networks is vulnerable to internal but also external risks. As a consequence, in order to ensure network stability, IoTs need a comprehensive security scheme. In IoTs, there are specifically two types of threats: internal and external threats. There are far more dangerous internal threats than external attacks. By violating the privileges of a node(s) within the internal attacks,

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the adversary manages to connect to the network and functions as a legitimate node within the network [2, 3]. After gaining entry, the hacker can initiate various kinds of attacks. Data between both the nodes will be determined, which will lead to the highly negative activity of the system. However, external attacks aim to generate traffic, false networking and compromise the network’s elegant efficiency [5, 6]. The threats were divided into active and inactive network attacks [12–15]. The intruder is active in malicious network attacks remotely and carries out various attacks, such as various protocols, fake accent, and DDoS. However, in DoS attack, while deliberately interfering in network elements, the attacker overhears network traffic. One of the passive risks is checking up [7].

5 Intrusion Detection System in IoTs IoTs are mobile wireless nodes that communicate via wireless devices. Inside the network type, there are many drawbacks, such as limited battery life, bandwidth limits, and stability. A primary problem with the related IoT ecosystem is taken into account in defence. The networks are responsible for forms of attacks node breach, eavesdropping routing attacks thanks to wireless communication connexions, dynamic shifting topologies. It may be a daunting challenge to define certain types of attacks. Detection of intrusion is an effective approach for detecting certain forms of IoT attacks. The IDS is an in-depth approach that actively tracks the actions of the network and, where applicable, takes the required action [16–18]. The IDSs are categorised into the following groups in compliance with the knowledge aggregation and identification mechanism; (i) based on signature, (ii) based on anomaly, and (iii) based on specification. To identify the identified attacks on the network, prior information is fed into signature-based IDS. During this pretty scheme, there is a downside that it cannot be extended to unknown threats. The behaviour of the system is monitored in an anomaly-based IDS where the anomaly is observed if a certain threshold deviates from the usual behaviour. For the operations or protocols, the certain limitations are set. IDS controls the process in compliance with the constraints.

6 Simulation and Results The first statement made to begin simulation is that malicious was identified by the IDS invaders using the FMIDS algorithm that uses forensic analysis in x iterations to get the log file at limit. After each iteration, the specified set of malicious nodes is narrowed with the addition of n new differential diagnoses and the final set R is created after IDS completes the ultimate iteration. During this analysis, the exact IDS iteration (loop) is described by IDSIN and within the experiment carried out. The six contiguous iterations used successively to generate the analysis are represented by Nε. The justification for invoking the Intrusion Prevention Algorithm in IoT (IPAI)

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Fig. 1 Fixing IoT size for simulations

would be any IDSIN. As a computer file, the report created by IDS is used to shape the blacklist file and then the guidelines for the preventive protocol are created. Our network consists of 200 nodes and the area for simulation is limited to 600 m2 , which requires the distribution of random and uniform nodes with a propagation range of 300 m [19–22]. We chose the AODV protocol for routing, and we chose the IEEE802.11 protocol for Linux. For example, the propagation is used with two ray reflection models. Using two FTP sources, authentic data traffic is simulated with the following information shown in Table 1, including IS = Ingress rate, PS = Packet Hand, and WS = Window size (default). WS 21 IS 0.5 Mbs. PS 1600 bytes. The source of the Constant Bit Rate (CBR) cannot simulate the attack traffic with a packet size of 512 bytes and an arrival time of 0.0005 s followed by a collaborative and synchronised operation against the same goal [23]. Ten CBR origins with varying cross-advent time and structure are simulated by the 10 and 20 attacker network, along with different source operating time, to simulate background traffic. Implicitly, the findings prove a coordinated and repeated operation between 10 and 20 attacks over the following three time periods from 0.1 to 0.3 Ts [5, 6] (Figs. 1, 2, 3, 4, 5 and 6).

7 Conclusion The intensity of DDoS and thus damage increased various different attack and thus provided an appropriate environment to harm the IoT technologies’ security and performance. The impact and duration of the attack could further aggravate the network’s performance and deter legitimate network users from using network services. This text stresses and recommends a prevention scheme that is favourable

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Fig. 2 IoT device placed at random position

Fig. 3 FixLocation of each IoT device with x, y coordinates with IDS will have IDS node

for applying to IoT networks responsible for DDoS attacks within the future defence strategy. They endorsed the basic structure and aims of the current IDS, in a very way per time we have paid ends up in the proposed algorithm. The proposed avoidance algorithm can be administratively and functionally adaptable in several forms to different security requirements and is additionally customisable in accordance with the existing blacklist table that can be revised at the same time. This will result in

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Fig. 4 Data fragmentation between nodes

Fig. 5 x, y coordinates of IoT

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Fig. 6 Malicious node detection graph

a reaction module recommendation being produced and thus approached to ensure the network.

References 1. Hamad T, Alumax A (2014) Hybrid approach using intrusion detection system, Int J Eng Res Technol 3(2):33–42 2. Indira N, Rukmani Devi S, Kalpana AV (2020) Light weight proactive padding based crypto security system in distributed cloud environment. Int J Comput Intell Sys 13(1):36–43. ISSN: 1875-6891. eISSN: 1875–6883. https://doi.org/10.2991/ijcis.d.200110.001. https://www.atl antis-press.com/journals/ijcis/ 3. Indira N, Rukmani Devi S, Kalpana AV (2020) R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment. Int J Ambient Intell Human Comput 11(3):1–12. https://doi.org/10.1007/s12652-020-01860-z 4. Desjardin AV, Bunya R (2016) Fog computing: helping the internet of things realize its potential, IEEE 49(8):112–116 5. Kalpana AV, Rukmani Devi S, Indira N (2018) An efficient localization for smart defense node connection based node position tracking and identification in wireless sensor network. J Web Eng 17(6):2452-2471SCIE, 0.311 6. Kalpana AV, Rukmani Devi S, Indira N (2019) A unique approach to 3D localization in wireless sensor network by using adaptive stochastic control algorithm. Appl Math Inf Sci 13(4):621– 628. https://doi.org/10.18576/amis/130414 7. Hemanth Kumar G, Ramesh GP (2019) Reducing power feasting and extend network life time of IoT devices through localization. Int J Adv Sci Technol 28(12):297–305 8. Alumax A, Hamad T (2016) A novel approach for detecting DDoS using artificial neural networks. Int J Comput Sci Netw Secur 16(12): 132–138

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9. Aldean A, Hamad T (2016) AAODV (aggrandized ad hoc on demand vector): a detection and prevention technique for manets. Int J Adv Comput Sci Appl (IJACSA) 7(10) 10. Kumar GH, Ramesh GP (2018) Novel gateway free device to device communication technique for IoT to enable direct communication between homogeneous devices. Int J Pure Appl Math 118(16):565–578 11. Ali A (2017) IoT based disaster detection and early warning device. Int J MC Square Sci Res 9(3):20–25 12. Ahanger TA, Aljumah A (2018) Internet of things: a comprehensive study of security issues and defense mechanisms. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2876939 13. Ahanger TA (2018) Defense scheme to protect IoT from cyber attacks using AI principles. Int J Comput Commun Control 13:915–926. https://doi.org/10.15837/ijccc.2018.6.3356 14. Rose K, Eldridge S, Chapin L (2015) The internet of things: an overview, understanding the issues and challenges of a more connected world. Global Initiative for Inclusive ICTs, Internet Society, pp 1–53 15. Weber RH (2010) Internet of things—new security and privacy challenges. Comput Law Secur Rev 26:23–30 16. Zhou J, Cao Z, Dong X, Vasilios AV (2017) Security and privacy for cloud-based IoT: challenges. IEEE Common Mag 55(1):26–33 17. Lin H, Bergmann NW (2016) IoT privacy and security challenges for smart home environments. In: MDPI 18. Petrolo R, Locris V, Mitton N (2014), Towards a smart city based on cloud of things. In: International ACM Mobihoc workshop on wireless and mobile technologies for smart cities, pp1–43 19. Dalai F, Railgun SY (2016) Security and privacy considerations for IoT application on smart grids: survey and research challenges. In: (2016) IEEE 4th international conference on future internet of things and cloud workshops (iCloud), pp 63–68 20. Jing Q, Vasilios AV, Wan J, Lu J, Qi D (2014) Security of the internet of things: perspectives and challenges. Wire Netw 20(8):2481–2501 21. Xu T, Wendt JB, Potomac M (2014) Security of IoT systems: design challenges and opportunities. IEEE 22. Stephen R, Rockier L (2017) Intrusion detection system to detect sinkhole attack on RPL protocol in internet of things. Int J Electra Electron Comput Sci Eng 4(4) (2017) 23. Xenakis C, Panes C, Stavrakis I (2011) A comparative evaluation of intrusion detection architectures for mobile ad hoc networks. Comput Secur 30(1):63–80

OCR-Based Automatic Toll Collection and Theft Vehicle Detection Using IoT A. V. Kalpana, K. Kavitharani, and M. Nandhini

Abstract The purpose of this work is to solve the problems facing toll plazas, and the system may also allow authorities to solve car theft cases very effectively. In the proposed scheme, to capture information on passing vehicles and make hasslefree automated transactions supporting the unique identity pin (UIP), the toll will be equipped with optical character recognition (OCR) and RFID sensing. The UIP is passed to the central server unit (CSU) where the amount of the car from the account is deducted. The balance is deducted by using RFID from the car owner’s account. The deduction number is tracked with the help of the RTC, i.e. the number will not be deducted if within a given time the same car crosses the same tollgate. It will signal TCS to unlock the barricade until the balance is deducted at CSU, and the vehicle is not permitted to move; if the vehicle is found to be robbed at CSU, it will mean that the TCS will not unlock the barricade. For further intervention, these information can be included. Keywords Central server unit · IoT · Microcontroller · Optical character recognition · RTC · RFID · Unique identification number

1 Introduction Road networks and its associated innovations have been becoming more innovative for decades. Consequently, states, private operators and customers are agreeing on creative strategies to advance road standards and protection on both regional and inner-city highways. Components of intelligent transport systems (ITS), such as onboard units (OBUs), have automobile facilities for contact with the location of the car. The usage of the OBU as a transponder, generally called a tag, has gained popularity among these facilities in toll roads so as to pay electronically without the need to wait in toll areas. From the standpoint of all toll authorities and thus the driver, electronic toll collection (ETC) schemes are preferable to physical approaches. The ETC is a A. V. Kalpana (B) · K. Kavitharani · M. Nandhini Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_17

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technology that validates the processing of toll payments automatically. Researchers have researched and processed it on numerous roads, bridges and tunnels that involve these processes. This method will ensure whether or not the vehicle is registered and also notify some agencies of toll payment breaches, debits and participating accounts. The technology’s main benefit is the ability to get rid of toll congestion, particularly on occasions. Fuel reduction and fewer mobile emissions are applied to other typical benefits for the motorist by minimising deceleration, waiting time and acceleration. The radio frequency identification (RFID) technology of the ETC system is mainly used in India. Nowadays, Time and Performance are the most prioritized things, the standard approach to be changed. This RFID technology is used to eliminate the key issues with traffic and time usage. The tag added to the vehicle’s windshield is detected by the RFID reader connected to the tollgate door. The article identification sensor senses the way the vehicle tag enters, and the toll deduction takes place via a prepaid card assigned to the connected RFID tag that belongs to the account of the user.

2 Literature Review Morphological process, edge detection for plate localization, histogram modulation and character segmentation are added to the automatic number plate recognition system (ANPR) [1]. For character classification and identification, artificial neural networks were applied. Using the general black box device, the paper gives an intelligent way to capture injury or safety information. Commonly, investigators nonsystematically seek potential clues by hand where something is needed following an accident or crime [2]. A new approach to the risk evaluation of highway networks on real-world highways has been developed [3]. Tollgate data represents the features of the highway, particularly origin–destination (OD) toll data, such as structural intersections and traffic movements. RFID technology uses vehicle-fixed tags from which RFID readers can detect the data generated on the tags [4]. The key philosophy of the article is to define the different current concepts when manually making ticket purchases and reviewing correct paperwork and overloading the safety of motorists and toll authorities. One of the critical aspects of traffic [5] is traffic management. IoT, however, makes it possible to regularly control and map position by linking vehicles with the main server. In the training process, data such as the number of cars, traffic rate and time are read through using the hybrid artificial neural with hidden Markov model (HANN-HMM) network with minimal time [6]. The IoT-TM could make better traffic control decisions than the current systems [7]. Automatic number plate identification is the equipment used to scan the number of a car from a moving image or car. There are differences in the form of plate and some atmospheric illuminations [8]. It uses various types of surveillance cameras to track and record the registrations and record the movements of the car. The highway toll scheme is not capable of supplying users with accurate route information. For much of the traffic, a substitute approach, the shortest route and the travel charge for using the alternate route is far

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from the very justifiable level [9, 10]. Multiple registered licence plate identification and acknowledgement from images becomes necessary. The suggested method consists of two steps: identification and acknowledgement of the number plate [11]. We apply both the Spanish and Indian licence plates in plate identification. Three separate licence plates are used that vary in form and height from each other [12]. The licence plate is detected from the captured image in the number plate identification section, and in the second process, the segmented plate is transferred to the plate recognition section that recognises numbers and characters [13].

3 OCR-Based ETC In our proposed system, there is an automatic money collecting system as well as barricades open at the same time whereas, in the existing system, it is not available. This also monitors each vehicle and finds the theft vehicle using image processing techniques. As per the theft vehicle is concerned, the RTO sends the details of the theft vehicle to each toll plazas, and when the theft vehicle reaches the tollgate, using image processing, the camera captures the number plate, and data pre-processing takes place. Once the number is identical to the number sent by RTO in the CSU, the amount gets debited automatically but the barricade does not open. If the theft vehicle number is mismatched with the scanned vehicle, then it displaces the normal vehicle, and the amount gets debited and the barricade opens as shown in Fig. 1. The amount deduction is basically monitored by real time clock (RTC), i.e. for a certain amount of period, a vehicle can cross the toll any number of times with the payment of only once, and the amount will not be debited for the second time [5, 6]. The approach is to use MATLAB image processing to capture accident or safety data. The algorithm finds the license plate numeral of an adjacent car. For license plate number detection, first the position of license plate is localised and then license plate is detected. The detected license plate is converted into binary image and fed into OCR for license plate number detections as shown in Fig. 2. Fig. 1 Electronic Toll Collection System

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Fig. 2 Steps in OCR

4 Block Diagram The working definition of the different elements used in the method is represented in brief, as seen in Fig. 3.

4.1 Arduino UNO It is an ATmega328P-supported micro-controller module. The core component of the whole device is the micro-controller. It takes responsibility for the execution of procedures. All the peripheral devices or modules linked inside the system will be tracked and also managed.

4.2 RFID Tag An electric field detects and records the tags connected to objects automatically. This consists of a transponder, receiver and transmitter for the radio.

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Fig. 3 Block diagram of the Proposed System

4.3 RFID Reader It is a technology where a reader captures the digital data encoded in the tag. The unique number will be read from the RFID tag, and the data will be sent to the microcontroller.

4.4 Liquid Crystal Display An LCD display framework can be used for different applications. It is a very simple module that constantly displays readings.

4.5 IOT Module It is a tiny portable computer that is built into objects, and the devices are linked to a wireless database that transfers and collects information.

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4.6 RTC It is a clock on a device that keeps track of the real time. The AM/PM indicator is given in either the 24-h or 12-h format.

4.7 Servo Motor A servo motor is a basic electrical system that has a servomechanism-controlled output shaft. It functions within a system of closed-loop management.

4.8 Power Supply Based on the units of the modules, this unit will provide the voltage needed. The transformer, rectifier, philtre and regulator are included.

5 Methodology If an individual owns a car, they have to register their car at the RTO office first. The vehicle number plate will be issued to the vehicle along with an RFID tag. A specific identifying number is found in the tag. The unique number is stored in the database. There should be a minimum down payment required for the card. The RFID tag is scanned with the RFID sensor at all times for the vehicle arriving at the toll, and the specific tag Id is checked with the database. If the car seems to be regular, the transaction begins and the amount will be deducted immediately dependent on the balance remaining. Often, within a certain period of time, the amount can be taken off only once during the various entries. The time will be recorded using the RTC. In the central server device, the programme further updates the data. And next, if a car burglary enters the office, an entry in the database is made for the vehicle number plate. Now, it is easy to identify any car arriving at the toll with the same unique Id relative to the database as a robbery car. In the type of a LAN, the central server links all toll points. Transaction notifications can be updated concurrently on the server [14, 15].

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6 Algorithm Optical character recognition (OCR) would be the use of computers, like a scanned paper record, to distinguish typed or handwritten text characters within digital representations of physical papers. OCR’s basic method includes reviewing a document’s text and converting the characters into code that can be used for analysis [16]. Usually, OCR is often noted as a device for text recognition. OCR systems consist of a variety of hardware and software used to translate physical documents into written text. To read the messages, hardware is hired, while advanced processing is for apps. Usually, this technique is used to translate valid or notable text documents into PDFs. Users will edit the pattern if put during this soft copy and locate the record as if it had been created through an application [17]. A typical OCR system as shown in Fig. 4 entails quite a lot of components. The first move is to get the analogue paper digitised. If the text-containing regions are obtained, each symbol is passed to the segmentation process. The symbols retrieved are pre-processed to eliminate noise and to enable the extraction of text characteristics [11–14]. By comparing the extracted features with definitions of the symbol groups obtained from the learning process, the identity of a symbol is sought. Finally, contextual material is used, as seen in Fig. 4, to recreate the words and numbers of the original text.

Fig. 4 Typical OCR System

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6.1 Optical Scan The digital version of the paper is generated at this stage. A transportation system and a sensing device are part of the scanning device to turn the light intensity obtained into a grey standard. First, the image obtained is transformed to a binary format by thresholding.

6.2 Location and Segmentation In order to recognise the constitution of the picture, this stage is used. The method of segmentation allows to differentiate between the text component of the graph in the picture and any non-text sections that are present.

6.3 Preprocessing Due to the scanner or the technique applied for thresholding, the image arising just after scanning process can involve some noise. This noise can cause fragmented characters, which can impede the precision of text recognition. In this step, we therefore eliminate the noise that is also known as digital image smoothing [18].

6.4 Feature Extraction The image will then be compared to the templates already loaded on the device, and the highest correlated template will be chosen and declared as the character.

6.5 Post Processing If there is some word that is unrecognised during the extraction point, so in this point, the term of letter is given a definition. This can be achieved by importing extra templates into the framework.

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7 Results and Discussion Figure 5 displays the investigational setup of the hardware. When the RFID tag is scanned, the LCD display in the kit shows whether the vehicle is normal or theft.If the vehicle is normal, the amount is debited from the user whenever they cross the toll and also for the theft vehicle but the servo motor does not open for theft. The amount detection is monitored with the help of RTC, i.e. the amount is debited only once for multiple entries in toll as shown in Fig. 6. All the entries are stored in the IoT server where the vehicle entry and exit is continuously monitored in the database based on the toll [9, 16]. Step 1: The input image is read in JPEG format as shown in Fig. 6. Step 2: Then the edge detection is applied to the image as shown in Fig. 7. Step 3: Then the location of the license plate is found as shown in Fig. 8. Step 4: The number plate images are segmented as shown in Fig. 9 to obtain a binarized image as shown in Fig. 10. Step 6: Finally, the vehicle number is identified, which is shown in Fig. 11. The input image captured is stored in JPEG format as shown in Fig. 6. Later on, it is converted into a grayscale image in MATLAB. The noises from the image are removed in the processing. Then the pictures in colour are transformed to grey images. It calculates the value of the grey value according to the value of R, G, B in the map and obtains the grey image at the same time. The license plate position is then recognised. Character segmentation is done to get the output of the extracted number plate using labelling components. And finally, the vehicle number is obtained.

Fig. 5 Experimental Setup

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Fig. 6 Input Images

Fig. 7 Application of Edge Detection

8 Conclusion Both highway toll booths are manually operated, where the toll workers receive the amount from the driver of the car and thus send a receipt. But this process is sluggish, which is why we always get into traffic jams on busy main roads at the toll booths. Automatic toll gathering strategies can save time, money, and manpower. The above method uses the RFID-based fastag toll collection system that speeds up the operation and hence minimises the queue at the toll gate in general. Instead, we build a framework to provide a fast and secure toll collection environment and

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Fig. 8 Location of License Plate

Fig. 9 Segmentation of Number Plate

automatically detect fraud vehicles by image processing at the toll stations. Payment can be supported from a bank account connected to credit cards through the fastag app. Thus, both theft vehicle identification and automated toll collection scheme are assured by the scheme.

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Fig. 10 Binarized Image

Fig. 11 Final Output

References 1. Fang S, Bian K, Xie K (2016) Vulnerability analysis of highway traffic networks using origindestination tollgate data. In: IEEE 19th international conference on intelligent transportation systems (ITSC). Rio de Janeiro, 1957–1963 2. Panjaitan AM, Silalahi RV, Andrew J (2014) Analysis of e-toll card usage at Pondok Ranji tollgate. In: 2nd international conference on information and communication technology (ICoICT). Bandung, pp 481–486 3. Gowrisubadra K, Jeevitha S, Selvarasi N (2017) A survey on rfid based automatic toll gate management. In: Fourth international conference on signal processing, communication and networking (ICSCN), Chennai, pp 1–6

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4. Eswaraprasad R, Raja L (2017) Improved intelligent transport system for reliable traffic control management by adapting internet of things. In: International conference on Infocom technologies and unmanned systems (trends and future directions) (ICTUS). Dubai, pp 597–601 5. Chopra SA, Ghadge AA, Padwal OA (2014) Optical character recognition. Int J Adv Res Comput Commun Eng 3(1):4956–4958 6. Patel C, Patel A, Patel D (2012) Optical character recognition by open source OCR tool tesseract a case study. Int J Comput Appl 55(10):50–56 7. Fang S, Bian K, Xie K, Cui D, Hong H (2016) The shortest path or not? Analyzing the ambiguity of path selection in China’s toll highway networks. In: IEEE 19th international conference on intelligent transportation systems (ITSC), Rio de, 1964–1969 8. Boliwala R, Pawar M (2016) Automatic number plate detection for varying illumination conditions. In: International conference on communication and signal processing (ICCSP), pp 0658–0661 9. Menon A, Omman B (2018) Detection and recognition of multiple license plate from still images. In: International conference on circuits and systems in digital enterprise technology (ICCSDET), pp 1–5 10. Hemanth K, Ramesh GP (2020) Energy efficiency and data packet security for wireless sensor networks using African Buffalo optimization. IJCA 13(02):944–954 11. Islam R, Sharif KF, Biswas S (2015) Automatic vehicle number plate recognition using structured elements. In: IEEE conference on systems, process and control (ICSPC), Bandar Sunway, pp 44–48 12. Badawi WA (2019) Underground pipeline water leakage monitoring based on IoT. Int J MC Square Sci Res 11(3):01–08 13. Ktata S, Benzarti F (2012) License plate detection using mathematical morphology. In: 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT), Sousse, pp 735–739 14. Indira N, Rukmani Devi S, Kalpana AV (2020) Light weight proactive padding based crypto security system in distributed cloud environment. Int J Comput Intell Syst 13(1):36–43. https:// doi.org/10.2991/ijcis.d.200110.001. ISSN: 1875-6891; eISSN: 1875-6883. https://www.atl antis-press.com/journals/ijcis/ 15. Indira N, Rukmani Devi S, Kalpana AV (2020) R2R-CSES: proactive security data process using random round crypto security encryption standard in cloud environment. J Ambient Intell Human Comput, pp 1–12 16. Kalpana AV, Rukmani Devi S, Indira N (2018) An efficient localization for smart defense node connection based node position tracking and identification in wireless sensor network. J Web Eng 17(6):2452–2471SCIE, 0.311 17. Tejas B, Omkar D, Rutuja D, Prajakta K, Bhakti P (2017) Number plate recognition and document verification using feature extraction OCR algorithm. In: International conference on intelligent computing and control systems (ICICCS), Madurai, pp 1317–1320 18. Kalpana AV, Rukmani Devi S, Indira N (2019) A unique approach to 3D localization in wireless sensor network by using adaptive stochastic control algorithm. Appl Math Inf Sci 13(4):621– 628. https://doi.org/10.18576/amis/130414

A Comprehensive Survey on Software-Defined Network Controllers Roaa Shubbar, Mohammad Alhisnawi, Aladdin Abdulhassan, and Mahmood Ahamdi

Abstract Software-defined networking (SDN) is seen as an important paradigm to fix many issues of the Internet involving multi-casting, complexity, security, power efficiency, and load balancing. SDN is an architecture that disconnects the control plane of any networking equipment (router or switch) from its data plane, making it suitable to observe, control, and administrate the entire network from a centralized entity (controller). In SDN paradigm, centralized nodes named “controllers” work in a similar way such as network operating systems (NOS) by running various applications that control and manage the entire network by deploying well-defined APIs. This paper offers an up-to-date survey of the SDN controllers including types, features, architectures, design, and many other important aspects. Keywords Software-defined networking · OpenFlow · OpenFlow controller · Network operating system

1 Introduction Despite that the term software-defined networking (SDN) [1] has been arisen recently, but the notion behind SDN has been developing since 1996 driven by the passion to support user-controlled administration of forwarding in network entities. The employments by manufacture groups and research involve: Ipsilon (1996) suggested global switch administration protocol, a Tempest (1998): a framework for safe, resource-assured, programmable networks, and ForCES (2000): decoupling of forwarding and control planes, finally path computation element (2004). Latterly, Ethane (2007) and OpenFlow [2] (2008) have presented the ability to implement the notion of SDN nearer to actuality. Ethane is a rule-based technique that R. Shubbar · M. Alhisnawi (B) · A. Abdulhassan College of Information Technology, University of Babylon, Babylon, Iraq e-mail: [email protected] M. Ahamdi College of Engineering, University of Razi, Kermanshah, Iran © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_18

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permits to network operators to specify rules that control network-level access for users, which involves quarantine for mistreating users and authentication. OpenFlow permits to an entity external to the forwarding elements to specify the entries in their flow tables. SDN is not restricted to any of these protocols but is a public idiom for the platform. Clearly, SDN is presented in this paper with the Open Networking Foundation (ONF) explanation: “From the architectural side of view, in SDN, the control and data elements are separated, network control is logically centralized, and the network details are isolated from the above applications” [3]. SDN has several fundamental features [4]: • Isolation between control and data planes. • Centralized controlling/view on the entire network. • Utilization of open interfaces to create a connection between control and data planes. • Allowing the outer applications to program the entire network. Generally, SDN simplifies the manner of modifying the behavior of the entire network (e.g., modifying the traffic directions dynamically). Consequently, the virtualization of SDN networks represents the essential characteristic that can enhance the achievement of communication networks [5]. The control and data planes are isolated in SDN, which make network intelligence (NI) logically centralized [6]. In this architecture, both planes can develop independently, so that carriers and enterprises can obtain automation, adorable programmability, and network control, allowing them to construct programmable and quite elastic networks [7]. OpenFlow is an enabler of SDN [8]. It is a modern open standard, a novel standard protocol, suggested by the ONF and with the purpose to support programmable networks. Commonly, in conventional network paradigm, the process of making decisions and forwarding functions is placed on the same device [9]. OpenFlow was introduced to make experiences on campus networks [10] and has been deployed earlier on networks of the universities. OpenFlow presents a standard upon which control and data planes can be abstracted [11]. This indicates that these two tasks no more must take place on the exact network equipment. This permits control logic to be shifted to an outer controller and the controller interacts with the data path through the network itself by utilizing the OpenFlow protocol. There exist considerable kinds of SDN controllers that have been designed using different programming languages: C and Ruby-based like Trema, Python and C++- based like NOX, and finally, Java-based like Maestro and Beacon [12]. A controller represents the crucial item in the SDN paradigm. It represents the fundamental backing entity for the control logic (i.e., Apps) to produce the arrangement of the network depending on the rules enforced by the network operator [13]. Like conventional operating system, SDN controller abstracts the details of the underlying forwarding equipments for the applications [14]. There are some related works like [15, 16], but we noticed that the researchers have been focused on the SDN as a whole and they mentioned some sections about the SDN controllers. Also, in [17], we noticed that this work did not focus on SDN

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controller from all sides of views. The contribution of this paper includes providing a state-of-the-art survey on the SDN controllers as a most important component in software-defined networks. We tried to reflect the most important aspects that are related to SDN controllers like architecture, implementation, challenges, evaluation tools, and how to select the suitable controller. The remainder of the paper is structured as follows. Section 2 presents a general description of the SDN controller. Section 3 presents a description to the architecture of SDN controllers. Furthermore, this section presents the fundamental concepts in the working of SDN controllers. Section 4 presents the implementation of SDN controller. Section 5 gives the most significant challenges in building SDN controller. Section 6 presents the role of SDN in the cloud environment. Section 7 gives a brief description for the most known controllers. Section 8 depicts the most popular tools for evaluating SDN controllers. Section 9 describes how to select the suitable controller. Finally, Sect. 10 provides a conclusion with a summary on some types of SDN controllers.

2 General View of the SDN Controller The three most important principles of SDN are: separating of control and data planes, network programmability, and centralized administration, regardless of the amount of centralization [7, 18]. The SDN controller is the implication of the SDN framework and, in many situations, is a repercussion of the framework [19]. Theoretically, SDN controller supports functions that can achieve a distributed control plane, as long as applying the notions of fugacious state centralization and management. Actually, any specific sample of a controller will achieve a bit or partial of this work, beside its own take on these notions [20]. The controller can be depicted as a software system or combination of systems that jointly offers the following: • Network state management, and in several situations, the distribution and management of this state, may include a database. These databases work as a depot for information extracted from the controlled network entities and concerning software beside the information controlled by SDN applications including learned topology, network state, some fragile management information, and control session information. Sometimes, the controller may have many, goal-driven data administration operations (i.e., relational and non-relational databases). In some other situations, other in-memory database techniques can be included as well. • A data model that picks up the connections among policies, controlled resources, and other services presented by the controller. In most cases, these models are built by using the Yang modeling language. • A recent, predominantly RESTful API is supplied that offers the controller services to an application [7]. This eases many of the communication between the controller and the application. Also, it is rendered from the data model that

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Fig. 1 Generic behavior of SDN controller

depicts the features and services of the controller. Sometimes, the controller and its API are portion of an expansion environment that produces the code of the API from the model. Several environments supply sturdy development environments that permit later spreading of APIs for new modules and expansion of core capabilities, including those that allowing to backing dynamic amplification of more controller possibilities. • The secure TCP control session between the controller and related factors in the network entities. A standard-based protocol for the supplying of applicationdriven network situation on network entities. • The equipment, topology, and service detection technique, a route calculation system. The recent platforms of controllers contain the commercial products of Juniper/Contrail, NEC (Trema), Nicira (NVP), VMware (vCloud/vSphere), and Big Switch Networks (Floodlight/BNC). Also, it includes some of the open-source controllers. Figure 1 depicts a generic behavior of SDN controller [20].

3 Architecture of SDN Controllers The present controllers can be classified based on several factors. Architecturally, the fundamental related aspect is that if they are centralized controllers or distributed. This represents the most important basic layout pivots of SDN controller platforms. Therefore, we will begin by presenting this side.

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3.1 Centralized Versus Distributed In a centralized environment, solely, one entity will manage all network equipments [14]. SDN offers centralized controllers to significantly increment the programmability of the network [21]. The controller represents the centralized controller of an OpenFlow network. It conserves topology information, sets up the entire OpenFlow equipments, and observes the global status of whole network. The OpenFlow equipment, like a router, switch or access point, is any device that has the ability to support OpenFlow functionality [22]. Each equipment preserves a flow table that holds the rules that are enforced on every incoming packet [23]. Centralized controller considers as an individual point of failure and also may have restrictions concerning the scalability issue. Also, only one controller may not be sufficient to handle a network with a great amount of network equipments [14, 24]. Many of these extensive data centers contain many thousands of servers combined with thousands of forwarding equipments (switch/router) in tree-like topologies (e.g., fat tree) that cannot readily be controlled by a solo centralized controller [25]. SDN [26] proposes logically centralized controllers to significantly maximize network programmability. Both flexibility and simplicity of a logical centralized controller may cause a limitation on the scalability of control plane [21]. Opposite to a centralized layout, a distributed network operating system (NOS) can be scaled up to satisfy the conditions of potentially any paradigm, from tiny to large networks. Distributed controllers have been suggested for SDN to overcome the problems of reliability and scalability that emerged with a centralized controller [27]. Figure 2 depicts the overall structure of the centralized and distributed controller. In

Fig. 2 a Centralized controller. b Distributed controller

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the distributed architecture, every single controller is in charge of managing solely a part of the devices. To preserve the uniformity of the network’s situation and run cooperatively, any single controller has the ability to communicate with other ones by utilizing east/westbound APIs [28]. A distributed controller [29] can be either a centralized cluster of entities or a physically distributed series of entities [30]. While the former alternative can bring great achievement for highly intensive data centers, the later can be extra flexible to various types of physical and logical failure [31]. Another popular feature of this kind of controllers is fault tolerance which means that when single device fails, the neighbor device should adopt the responsibilities of the failed device. Yet, in spite of some controllers tolerating smash failures but not for arbitrary failures, it indicates that any entity that misbehaves will not be replaced by another well-behaved one [14]. Furthermore, OpenFlow permits the connection of forwarding device (switch/router) to several controllers simultaneously, which would permit to other controllers to work as backup in the case of a failure. Onix [32] and HyperFlow [33] expand this notion by preserving a logically centralized but physically distributed controllers. This minimizes the overhead of look-up by allowing interaction with topical controllers, while permitting Apps to be created with a more straightforward central vision of the hole network. The possible issues are trade-offs [34] concerning to weakness and consistency when distributing state over the controller. A hybrid way, like Kandoo [35], has the ability to use local controllers for some applications and forward to a public controller for resolutions that demand centralized network view. This has the effect of minimizing the burden on the public controller by allowing to filter the amount of novel flow demands, while also supporting the datapath with more quick replies for demands that can be treated by a local control App. Furthermore, SDN can offer some grade of logical decentralization, with many logical controllers. An important kind of proxy controller, named Flowvisor [36], can be exploited to insert a level of virtualization to OpenFlow networks and permit many controllers to manage simultaneously an overlapping set of physical entities. An example, where the logically decentralized controller is needed, will be in the case of an internetwork extending numerous administrative scopes [37]. In [38], an attempt has been done to solve the problem of choosing the best testbed for implementing a distributed controller. They noticed that several developers, users, and testers of distributed controllers and Apps have constructed virtual testbeds by utilizing complete equipment virtualization and/or heavyweight containers, usually for hosting the network OS or Apps. This mechanism decreases both the scalability and the usability of the development platform because of its complexity and consumed overhead. In such a fat testbed, a supplementary (almost, extremely dispensable) orchestration system may be needed, like Docker (which does not support live migration [39]), OpenStack, or Vagrant. VMs and heavyweight containers consume considerable hardware resources and frequently need sophisticated administration. They mentioned that a better way is to use Mininet [40, 41] to dynamically generate virtual testbeds on the fly.

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3.2 Northbound and Southbound Interfaces An interface that permits a specific equipment of a network to interact with abovelevel once is called a northbound interface. Contrariwise, a southbound interface permits a specific network equipment to interact with a lower-level entity [42]. In a symbolic meaning, southbound flow can be considered as moving down, while northbound flow can be considered as moving up. In architectural graphs, southbound interfaces are sketched at the bottom of the applicable entity, while northbound interfaces are sketched at the top of the entity. While the idioms southbound and northbound can apply to almost any kind of computer system or network, recently they have been utilized increasingly in coupling with APIs in SDN [43]. In software-defined networking, the southbound interface is either OpenFlow or other ersatz protocol [42]. Its fundamental job is to allow interaction between the controller and the network entities (which include both virtual and physical devices). Thus, the router has the ability to discover topology of the network, specify network flows, and perform demands relayed to it by northbound APIs. The main tasks of northbound interfaces include sharing of required data among many systems and controlling both automation and synchronization. In contrast, the main tasks of southbound interfaces include unification of a distributed computing network, supporting the protocols of network abstraction, contacting with the underlying network entities [44]. Figure 3 depicts the position of northbound and southbound APIs.

Fig. 3 Northbound and southbound APIs

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Yet, NETCONF and OpenFlow represent the main southbound interfaces for SDN networks. For the incumbent networking vendors, Cisco presents OnePK, Juniper presents Junos Space, and Brocade has their OpenScript engine that might show viable alternatives if you like closed solutions [45]. The main function that the SDN controller provides to network applications falls in providing API for reaching the network. In some situations, this northbound API represents a low-level interface, supplying reach to the network entities in a prevalent and convenient way. In this situation, that application is familiar with single network device, but it is abstracted from their variations. In other situations, the controller may support high-level APIs that provide an abstraction of the network itself, such that the developer will not be worried about dealing with network equipments separately but with the entire network. Currently, there is no northbound interface to the southbound OpenFlow standard interface or even the actual heritage standards. This absence of a standard northbound interface is represented as existing defect in SDN, and several researchers are improving suggestions to standardize it [42, 44]. The lack of a standard northbound interface has been presented in many different formats; for instance, a Floodlight [46] employs a Java API and RESTful API [47]. The OpenDaylight controller [48] offers a RESTful API for applications working on independent equipments. The northbound interface considers a chance for the invention and the cooperation among the vendors and the open-source society [49]. The main aims for the northbound and southbound Interfaces are [50]: • Supply mobile, steady, and extensible APIs to the SDN controller, network application developers and network functions. • Increment portability of software originated to communicate with the controllers. This can be accomplished by identifying several APIs at different levels of abstraction of domain to increase the network mobility and programmability. • Northbound Interfaces The two key elements of the ecosystem are northbound and southbound interfaces. The widely accepted OpenFlow is a southbound, and an open issue is a northbound interface. Now, it is early to set a standard northbound API interface [47]. In any case, it is expecting to arise the northbound interface with development of SDN. Northbound interface represents an abstraction that allows to network applications to investigate all the possibilities of SDN. Unlike the southbound, which is a hardware ecosystem, the northbound interface is a software-based ecosystem. The application is the top driver in these ecosystems while standards arise subsequently and are basically driven by adoption [33]. However, an elementary standard for northbound interfaces stays playing a substantial function for the futurity of SDN. This issue has been discueed in [47, 51] and it is very premature to determine one standard for northbound interface at present. The progressing of various controllers will be the foundation for going out with a popular application-level interface. • Southbound Interfaces

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The southbound APIs can be found as a layer of equipment drivers on the lower level of control system. They allow a control platform to utilize various southbound APIs while supply an interface with the upper layers, and protocol plugins to administrate physical or virtual equipments (e.g., NetConf, BGP, SNMP). This is necessary for backward of heterogeneity and compatibility (i.e., to permit a connectivity of several protocols and device administration). So, a mixing of virtual and physical equipments (such as Open vSwitch [52, 53], vRouter [54]) and a diversity of device interfaces (such as OVSDB, OpenFlow, OF-config [55], SNMP and NetConf) can coexist. A few of controllers present a large number of southbound interface and/or protocol plugins, such as HP VAN SDN, OpenDaylight [56], and Onix controller. While most of them support solely OpenFlow as a southbound API, Onix offers both the OVSDB and OpenFlow protocols. The HP VAN controller can support another southbound API like L2 and L3 factors. An OpenDaylight go further through the provision of a service layer abstraction (SLA) which permits many protocols and southbound APIs to remain in the control plane. For example, its basic structure was suggested to offer more than seven various protocols and plugins: OVSDB [57], NETCONF [58], PCEP [59], SNMP [60], BGP [61], LISP Flow Mapping [62], and OpenFlow. From here, OpenDaylight is one of a little control environment being assumed for supporting a wider integration of techniques in a control environment. • Eastbound and Westbound Interfaces East/westbound interfaces, as shown in Fig. 4, are considered as particular instances that are used only in distributed controllers. Recently, every SDN controller uses an east/westbound API. Interfaces can make more than one function which involves

Fig. 4 Position of Eastbound and Westbound interfaces

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exchanging data among controllers, observation/notification potentials, and algorithms for data consistency models. East/westbound APIs, like southbound and northbound interfaces, are fundamental partitions of distributed controllers. The existence of a standard east/westbound APIs is necessary to support common compatibility among various controllers. For example, SDNi [63] identifies common needs for coordination flow setup and interchange accessibility information through numerous domains. Substantially, these protocols can be utilized in an interoperable and orchestrated manner to generate extra dependable and scalable distributed control environments. Interoperability can be used to maximize the variety of the control platform item. The variety maximizes the system reliability by minimizing the possibility of familiar faults, like a software fault [64].

4 Implementation of SDN Controllers 4.1 Proactive Versus Reactive To adjust the information of flow tables, SDN controller sends flowmods packets to a switch. For adding rules in the device, the controller has two methods: (I) proactively: that means the controller starts the initiative and inserts rules prior to arriving of the first packet to the network or (II) reactively: which means the controller reacts depending on the network, as previously unrecognized packet [65]. In a reactive control mode, like the one suggested by Ethane [66], the forwarding items should inform a controller whenever they cannot make a decision, like when a packet reaches from a novel flow to a device. In this mode, SDN controller exchanges many messages with the OpenFlow switches [67]. The first packet in every new flow will face some kind of delay which results from sending this packet to the controller to take the proper decision (such as, drop or forward). Later, the reminder packets in this flow will be forwarded at a rate of the forwarding device. While the delay that occurs with a first packet may be trivial in more situations, it may be considerable if a controller is located far [33] or if several flows are shortlifespan,like a monocularpacket flows. Furthermore, in larger networks, there are several scalability issues in which a controller should manage a greater number of new flow demands. Instead of that, proactive control methods put policies for the rules from the controller to the entities. An instance for the proactive control is DIFANE [68], which divides the rules through a number of hierarchical equipments. Experimentally, the proactive DIFANE minimizes the first packet delay from 10 ms average RTT to 0.4 ms average RTT with a centralized NOX controller for novel one packet flow. It was appeared to maximize new flow performance, for example, the DIFANE accomplished 800,000 packet/s while in NOX accomplished a maximum of 50,000 packet/s [37].

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4.2 Implementation of SDN Controllers Traditional computer networks are in charge of carrying out several operations, like a server workload balancing, monitoring of network traffic, access control in the network, and routing of data. In addition, computer networks are consisting of many various kinds of equipments, like a collection of routers, firewall, switches, etc. This diversity of modules existing in the networks makes the network administrating a sophisticated and hard mission. An easy solving is presented by SDN through accomplishing the mission of network resource administration by utilizing an easy interface among many various equipments and the software module utilized to manage them [69]. As explained earlier, OpenFlow is an advanced concept where a standard protocol is utilized to change the attitude of equipments by utilizing low-level API which simulates the implied hardware entity. An instance of this is the Frenetic project, where its aim is to support a normal and maximum degree of abstraction for creating applications with three essential areas in mind: (1) observing the traffic in a network, (2) administrating (generation and configuration) packet forwarding policy rules in the network managing, and (3) guaranteeing the consistency in upgrading those policy rules [70, 71]. By supporting these abstractions, the programmer’s job becomes more efficient and simpler in creating and handling additional network applications for SDN without any concern about the underlying details [72, 73]. In general, topmost programming languages that are used for writing SDN controllers are: C, Ruby, Java, and Python.

4.3 SDN Beyond Data Center Evolving SDN between and beyond data center needs considering the integration and interaction with heritage control planes and supporting conventional routing, switching, operation administration, and management (OAM) missions [21]. Generally, in any kind of network, to accomplish a path (the better one) between a source–destination pair, there is a necessity to create a communication among various nodes [74, 75]. In recent networks, there exist two methods to create a path: by instantly arranging the data plane at every entity along a selection source–destination path or by the utilization of a distributed protocol that run at every entity. An architecture called hybrid switch has been introduced to make an equipment behave as OpenFlow switch or as legacy switch, or as both at the same time. One instance of software-based OpenFlow device is OpenVSwitch (OVS) which has the ability to work either in an OpenFlow only mode or in hybrid mode [74]. Hybrid networking in the SDN environment is a network that permits to support OpenFlow for a partial of all flows only, supporting choices to allow OpenFlow on a subset of equipments and/or ports.

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• Integrating OpenFlow and Legacy Networks Currently, to combine various OpenFlow islands, there is a need for various handcrafted techniques and engineering on legacy network equipments and their protocols. An automatic and transparent way to integrate legacy network is untreated problem till now, because of the existing legacy protocol imitation applications that exist on SDN controllers [74, 76]. The issue is that many of these techniques do not provide fundamental characteristics for paradigm development or virtualization depending on slicing and programmability. As instance, GMPLS is a conservative protocol suite, which presents a modicum room for invention since that new features are extremely hard to integrate in the distributed control plane. Moreover, OpenFlow can be simply employed for various dimensions while permitting the system performance to be enhanced and the network control layer to be sliced [76]. • Hybrid Networks from an SDN Perspective There is a requirement to investigate the hybrid networks from an SDN perspective depending on the essential feature of OpenFlow-enabled networks, which is the ingrained centralized control of network entities. The most three fundamental perspective mechanisms can be utilized for hybrid networking [21]: • Make OpenFlow entity act as a router, so it does not problem if such entity is linked to a legacy router or to another OpenFlow entity, assuming that the legacy entities can recognize the respective L3 protocol. • Make OpenFlow nodes recognize circuit mechanism signaling to create label switching paths (LSPs), while assuming that all legacy entities along the path also can recognize the exact signaling protocol. • Utilize an OpenFlow-based control plane for the entire hybrid network and take over a moderate layer to transpose OpenFlow forwarding principles to the arrangement syntax of every legacy non-OpenFlow device. The following considerations emerge regards to SDN controllers: • One or more SDN controllers may take control of each OpenFlow domain for each approach above, so subsets of the entire OpenFlow domain are allowed. • For the approach number 3, permit various SDN controllers and treat OpenFlow as the regular manner to control the entire hybrid network. • There is a possibility to put approach numbers 1 or 2 on top of approach number 3. Consequently, the entire hybrid network will behave like an OpenFlow network performing L3 routing or MPLS signaling [76].

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5 Most Important Challenges in Designing SDN Controller 5.1 Controller Security Issues As explained earlier, the controller is logically centralized which means that it preserves a general vision of the entire network, while it seems as a logically single entity to the applications. So, the majority of networking security is evolving around controller which is locked down. Also, the network will be completely susceptible to untrusted changes or harmful attacks without proper security arrangements, both of which can take your network completely down [15, 77]. SDN controllers [78, 79] do not have the ability (as a standalone) to capture TCP packet in a uniform way and save TCP session data [80], among other basic state tracking data, which is wanted to improve security operation tools [80–82] (as instance, IP reputation and TCP connection status). Nevertheless, it can use a specialized application to do that. When employing TCP, it is better to utilize ersatz security procedures to prohibit snooping, intrusion, or other attacks on the OpenFlow SSL channel [79]. Several key questions are arising when talking about centralized controls: • Who accesses a controller? • Is the controller obtainable for the business continuity potential at beneficial time? • Is there is a security in the connection between controller and network entities or not? Essentially, there are two issues: The first focuses on using SDN to enhance network security, while the other focuses on enhancing the security of SDN itself. It is possible to use SDN to enhance the safety of recent networks. Perhaps, the headmost example of SDN was an application for enforcing safety principles. SDN permits the enforcement to be achieved on the headmost entry point to the network [83] (as instance, the device that is directly connected to the user). Instead of that, in a hybrid network, applying security policies can be done on a larger network circumference through programmable equipments, and hence, there is no demand to move the whole infrastructure to OpenFlow [84]. With either application, prior to getting into the decisive areas of the network, the malicious actions can be blocked. SDN has been successfully utilized for other important security targets such as active security [85] and discovering of DDoS flooding attacks [86]. In a convenient way, it is extremely handy for algorithms, especially the algorithms for detecting DDoS flooding attacks. There exist some study ideas on determining the crucial security menaces of SDNs and on increasing its security and confidently [78, 81, 87]. Early techniques attempt to enforce straightforward technologies, like using policy prioritization and grouping applications, to guarantee that policies created by security applications will not be replaced by less preference applications [78]. Other approaches attempt to provide a framework for improving security applications in SDNs [81]. Nevertheless, the

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dependability and security of SDN environments still have a long way to be developed [87].

5.2 Controller Placement SDN technology separates control planes that have caused many unanswered questions regarding availability, scalability, reliability, performance, and network interchange time when compared to more classical distributed systems [88]. The centralized control structure is more vulnerable, which requires convenient workflow. More astonishingly, the existing reaction time requirements can be meet sufficiently by one controller location in network topology. Many questions concerning controller placement arise: • How many SDN controllers are required? • How does controller placement affect latency? • Where should controller go in the topology? The authors of [88] analyzed controller placement problem in 256 different topologies that involve a varied range geographic areas with (continental, regional, and global), topologies (line, ring, hub, tree, and mesh), and 8–200 nodes. This analysis provides some conjecture for controller placement discussion in network topology. About three controllers are required to eliminate a half of latency, while, on the other hand, four controllers required for same reduction for worst-case latency for different topology scenarios.

5.3 Controller Scalability To stay in reasonable time response (less than 10 ms), NOX controller [89] can initiate solely 30,000 flow request/s, and it is a challenge to serve a lot of flows and stay in a reasonable response time within little period. Control plane architecture is scalable if it became qualified to serve more flow demands with the growing complication and scale of network while keeping the acceptable level of service quality [90]. The proposing of a centralized control plane to network may come in scaling bottleneck when several events need to be handled by the centralized controller. Several systems attempt to enhance scaling by minimizing the amount of cases that should be managed by the controller. For example, Devoflow [91] copes scalability by decoupling the control and global centralized clarity and refactoring the OpenFlow API. NOX-MT [92], Beacon [93], and Maestro [94] network controllers utilize multicore servers. Specially, NOX-MT modifies NOX for better using of CPUs by classifying system calls and by exploiting Boost [95] C++ libraries for threading and IO notification. Beacon [93] uses a different approach that assign single OS thread for

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each core and stably assigns particular core to handle one switch when it connects to the controller. Tootoonchian [92] explains that NOX-MT, Beacon, and Maestro process 2 million, 100,000, and 300,000 OpenFlow requests/s, respectively, when controlling 64 switches with eight OS threads. Later measurements by A.Voellmy [96] explained that by using 10 CPU cores, NOX-MT can be scaled to 5 million flow requests/s, and by using 20 CPU cores, Beacon events handling rate scaled to 13 million event/s. Other systems offer multi-server implementations of OpenFlow network controllers, to load balance event that loaded through several control servers. Onix [32] divides network state through several distributed controllers, mitigating fault-tolerance concerns and scalability [89]. ElastiCon [97] performs a distributed SDN controller. Now, the important question is: What is the way to allow the controller to offer a general vision on the entire network? Supposing that a hybrid architecture can satisfy the required performance, another issue is SDN scalability that has some consideration with bounded solution. The issue can be divided into controller scalability and the scalability of network nodes. The emphasis here is on controller scalability in which three set issues are recognized. First, it is the delay that presented by interchanging network packets from/to a controller and multiple nodes. Second, it is how SDN controllers make the connection with other controllers by utilizing east-westbound APIs [98]. To solve the first challenge, a P2P or distributed controller should share the controller’s communication load. However, this method does not remove the controller-to-controller connection challenge which requires a global network view [99]. Traditional networks do not suffer from the scalable problem, a network node has been designed as independent, and it requires only bounded knowledge from its neighbor’s nodes. In order to originate elastic networks, secondary supply and alternate paths are required. To ensure the occurrence of the failure, there are no or a few interruptions in service. It is necessary then to turn on some events between systems. Such system may require load balancers and firewalls network elements [100]. There are numbers of other approaches with goals look like SDN’s goals with the existing routing protocols such as the orchestration which utilize an API used by application elements to demand required performance from the transport layer. Data model has been proposed as extension to the Application-Layer Traffic Optimization (ALTO) by several organizations. The main goal of ALTO is to provide the desired resource to the applications of one or several hosts by directing them in their options. A straight architecture was introduced in [101] with bi-directional connection between each ALTO server and the SDN controller to support the general view for network nodes. SDN with ALTO should be a robust tool in terms of improving software performance. HyperFlow [33] is a particular solution to controller scalability problem; it is a controller application that resides on NOX controller and applied on an event broadcasting system. The HyperFlow application broadcasts events that can make a modification in the conditions of the system, and other controllers answer all the

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broadcast events and the conditions can be rebuild. The same convenient network global view shared among controllers by this means. Indeed, by distributing the conditions over multi-controller, the global network view can be accomplished. This is highlighted in [89] in which a number of resolutions to controller scalability are explained. In [89], the authors conclude that the SDN flexibility provides a scope in functional controller scalability and network manageability. To obtain full SDN scalability, an evolutionary architecture will be necessary to network programmability. For example, an extended query can be determined in the node CPU with the hybrid architecture, which would be moved to the controller for management. This can affect the communication between the controller and its devices and on the size of controller’s database.

5.4 Reliability The SDN has increased the network availability by providing the design validation as a part of network configuration performed by intelligence controller which eliminates manual errors. This design validation is one value provided by the controller. Although with this feature of SDN controller, one of the most weaknesses of SDN is the monocular point of failure. To counter that weakness when evaluating controllers, the functionality that the controller offers needs to take into account which are excesses network reliability [102]. The controller ability to detect multi-paths from the source to the destination can be used to maximize network reliability. When SDN controller sets up several paths between the source and the destination, then the availability shall not impact by a single link. On contrast, when SDN controller sets up one single path only from source to destination, the SDN controller should has the capability to reroute the traffic flow quickly to an active link when a link failure occurs. To do that, the network topology should be continually monitored [102]. In terms of controller availability, it is important that the controller is developed by utilizing hardware and software redundant features. For example, in an active/standby mode, the utilization of couple of SDN controllers can excess the reliability and the utilization of three or more SDN controllers can excesses the scalability, availability, and achievement of the controller. Nevertheless, it is necessary that the solution maintains the memory synchronization between the standby and active controllers [102].

5.5 Controller Performance Great throughput (until 1 M responses/s) and low latency (10–100 ms event management) are the first performance metrics that are required by a large-scale data center

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and high-performance networks. Furthermore, there are a lot of other performance aspects such as [103]: • • • • •

Time of discovering network topology. Rate and time of asynchronous message handling. Rate and time of reactive path supplying. Rate and time of proactive path supplying. Time of network topology discovering and modification.

All the above aspects are related to controller throughput and latency, so we will focus only on these two aspects. • Controller Throughput The throughput of a controller can be described as “what is the highest number of packets that a controller can manage in a second.” There are a lot of works that try to achieve the large-scale data center requirement, and we will list the most important ones. In [104], the authors tested the Floodlight and OpenDaylight controllers for throughput mode by using Cbench tools, and they conclude that Floodlight’s average responses/sec with eight devices offer perfect results of (18,001), whereas OpenDaylight average responses/sec were unexpectedly very low (270) for eight switches. In [105], the authors made a performance analysis of Python-based OpenFlow controllers (POX, RYU, and Pyretic), and they concluded that Ryu controller has the highest throughput, among others. Thus, they showed that the achievement of Ryu was the best, POX achievement was acceptable, but Pyretic achievement was the worst. Each NOX instance can handle about 30 k flow installs per second. There are some works that try to improve NOX achievement. One of these works is [106] in which they presented NOX-MT (NOX-Multi-thread) and showed that it can handle 1.6 million requests per second. In [33], they used the HyperFlow application and implemented atop NOX by spreading events that effect on the controller state. However, it can typically process a larger number of events which have no interaction with a switch. Events published through HyperFlow have no interaction with controllers and affect controller state only. They showed that HyperFlow under specific conditions can guarantee a bounded window of inconsistency among controllers. In [107], a problem in the design of NOX controller has been detected which made it unsettled in the existence of background traffic, and they introduce an IP search technique in the controller that helps to make the system stable by minimizing the amount of packets produced by the controller. After analyzing the Maestro and Beacon controllers by [108], it has been showed that the largest throughput that Maestro controller can achieve is (600,000 requests per second). In [93], it has been showed that Beacon controller has high performance and can handle up to 12.8 million demands per second with twelve cores. In [109], two ONOS controller prototypes have been described and evaluated. The authors have been developed the first ONOS prototype over a four-month course, and hence, the performance had not reached the requirement of the high-performance networks. The remote data operations were the biggest performance bottlenecks of their first

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prototype. Therefore, they got a very worse latency so that it has been taken 30 s to reacts to a link failure. They focused on improving the performance in their second prototype of ONOS. They addressed their first prototype issue by emphasizing on enhancing the speed of their remote database processes, and for their specific use case, they designed a new optimized data model, but they saw that they yet do not satisfy their target of the path setup throughput (1 Mpath/s), and they think that they can maximize the throughput by distributing the path calculation load through multi-ONOS situations. • Controller Latency Controller latency is defined as “what is the consumed time by the controller to handle one packet.” For achieving a good performance, the control platform should keep the control plane latency in a reasonable bound to avoid the impeding of management software. In [104], the authors also tested the Floodlight and OpenDaylight controllers for latency performance mode, and from the results, with eight devices, on average, Floodlight manages 1214 responses/s per device that means to process a single packet, it takes 823 µ, where the numbers of responses for OpenDaylight were extremely low. The response recorded was 55/s with eight switches that is (18,181 µ) to process a single packet. They also discovered that the delay achievement of Floodlight grows when increasing the number of devices but ODL showed unforeseen results. When the number of devices is increased the numbers of responses decrease. A comparison between Ryu and NOX controllers has been made in [110]. The authors showed in their experiments that Ryu does not have the spanning tree protocol implementation and suffers from packet broadcast storm issue when managing a network with loops. Also, they showed that in a network controlled by Ryu or NOX after a link failure, TCP flow cannot switch to a novel path when ARP is handicapped. But when ARP is enabled, NOX enables a TCP flow to shift to a novel path. They showed that the attitude of Ryu and NOX are extremely various, and their implementations remain to have numerous rooms to be enhanced. Despite the several attempts that have been made to improve the work of the SDN controller but they still unable to meet the high network requirements like growing flow requests and response latency restrictions. The current studies of several production paradigms suggested that the achievement of one controller is yet away from adequation [17].

6 SDN-Based Cloud Computing Cloud computing is a rising computing architecture with scalability, elasticity, and massiveness that targets to move the spreading of a computing infrastructure to the network to relieve the charge of management and maintenance of software and hardware resources. Virtual network devices and machines are utilized to present the services of cloud computing over the Internet [111].

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In cloud computing, virtualized applications are residing in private or public cloud which at the end allow the itinerant users to access to devices from everywhere. The fundamental technology of cloud computing is the virtualization, and the utilization of virtual computing has a fundamental impact on maximizing the significance of the network infrastructure. Furthermore, this virtualization allows every user to control its traffic and to utilize its own network topology. To ensure the reliability, conventional networks have been built by utilizing network devices in a distributed way. However, this architecture is not prepared for high domain of employment scenarios of cloud computing. Thus, it has no ability to manage other demands like grate number of VLANs, load balancing, and QoS. In cloud paradigm, it has become extra complicate to offer novel end-to end services and applications in a costly manner. Besides that, the diversity of physical machines and the difference among the interfaces of network devices have a great impact on maximizing the burden of management and minimizing the achievement of data center [112]. To cope with the simultaneous requirements for manageability, security, virtualization, agility, and mobility in cloud computing paradigm, supplying novel services and the dependable application delivery in a dynamic IT paradigm can be obtained by the SDN. SDN proposed a standard interface between applications of the controller and forwarding tables inside the switches, and thus, it is a natural environment for network virtualization. SDN allows the network to be a programmable part of the biggest cloud infrastructure. As we mentioned earlier, the main characteristics of SDN involve isolation of data and control planes, supplying interfaces/APIs between control and data planes. By using SDN, network administrator has the ability to introduce a novel function by organizing a software that can be easily affects the logical map of a slice of the network. Moreover, SDN controller can support a useful set of network services including service differentiation, custom addressing, network isolation, and middle box support (e.g., firewalls and intrusion detection systems) [133]. Another advantage of SDN controller is that it alleviates the cloud administrator of the burden of interacting directly with the network equipments via a set of network administration protocols. This architecture will simplify the controller to enable supporting multi-cloud platforms (e.g., VirtualBox, KVM, Xen, VMware, OpenStack) in addition to support multi-device management protocols (e.g., SNMP, OpenFlow) [134]. Cloud computing imposes novel DDoS challenges (i.e., extended vindication circumference and dynamical topology that is because of its own new operation paradigm). To actively cope with these issues, the cloud provider must have the ability to (1) readily envoy the control of its network to cloud users (2) rapid reconfigure the control depending on the changes of network topology resulted by dynamic emigrations and allocations. It can take advantage of both the centralized controller and the virtualization of SDN [113]. Concerning SDN controller, the main challenges in building SDN-based cloud computing is how to use SDN controller that can provide load balancing, power management, and monitoring that are extremely critical aspects in building such system.

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7 List of Most Popular SDN Controllers This section provides a brief explanation for the topmost twelve controllers that we worked on them.

7.1 OpenDaylight OpenDaylight (ODL) [62] is a greatly obtainable, scalable, extensible, modular, and multi-protocol controller platform designed for SDN deployments on recent varied several- supplier networks. It supplies a model-driven service abstraction environment that permits users to build applications that facilely run through a large diversity of hardware and southbound protocols.

7.2 Onix Onix [32] is a platform that allows to network control plane to work as distributed system. Control planes that programmed with Onix work on a wide vision of network using a fundamental distribution plain. This state may be supplied by a platform. So, Onix supplies a global API for the control plane implementations, whereas permitting them to do their trade-offs within scalability, durability, and consistency.

7.3 ONOS ONOS [114] is the foremost open-source SDN network OS aimed particularly at the service provider networks. ONOS is built to offer the scale out, high availability (HA), and high performance for networks. Additionally, it has generated valuable northbound APIs and abstractions to enable application innovation and southbound interfaces to permit for easier controlling of OpenFlow/legacy network devices.

7.4 HyperFlow HyperFlow [33] is a distributed controller for OpenFlow, which permits network administrators to utilize any number of controllers inside their networks. It supplies scalability while conserving network control logically centralized. Every controller shares the exact harmonious network global view and locally manages the flow demands without connecting to any remote active entity, thus reducing the flow

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setup times. Moreover, HyperFlow does not demand any changes to the OpenFlow standard and only requires minor adjustments to presenting control applications.

7.5 Floodlight The Floodlight [115] is a project-class SDN controller available under Apache License, build by Java. It is offered by a society of developers with some engineers from big switch networks. It was proposed to run with the increasing number of network equipments that build with OpenFlow principle.

7.6 NOX NOX [86] is a part of SDN platform, and it was the first controller. Specially, it is a platform for constructing network control applications. NOX was originally developed at Nicira networks part-by-part with OpenFlow. Nicira granted NOX to the research field in 2008, and since then, it has been the basic for different study areas in the beginning investigation of SDN area.

7.7 POX POX [116] is fantastic for exploring into SDN using Linux, Python on Windows, Mac OS. It is targeted largely at education and research and can be used for outstanding work on determining techniques and key abstractions for controller design. It is an environment for the fast improvement of network control applications by utilizing Python[117].

7.8 MuL Open MuL [61] is an OpenFlow support SDN controller. It has been programmed by C language ,and it is based on several threaded infrastructures at its core. It can support a several-level northbound interface for enabling the applications to communicate with the underline network nodes. It was targeted to support different SDN enabling southbound APIs such as OVSDB, OF-config, along with OpenFlow 1.4, 1.3, and 1.0. It is designed for reliability and performance which are the requirements for deployment in critical tasks networks. Also, it is modular, highly flexible, and simple to learn.

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7.9 Terma Trema [118, 119] is an SDN controller framework. It contains the main requirements to build SDN controllers under two programming languages (Ruby and C). It also provides several sample softwares that developed on top of Trema and can be running as SDN controller’s samples. Trema supports C and Ruby languages, and the developer has a freedom to choose based on his required performance.

7.10 Beacon Beacon [93] is a powerful SDN controller which has the following specifications (quick, cross-platform, modular, and offers both event/thread-based processes). The stability and language programming are the most important features in Beacon. It was programmed in Java and works on several environments like the high-end Linux servers and simple Android phones. Beacon is a dynamic controller; at running time, the code bundles in Beacon can be launched, refreshed, stopped, and installed.

7.11 Ryu Ryu [120] is SDN framework that offers APIs with software components that give the developers a way to create a novel control and network administration applications. Ryu upholds many of the network management protocols, like Netconf, OF-config, OpenFlow, etc. Ryu supports, Nicira and 1.0, 1.2, 1.3, 1.4, 1.5 extensions of OpenFlow and the entire codes are available for free under the Apache 2.0 license.

7.12 Maestro Maestro [121] is an OS to orchestrate the network control software. Maestro written by Java provides interfaces that can be used for implementing network control modular software which can reach and alter the conditions of the network and can coordinate their activity. By using this modularized software, Maestro acts as a platform for achieving programmable and automatic network control tasks.

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8 Evaluation Tools for SDN Controllers In this section, we will present the most important and widely used controller evaluation tools that estimate the achievement of SDN controllers by simulating the load of large-scale networks (as instance, PktBlaster [124], OFCBenchmark [125], Cbench [126]). Among all these tools, Cbench represents the standard tool utilized for evaluating SDN controllers, and it is widely used. So, we will focus on it. Cbench [106] is the most general (stress and a blind) tool used for SDN controller benchmarking. If several models of Cbench are related to one controller, Cbench models are insensible to each other. Cbench tests run in one of two modes. In latency mode, Cbench forwards a special packet to the SDN controller and stay waiting for a reply. Also, each simulated switch preserves precisely one outstanding novel flow demand, standing for a replay before encouraging the later demand. Latency mode counts the SDN controller’s request management time under low-load situations. In throughput mode, Cbench sends flow of packet in messages to the controller for certain interval of time. As well, every switch preserves as many random requests as its buffer will accommodate until the TCP sends a buffer overflow single and so on [127, 128]. In both situations, Cbench measures several replies from a controller. Cbench can operate in a hybrid style. In many researches, it has been explained that sophisticated probabilistic models can prophesy data center traffic [129, 130]. Cbench did not have any way to support these models.

9 Selecting the Suitable Controller Recently, with the variety of utilizations and various controller options, the following question will arise: Which of the controllers should be selected and used? This issue can be disturbing as it is hard to specify the appropriate aspects, and the number of controllers is continuously growing. The controllers have various characteristics. Choosing a controller by utilizing one characteristic is measly. Nevertheless, choosing a controller depending on several characteristics is a multi-criteria decisionmaking (MCDM) issue [135]. To overcome such issue, many ways are available in management science such as analytic hierarchy process (AHP). In [16], they utilized AHP to choose the better controller for twice purposes. First, it utilizes pairwise prioritization. Second, it has an integrated consistency examining technique. Nevertheless, to choose the better controller automatically, the AH requires to be adapted as it does not support a technique to map the value of characteristics to the pairwise prioritization scale. The adaptation is accomplished by utilizing a monotonic interpolation/extrapolation technique. For choosing the controller, we suggest considering several selection standards like interfaces, modularity, GUI, and maturity. In [136], several key attributes have been specified to consider when picking SDN controller. These attributes include:

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• Application ecosystem and maturity of northbound APIs: SDN controllers are deployed to overcome network issues in association with SDN applications. Giving interesting to the business issues and recognizing what applications available for the controller platform and whether those applications are a perfect fit for specific business issues is one of the interesting features for investigation. • Use case fit: Just as important as understanding whether SDN applications exist to solve a specific problem, there is a need to understand if the controller was designed for the specific areas of deployment. As instance, controllers that are created for WAN deployments might not run as well in the data center environment. • Open-source versus proprietary: Certainly, open-source solutions will minimize the likelihood of vendor lock-in restrictions. Also, extra attention should be paid toward operations and fit, as well as other characteristics. • Maturity and robustness of the controller and compatibility with networking equipment: Since the controller will be a key element of the network, it is significant to look at the maturity of the solution under consideration and in particular, pay attention to the number of production deployments, areas of deployments and how long these deployments have been running. • Clarity of roadmap and commitment to stable APIs: Related to the open-source versus proprietary attribute, it is obvious that clarity and stability of the vendor or open-source project roadmap are significant, as the vendor commitment to stable APIs (which hopefully are also open APIs). • Smoothness of integration into orchestration platforms: Almost every commercial controller and many open-source controllers provide OpenStack support in the data center.

10 Conclusion A controller is a crucial entity in the infrastructure of SDN as it is the main backing element for the control logic to create network arrangement depending on the rules specified by the network administrator. SDN controllers have been previewed to select among the most known twelve controllers depending on their recent employment. Those twelve controllers have been examined to gather their statistics such as architecture, interfaces, and platform. We presented an updated and inclusive survey of the SDN controller from a different side of views. Table 1 depicts a summary of most known twelve controllers with their respective paradigms and features. As can be noticed, most controllers are centralized. Curiously, the northbound API is very numerous. Especially, three controllers (Onix, Floodlight, and MuL) give a little more interest to this interface. Lastly, we can notice that just Ryu controller can use all three versions (i.e., 1.0, 1.2, 1.3, 1.4, 1.5) of OpenFlow as a southbound interface. To deduce, it is significant to confirm that the controller is one of the decisive aspects for the prosperity of SDN. The presented work can be utilized as a reference for SDN researchers or developers to simplify the process of choosing SDN controller.

Contreoller

C++ Python C C, Ruby Java Python

NOX [86]

POX [116]

MuL [61]

Trema [122]

Maestro [108]

Ryu NOS [123]

C++

HyperFlow [33] Java

Java

ONOS [114]

Floodlight [115]

Python, C

Onix [18]

Centralized

Java

OpenDaylight [62]

Distributed

Programming language

Controller name

Architecture

Table 1 Classification of topmost twelve controllers

Ad hoc API

Ad hoc API

Ad hoc API

Multi-level interface

Ad hoc API

Ad hoc API

RESTful API

–

RESTful API

NVP NBAPI

REST, RESTCONF, Java APIs

Northbound API

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow

OpenFlow, OVSDB

OpenFlow, OVSDB, SNMP, PCEP, BGP, NETCONF

Southbound API

No

No

No

No

No

No

No

Weak

Weak, strong

Weak, strong

Weak

Consistency

v1.0; 2; 3; 4; 5

v1.0

v1.0

v1.0

v1.0

v1.0

v1.1

v1.0

v1.0

v1.0

v1.0; 3

OpenFlow support

Most supported on Linux

Linux

Linux

–

Linux, Mac OS, Windows

Linux, Mac OS, Windows

Linux, Mac OS, Windows

–

–

Linux

Linux

Platform support

(continued)

No

–

No

–

No

No

No

–

No

Yes

Yes

Balancing

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Table 1 (continued)

Beacon [93]

Java

Ad hoc, API (based on OpenFlow events)

OpenFlow

No

v1.0

Linux, Android

Yes

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Performance Evaluation of Nanorobot Drug Delivery Mechanism for Breast Cancer A. R. Venmathi and L. Vanitha

Abstract Breast phantoms are used as experimental models for the evaluation of screening parameters while using mammographic screening. The calcification regions in mammogram images are segmented using an area growing algorithm. The conventional cancer treatment procedures are excruciating, which makes the patients withdraw from treatment, and hence, researchers are working to overcome this drawback. Nanotechnology, the developing trend, overcomes this drawback. This technology uses active drug delivery mechanisms that deliver nanomedicines to the segmented target region. Nanorobots carry the nanomedicines. Laser sintering is the printing technology for new organs like breast phantoms. The calcification regions made using eggshells with varying sizes. This paper explains the efficient nano-drug delivery method, which reduces risk and tedious tasks. Keywords Microcalcification · Nano-drug delivery · Region-based · Detection · Organ printing

1 Introduction The phantoms are numerical models used for clinical validation and representation of development in technology for the reduction of pain and risk of patients while they undergo medical checkups. The appropriateness of phantoms with the human breast contributed to the benefits of undesirable experiments on patients. Singlet phantom fabricated with a single material urethane-based polymer and microcalcification clusters were induced inside the structure using crushed eggshells. These crushed structures vary in size to represent benign and malignant tumors. Typically regular shape of eggshells was fabricated to model benign tumors while irregular shapes made to model malignant tumors. The fabricated breast phantoms have great extended clinical utility and also avoid unwanted expenses on design for clinical A. R. Venmathi (B) · L. Vanitha Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_19

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trials by preventing the use of human organs. Nanotechnology involves a significant role in processing these phantoms from a dedicated CAD system into tissue classes, glandular density, and skin structure. Organ printing is the emerging method in nanospecialization to fulfill the requirements in the medical field, which can be on Petri dishes instead of papers. At the same time, cells, chemicals, and linkers replace the ink. 3D printing is the additive manufacturing technique for providing clear resolution printing with reasonable cost to screen breast phantoms. Singlet phantoms fabricated with a single material for representing fibro granular tissues and skin [1]. Also, singlet has attenuation similar to breast tissues. The microcalcification clusters included inside the phantom with the help of eggshells which, has similar X-ray attenuation properties. Breast calcifications contain an enormous amount of calcium oxalate and represented in breast phantoms using eggshells since they contain massive calcium content. Simulated microcalcification clusters were of fine powders to represent benign tumors and irregular shape shells to show malignant tumors. The phantoms were imaged and characterized visually and quantitatively. Singlet uses a single rigid material like bee wax to mimic granular tissue and skin. Cancer affected regions targeted with various nano-drug delivery mechanisms. The analysis of these mechanisms highlights the possibilities of using the nano-drug delivery system as well as early detection and cure of the diseases with reduced risk.

2 Literature Review Nanoparticles are also used currently in various hospitals for the diversity of conclusions. Researchers from the Institute of Structural Biology and Pharmacology, Toulouse (2006), gave an idea to work with nanoscale to deliver the drugs directly to the tumor cells, where the side effects significantly reduced. Also, IEEE 12th International Conference presents the methods adopted by NanoScience. Cancer rates in developing nations have increased in recent years; Bangham et al. [2] demonstrated methods for delivery of macromolecules. The clinical trials for combination therapies are under tremendous progress and respond to the situation, which facilitates drug delivery using phantom [3]. Al-Jamal et al. [4] used the cancer therapies, and Alexis et al. [5, 6] analyzed the causes which affect the biodistribution of polymeric nanoparticles and clearance. In 1996, Langer and Folkman demonstrated the controlled delivery of macromolecules with polymers. In the year 1990, Klibanov et al., and also in the year 1994, Gref et al., proved that the introduction of Glycoproteins increases the delivery time or the circulation time. The vesicle delivery system also is shown to be efficient for multiple types of cancers used the protein carriers. The drug delivery using bacterial nanoparticles is described in the year 2008 by Hawkins et al. and a similar concept explained by Wang and Uludagin in the year 2008.

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3 Breast Phantom Fabrication 3.1 Selective Laser Sintering (SLS) The new objects or organs printed using power like material on the substrate. The laser draws the shape of the organ on the powder and fuses together to form the organ. Bio-ink is a material made up of living cells loaded into a cartridge, and its components are collagen, alginate fibrin, and bio-papers are agoras and gelatin. Alginate hydrogel is an excellent material for bio-fabrication, and granular density indicates the fiber volume in the breast tissue. The virtual phantom compressed to represent typical breast thickness to simulate mammography. The outer layers of these tissues described as mesh-like structures. These mesh-like structures are capable of capturing the calcium deposit regions to produce malignancy results. Biopsy phantoms of breast developed for ultrasound images shown in Figs. 1 and 2 show the breast phantom, mammogram of a patient, and mammogram of a patient. Fig. 1 Biopsy phantoms of breast developed for ultrasound images

Fig. 2 a Breast phantom, b mammogram of a patient, c phantom mammogram

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4 Requirements on Nano-Drug System The fight against cancer is half the way achieved by early detection. The image handling process, such as enhancement, segmentation [7], and restoration, enhances the process of cancer detection and classification. Though all these processes diagnose cancer, the eradication of cancer is very much essential to redeem society from the deadly disease. Nanotechnology provides new methodologies to find therapies with the cell level for the complete elimination of cancer virus. The current scenario meets with chemotherapy and surgery with which patients undergo a painful process, and also drugs like Decetaxel and Fingolimod used for chemotherapy are very toxic and also have side effects like nausea and headaches [5]. It is very much essential to have a regular checkup for the body due to this continuous monitoring above the age of 40 and can diagnose if there is any abnormal PSA level. The biomarker levels also monitored to detect cancer at an early stage. Biomarker level 1–4 gives an alert to trace disease. The typical drug delivery to the cancer cells may damage the neighboring cells in association with the cancer cells [6]. This drawback can be overcome by theranostics, which can diagnose as well as carry the therapeutic agents [8]. The application of DNA nanotechnology is efficient therapeutics and also for the delivery of drugs. DNA is a genetic material that can carry information, and also it is a biocompatible material. DNA origami is a new technique that synthesis different structures of DNA, with main components like sugar, phosphate, and the nitrogen base. It also has some specific characters named hydrophilic and hydrophobic so that the drug carried by DNA material can stick on the cancer cell for the delivery as well as, and it can repel the water molecule, which is not necessary inside the particle. DNA also can act as a cage to carry any kind of drug to deliver into the cell. Also, the target is made perfect without affecting the neighboring cells. The advantage of nanoparticles is that it persists in the blood circulation for an extended period. Another advantage is that it ensures the discharge of drugs as per the direction given. Since the drugs are in the nanoform, they can penetrate the tissues and enhance the quick uptake of the drugs by the cells. This action promotes the medications to the targets for their absorption. Hence, the efficiency of intake and prevention of disease is greater with nil side effects.

5 Drug Delivery Mechanisms to Target the Tumor Even though the drugs in the nanoform penetrate the tissues with a quick uptake by the cells, there should be a delivery mechanism [9] to take the medicines up to the target level. The discussion on these mechanisms can specify the best system to adopt. These nanorobots take the drug to the targets through the blood circulatory system. The tools discussed below used different wavelengths and technologies to achieve the objectives. Figure 3 shows the drug delivery system.

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Fig. 3 a Poly chemical structure of nanoparticle platform for drug delivery, b drug delivery system

Fig. 4 Nano-drug delivering mechanisms

Figure 4 shows different drug delivery mechanisms and the materials used for the purpose. The picture concludes that early diagnosis reduces the treatment procedures, and the prevention rate also increased.

5.1 Quantum Dots (QD) These are the single crystal of semiconductor materials in nanosize having fluorescent characters, the size varies from 5 to 15 nm, and the size determines its color [10, 11]. These quantum dots injected into the targets, and the emerging light detected the tumor cells, especially the wavelength that emerged determines the color and also the diameter. The optical properties of the nanoparticle depend on the structure. The materials identified are Cd, Se, and ZnS, which are responsible for its color and

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brightness. Once if the Quantum dots injected into the target and exposed to the UV light, they can emit light, and therefore, it can find the cancer signature. As a result, cancer cells with different phenotypes colored differently, and the peak intensity correlates the seriousness of the tumor. The metastatic behavior can be determined using the strength of the peaks. The application of Quantum Dots in Biotechnology and medical diagnosis holds great potential.

5.2 Carbon Dots (QD) The QD, which are toxic, thus alternatively, Carbon Dots are used. These C-dots are fluorescent nanomaterials, accepted for their biocompatibility, and eco-friendly behavior. The wavelength of the light depends on the size, which ranges between 2 and 15 nm. It is mainly composed of carbon elements, unique optical properties, and high photostability broad excitation spectra. The synthesis is simple having –COOH, – NH2 –OH fictionalization and, therefore, readily water-soluble. The microwave pyrolysis method plays a vital role in the combination of C-dots. Cancer also detected by using Carbon nanotubes. The synthesis is simple, and also it can use the microwave pyrolysis method for fabrication. Carbon Dots or C-dots are extraordinary nanomaterials with prominent versatility. They are becoming a popular platform for the attachment of cancer-curing drugs due to the availability of primary functional groups. The synthesis of C-Dots can be with a top-down approach or bottom-up approach. The synthesis methods also can vary the composition of C-Dots. The form leads to variation in size and activity of C-dots whose wavelength and the emission ranging from 300 to 700 nm. They are non-toxic and efficiently enhance cancer suppressor gene functioning. C-Dots also have a high binding accord and improve penetration of the drugs inside the cell. Therefore, C-dots constitute a primary as well as an efficient drug delivery system.

5.3 Biological Scaffolds Another necessary slow and steady drug release is with biological scaffolds. The scaffolds are highly biocompatible and biodegradable so that it can allow the cells to attach, grow, and also diffuse into the cells similar to the extracellular matrix. Collagen and Matrigel are the two useful scaffold protein contents having the size 5–200 nm diameters. In this technique, the damaged tissues replaced by attaching a scaffold that replaces the old by the growth of new tissues.

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5.4 Liposomes The next advantageous drug delivery is with the help of lipid-like self-assembly structures. They are with hydrophobic tails and hydrophilic heads to make themselves assembled in water. Amino acid-like aspartic acid and glutamic acid are the main lipids [4]. The hydrophilic heads can use with lysine or arginine, whereas hydrophobic tails made with Alamine or Leucine. They are helpful to repel and to attract the cells in the water medium. The main advantage of this lipid self-assembly is fine tinning. The zoom in or the zoom out of cells is possible based on the size of the cell. This character of lipid self-assembly is useful in preparing nanotubes, and since it contains protein reduces the toxicity. The cationic peptides like A6K with positive shock utilized to combine with the DNA having negative charge nuclei so that the drug can be quickly delivered. Lipid nanotechnology which explains the lipidbased carrier system. Lipid is significant bimolecular, which includes fatty acids, waxy glycol, triacylglycerol, phospholipids, and cholesterol [11]. This technology is having a high loading capacity, prolonged circulation, and tumor accumulation. Since the toxicity is a low risk, it is an excellent drug carrier and drug deliverer. Another valuable property is the exterior lipid bi-layer, which is chemically reactive, and this property helps in tagging antibodies, and the target is the liposome. They are classified based on their size and called vesicles. They are categorizing as SUV, MUV, and LUV prepared by sonication extrusion methods. These particles arrived in these methods range up to 100 nm diameter. These nanoparticles attack the liposome in the cells. The clathrin-coated endosome reaches the liposome and degrades into endolysosome. Hence, the drug is left behind in the cytoplasm. The preparation of lipids adds a small volume of water content, and this dehydration process performed using Dehydrated and Rehydrated vesicles (DRV) method [12]. The repeating cycles of freezing and drying can evolve LUV. Another process called dialysis need excess buffer solution at 40 °C., but for every 4–5 h buffer should be removed since the fluorescent dye is detected. Noisome overlooks all these drawbacks in the liposome, which can enhance the penetration of drugs into the cells [13, 14]. The difference identified is Noisome, with a solid base and liposome with a liquid core, and robust nature of Noisome prolonged drug delivery with control through diffusion or membrane fusion. Usually, the tumor location suffering from leaky blood vessels are helpful for the nanoparticles to accumulate in the tumor location and deliver the drugs.

5.5 Glycoproteins The peptide link has three important regions; one is ligand responsible for cell recognition. The second one is anchor mainly for attachment and linker for physical separation between surfaces. Once if the peptide enters the body, the immune system targets the foreign entity to leave out from the body system. The attacking action

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Fig. 5 Working a Quantum dots, b carbon dots, c biological scaffolds, d liposomes, e glycoproteins

forms the link to focus on the target to deliver the drugs. Glyco-nanotechnology is the combination of Glycobiology, Biotechnology, and material science that clearly explains the carbohydrate interactions. A similar application with DNA and proteins, carbohydrates also been used as stabilizing and coordinating agents. Lin’s technique is expressing the interactions of carbohydrates and proteins. This method mainly focused on Escherichia coli bacteria. The nanoparticle coated with nano sugar specific to target these bacteria. The lactose-based Glyco-nanoparticles also can be used for cancer therapy. Figure 5 shows the different drug delivery methods.

6 Results and Discussion The essential point while detecting a region is the selection of seed points with the help of the threshold value. A preprocessing technique typically removes the unwanted artifacts of the images. A smoothening filter also removes the smooth edges as well as the abrupt edges. Thus by concentrating only on the area of interest, the cancer region of the breast phantom can easily be detected. The max–min algorithm focused on boundaries and handled some textures. The review discusses the current advances in nano-drug delivery mechanisms. The nanomaterials deliver to the targets activate the cells and the tissues. The use of nanotechnology enhances the absorption and administers the release of drugs. Theranautics is the nanotechnology which integrates the diagnosis and therapy procedures, and this mechanism is safe, with reduced toxicity and hence provides a fruitful treatment [15]. In the future, the nano-drug delivery mechanisms ensure the comforts of the patients who can escape from the painful procedures and also benefited by using nanomedicines to reach the target easily. Nanomedicines has the significant advantage of attending only cancer affected regions and not spoiling the neighboring tissues. Table 1 and Fig. 6 show the comparison between conventional therapy and theranautics for breast cancer treatment.

Performance Evaluation of Nanorobot Drug Delivery Mechanism … Table 1 Difference between conventional therapy and theranautics

241

Reaction factors

Conventional therapy (%)

Theranautics (%)

Neighboring cell damage

100

10

Biocompatibility

50

90

Hydrophilic and hydrophobic

20

85

Toxicity

85

20

Chemical reaction

100

10

Drug release control 20

Ordinary Therapy

Percentage

120 100 80 60 40 20 0

95

TheranauƟcs

100

100 90

85

95

85

55

20

20

20 10

10

ReacƟon Factors

Fig. 6 Comparison between ordinary therapy and theranautics

References 1. Ahmed R, Mourad Z, Ahmed BH, Mohamed B (2009) An optimal unsupervised satellite image segmentation approach based on pearson system and k-means clustering algorithm. Initialization Int J Electron Commun Eng 3(11):2181–2188 2. Bangham AD, Standish MM, Watkins JC (1965) Diffusion of univalent ions across the lamellae of swollen phospholipids. J Mol Biol 13(1):238–252 3. Buch K, Li B, Qureshi MM, Kuno H, Anderson SW, Sakai O (2017) Quantitative assessment of variation in CT parameters on texture features: pilot study using a nonanatomic phantom. Am J Neuroradiol 38(5):981–985 4. Al-Jamal WT, Al-Jamal KT, Bomans PH, Frederik PM, Kostarelos K (2008) Functionalizedquantum-dot- liposome hybrids as multimodal nanoparticles for cancer 4(9):1406–1415 5. Alexis F, Pridgen E, Molnar LK, Farokhzad OC (2008) Factors affecting the clearance and biodistribution of polymeric nanoparticles. Mol Pharm 5(4):505–515

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6. Alexis F, Rhee JW, Richie JP, Radovic-Moreno AF, Langer R, Farokhzad OC (2008) New frontiers in nanotechnology for cancer treatment. Urol Oncol 26(1):74–85 7. Maitra IK, Samir SN, Bandyopadhyay K (2011) Automated digital mammogram segmentation for detection of abnormal masses using binary homogeneity enhancement algorithm. Indian J Comput Sci Eng 2(3):416–427 8. Ahmed F, Pakunlu RI, Srinivas G, Brannan A, Bates F, Klein ML, Minko T, Discher DE (2006) Shrinkage of a rapidly growing tumor by drug-loaded polymersomes: pH-triggered release through copolymer degradation. Mol Pharm 3(3):340–350 9. Bagalkot V, Farokhzad OC, Langer R, Jon S (2006) An aptamer-doxorubicin physical conjugate as a novel targeted drug-delivery platform. AngewandteChemie Int Ed 45(48):8149–8152 10. Bagalkot V, Zhang L, Levy-Nissenbaum E, Jon S, Kantoff PW, Langer R, Farokhzad OC (2007) Quantum dot-aptamer conjugates for synchronous cancer imaging, therapy, and sensing of drug delivery based on bi- fluorescence resonance energy transfer. Nano Lett 7(10):3065–3070 11. Rajan A, Ramesh GP (2016) Glaucoma detection in optical coherence tomography images using undecimated wavelet transform 7:878–885 12. Andresen TL, Davidsen J, Begtrup M, Mouritsen OG, Jorgensen K (2004) Enzymatic release of antitumor ether lipids by specific phospholipase A2 activation of liposome-forming prodrugs. J Med Chem 47(7):1694–1703 13. Kumarapandian S (2018) Melanoma classification using multiwavelet transform and support vector machine. Int J MC Square Sci Res 10(3):01–07 14. Singh KK, Singh A (2010) A study of image segmentation algorithms for different types of images. Int J Comput Sci Issues 7(5):414–417 15. Satpathy RB, Ramesh GP (2020) Advance approach for effective EEG artefacts removal. In: Balas V, Kumar R, Srivastava R (eds) Recent trends and advances in artificial intelligence and internet of things. Intelligent systems reference library, vol 172. Springer, Cham

A Correlation and Slope-Based Neighbor Selection Model for Recommender Systems Jehan Kadhim Shareef Al-Safi and Cihan Kaleli

Abstract The recommendation system is used to render personal suggestions to its clients while choosing a product from a list of items. Collaborative filtering is the most commonly used method for recommendation systems. In a collaborative filtering algorithm, the similarity factor employed in discovering the users with the same actions with respect to the chosen products is one of the key components of recommendations without considering any other information related to the recommender system entity. In this paper, we present a new correlation and slope-based neighbor selection model which concentrates on measuring the importance effecting of entities on each other. The proposed model picks the similarity in the importance of the correlated entities and takes it into account. Additionally, we suggest a new rating prediction model based on the first available rating from the nearest neighbors. We tested other new models in addition to our proposed model. The experiments are performed on three real datasets to test our model accuracy and compare with many state-of-the-art similarity measures accuracy. Experimental outcome confirms that the proposed model surpasses the recommendations accuracy of the previous methods. The results authenticate the effect of using the recommendation system entities information like the degree of importance to identify suitable neighborhoods and obtain a fitted recommendation system. Therefore, we can consider that our proposal is an effective method to select the nearest neighbors successfully and support the recommendation process carefully. Keywords Collaborative filtering · Correlation · Nearest neighbor · Recommendation system · Regression-based similarity

J. K. S. Al-Safi (B) · C. Kaleli Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkey J. K. S. Al-Safi Digital Media Department, Thi-Qar University, Thi-Qar, Iraq © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_20

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1 Introduction A recommender system (RS) means to the gathering of information on Web sites associated with the user interests for an assortment of items. The RS operates diverse online information sources to divine the users’ favorites for certain items; therefore, it represents an essential role in the purchase of commodities or services [1]. RSs are established on an assortment of processes such as collaborative approach (CF) [2–4], content-based approaches (CB) [5, 6], and hybrid approaches [7, 8]. Collaborative filtering (CF) is a well-known tool utilized in RS. CF has successfully been implemented by many commercial systems such as Amazon and eBay [9]. CF mainly categorized into two prime categories: memory-based and model-based CF. Memory-based CF is available in user-based and item-based models [2, 10–12]. The basic principle of CF is to identify the like-minded consumer and to suggest things that they have always favored. This methodology is regarded as user-based CF. The item-based CF, on the other hand, suggests products close to those for which the customer has previously chosen [13]. Latent semantic models, Bayesian belief networks, clustering matrix factorization, Markov decision process [14], and neural networks [15] are popular models of model-based CF approaches [13]. In those methods, the recommendation mainly supports the rating prediction for unseen items by the current user. Users should be recommended for items with highly predicted ratings. The accuracy of recommendation, computation time, and the recommendation relevancy in a model-based recommender system is superior to the memory-based recommender system [16]. One of the most important elements of successful recommendation prediction in the CF algorithm is the selection of the nearest neighbors (NN) to the active entities. K-NN based CF algorithms used Pearson’s correlation coefficient (PCC) [17] or adjusted cosine-based similarity (ACS) measure [18] to compose a neighborhood selection for entities. But, k-NN based CF algorithms still struggle to quantify the similarity between entities. These methods calculate the similarity using items’ user ratings. Many commercial companies currently retain a huge volume of products; therefore, the user-item rating matrix used by CF is extremely sparse. The data sparsity is one of the challenges of CF research since it usually produces unreliable similarities [19]. A variety of basic approaches have been suggested in the literature in order to mitigate this unavoidable issue of data sparsity and at the same time to provide accurate recommendations and develop the NN selection strength of CF algorithms. For instance, the CF boosted default vote and imputation attempt to fill in the missing rating data [2]. Other approaches used additional information and combined it with traditional similarity measures in order to define a new similarity measure instead of supplementing the rating data. Such information generally relies on a heuristic approach, which determines the number of common items between two users to calculate the similarity between them. In addition, it is often treated independently without taking into account the associated traditional similarity measures [20].

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Table 1 Sample of the user-item dataset User/Genre

I1:Drama

I2:Fantasy

I3:Drama

I4:Fantasy

I5:Fantasy

I6:Drama

U1

4

3

1

1

4

4

U2

4

3

1

1

4

5

U3

4

3

1

2

4

5

U4

2

3

3

4

2

2

U5

3

3

1

4

1

2

U6

3

3

2

4

1

1

U7

2

5

3

5

1

?

However, CF algorithms have several problems. Despite the success of the userbased CF, it has limitations of scalability and real-time performance. The complexity of the numerical processes grows linearly with the number of clients, which may be rising to many million in traditional business applications. Therefore, user-based CF is difficult to include an explanation of the recommendation. Furthermore, CF cannot handle the recommendations of multiple interest users. If users have different interests, it gives poor recommendations [21]. These limitations make the client mistrust the RS. The proposed research concentrates on those CF limitations and attempt to overcome it by implementing a new neighborhood selection method. In CF algorithm, the predictions of the desired item for the active user are dependent on the neighbor preference for the rated item [2]. But, it is normal for one person to have several interests. For example, a user can simultaneously be interested in the ‘Fantasy’ and ‘Drama’ movie genre. The prediction findings are questionable if we forecast the genre “Drama” utilizing value expectations for the “Fantasy” movie genre. Like the following case: Table 1 represents the user-item data matrix; there are seven users and six items. For explanation, among the six items, I1: Drama, I3: Drama, and I6: Drama means their contents are on ‘Drama’. So, the three items are similar, but are different items; same sense with I2: Fantasy, I4: Fantasy, I5: Fantasy. Now, the question is: what is the rating value of U7 for I6; R76 = ? I: the item. U: the user. Here, we assume that each user has three users neighbor for interpretation. According to the user-based CF algorithm [2], U7 has the U4, U5, and U6 as nearest neighbors; thus, the prediction value R76 = 1. But, their common interest is the ‘Fantasy’ movie genre. The interest on ‘Fantasy’ used to forecast the interest on ‘Drama’, but ‘Fantasy’ and ‘Drama’ are not related, so the rating prediction is dubitable and not precise. From these limitations, it might be concluded the importance of selecting neighborhood with similar content in order to gain an accurate rating prediction. If two correlated users have similar slope values, they can be appropriate neighbors to each other and that will improve RS accuracy. We pursue to merge the traditional similarity base correlation [22] with the regression coefficient (slope) value [23]

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of entities to structure a more accurate NN selection model. The proposed model takes the users’ and items’ rating behavior impact on items’ and users’ weight into account by employing the multiple regression method on correlated user ratings. It is based on the traditional similarity measures by Pearson correlation relationship (PCC) and the average regression coefficient, i.e., slope. Additionally, we suggest a new rating prediction model based on the first available rating from the nearest neighbors. We estimate the effectiveness of our model on many state-of-the-art similarity models, using three benchmark datasets. Experimental results confirm that the proposed models surpass the recommendation accuracy of traditional CF algorithms and improve the rating prediction accuracy, on sparse datasets in particular. The most important contributions of this research are: 1. 2. 3.

4.

Discovering the mean impact of users rating on the weight of items and the mean impact of items rating on the weight of the users. Proposing a new PCC-slope-based neighbor selection method to form a similarity model between entities. Proposing a simple prediction model based on dynamic k-NN and a subset of a dataset, minimizing the sparsity, computation time, and complexity of the model. Improving the accuracy of recommender system recommendations.

The rest of this paper is organized as follows. We review existing similarity measures suggested for CF systems in Sect. 2. In Sect. 3, the background methods of the proposed model are explained, followed by the proposed correlation-regressionbased similarity model in Sect. 4. Section 5 the experiment and results of this research. Finally, our conclusion and future work are presented in Sect. 6.

2 Related Work CF is quite spreading and impacts most of the recommender systems. K-NN algorithm is the adaptation algorithm in a memory-based CF recommendation procedure which is exercised to suggest recommendations. K-NN based recommender systems recommendation processes are dependable and provide accurate recommendations with CF [24, 25]. Recently, researchers conducted a number of studies in the recommendation process in order to find more effective NN algorithms. In [17], the first comprehensive research is carried out on the neighborly selection of CF. According to their comprehensive analysis results, PCC is an efficient alternative for measuring user correlations, and the best solution to creating a neighborhood is hiring the most similar k users. An efficient threshold-based method of selecting the neighborhood for CF is proposed by researchers in [26]. The model used the replacement of neighbors for a customer having uncommon interests. In [27], dynamic changes in user profiles were taken into account, and an adaptive neighborhood selection method was introduced,

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which relies on users’ preference for target products. According to the experimental findings, the suggested method increases the accuracy of recommendation forecasts. CF suffers from data sparsity, in the existing similarity behavior, the early approaches to the issue of data sparsity incorporated similarities’ weight. This weight is usually determined by the number of products that are co-rated by two consumers. This approach is based on the assumption that there would be a higher similarity between two users with more common products [28]. A new similarity is adopted by [29]; they combined Jaccard index with mean squared differences. In [30], the PCC and adjusted cosine similarity are added to the balance factor to reflect the rating differences between various users. The expanded Jaccard index definition has been developed and used for similarity estimation in [31]. Although it has been experimental to mitigate the problem of sparsity by combining a number of co-rated items with traditional similarity measures, the strategy takes no contextual information from rating data. Therefore, many attempts were made to acquire any data context information. In [32], the goal of the heuristic similitude measure was to improve recommendation efficiency under cold-start conditions, called proximity-impact-popularity (PIP) where only a limited number of ratings are necessary for each user to quantify the similarity. In [33], an entropy-based model has been proposed. To discover the NN, the model collects the most similar entities based on the minimum entropy difference with active users’ neighbors. It meaningfully enhanced the recommendation accuracy of traditional CF algorithms. In the meantime, in [34], the model merged trusting data with user similarity in order to improve the slope one algorithm and develop the final recommendation formula. In [35], the model forecasts ratings based on a continuous scale, and instead of classification, the model used the regression approach. The standard k-NN algorithm using PCC and cosine as similarity measures was confronted with the SVM regression. The multiple regression methods had been used to estimate the compound attributes’ weights, while the model and estimate of utility values were calculated by ontologies. In [36], a new NN selection model that combines structural and rating-based similarity calculations have been suggested. The experimental findings are substantial that the memory-based CF model outperforms the model-based CF model in terms of prediction accuracy. This model achieved on a few neighbors comparing with the traditional memory-based CF models. The proposed model in [37] examines the way of discovering user correlations and determines the NN based on a subset of a dataset instead of the entire dataset. They propositioned a novel model in CF for selecting the active users’ neighbors by using the similarity variance between users. This model recovered the CF prediction accuracy. In [38], a regression-based three-way RS have been proposed, the model aims to minimize the average cost for different behaviors in RS. They utilized a regressionbased model for a binary recommendation using the slope one [39] and k-NN algorithm [18] for rating prediction.

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One of the most important steps in CF is how to select neighbors for active users. Most CF models measure user comparison on the whole ratings and ordered user relations with active users to select the NNs. But we debate that this is irregular since we consider that users have a partial similar interest, not on the whole rated items [37]. Because of this doubt, in our experiments, we based on a subset of the dataset. Our model is based on a subset of the dataset which has users and items of the high rating numbers from the benchmark datasets instead of the whole dataset, minimizing the sparsity, complexity, and computation time of the model. However, distinct from traditional CF algorithms that use rating-based similarity measurements such as adjusted cosine, or Pearson correlation coefficient alone, we propose to combine both correlation relationship with the regression coefficient (slope) values to build a neighbor selection model (PCC-slope-based model) to determine the role of users’ rating on items’ weight in user-base model and the role of items’ rating on users’ weight in item-based model in selecting appropriate NNs. Furthermore, we propose a new rating prediction model that simply predicts the entity rating based on the entity’ nearest neighbors. Since our proposed model harness the traditional similarity and regression model among entities, it could be considered as a new similarity model for CF. Our work varies from the aforementioned models with the following points: 1 2 3

New memory-based PCC-slope-based neighbor selection model for the NN selection as a user- and item-based model. New rating prediction model based on dynamic k-NN and subset of a dataset, minimizing the sparsity, computation time, and complexity of the model. Improve CF accuracy for RS.

3 Background In this section, the methods to create the proposed model are introduced. Several methods have been used to calculate the similarities between various users and items in CF. In this paper, similarities based on users and items are focused on. This section presents the multiple regression methods, Pearson correlation coefficient (PCC)-based similarity, and NN CF algorithms to prepare the proposed model.

3.1 Pearson Correlation Coefficient (PCC) Based Similarity The PCC of the user a, and u is determined as Eq. 1:   n  i=1 ra,i − ra r u,i − r u sim(a, u) =   2 n  2 n 1 ra,i − ra 1 r u,i − r u

(1)

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where sim(a, u) ra,i ra n

represent the similarity between two users a and u, is the rating value of user a for item i, is the average rating for user a, and is the total number of items in this user-item space.

Although two approaches have been successfully proved, the Pearson correlation and adjusted cosine similarity, they are limited in certain situations [22, 40]; they examine only the linear correlations similarity and ignore the nonlinearity entities correlation and the entities’ weight impaction on the similarities. Thus, the similarity based on entities’ weight impaction can obtain much more authentic similarities’ values. This is where this paper begins.

3.2 Regression Method The linear regression method can be used to detect which features have a critical impact on the forecasted output and discover the relationship between different variables. It determines the dependent variable Y value based on the independent variable X, so it is performing a linear relationship between x as input and Y as output. We can write the regression equation with one independent variable as the following equation: Y = a + bX + e

(2)

where Y a b X e

is the variable measurable score (items’ and users’ weight in our present case), is the intercept point of X and Y, is the slope or coefficient, is the independent variable (the users’ and item’ ratings in our present case), and is the error or residual.

However, our datasets in this research have multiple features, and therefore, multiple linear regressions are used in our proposed model. So, we should apply the multiple linear regression to all independent variables as the following the following equation: Y = a + b1 X 1 + b2 X 2 + · · · + bk X k + e

(3)

We have k different independent variables, and every variable has its own slope, but there is one error and intercept for all variables. For k > 2 independent variables, locating the b values (slopes) is complicated, so we need matrix algebra for calculations.

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For k = 2 of independent variables, it is simple. However, the core concepts of regression remain the same irrespective of how many different variables we have [23]. The b in the two variables case:      2  x1 y − x2 y x1 x2 x2 (4) b1 =  2  2   2 x1 x2 − x1 x2 and  b2 =

     x1 x2 x2 y − x1 y x12  2  2   2 x1 x2 − x1 x2

(5)

3.3 Neighbor-Based CF Algorithm CF recommender systems generally consider the rating value as input and recommender accuracy as a form of estimation metrics. Neighbor-based or NN CF algorithm predicts the interests of active users based on the rating value of this user to items [2]. NN CF recommendations commonly employ the similarity measurements like Pearson’s correlation coefficient [41], vector space similarity [42], and cosine-based similarity [18, 43] to find the distance between users or items. It is mainly available in two types [44, 45]: user-based and item-based CF. The user-based CF depends on other similar-minded users to recommend the item to the active user. This model is based on finding the set of nearest similar users that sharing similar tastes or preferences. Then, the prediction of active users’ unrated items based on the rating of similar users for the same item. While the item-based CF model recommends items to the active user based on other highly correlated or similar items with the target item, the item-based model is based on finding a set of the nearest similar items, and the prediction of user’ rating is based on the ratings of this user for the neighbor items of the target item. CF is commonly based on the k-NN algorithm [18, 46, 47] to indicate the recommended users or items.

4 A New NN Selection Proposed Model In this section, we present our suggested similarity measurement to express the similarity between users and between items. Additionally, we explained a new rating prediction model.

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4.1 The PCC-Slope-Based Neighbor Selection Model A reliable rating prediction model needs to hire all obtainable bits of knowledge from the entities’ profile. Because of the variety in the users’ rating and interest, it is important to study the impact of user rating on item rating weight and the impact of item rating on user rating weight to form a reliable neighbor selection model that increases the RS accuracy. The entities’ impact can be obtained by the multiple regression method. Therefore, we suggest computing the importance of correlated entities to harness them in similarity measures and finding active users’ NN. According to PCC Eq. (1) [22], the correlation relationship between entities has been calculated to form the first part of the similarity measure in our model as written in Eq. (6). Sim1 (a, u) = corr (a, u)

(6)

where Sim1 (a, u)

represent the first part of similarity between a and user u.

Then, for those correlated users with a, the second part of the similarity is calculated based on the multi-regression method [23], and the average regression coefficient C(u) (slope) value represents the importance value for each correlated entity. The second similarity between entities formed as the absolute differences in the slope value between a and users to form NNs as formed in Eq. (7) Sim2 (a, u) = |C(a) − C(u)|, u ∈ corra

(7)

where Sim2 (a, u) C(a) corra

represent the regression-based similarity between a and u. is the average regression coefficient of a ratings on the items’ rating weight in the regression model, similarly for (u). represents the set of correlated users with a.

The average regression coefficient of users C(u) is calculated as Eqs. (4 and 5) in the regression method in Sect. 3.2. The values of the Sim 2 (a, u) are sorted ascendingly to discover the NN’ set to a. Similarly, for the item-based similarity measures, the proposed procedure is applied over the item rating vectors, the user rating weight is the output of the regression model to infer the item rating importance value on user rating weight and forming NNs for the active item. In Sim1 (a, u), the value of the correlation between entities is in the interval [0, 1] and the same for Sim2 (a, u). Thus, it is realizable to compare between entities’ similarity. With traditional CF algorithms, the similarity is computed based on only entities’ rating similarity using PCC [17] or ACS [18] to select NNs. While in our proposed

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Fig. 1 PCC-slope-based neighbor selection proposed model

model, the NNs are selected based on additional information which is the difference in the value of importance between correlated entities. Figure 1 shows the proposed model steps.

4.2 New Prediction Procedure One of the most important steps in the k-NN algorithm is to determine the number of suitable k neighbors, because if the value of k is small, then it might lose some close and similar neighbors. While if the value of k is large, it is possible to choose relatively dissimilar neighbors, which leads to deterioration in the predictive accuracy of recommendations. Thus, the different values of k could support prediction accuracy. Wherefore, our model is based on dynamic k-value for NNs [5, 48]. Therefore, in the proposed user-based model, after getting the NNs for a, and sorted them ascendingly according to the difference in the value of importance between a and his/her correlated NNs as illustrated in (Sect. 4.1), a new rating prediction model based on the first available rating from NN has been suggested. In order to check the prediction accuracy, we considered the common items i between users ( i ∈ ra,b ). r a,b

represent the common rating set of a and user b.

The user-based model prediction formula is explained in the following equation: pa,i=rb,i If and only if b ∈ K a , and i ∈ rb , K = 1, 2 … 50.

(8)

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where pa,i rb,i Ka rb

is active user’s a predicted rating value of unrated item i, is the user b’s rating value to item i, represents the neighbor set ofa, and represents the user b’s rating set.

Similarly, in the item-based model, the procedure is applied to items instead of users.

5 Experiments We executed experiments on three commonly used datasets to examine the effects of employing the suggested NN selection model and proposed rating prediction model in user-based and item-based CF models.

5.1 Datasets: Movie Lens Dataset We assessed the performance of our model proposed in this research on three common datasets that are used in CF research literature for comparison purposes: Movie Lens 100k (ML 100k), Movie Lens 1M (ML 1M), and Movie Lens 20M (ML 20M) [49]. This dataset collected by the Group Lens Research Group at the University of Minnesota (https://www.grouplens.org) is to be used as a product of the Movie Lens movie RS. In all datasets, each user rated at least 20 movies. The description of the datasets as follows: ML 100k Dataset In this dataset, there are 100,000 ratings from 943 users on 1,682 movies. The rating scales are from 1 as the worst rating to 5 as the best rating. The sparsity level of this dataset is 93%. The sparsity level is obtained from the following formula [50]:   all observed ratings Sparsity = 100 ∗ 1 − m∗n

(9)

where m represents the total number of users and n represents the total number of items. ML 1M Dataset It includes 1,000,209 ratings from 6,040 users on 3,900 movies. It has a 95.00% sparsity level. The rating scale is like the ML 100 k dataset, 1 (worst rating) to 5 (best rating). ML 20M Dataset This dataset contains 20,000,263 ratings from 138,493 users on 27,278 movies. It has a 99.00% sparsity level. The available dataset is only 1,048,575

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Table 2 Datasets detail (http://movielens.umn.edu) Name

Rating scale

ML 100K

1–5 stars

User

ML 1M

1–5 stars

6040

3706

1,000,209

95.00

ML 20M

0.5–5 stars

7120

27,278

1,048,575

99.00

User-based model ML 20M

0.5–5 stars

1500

7500

676,312

93.96

Item-based model ML 20M

0.5–5 stars

7000

1500

781,798

92.55

943

Movie

Ratings

Sparsity (%)

1682

100,000

93.00

records of three columns (user_id, movie_id, and ratings), 7120 users, 27,278 movies, additional to the dataset of movie titles. The rating scale is from 0.5 as the worst rating to 5 as the best rating [51]. In our proposed model, we analyzed the ML 20M dataset. While the other datasets have been used for comparison with previous researches, the details of the dataset we have adopted are as given in Table 2.

5.2 Items’ and Users’ Weight: Bayesian Posterior Mean Method (BPM) We need items’ weight as outputs in the user-based regression model, and users’ weight as outputs in the item-based regression model. We used the Bayesian posterior mean method for those needs. BPM is a statistical theory established on the Bayesian interpretation probability that extracts confidence’s degree in an event. The degree of confidence might be established on previous information about the event or previous experiments. Mathematically, the formula for finding the weight for each item as the following formula (item weight Iw is equivalent to a Bayesian posterior mean) [52]:  Iw =

   min(Ui ) Ui .µi + .µ Ui + min(Ui ) Ui + min(Ui )

(10)

where • Ui : the set of all users that rated item i, • min(Ui ): the minimum ratings required to the item to be listed in the selected dataset. • µi : the mean rating of item i, and • µ: the mean of all dataset ratings. The formula for finding the weight for each user is the following:  Uw =

   min(Iu ) Iu .µu + .µ Iu + min(Iu ) Iu + min(Iu )

(11)

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where Iu Min(I u ) µu µ

the set of all items rated by user u; the minimum ratings required from the user to be listed in the selected dataset; the mean of all ratings given by user u; and the mean of all ratings.

5.3 Evaluation Metrics: MAE, MSE, and RMSE Evaluation metrics are an indispensable part and important to calculate the prediction in the machine learning models. In this research, three evaluation metrics: mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are used to estimate the prediction performance. The calculation technique of MAE, MSE [53, 54], and RMSE [55] is in equations below: MAE =

n 1 |ri − pi | n i=1 2

1 |ri − pi | MSE = n i=1

n

1  RMSE =  (ri − pi )2 n i=1 n

(12)

(13)

(14)

where r = actual rating; p = predicted rating; n = the total number of rating. MAE describes the average of the absolute error between actual and predicted ratings, and MSE is a quality amount that processes the mean squared error between the actual rating and the predicted rating, while RMSE reflects the standard deviation between the predicted and the original rating value. The lower values of MAE, MSE, and RMSE reflect the better prediction model.

5.4 Experimentation Methodology Our experiments have been done over Movie Lens dataset. All the code that were initiated for this experimental estimation were written in Python 3.9 and carried out on the PyCharm 2020.2.1 environment. In our models, firstly, we prepared the ML-20 M dataset. In order to avoid the data sparsity problem, reducing model complexity, and computational time, we selected a subset of this movie lens dataset.

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In the user-based model, this subset contains the first 1500 users who have the highest ratings amount in the dataset. The highest user has a 2711 rating number, and the lowest user has a 190 rating number. For the item-based model, we depend on the first 1500 items that have the highest ratings amount in the data set. The highest item has a 3498 rating number, and the lowest item containing a 165 rating number. Secondly, the weight of each user and item has been computed by the BPM [52] (Sect. 5.2), and the average impact of each entities was calculated based on multi-regression coefficient value (slope). In the case of the user-based similarity model, the weight of the items is measured, to calculate the effect of the user rating on the weight of the items. On the other hand, for the item-based similarity model, the weight of the users is measured. For the rating prediction process, we applied our proposed k-NN based rating prediction model on the selected sub-datasets. Here, k has a dynamic value in the user-based and item-based models. We applied the rating prediction model to the highest 250 ratings for each user. For a user who has less than 250 ratings, all of his ratings were predicted. In the item-based model, we applied the same procedures too. Then, with the resulting dataset, in order to test our proposed model quality, the closest neighbors were ranked in different ways: firstly, according to the correlation relationship (PCC) of users (items) with the active user (item); secondly, according to the differences in regression value (slope) between active user (item) and the NNs; thirdly, according to the differences in R-squared (R2 ) value between active user (item) and NNs; (R-squared (R2 ) is a mathematical indicator describing a proportion of the variance of a dependent variable described in regression analysis by the independent variable or variables. R-squared demonstrates to what range the variance of one variable explains the variance of another variable [56]). fourthly, according to the difference value in R-squared (R2 ) value between the active user (item) and his correlated users (i.e. according to PCC and R2 ); and finally, according to our proposed model which is combined the effects of the PCC and regression model (PCC + Slope), especially the slope value.

5.5 Results and Discussion With a view to interpret the reason for suggesting PCC-slope-based neighbor selection model, we analyze our model results and compared it to many new models and state-of-the-art similarity measures. From the rating vectors of the nearest neighbors that selected by our proposed models, in user-based, we can notice that the active user has the same preferences as the NNs, as they like approximately the same items and dislike the same items as well, as given in Tables 3 and 4. For the item-based model too, the active item has similar rating values with its nearest neighbors, which means that the user likes the active item and its similar items (its neighbors). This represents the similarity between the items’ content.

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Table 3 Sample of NN for a a

U1

U2

U3

U4

5

5

5

5

5

5

5

4.5

0

5

5

5

4

4

5

5

4.5

4

4

5

Table 4 Sample of NN for active item I I

I1

I2

I3

I4

5

0

5

0

0

5

0

5

0

0

5

0

5

0

5

4

4

5

0

5

We assume that by harness entity, importance values on the correlated entities can increase the information of the traditional CF algorithms, obtain more appropriate NNs, reduce the effects of data sparsity in RS, and improve the RS accuracy. Therefore, the first part of our experiments is carried out to reveal accuracy improvement if our model is applied for user-based rating prediction compared to other new models. We show average MAE value of only ten users to display clearly the change in the prediction accuracy of user ratings when compared with the indicated models. The experimental findings confirm our proposed NNs selection and rating prediction methods accuracy. Figure 2 shows that our proposed model prevails over the above-mentioned models since it records the lowest MAE value with almost all of the users; MAE = 0.28 with U4 followed by the PCC + R-sqr model. The worst MAE value is recorded by the PCC model where the same user has MAE = 0.68. As a result, we can assert that the use of correlation model alone, and slope value alone, and R-squared value alone is not sufficient to select the appropriate neighbors in the RSs. Thus, by mixing the proposed model with traditional entity similarity, the amount of information increased in the CF algorithm and we confirmed our assumptions. We obtained the same results in the item-based model, and our model continued to record the lowest MAE value compared to the rest of the compared models (Fig. 3). Although we tested 50 neighbors (k = 50), the rating prediction value did not exceed k = 5 either in user- or item-based model. Tables 5 and 6 show the evaluation criteria according to the MAE, MSE, and RMSE for the user- and item-based model. In both tables, our proposed models are clearly ahead over the other models in terms of prediction accuracy. Secondly, we compare our user-based model with our item-based model. According the evaluation metrics values, results show that the user-based model

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J. K. S. Al-Safi and C. Kaleli PCC

Rsqr

slope

PCC+Rsqr

PCC+slope

0.800 0.700

MAE

0.600 0.500 0.400 0.300 0.200 U1

U2

U3

U4

U5

U6

U7

U8

U9

U10

SAMPLE OF USERS

Fig. 2 Comparison of our user-based model and other models in terms of MAE value

PCC

Rsqr

slope

PCC+Rsqr

PCC+slope

0.9

MAE

0.8 0.7 0.6 0.5 0.4

I1

I2

I3

I4

I5

I6

I7

I8

I9

I10

SAMPLE OF ITEMS

Fig. 3 Comparison of our item-based model and other models in terms of MAE value

accuracy outweighs the item-based model accuracy because the user-based model registers MAE = 0.497 while MAE = 0.52 with item-based model (Fig. 4). Figures 5 and 6 illustrate this comparison results according to MSE and RMSE value. This result leads us to rely on users rather than items to build an accurate recommendation system. As already stated, there are various studies to improve the efficiency of the neighborhood for CF systems, either by enhancing the technique of neighbor collection or add modern tests of similarity. In order to show how our method enhances the accuracy of the previous research results, we compared our method to previous researches. Wang et al. (2020) in [39] their model combine structural and rating-based similarity

PCC

0.580

0.622

0.670

0.570

0.594

User-id

U1

U2

U3

U4

U5

MAE

0.531

0.592

0.652

0.611

0.569

R-sqr

0.560

0.559

0.580

0.579

0.586

slope

0.551

0.463

0.542

0.531

0.439

PCC + Rsqr

0.471

0.440

0.420

0.379

0.286

PCC + Slope

Table 5 Sample of user and their evaluation metrics

0.660

0.650

0.630

0.560

0.490

PCC

MSE

0.484

0.580

0.570

0.580

0.470

R-sqr

0.414

0.558

0.520

0.540

0.410

Slope

0.464

0.638

0.550

0.580

0.420

PCC + Rsqr

0.440

0.480

0.460

0.410

0.380

PCC + Slope

0.812

0.806

0.794

0.748

0.700

PCC

RMSE

0.695

0.762

0.755

0.762

0.686

R-sqr

0.643

0.747

0.721

0.735

0.640

Slope

0.681

0.799

0.742

0.762

0.648

PCC + Rsqr

0.663

0.693

0.678

0.640

0.616

PCC + Slope

A Correlation and Slope-Based Neighbor Selection Model … 259

PCC

0.586

0.831

0.571

0.861

0.616

Item-id

I1

I2

I3

I4

I5

MAE

0.613

0.678

0.540

0.654

0.630

R2

0.580

0.690

0.530

0.660

0.610

Slope

0.560

0.650

0.550

0.640

0.590

PCC + R2

0.520

0.510

0.468

0.431

0.414

PCC + Slope

Table 6 Sample of items and their evaluation metrics

0.680

0.910

0.610

0.890

0.640

PCC

MSE

0.660

0.710

0.590

0.690

0.670

R2

0.680

0.830

0.580

0.680

0.650

Slope

0.690

0.850

0.570

0.690

0.660

PCC + R2

0.570

0.560

0.510

0.480

0.490

PCC + Slope

0.825

0.954

0.781

0.943

0.800

PCC

0.812

0.843

0.768

0.831

0.819

R2

RMSE

0.825

0.911

0.762

0.825

0.806

Slope

0.755

0.748

0.714

0.693

0.700

PCC + R2

0.742

0.748

0.714

0.693

0.663

PCC + slope

260 J. K. S. Al-Safi and C. Kaleli

A Correlation and Slope-Based Neighbor Selection Model … Fig. 4 Average MAE of user- and item-based models

261

0.525 0.520 0.520 0.515

MAE

0.510 0.505 0.500

0.497

0.495 0.490 0.485 user-based

item-based

MODELS

Fig. 5 Average MSE of user- and item-based models

0.590 0.578

0.580 0.570

MSE

0.560 0.550 0.540 0.530

0.526

0.520 0.510 0.500 user-based

item-based

MODELS

Fig. 6 Average RMSE of user- and item-based models

0.755

0.751

0.750 0.745

RMSE

0.740 0.735 0.730 0.725

0.723

0.720 0.715 0.710 0.705 user-based

item-based

MODELS

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calculations. We used the same amount of movie lens datasets in order to compare the efficiency and accuracy of our proposed models with their model. The results are as shown in Tables 7 and 8 and Figs. 7, 8, 9, and 10. As shown in the comparison tables and figures, our suggested models recorded lower MAE and RMSE values, with ML-100K: MAE = 0.53, RMSE = 0.65. With ML-1M: MAE = 0.52, RMSE = 0.68. It appears from the comparison result that our proposed model overcame the previously mentioned models. This result underscores the importance of adding the recommendation system entities information to identify suitable neighbors and obtain an accurate recommendation system. Therefore, we can consider that our proposed model is an effective way to select the nearest neighbors successfully and support the recommendation process carefully. Since our rating prediction model needs a Table 7 Comparison of our models and state-of-the-art CFs in terms of prediction accuracy on ML-100K dataset. MAE value is ranked in descending order, and our models are in bold font Method

Parameterization

MAE

RMSE

ItemKNNPearson

K = 60

0.736125

0.936507

ItemKNNAdjustedCosine

K = 60

0.733618

0.935528

ItemKNNPearsonReg

K = 60

0.733399

0.932648

ItemKNNAdjustedCosineReg

K = 60

0.729538

0.929991

ItemKNNBCosine

K = 25

0.725362

0.924898

BiasedMatrixFactorization

num_factors = 40, bias_reg = 0.1, reg_u = 1.0, reg_i = 1.2, learn_rate = 0.07, num_iter = 100,

0.721715

0.912668

ItemKNNPearsonShrink

K = 40

0.718144

0.915559

ItemKNNAdjustedCosineShrink

K = 40

0.71739

0.91571

SigmoidUserAsymmetricFactorModel

num_factors = 5, regularization = 0.003, bias_reg = 0.01, learn_rate = 0.006, bias_learn_rate = 0.7,

0.71655

0.910353

SVDPlusPlus

num_factors = 50, regularization = 1, bias_reg = 0.005, learn_rate = 0.01, bias_learn_rate = 0.07,

0.715941

0.909214

ItemKNNCombined

K = 25

0.708815

0.909131

ItemKNNCombinedReg

K = 25

0.704588

0.903682

PCC-slope item-based

Dynamic Kfrom 1 to 50 0.596141

0.7359

PCC-slope user-based

Dynamic Kfrom 1 to 50 0.53

0.65

Bold indicates proposed models in this research

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Table 8 Comparison of our models and state-of-the-art CFs in terms of prediction accuracy on ML-1M dataset. MAE value is ranked in descending order, and our models are in bold font Method

Parameterization

MAE

RMSE

ItemKNNAdjustedCosine

K = 80

0.690876

0.87984

ItemKNNAdjustedCosine

K = 80

0.690622

0.879012

BiasedMatrixFactorization

num_factors = 120, bias_reg = 0.001, regularization = 0.055, learn_rate = 0.07, num_iter = 100,

0.675454

0.853102

SVDPlusPlus

num_factors = 20, num_iter = 80, reg = 0.05, learn_rate = 0.005

0.667725

0.853002

ItemKNNCombinedReg

K = 20

0.664384

0.852439

ItemKNNCombined

K = 20

0.66427

0.852514

PCC-slope item-based

Dynamic Kfrom 1 to 50

0.576141

0.7267

PCC-slope user-based

Dynamic Kfrom 1 to 50

0.526

0.68

MAE

Bold indicates proposed models in this research 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Methods Fig. 7 Comparison MAE of our models and state-of-the-art CFs on ML-100K dataset

small number of NNs to compute entity rating prediction, this reduces the model complexity and its computational time.

6 Conclusion and Future Work There are many models to measure similarities among users in the RS. But, there are not a lot of methods interested in measuring the similarity between the impact of the multi-interests user and how much the item (user) rating weight depends on user (item) ratings. We assumed that, for a fairly accurate recommendation system,

J. K. S. Al-Safi and C. Kaleli

RMSE

264

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Methods

MAE

Fig. 8 Comparison RMSE of our models and state-of-the-art CFs on ML-100K dataset 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Methods Fig. 9 Comparison MAE of our models and state-of-the-art CFs on ML-1M dataset

it is necessary to take into account all necessary information about this system’s entities. Since the regression method can be used to detect which features have a critical impact on the forecasted output and discover the relationship between different variables, we consider that it is possible to measure the critical impact of the entities by regression method and we include the critical impact differences on the correlated entities to form suitable neighbors for entities. Thus, in this paper, we suggest a new PCC- and regression-based neighbor selection model named the PCCslope-based model to support CFs. We depend on our rating prediction model which is based on the first available rating from NN and dynamic k-value to predict the rating of entities. We compare our model with other new models of selecting NNs, additional to

RMSE

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265

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Methods Fig. 10 Comparison RMSE of our models and state-of-the-art CFs on ML-1 M dataset

other state-of-the-art similarity measures models. Empirical evaluations demonstrate that our model accuracy surpasses the above-mentioned NNs selection models, and the nearest neighbors should have a strong correlation relationship and a minimum impact difference with the active user. The results confirm the importance of using the recommendation system entities information like the degree of importance to identify suitable neighborhood and obtain a qualified recommendation system. Usually, the neighbors’ size affects the prediction accuracy somewhat. But, this proposal has proven its quality in terms of accuracy even with small and dynamic neighbors’ size. For future work, we would like to use a combination of techniques to develop a hybrid model for the user- or item-based RS that could be more accurate to predict multiple-interests users’ behavior. Also, we plan to conduct a further examination of the performance with a dense dataset. Additional tests may also be performed to predict results for various latest metrics, including efficiency and diversity.

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Intrinsic and Simplified Complex Network Embedding Model Ahmad F. Al Musawi and Preetam Ghosh

Abstract Most naturally occurring complex networks exhibit a power-law-based degree distribution. In such networks, most of the nodes possess the same few lowest degrees (mostly degrees of 1, 2, 3), and the number of nodes reduces exponentially as we increase the degree. This creates redundant structures in the network where many nodes have identical connections. In this paper, we transform such complex networks into their intrinsic and simplified structure such that the redundant nodes and connections are removed intelligently while preserving their overall structural features and complexity. A network intrinsic model is proposed to group similar topological structures (nodes and edges) into single nodes that preserves the network features. We implemented our network intrinsic model on several different types of complex networks, and our results demonstrate different degrees of network complexity reduction depending on the size and topology of the networks. The proposed model can be used to minimize the overhead of processing complex networks with no loss of topology or data and thus reduces the computing time of different complex networks algorithms related to its topology besides having potential applications in problems related to network alignment. Keywords Complex network · Simplification · Biological networks · Network centrality

1 Introduction Exploring the diverse patterns of relationships existing in complex networks requires newer approaches to reduce the dimensions of the dataset that can result in a reduction A. F. Al Musawi (B) Department of Information Technology, College of Computer Science and Mathematics, University of Thi-Qar, Thi Qar, Iraq e-mail: [email protected] P. Ghosh Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_21

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in their processing times. Complex network analysis, however, is the field of study that describes a variety of natural and industrial complex systems in terms of the complex structure of their components (or nodes) and the relationships (or connections) among these components. There are many examples of complex networks. In nature, systems biology [1] refers to the understanding of the dynamics of activities achieved within cells at molecular and chemical components’ levels. For example, genetic regulatory networks refer to the sequence of genes’ and transcriptional factors’ regulation of each other which results in achieving specific functionality within the cell, such as protein synthesis. In industrial communication networks, computers and servers transfer information between each other through a connection media (wired or wireless). Moreover, social media (such as Facebook, Twitter, and so on) offers a wide range of relationships and connections between virtual identities of individuals. The analysis of complex network applications deals with the understanding of how their complex structures function and provides insights toward targeting the building blocks [2], network robustness improvement [3–8], and recovery from possible failures [9, 10]. These networks have different topologies where several models and algorithms have been proposed to understand their structure. Network topology refers to the connection patterns among the nodes. Many models were also proposed to mathematically explain how such topologies grow and evolve into complex industrial or natural networks, such as Erdos–Renyi, Barabasi–Albert [11] amongs others [12]. Most complex networks follow a power-law degree distribution [11] such that the probability of a node having a degree k equals to a power law of P(k) = k −γ , where γ denotes the degree exponent. Such phenomena are well known as the “rich become richer” which reflects the probability that nodes with higher connectivity (degree) tend to have more connections than nodes with lower connectivity. This phenomenon can also be observed when a huge number of low popular websites cite highly ranked websites such as Google and Facebook. However, in such a network, most of the nodes possess the same few lowest degrees (mostly 1°, 2°, 3°), and the number of nodes reduces exponentially as we increase the degree. The few nodes having a very high degree depict the long tail of the power-law degree distribution. This creates redundant structures where nodes having identical topological features are found within the network. One popular framework of simplifying the topology of complex networks is the clustering paradigm. Clustering (or community detection) algorithms aim at grouping the set of nodes that are highly connected and have much fewer connections to other nodes of the network [13]. Such a simplification provides a fast understanding of the homophily of the nodes into clusters (tendency to have connection with similar nodes) [14] and provides a simplified model for processing and assessment of complex networks, along with the grouping of different structures (based on similarity or diversity) into new forms with less number of nodes and connections. The process of clustering would extract new knowledge from the network and plays a critical role in the analysis of a variety of applications for feature recognition and knowledge mining [15].

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The centrality of the nodes is another feature of interest that computes the influence of one node onto other nodes. Many centrality [16] measures are available, and several nodes may possess identical centralities due to their identical connectivity within the network, see (Fig. 1) and (Table 1). Besides other features, complex networks are rich in redundant components (nodes and connections). Another important aspect to mention is the network similarity problem. Due to the complexity of large-scale complex networks, it is very difficult to measure the similarity (or even measure the difference) between two given networks. This topic has been the focus of many studies [17] as it has a significant impact on different fields. Complex networks’ similarity models use the aspect of scoring (especially on comparing the best match of two networks). However, similarity can be measured by comparing the general properties of two or more networks (network comparison) [17] or by measuring the best subgraph alignment of two or more networks (networks alignment) [18]. Distribution of recurrent structures such as motifs [2] and graphlets [19] is used to check the similarity patterns of the compared networks. These repeated components add more complexity to the network analysis in terms of the memory required for storage and the computation time required for calculating different properties of the network exhaustively. Although current algorithms do consider run-time complexity, they do not scale properly for larger networks (having more than a thousand nodes). On the other hand, using the original complex network topologies without any modification may not properly reflect their unique structures because many nodes share the very same topological structures. The succinct presentation of the topology of complex networks requires the assembling of redundant nodes and edges into a single node and a single edge to show the core topology of the network. All nodes must be presented uniquely within the intrinsic network

Fig. 1 Three synthetic networks with their simplified versions. In (A—Top), sample network of size 22 nodes, shaded nodes share the same connection within the network. In (A—Middle), nodes with same connection are grouped in one topological group. (A—Bottom), groups are represented as one node, each node labeled with its group ID (GID). The reduction of the number of nodes was about 40.91%

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Table 1 Measuring set of topological features of (Fig. 1—network a)—bottom simplified network. Each node in the simplified network is a group of nodes shares the same connectivity in the original network. Although measurements’ values were different, they preserve the basic Sequence and modified topological value Node

EC

1, 2, 3, 4, 5, 6

0.0499 0.0996 0

PR

CC HC 0

BC A 0

H

0.2909 0

In Out D 1

0

1

7

0

0.0777 0.8

0.8750 0

0

0.6611 0

3

3

8, 9, 10

1

0.1810 0

0

0

0

0

1

0

1

11

0.0999 0.1216 1

1

2

0.6057 0

2

1

3

12, 13

0.0999 0.1216 0

0

0

0.6057 0

2

0

2

14

0

0.875

0

0

0.7154 0

3

3

15, 16, 17, 18, 19

0.0999 0.1326 0

0

0

0.4143 0

2

0

2

20

0

0.0777 1

1

0

0

0.2261 0

2

2

21, 22

0.0499 0.1106 0

0

0

0.0995 0

0

1

0.0777 0.8

1

EC: Eigen centrality[20], PR: PageRank [21], CC: Closeness centrality [22], HC: Harmonic closeness centrality [23], BC: betweenness centrality [22], A: Authority centrality [23], H: Hub centrality [23], In: Indegree, Out: Outdegree, D: Degree, D = In + Out.

such that no two nodes share the same connection. Such network simplification may uniquely show the direct impact of the core and reduced set of nodes and edges of different complex networks. Our hypothesis is that as most of the nodes in complex networks share the same topological features (centralities), they can be grouped into a new structure or even can be removed from process. The new format simplifies the original network by selecting genuine nodes and edges that preserves the explicit paths of the original network. Our simplification method is motivated by the dimension reduction of the network adjacency matrix to a new simplified adjacency matrix. This strategy can reduce the execution time of any topology related algorithm due to the reduction achieved on the number of nodes and edges to be processed. The features of the original network are equal to the features of the new simplified network, and all network properties can be computed simply by using the original number of nodes within the proposed formulae of the features of the simplified network.

2 Related Works Complex network simplification is somewhat fuzzy, yet logical, solution to the problem of understanding the connectivity of many nodes and links within the networks. Many researchers explain the patterns occurring in complex networks, by exploring specific features shared by classifying nodes and links of the network into smaller and easier to interpret representations. Several approaches existed for such an objective:

Intrinsic and Simplified Complex Network Embedding Model

1.

2.

3.

4.

5.

273

Simplification [24]: It is the process of size reduction of a network by decreasing the number of nodes and links and has been studied extensively. Zhou et al. sample the weighted network by pruning a subset of links that maintains the overall network connectivity. In such methods, losses of edges and nodes are acceptable. Motif simplification [25] is another technique in which nodes that participate in two network motifs [2] (fan and parallel motifs) were grouped into sociogram structures. Moreover, network simplification can also produce new networks that preserve the same features of the original network under a specific threshold. In flow oriented simplification [26], vertices and edges are deleted if they do not contribute to the source to sink flow. In pathfinder networks [27], edges were pruned if there is a better path of at most q edges, q being a user defined parameter. In relative neighborhood graphs [28], only close pairs of nodes are relatively connected. In path oriented simplification [29], edges are removed such that the quality of best paths between any node pair is not affected. Network Partitioning (clustering): It refers to the process of dividing the networks into two or more partitions of highly connected communities. Several models have been proposed for grouping nodes with different topological features into one cluster (community) [30–32]. A cluster, as a result, is a subgraph that assigns specific values (clustering value) or merges nodes and links into one hyper node (or cluster). For example, such merging is achieved by grouping nodes based on distances in so-called self-similarity and box filling renormalization [33], [34]. Such operations help in viewing complex networks with simplified visual structures. Network Sampling: It is the process of generating a smaller network which has the properties of the original networks (such as degree distribution, diameter, or the size distribution of connected components). This is achieved by the selection of a random sample of nodes and/or links of the original network [35], e.g., snowball sampling [36] or random walk sampling [29], [37]. The preservation of similarity and same network structure (topology) of the nodes/edges of the transformed networks is still as open challenges. Network Compression: It refers to the process of transforming the network into a smaller representation (fewer number of nodes and edges) while preserving the network’s structural features. Network compression was approached from two perspectives: algorithmic and semantical. In algorithmic/information theoretic approach [38], compression is used to reduce time, memory size, and complexity. In semantical/structural approach [39], networks are compressed to produce simplified graphs and mostly used in search engines and information retrieval. Summarization of Complex Networks: It refers to the descriptive [40] or quantitative reporting approaches of the properties of complex networks. Such research mainly focuses on topological properties, attributes of nodes or links, and clustering paradigms [32]. Lie et al. [41] implemented clustering techniques toward network summarization into representative structures.

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3 Material and Methods Consider a network G = (V, E), V is a set of vertices (or nodes), and E is set of edges. A network can be directed or undirected. In directed networks, an edge is oriented from source node to destination node. In an undirected network, an edge has no orientation, and direction is not used among two nodes. A N ×N is an adjacency matrix that holds information about edges among G’s nodes. N = |V|, i.e., N is the number of nodes in G. To target redundant nodes that share similar connections within the network, a similarity measurement is needed to check the distance between two given nodes. There are several measurements to measure the distance between two objects. A traditional distance measurement that can be used in measuring the similarity between two nodes (say u, v) is Minkowski measures. The entries of the Minkowski distance are the vector of the connection of the current node. Undirected network G, A N ×N , and N = number of nodes in G would compare the values of Au (i’th row and i’th column are identical) with values of Av such that:   P  P  P  P1 du,v =  Au,1 − Av,1  +  Au,2 − Av,2  + · · · +  Au,N − Av,N  where du,v is the distance between nodes u, v, P is a positive integer, if P = 1, then d is Manhattan distance. If P = 2, then d is Euclidean distance. In directed networks, a distance would be measured for incoming, outgoing links (rows and columns) independently. If the two nodes share the same two distances, then they are similar. If we consider applying this format to measure the distance between two nodes, then we must scan all corresponding edges from both nodes within the adjacency matrix, which is very expensive in execution. However, other distance measurements can be used that consider nodes with shared neighbors only, such as Jaccard, Cosine, or many others [42]. In these measures, a set of connected nodes (neighbors) are determined first. Then, we can find  du,v as number of shared nodes between u, v, as |Hu ∩Hv | in Jaccard index du,v = |Hu ∪Hv | , s.t. Hi is neighbor of node i. In directed networks, a distance would be measured for incoming and outgoing links independently. If the two nodes share the same two distances, then they are similar. We have modified the Jaccard indexto work for directed networks, so that the Jaccard index for two nodes  in ∩vin )∪(u out ∩vout ) , s.t. u (u, v) will be du,v = (u in refers to set of nodes that have edges (u in ∪vin )∪(u out ∪vout ) (incoming to) vertex u, u out refers to the set of nodes that have edges (outgoing from) node u. One intuitive method to check similarity in networks is by comparing all nodes with all other nodes using Jaccard index. In a directed network, this method should be implemented by considering the Jaccard index for the directed nodes, such that each node will have two sets of nodes (u in , u out ). In an undirected network, the

Intrinsic and Simplified Complex Network Embedding Model

275

iteration can be reduced by skipping visited tuples. This process will reduce the nodes’ comparisons into the half. However, nodes are not necessarily connected with all other nodes. The resulted model (Algorithm 1) has a complexity of O(N 2 log(N )), as similarity function requires O(log(N)). Algorithm 1 N = Number of nodes, Visited = [] For w in Nodes B(w) = Nieghbors(w) For i=1 to len(B(w))-1 u = Node(i) if u not in Visited For j=i+1 to len(B(w)) v = Node(j) if v not in Visited Visited.append(v) End if End for End if End for End for

A better implementation of searching for similar nodes is to search the network based on shared neighborhoods. Specifically, instead of implementing a nested loop for comparing nodes with all other nodes, we propose the comparison of neighbors of all nodes but with nodes existed within the same neighborhood only. Initially, we enumerate the neighbor nodes Sx (most preferred as adjacency list) for all nodes (x ∈ nodes). Then, we implement Algorithm 2 into S.

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Algorithm 2 Define B(N,N) as Boolean Matrix Define D(N) as Boolean = False For each node in Nodes If = False then For i=1 to | |-1 For j=i+1 to | | u= v = (j) False then If

If End If End For End For End If End For

= True =1 then

= False

The B matrix stores the binary values of whether the two nodes been compared (True) or not (False). The D matrix stores the binary values of whether current node is found as similar to another node(s) (True) or not (False). This algorithm has a complexity of O (N log N ). Nodes with similar topology are pruned (deleted). However, grouping action can be made to nodes with similar topologies to show number of similar nodes are existed within the network.

4 Results and Discussion In this section, our embedding model is conducted on different types of networks. The aim of this experiment is to see how the different types of networks and centralities are going to be under different distance scales embedding. An embedding ratio is measured by dividing the new measured value of the given factor (degree or centrality) divided by the original value that will show how much difference has achieved on the new network on the given measure in comparison to the original network.

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4.1 Networks Dataset Our complex network simplification model was implemented on different types of networks, considering their sophisticated differences in structures and centralities. Networks under consideration can be classified into synthetic networks and natural networks, see Table 2. Two synthetic networks were used: Erdos–Renyi (ER) model and Barabasi–Albert model (BA). In ER model, a node is added to the network with an edge based on a probability value of P. In BA model, a node is added to the network based on the likelihood of attachment to nodes with higher degree node. A node (to be attached to) is selected based on a probability model which uses a random value of r such that r lies in the range of the probability distribution of degrees. Several natural networks were used as follows: biological, ecological, collaboration, and social networks. In biological networks, a node may have different types of biological representations which can be a gene, a transcription factor, or proteins which result in different types of networks such as genetic regulatory network (GRN) or protein– protein interaction network (PPI or PIN). However, in GRN, genes or transcription factor (nodes) regulates (as an edge) the work of another gene or transcription factor. In PIN, proteins are interacting with each other to produce some metabolic functionality in the cell. In ecological networks, different types of representation among species (nodes) can be happen such as eating, competing for resources, or any mutual advantageous interactions. In collaboration and social networks, a node would represent a person and an edge represents any kind of interaction between the two nodes, such as emailing, citing, sharing files, participating in teams, or been a friend in social media.

4.2 Network Overall Measures To evaluate the effectiveness of the embedding model on the different types of network, we measured the most common centralities that describe the overall functionality of the network, been embedded on different distance scales. Herein, the number of nodes and number of edges in the original and simplified networks are measured (see Table 3), along with the most basic centralities in network science, as given in Table 4. By implementing the embedding model on the given network, a new network will reflect the embedded network, where redundancy in nodes has been removed based on distance scale. Apparently, this will affect the network adjacency matrix as it would result in dimension reduction of the network (which is based on the number of nodes) into the new smaller one.

ba_1k_2k, BA model [43]

ba_1k_40k, BA model [43]

er_graph_1k_6k, ER model [43]

er_graph_1k_4k, ER model [43]

bio-CE-PG PIN [43]

Ecoli. GRN [44]

bio-yeast PIN [45]

bio-CE-LC [43]

ca-GrQc [43]

ca-CSphd [43]

mammalia-voles-kcs-trapping [43]

socfb-Simmons81 [43]

socfb-Haverford76 [43]

socfb-Reed98 [43]

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Network name

SN

SN

SN

EN

CN

CN

BN

BN

BN

BN

AN

AN

AN

AN

Type

962

1446

1518

1218

1882

4158

1387

1458

1565

1871

1000

1000

1000

1000

|V |

18,812

59,589

32,988

3697

1740

13,422

1648

1948

3758

47,754

4000

6000

40,236

1996

|E|

0.15918

0.161479

0.157469

0.235139

0.002533

0.278439

0.037854

0.035415

0.132829

0.210138

0.004034

0.006407

0.078691

0.015928

CC

0,000,615

0.000428

0.000434

0.000766

2.58E–06

8.11E–06

4.70E–06

1.07E–05

4.65E–06

0.000226

0.000282

0.000498

0.000555

3.73E–05

BC

0.020349

0.028519

0.014325

0.002494

0.000492

0.000777

0.000857

0.000917

0.001535

0.013649

0.004004

0.006006

0.040276

0.001998

D

0.00104

0.000692

0.000659

0.000821

0.000531

0.000241

0.000721

0.000686

0.000639

0.000534

0.001

0.001

0.001

0.001

PR

0.134141

0.177036

0.124459

0.030049

0.000763

0.002844

0.002788

0.002316

0.002448

0.095186

0.021999

0.047381

0.18755

0.005667

CLC

143.8125

281.2147

211.0672

43.35351

1.851792

14.30406

4.1948

4.058927

4.587561

197.4413

26.59369

55.23836

209.0712

7.005974

HC

0.012732235

0.007615551

0.010271041

0.026304324

0.023031616

0.006543004

0.025674923

0.025393058

0.024824156

0.004861498

0.030334602

0.029293306

0.023985789

0.031621176

KATZ

Table 2 Structural properties and centralities of selected natural and synthetic networks studied in this work. |V |, |E| are number of nodes and edges, respectively. CC is the clustering coefficient, BC is the betweenness centrality, D is the density of the network, PR is the PageRank, CLC is the closeness centrality, HC is the harmonic centrality, and Katz is the Katz centrality. All centralities averaged networks: AN—Synthetic, BN—Biological, CN—Collaboration, EN—Ecological, and SN—Social

278 A. F. Al Musawi and P. Ghosh

AN AN AN BN BN BN BN BN BN BN BN BN BN BN BN BN CN CN EN SN

ba_1k_2k, BA model [43]

Erdos–Renyi model

Barabasi–Albert model

bio-CE-PG PIN [43]

Ecoli. GRN [44]

bio-yeast PIN [45]

bio-CE-LC [43]

Yeast genetic regulatory network [44]

Interolog, C. Elegans [45]

C. Elegans Protein interactions (InAct) [45]

Y2H Union [46]

CCSB-Y2H, S. cerevisiae [46]

Wi2007, C. Elegans [45]

Protein interaction network of Yeast [45]

Rat protein interaction [45]

Cattle protein interaction [45]

ca-GrQc [43]

ca-CSphd [43]

mammalia-voles-kcs-trapping [43]

socfb-Simmons81 [43]

Directed

Directed

Directed

Directed

Undirected

Undirected

Undirected

Undirected

Undirected

Undirected

Undirected

Undirected

Undirected

Directed

Directed

Directed

Directed

Undirected

Undirected

Directed

1518

1218

1882

4158

120

1167

1457

1113

965

1647

3031

2377

4441

1387

1458

1565

1871

1499

1500

1000

32,988

3697

1740

13,422

184

1451

1934

1493

1467

2502

5409

12,924

12,873

1648

1948

3758

47,754

767

1364

1996

0.131752

2.955665

35.01594

12.07311

71.66667

71.37961

28.07138

38.72417

25.69948

21.91864

33.22336

27.80816

52.39811

37.49099

24.4856

66.07029

5.558525

48.833%

9.067%

0.4

1516

1182

1223

3656

34

334

1048

682

717

1286

2024

1716

2114

867

1101

531

1767

1499

2490

996

32,984

3612

1075

10,777

54

510

1514

1046

1204

2122

4376

7818

8703

1081

1565

1345

47,483

387

2279

1988

(continued)

0.012126

2.299161

38.21839

19.70645

70.65217

64.85183

21.71665

29.93972

17.92774

15.18785

19.0978

39.50789

32.39338

34.40534

19.66119

64.20969

0.567492

74.183%

8.474%

0.400802

Table 3 Results of applying our model on different complex networks samples. Model of networks is as follows: AN—Synthetic network, BN—Biological network, CN—Collaboration network, EN—Ecological network, and SN—Social network      ˆ  ˆ |V | |E| Name Model Type εV εE V  E

Intrinsic and Simplified Complex Network Embedding Model 279

SN SN SN SN SN

socfb-Haverford76 [43]

socfb-Reed98 [43]

Social network 1 (Facebook) [47]

Social network 2 (Facebook) [47]

Social network 3 (Facebook) [47]

Undirected

Undirected

Undirected

Directed

Directed

Type

746

149

665

962

1446

|V |

50,075

2726

9370

18,812

59,589

   ˆ V 

0.268097

0.671141

1.503759

0.623701

0

εV

744

148

655

956

1446

|E|

46,025

2451

9351

18,806

59,589

   ˆ E

  ˆ Original network edges,  E : Embedded network edges, ε E : Embedding percentage achieved on edges,ε E = 100 −

  ˆ  E ∗100 |E| .

  ˆ  V ∗100 |V | ,

|E|:

8.087868

10.08804

0.202775

0.031895

0

εE

    |V |: Original network number of nodes,  Vˆ : Embedded networks number of nodes, ε V : Embedding percentage achieved on nodes,ε V = 100 −

Model

Name

Table 3 (continued)

280 A. F. Al Musawi and P. Ghosh

Intrinsic and Simplified Complex Network Embedding Model

281

Table 4 List of structural and centralities used in the experiments Centrality Equation

Description

|V|, |E|

Number of nodes and edges in the network

CC

BC

(number of triangles)×3 C = (number of connected triples) Connected triples is three nodes, uvw s.t. there exist edges (u, v), (v, w), and may be (u, w)  σst (v) BC(v) = σst s=v=t

σst (v) is the total number of shortest paths from node s to node t that pass through node v D

PR

d=

2m n(n−1) m n(n−1)

. . . for undirected graph

d= . . . for directed graph n, m are the number of nodes and edges, respectively, in the given graph  PR(v) = α Avu PR(u) L(u) + β u∈V

α, β are constant values, A is an adjacency matrix. L(u) number of edges from u CLC

HC

CC(v) =

 t∈V

1 dG (v,t)

s.t. dG (v, t) is the distance from v to t  1 HC(v) = d(t,v) d(t,v) 0.5 and normalization of E(x)dstip < 0.5 (10) Normal = normalization of E(x) srcip ≤ 0.5|normalization of E(x) dstip ≥ 0.5 (11)

8 Blockchain Technology Blockchain technology is public and decentralized and provides a trusted consensus. Blockchain connects blocks one by one with encryption. Several secure hash algorithms (SHAs), for example, SHA-1, SHA-256, SHA-384, and SHA-512, can be used to solve the condensing of the message problem in the current block to product a digest of the message. SHA is a one-way iterative hash function, with different tasks that generate different structures and dimensions fora digest of the message. The hash functions have individual properties cryptographically that can connect any block with other blocks. First, it is difficult to reverse the hash function, that is, mathematically, the input message cannot be found, based on the digest of the corresponding output message. Second, it is not mathematically possible to have two various files that give the same message. Third, any changes to the file (with overwhelming potential) lead to a different file digest [24, 25]. The contents of a blockchain in this work are entropy values, normalization of entropy values, and classifier algorithm, as shown in Fig. 4.

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Fig. 4 Blockchain in the proposed system

9 Results and Discussion We display the results of our experiment, and the detection of entropy values is analyzed depending on time-based detection method to prevent DDoS attacks. In this work, the dynamic window size is set to each 1 s. Network topology, which contains nine switches and 64 hosts, is used. The total dataset is collected in 3 h to train and test the model to detect the attack. During the period of dataset collection, a regular packet is generated from hosts two and ten, and each host creates a packet in 0.1 s to a different destination. DDoS attack packets are generated from hosts one and nine, and each host makes packet in 0.025 s to the victim host 60 with fake source IPs. Figures 5 and 6 show the influence of normalizing entropy values before and during the attack. From the graphs shown earlier, the values of the entropy are seen. When the attack of DDoS happens, there will be a rapid increase of source IP entropy due to increased randomness and also a rapid decrease of destination IP entropy due to decreased randomness. Three classifiers like Naive Bayes classifier, SGD, and Hoeffding trees are used. It is noticed that the Hoeffding trees are the best in our proposed system because the precision is 98.88%. The results among the classifiers are shown in Table 1.

Fig. 5 Normalization of entropy values for source and destination IP

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Fig. 6 Normalization of entropy values for source and destination ports

Table 1 Results of classifier in the proposed system

Items

Naïve Bayes

SGD

Hoeffding tree

True positive rate

96.0235

97.3097

98.8807

True negative rate

97.5088

98.1527

99.0131

Precision

96.3956

97.4485

98.8893

Recall

96.0235

97.3097

98.8807

F-measure

96.0547

97.3234

98.8821

3.8431

2.6902

1.8922

18.7750

16.4019

9.6152

Mean absolute error Root mean square error

10 Conclusion A framework of SDN for detection and prevention DDoS attacks on the controller and data plane is produced. This framework involves the training of algorithms in the model of machine learning by using data capturing and training to predict attacks of DDoS. When the DDoS attack occurs, we use the script to make decisions and build defense rules to drop DDoS packets in our SDN framework. Naive Bayes classifier, SGD, and Hoeffding trees are evaluated with traffic attribute extraction in real time. The results of the experiment show Hoeffding trees to be the most appropriate classifier for our network.

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References 1. Ye J, Cheng X, Zhu J, Feng L, Song L (2018) A DDoS attack detection method based on SVM in software defined network. Secur Commun Netw 2018 2. Idhammad M, Afdel K, Belouch M (2018) Semi-supervised machine learning approach for DDoS detection. Appl Intell 48(10):3193–3208 3. Cui J, Wang M, Luo Y, Zhong H (2019) DDoS detection and defense mechanism based on cognitive-inspired computing in SDN. Futur Gener Comput Syst 97:275–283 4. Behal S, Kumar K (2017) Detection of DDoS attacks and flash events using novel information theory metrics. Comput Netw 116:96–110 5. Shafi Q, Basit A (2019) DDoS botnet prevention using blockchain in software defined internet of things. In: Proceedings of 2019 16th International Bhurban Conference on Applied Sciences and Technology. IBCAST 2019, pp 624–628 6. Alzahrani S, Hong L (2018) Generation of DDoS attack dataset for effective ids development and evaluation. J Inf Secur 09(04):225–241 7. Xu Y, Liu Y (2016) DDoS attack detection under SDN context. In: Proceedings of IEEE INFOCOM, vol 2016, July 2016 8. Ma X, Chen Y (2014) DDoS detection method based on chaos analysis of network traffic entropy. IEEE Commun Lett 18(1):114–117 9. Lee K, Kim J, Kwon KH, Han Y, Kim S (2008) DDoS attack detection method using cluster analysis. Expert Syst Appl 34(3):1659–1665 10. Karanbir S, Kanwalvir Singh D, Bharat B (2018) Threshold-based distributed DDoS attack detection in ISP networks. Turkish J Electr Eng Comput Sci 26(4):1796–1811 11. Mousavi SM, St-Hilaire M (2018) Early detection of DDoS attacks against software defined network controllers. J Netw Syst Manag 26(3):573–591 12. Wu X, Liu M, Dou W, Yu S (2016) DDoS attacks on data plane of software-defined network: are they possible? 13. You X, Feng Y, Sakurai K (2017) Packet in message based DDoS attack detection in SDN network using OpenFlow. In: Proceedings of 2017 5th International Symposium on Computing and Networking, CANDAR 2017, vol 2018, Jan, pp 522–528 14. Phan TV, Park M (2019) Efficient distributed denial-of-service attack defense in sdn-based cloud. IEEE Access 7:18701–18714 15. Myint Oo M, Kamolphiwong S, Kamolphiwong T, Vasupongayya S (2019) Advanced support vector machine-(ASVM-) based detection for distributed denial of service (DDoS) attack on software defined networking (SDN). J Comput Netw Commun 2019 16. Yin D, Zhang L, Yang K (2018) A DDoS attack detection and mitigation with software-defined internet of things framework. IEEE Access 6:24694–24705 17. Bhaya W, Ebadymanaa M (2017) DDoS attack detection approach using an efficient cluster analysis in large data scale. In: 2017 Annual conference on new trends in information and communications technology. Appl. NTICT 2017, July, pp 168–173 18. Shalev-Shwartz S, Ben-David S (2013) Understanding machine learning: from theory to algorithms, vol 9781107057 19. Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J Supercomput 74(10):5156–5170 20. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010, pp 177–186 21. Ibrahim NM, Zainal A (2020) A distributed intrusion detection scheme for cloud computing. Int J Distrib Syst Technol 11(1):68–82 22. Muallem A, Shetty S, Hong L, Pan JW (2019) TDDEHT: Threat detection using distributed ensembles of hoeffding trees on streaming cyber datasets. In: Proceedings of IEEE Military Communications Conference. MILCOM, vol 2019-Oct, pp 219–224 23. Srimani PK, Patil MM (2015) Performance analysis of Hoeffding trees in data streams by using massive online analysis framework. Int J Data Mining Model. Manag 7(4):293–313

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24. Bruno Rodrigues, Bocek T, Stiller B (2017) Multi-domain DDoS mitigation based on blockchains, pp 185–190 25. Liang G, Weller SR, Luo F, Zhao J, Dong ZY (2019) Distributed Blockchain-based data protection framework for modern power systems against cyber attacks. IEEE Trans Smart Grid 10(3):3162–3173

Wireless Sensor Network (WSN) Routing Optimization via the Implementation of Fuzzy Ant Colony (FACO) Algorithm: Towards Enhanced Energy Conservation Ahmed J. Obaid

Abstract To realize efficient performance in industrial applications, wireless sensor networks (WSNs) have witnessed routes developed. In WSN operations, it is important to note that they rely upon and operate on battery, which implies that there is energy restriction. In the current study, the aim was to design a routing protocol through which improved WSN energy conservation could be achieved, hence preserving the battery life. In the study, three variables were on the focus and aided in making informed decisions about routes that would be deemed appropriate. These parameters included the distance needed for the successful sending of packets to the destination node (from the source and in meters), the traffic amount in Erlang, and the sensor energy in joules. In the proposed routing protocol, it is important to note that it was based on FACO (fuzzy logic and ant colony optimization). Indeed, the role of employing fuzzy logic lay in the calculation of the total cost of the node–gateway intersection relative to the node’s energy, as well as its traffic load. Similarly, the implementation of ACO was informed by the need for searching and establishing distances that would prove the shortest between the sources to the destination sensor nodes, with the shortest distances aiding in system performance evaluation and inference-making. With MATLAB simulation adopted, findings demonstrated significant improvements in system performance, especially in terms of energy conservation. Particularly, results from FACO implementation, relative to the energy conservation parameter, suggested its superiority, as it outperformed ACO, having implemented the two algorithms under the same experimental conditions and with the same experimental parameters. The future implication for industrial applications is that the routing algorithm associated with system improvements via more energy conservation could be implemented via the use of WSN. Keywords Routing optimization · Energy conservation · ACO · FACO · WSN

A. J. Obaid (B) Faculty of Computer Science and Mathematics, University of Kufa, City, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_33

413

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1 Introduction Wireless sensor networks (WSNs) constituted deterministically and randomly distributed sensors. The sensors aid in acquiring data and sensing it to gateways, allowing for further analyses [1]. Some of the industrial applications in which WSNs gain usage include greenhouses (to monitor humidity, temperature, soil, and water levels), healthcare systems (to monitor patient health conditions), water municipals, smart grids, and street lighting [2]. Other areas include monitoring and detecting industrial machinery malfunctioning and strength monitoring in bridges [3]. The eventuality is that the WSNs play the primary role of taking a given application’s measurements and sending the resultant data to gateways [4]. Hence, it is at the gateways that system users engage in information interpretation, eventually achieving the intended purpose [5]. At this point, it remains worth inferable that for WSNs, their main importance lies in the attribute of machine monitoring. This importance arises from the affirmation that WSNs can be placed in regions that are unreachable by engineers, as well as rotating machinery—where hard-wired sensors are not applicable [6]. In contemporary society, this observation demonstrates that WSNs are continually replacing wired sensors because the former is cheaper when compared to the latter [7]. For sensor nodes, they contain in-built radio transceivers, which have electronic circuits, microcontrollers, and external and internal antennas. These units aid in interfacing with energy sources and the sensors. The energy sources, in this case, are batteries. There are also platforms that allow for the supply of external energy as deemed appropriate, with a particular focus on the sink node. Hence, for firms that engage in WSN manufacturing, the majority are out to ensure that complete sensor systems based on microelectromechanical systems are produced [6]. However, for WSNs, one major challenge entails adaptation and self-configuration, robustness, responsiveness, and energy efficiency [7–9]. From the literature, most studies have concentrated on the establishment of paths that could be deemed optimal for WSNs, especially those that promise energy conservation maximization between the destination nodes and the source nodes [10]. Despite such efforts, however, a major problem has been witnessed in terms of the manner in which a good routing algorithm could be created while considering the aforementioned challenges [9–11]. Once installed, it is important to note that WSNs require little or no cost of maintenance and that they are cheaper in cost. For various applications, therefore, they have been used increasingly [12]. For WSN routing protocols, their design emanates from the effort of ensuring that routes between the destination nodes and the sources are established. Hence, the functionality of the routing protocols involves network decomposition to achieve pieces that are more manageable, eventually allowing for information sharing among the neighbors before extending the same trend to the remainder of the entire network [13]. To achieve efficiency and reliability, WSN applications ought to be designed in a way involving routing optimization to ensure that even as WSN communications are managed, the process occurs in distance, traffic, and energy-aware mechanisms [14, 15]. In this study, the central purpose

Wireless Sensor Network (WSN) Routing Optimization …

415

was to achieve WSN routing optimization. The study’s specific objective lay in the finding of an optimum path that would be deemed the best for the sensors’ sending of the needed data to sink nodes relative to the sensor nodes’ energy, as well as source node-destination node distance and the network traffic. Hence, ACO and FACO algorithms were used in performing optimal routing while striving to achieve maximum energy conservation and minimum energy consumption.

2 Related Works In the last few years, scholarly studies have concentrated on WSN optimal path design to minimize the consumption of energy between the destination nodes and the source nodes [16]. Despite these efforts, realizing the best routing algorithm that would consider all factors determining WSN energy consumption has remained problematic. In [17], the focus was on ACO protocol implementation and how WSNs’ optimal routes could be established. In the study, it was noticed that ants tend to assume the shortest routes after completing the search process successfully [18]. Also, in the study, backward ants and forward ants were discussed. Hence, the ACO pheromone amount would determine the route to be followed. While the findings were informed, it is notable that the study failed to account for all parameters affecting WSN energy consumption. In [19], the ACO algorithm was also implemented relative to WSN performance optimization. In this investigation, a sensor node’s energy was added as a variable that would determine the shortest route realization. In the findings, it was reported that the path to be followed would be based on the sensor node’s remaining energy and the shortest route’s amount of ACO pheromone. In another study [20], the ACO algorithm was implemented along with the tree-based Breadth-First Search (BFS). The objective was to enhance the best path selection’s accuracy. Also, the proposed ACO algorithm exhibited three types of ants, which included the backward ants, the Bfrontward ants, and the frontward ants. In the findings, the cost of selecting the next node as using it as a path through which data could be sent for the transportation level that follows was better predicted by the Bfrontward ants. It is also worth indicating that additional studies have employed a hybrid of ACO and GA (Genetic algorithm) and also a termite hill algorithm for WSN system routing [8, 9]. Indeed, from the related works reviewed above, it is evident that most routing protocols that have been implemented rely on the criteria for single routings, such as considering only the distance (hop count) or the energy level only. With the danger of such approaches apparent in relation to a possibility of draining the energy or overloading the sensor nodes (hence, unbalanced sensor node energy levels), it becomes critical to establish a routing algorithm that proves aware of the parameters of the hop count, the energy level, and the traffic congestion towards realizing energy efficiency and reliability in industrial application-based sensors. In the current study, the focus is on the modification of the routing algorithm to account for additional considerations. Thus, the proposed system strives to stretch beyond aspects of the

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minimal distance between the sink node and the start node (and also the energy available in a given node) and consider or incorporate the sensor node traffic load.

3 Methodology In this research, the fuzzy system was considered because the tool allows for the incorporation of human-related reasoning towards efficient system operation. Hence, fuzzy logic allowed for the incorporation of crucial parameters through which the best optimum route could be determined, leading to packet sending to the gateways (from source sensor nodes). Thus, the fuzzy logic application allowed for optimum node cost evaluation, especially in relation to the parameters of the traffic and the energy of sensor networks. With this study’s main aim being the development of traffic-aware, distance-aware, and energy-aware routing model, the focus on parameter behavior was on two issues, which included packet delivery and energy consumption. From the literature, fuzzy logic aids in determining the most appropriate nodes that are worth picking, achieved via cost calculation. In turn, the best node would inform the best route selection (Fig. 1). In the flowchart above, each input’s fuzzy membership functions is shown. Both the traffic in the queue and the energy form the system’s inputs. However, the sensors’ reliance on batteries implies that the WSN’s energy remains limited. Therefore, maximizing energy conservation and minimizing energy consumption imply that the input forms a critical variable through which the best node that is worth selecting

Fig. 1 proposed routing model

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417

is determined—relative to the node’s cost. For the fuzzy variables, they are high, medium, and low, while a range of 0–5 J represents the energy limits (Fig. 2). For the case of the energy model, it is represented by the previously described firstorder radio model. Indeed, the first-order radio model’s inclusion was informed by the need to ensure that the priority was given to the nodes exhibiting the highest energy. Hence, upon a packet K’s transmission from a given source node to a destination node via the nodes that are selected, there is energy consumption, which makes certain nodes to be dead. The number of rounds simulated would determine the number of nodes that may be dead. Upon having a fixed distance, the eventuality would be that there is a direct proportionality between the number of data bits and energy usage. As indicated earlier, the traffic forms another fuzzy input. From the literature, the traffic comes in terms of the number of packets K in a given queue, responsible for determining the duration that a given packet is likely to take before reaching the destination (from the source node). From the viewpoint of the route selection process, nodes exhibiting less traffic would also be associated with higher possibilities of being selected, as there is the minimization of traffic in the queue. With fuzzy variables being high, medium, and low, a range of 0–10 bits per second represented the traffic limits. A dimensionless unit, Erlang, was used in measuring the degree of traffic intensity. From previous studies, given a wireless network, traffic intensity refers to a measure of the carried load or the offered load [2–4]. Regarding the system’s fuzzy output, it came in the form of the respective nodes’ cost. Relative to the interplay involving the least traffic and the highest energy, the node exhibiting the highest cost would be selected. Also, a range of 0 to 10 represented the cost limits. Upon node selection relative to the cost with which it would be associated, there was the implementation of the ACO algorithm. This implementation sought to establish the optimum route that would be worth using, with the shortest distance dictating the decision made. Imperative to highlight is that two different ways are operated by ants and involved backward and forward ways. For the case of the backward direction, the ants originate from the destination node to the source node. On the other hand, the forward direction implies that the ants’ movement originates from the source node to the destination node. Indeed, the direction to adopt is expected to be dictated probabilistically by the pheromone trail amount that other ants might have deposited. For the backward ants, therefore, they would retrace a previously followed route while seeking the destination node, and as they pass the arcs, they leave pheromone. At this point, the role of the cost parameter lies in the determination of the pheromone amount that the ants deposit as they proceed with their backward model. Should there be a deposition of more pheromone, this outcome would imply and be associated with short paths. Given some period, there tends to be a reduction in the pheromone amount deposited previously due to the evaporation effect. In this case, pheromone evaporation plays the role of ensuring that there is a reduction in the pheromone amount previously deposited during the process of finding the given path. Hence, as the process of searching begins, there is the assigning of certain pheromone amounts to each arc. Therefore, given an ant and a source node, it is expected that the pheromone

418

Fig. 2 Output and input membership functions

A. J. Obaid

Wireless Sensor Network (WSN) Routing Optimization … Table 1 Fuzzy parameter intersections

Energy

Traffic

419 Cost

Low

Low

Medium

Medium

Low

Large

High

Low

Large

Low

Medium

Small

Medium

Medium

Medium

High

Medium

Large

Low

High

Small

Medium

High

Small

High

High

Medium

trails are utilized towards evaluating a node that could probably be chosen as the next one. It is also worth highlighting that during the process of searching the route, each ant ends up taking various routes, and it is expected that the ant that reaches the destination node faster would have used the least path. Upon reaching the destination, the ant is projected to assume a backward direction movement (from a forward direction movement). As the backward trip proceeds, the ant end sup depositing a certain pheromone amount on the arcs that it visits. For such an ant, it ends up increasing possibilities that the rest of the ends will adopt the same arc that connects the nodes. Also, for these ants, having followed the shortest route, it is expected that the amount of time taken would be shorter, implying further that there will be the deposition of more pheromone in the otherwise shorter route. For pheromone evaporation, it is important that it occurs to ensure that ants are discouraged from converging in certain routes that are deemed suboptimal. Similarly, the rate at which there is the evaporation of pheromone trails remains exponential. In this study, therefore, there was the achievement of ACO based on three parameters that allowed for the location of an optimum path. These parameters included pheromone deposit, pheromone evaporation, and the cycle of the movement of the ants (Table 1). MATLAB C code was used to implement the simulation. Indeed, there was a random deployment of sensors. The area of operation was 100 × 100 m2 . The position 50,175 was that at which there was the location of the sink node. As indicated earlier, the first-order radio framework was utilized by the model, whereby 6400 bits were the packet size, while a range of 0 to 10 was the traffic load. Also, given two sensor nodes, the distance between them was set at 50 m in the simulation. Some of the ant colony parameters that were initialized included the effects of the cycles and sight of the ants, the traces effect, the number of ants, and the evaporation coefficients (Table 2).

420 Table 2 Simulation study parameters and conditions

A. J. Obaid Parameters

Value

X * Y simulation area

100 * 100 m2

Number of nodes

100

Sink node location

50 * 175

Node initial energy

5J

Number of maximum traffic load

10

Packet length

6400 bits

Transmission distance (d)

50 m

amp

50 nj/bits

E e−tx

100 pJ/bits/m2

Number of ants (k)

100

Evaporation coefficient (e)

0.1

Number of cycles

100

Effects of ants sight (α)

1

Traces effect (β)

5

4 Results and Discussion In this study, there was a comparison of the performance of the selected technique with that which had been reported for the case of ACO as a contemporary routing algorithm. In the simulation study’s case, there was fuzzy logic addition to the ACO. This modification sought to discern the respective nodes’ perceived optimum cost (Fig. 3). The figure above shows a comparative analysis of how FACO performed relative to the case of ACO. From the findings, FACO, upon implementation, had the packet number delivery to the sink number prove higher when compared to the case of ACO. Indeed, the superiority of FACO was attributed to its capacity to ensure that many parameters are included, with previous studies suggesting that the inclusion of many parameters poses a critical impact on WSN battery lifetime. Also, it can be seen that upon implementing FACO, the algorithm accounted for the energy amount that remained in the node, as well as the source node-sink node distance and also the traffic amount in the queue. On the other hand, ACO as a routing model was found to only engage in the shortest distance evaluation between the source node and the sink node. The latter outcome implied that for ACO, its implementation would yield a lower number of packets that were delivered. Similarly, the implementation of ACO implied that the path that was followed was that which was associated with the shortest distance but the highest traffic, causing delays in packet number delivery. Indeed, it is for these reasons that FACO outperformed ACO, as demonstrated in Fig. 4. At this point, there was a comparison of ACO and FACO performance relative to the variable of the number of dead nodes. As illustrated in Fig. 4, the ACO routing

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Fig. 3 ACO and FACO algorithms’ outcome performance

technique was associated with a higher number of dead nodes when compared to the case of FACO. The inference that was made at this point was that with an increase in the number of cycles, more dead nodes are likely to be dead when the ACO routing approach is implemented. Hence, the algorithm was found to be less energy efficient. Also, when the 45th cycle was reached, the implementation of ACO was associated with an increase of 20% in the dead node number, implying that even as the number of cycles increased, FACO implementation exhibited fewer dead nodes. Hence, the proposed routing protocol yielded improvements in system performance. It was also observed that at 51 cycles, when ACO and FACO had their performances compared, FACO protocol-based nodes exhibited a lower rate of energy consumption while ACO protocol-based nodes exhibited higher energy consumption at the same number of cycles (51). Hence, FACO proved to be more energy efficient. Of importance to document is that in WSN nodes, they are deemed dead after depleting all their energy [7]. In this case, when the 20th cycle was evaluated, it could be seen that for ACO, 1.4 J was the amount of the remainder of energy, while 2.8 J was the remainder of the energy amount after implementing FACO routing protocol. Hence, it was inferred that when FACO was used, there was significant conservation of energy than the case of ACO implementation as a routing protocol, especially because the latter technique was associated with relatively significant

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Fig. 4 A comparative analysis of ACO and FACO routing algorithm performance for packet delivery

energy loss. Hence, the proposed algorithm yielded superior performance, implying that if WSNs adopted it, they are likely to be more efficient and reliable (Fig. 5).

Fig. 5 A comparative analysis of the algorithms’ performance regarding the energy remaining

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The impact of three other parameters was also evaluated, and the outcomes obtained after implementing the protocols compared accordingly. These parameters included the number of dead nodes during the 100th cycle, the energy remaining during the 50th cycle, and the number of packets that were relayed to the sink node during the 50th cycle. Regarding the number of dead nodes that were recorded after 100 cycles, ACO had 45 while FACO had 25. In relation to the energy that remained after 50 cycles, ACO’s record showed 0.4 J, while the case of FACO depicted 1.6 J. From the perspective of the number of packets that were relayed to the sink node, having considered 50 cycles, ACO had 83 beats per second, while FACO exhibited 89 beats per second. Hence, when ACO was used, the number of dead nodes was larger. Also, ACO was associated with higher energy consumption. On the other hand, FACO outperformed ACO on these parameters in such a way that a higher number of packets were delivered. Hence, it was summed that the proposed routing protocol exhibits superior performance in relation to WSN energy conservation, outperforming ACO.

5 Conclusion In summary, the proposed routing protocol involved a hybrid system that combined ACO and fuzzy logic. The choice of fuzzy logic was informed by the need to select nodes deemed the best, especially those that account for the parameters of the remaining energy and the queue in the traffic. Notably, the use of ACO sought to allow for the searching of the optimum path between the source node and the sink node, with a particular focus on the shortest distance between the nodes. From the findings, the proposed algorithm was found to be capable of enhancing network lifetime because of its ability to balance between the network traffic in a given queue and the energy consumption. Particularly, FACO’s implementation outperformed the case of ACO in such a way that with an increase in the number of cycles, there was an increase in the number of packets delivered while ensuring tat more energy was conserved. Also, it is important to note that from the literature, an increase in the number of cycles tends to increase the number of dead nodes. However, in this case, FACO exhibited fewer dead nodes (with an increase in the number of cycles) than ACO. From the near and far future industrial perspective, therefore, this study infers that the proposed routing protocol is better placed to gain application for WSN routing. From a scholarly perspective, it is recommended that additional studies examine the degree to which the proposed system is likely to perform relative to other comparative routing algorithms that were not considered in this study.

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References 1. Goswami N, Malhotra R (2015) A survey on ANT based routing in WSN. Int J Comput Sci Manag Stud 15(6):11–14 2. Arya R, Sharma SC (2015) Analysis and optimization of energy of sensor node using ACO in wireless sensor network, vol 45. Elsevier B.V, pp 681–686 3. Khoshkangini R, Zaboli S, Conti M (2014) Efficient routing protocol via ant colony optimization (ACO) and Breadth first search (BFS). In: IEEE International Conference on Internet of Things, Green Computing and Communication, pp 374–380 4. Chandni SK, Monga H (2013) Improved termite hill routing protocol using ACO in WSN. In: International computer science and engineering conference, pp 365–370 5. Das S, Wagh S (2015) Prolonging the lifetime of wireless sensor networks based on blending of genetic algorithm and ant colony optimization. J Green Eng 4:245–260 6. Karray FO, Silva CD (2004) soft computing and intelligent systems design. Pearson Education Limited, pp 57–162 7. Rich E, Knight K, Shivashamkar B (2010) Artificial intelligence, 3rd edn. Tata McGraw Hill Education Private Limited, pp 300–400 8. Simon D (2013) Evolutionary optimization algorithms. Wiley, New York, pp 50–87 9. Pizzo J (2015) Ant colony optimization. Clanrye Int 101–200 10. Yan R, Sun H, Qian Y (2013) Energy aware sensor node design with its applications in wireless sensor networks. IEEE Trans Instrum Meas 62(5):1183–1191 11. Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, London, pp 25–63 12. Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks technology, protocols and applications, 1st edn. Wiley, New York, pp 20–60 13. Kamila NK (2016) Handbook of research on wireless sensor network trends, technologies and applications. IGI global, India, pp 1–34 14. Karray (2012) soft computing and intelligent system design theory tools and application. Pearson Addison Wesley, United Kingdom, pp 0–70 15. Yinbao S, Lee K, Lanctot P (2014) Internet of things wireless sensor networks. IEC market strategy board White paper. The IEEE Website, pp 43–57 16. Jamal N (2012) Routing techniques in wireless sensor networks a survey. IEEE Wireless Commun 11(6) 17. Ghaffari A (2017) An energy efficient routing protocol for wireless sensor networks using A-star algorithm. J Appl Res Technol 815–822 18. Wu Q, Yan Y (2014) LEACH routing protocol based on wireless sensor networks. Int J Fut Gener Commun Netw 7(5):251–258 19. Abu-Baker AK (2016) Energy-efficient routing in cluster-based wireless sensor networks optimization and analysis. Jordan J Electr Eng 2(2):146–159 20. Alkadhmawee AA, Lu S (2016) Prolonging the network lifetime based on LPA-Star algorithm and fuzzy logic in Wireless sensor network. In: 12th World Congress on intelligent control and automation, June 2016, pp 1448–1453

Efficient Design for Square RFID Tag Furkan Rabee and Daniah Mohammed

Abstract Radio frequency identification system (RFID) is an automatic technology for communication between two objects that are reader and tag. The fundamental challenge in the design of chipless RFID tag is the way to encode the data without a chip. This challenge is overcome by the utilization of electromagnetic properties of the resonators, filters and est to encode the data bits. The structure performs deep absorptions of the impinging signal at several frequencies related to the loop resonators. Keywords RFID tag · Chipless tag · Passive tag · Radio frequency to binary conversion

1 Introduction Radio frequency identification (RFID) is an automatic technology to identify people, objects, record metadata or control individual target through transmitting data using electromagnetic waves [1, 1]. RFID technology was developed soon after World War II, and it took almost 50 years before RFID technology advanced to present level and it is presented as a possible alternative to substitute the process of barcode identification [3, 3]. Chip-based radio frequency identification (RFID) tags are an integral constituent of various advanced applications spanning various industries such as wireless sensing [5], smart logistics [6], and pharmaceutical tracking [7]. This widespread acceptance is due to a number of advantages exclusive to RFID including improved interrogation distance, swift reading rate, and non-line-of-sight This paper shows the performance of The square tag to encode 8 data bits, Rogers RO4003C substrate has been used that spans 12 × 12 mm2 , that shows the possibility of obtaining good data capacity with a small area. F. Rabee (B) · D. Mohammed Computer Science Department, Faculty of Computer Science and Mathematics, University of Kufa, Kufa, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_34

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Fig. 1 RFID system operation

communication. However, chip-based RFID tags are not financially feasible for wide dissemination [8] (Fig. 1). The main function of antenna of RFID tag is to respond to an interrogation signal transmitted from the RFID reader. This method allows the tag and reader communicating with each other. The types of tag antenna can be classified into three different classifications: active, semi-active, and passive. The active and semiactive tags require a battery, and it will limit the time of life. Passive tags have no problems with energy limitations. RCS-based tags do not require antennas to operate, unlike circuit-based tags. Time domain chipless RFID tags are designed to encode bit sequences through presence or absence of resonant peaks, signified as data bits. In time-domain, chipless RFID tags use radiating structures to translate ID information into a uniquely distinguishable electromagnetic signature. The radiating structures are typically made up of metallic scatterers and reflective strands, such as square-shaped [9], triangular [10], and circular resonators [11]. This letter proposes a significant chipless RFID tag that offer higher bit density at different designs. The proposed tag encodes ‘1’ and ‘0’ bits by means of absorbing and reflecting properties of the resonators’ peculiar structures. Most RFID tags use an antenna made of copper, silver, or aluminum [12] due to the high conductivity of these materials. Conductivity, resistivity of metals Material

Resistivity p ( m) at 20 °C

Conductivity σ (S/m) at 20 °C

Silver

1.59 ×

10–8

6.30 × 107

Copper

1.68 ×

10–8

5.98 × 107

Annealed copper

1.72 × 10–8

5.80 × 107

Gold

2.44 ×

10–8

4.52 × 107

Aluminum

2.82 ×

10–8

3.5 × 107

Efficient Design for Square RFID Tag Table 1 Advantages and disadvantages of RFID

Advantages

427 Disadvantages

High speed

Interference

Multipurpose and many format

High cost

Reduce man-power

Some materials may create signal problem

High accuracy

Overhead reading (fail to read)

Multiple reading Complex duplication

When considering the resistivity of various metals, a lower resistivity means you will need less of a metal, but high resistivity is not bad in itself, because you can compensate by making the conductor thicker and end up with the same resistance. Maybe copper or silver is the best choice, but when making mechanical and cost considerations, it may find aluminum is better. This is because for a sample of copper and another of aluminum of equal resistance, the aluminum sample will be bigger, but stiffer, lighter, and cheaper. A.

Advantages and limitations of RFID system

The RFID technology has numerous advantages. While having advantages, this technology also has some drawbacks [13] (Table 1). B.

RFID antenna

RFID antenna is utilized to collect information about any object, and there are many types of RFID antenna such as stick antennas and gate antenna. According to the researchers, the RFID antenna must satisfy many requirement [13]: • • • •

Its size should be small. should have omnidirectional or hemispherical coverage. must provide maximum possible signal to the microchip. be robust and be very cheap.

(C)

Antenna Characteristics

Generally, a tag antenna consists of the conducting metal line and the dielectric substrate. The readable range rapidly decreases when the antenna size is reduced since the radiation efficiency of the antenna is dropped. As expected, the efficiency decreases as the thickness of substrate increases. In some real implementation, RFID tag antennas have attractive features such as compact, lightweight, and easy to fabricate for mass production [14]. Moreover, they have to be structured with an omnidirectional radiation pattern that facilitates communication with the reader regardless of their relative direction [15]. Most of RFID tags use antenna made from copper, silver, or aluminum [12]. For the development of the RFID tag, antenna was needed the assistance of CST software, which is a software that performs electromagnetic simulation.

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2 Design of RFID Tag The idea behind the RFID tag is to store a unique identification number and each resonator resonates at unique frequency In addition, the resonating frequency can be modified by changing the dimensions of the loop resonators. The tag should be characterized by its good response, small size to reduce production costs but the readable range rapidly decreases when the antenna size is reduced since the radiation efficiency of the antenna is dropped. With many experiments, an appropriate size and good response were achieved, as shown in the square RFID tag. The performance of the tag antenna changes according to a variety of substrate materials, so the Rogers RO4003C substrate has been used in the Square RFID tag design that given better results. Also, the efficiency increases as the substrate thickness decreases. A loop-based design methodology is the key to store multiple data bits within a small physical tag footprint. The geometrical dimensions as well as the number of nested resonant loops are determined iteratively using CST MICROWAVE STUDIO. The simultaneous existence of inductive and capacitive components distributed along a single loop creates reverberation at a specific value of frequency. The reverberation is readily identifiable as a pronounced dip in the radar cross-sectional (RCS) response, representing to a ‘1’ data bit. When there is no reverberation associated with the structure, total reflection of the impinging electromagnetic waves happens signifying a ‘0’ data bit. By meticulously designing a multiresonant circuit dependent on this phenomenon, various information bits can be installed in a same structure. Square RFID TAG This square-loop tag consists of a few concentric metal square rings placed on a 1.524 mm thick Rogers RO4003C dielectric substrate with the relative permittivity of 3.38. The simulations have been done with a 15 by 15 mm silver sheet placed behind the substrate of the tag antenna’s ground. The simulated antenna geometry is shown in Fig. 3. The parameter values to describe the geometry of the final tag antenna design are listed at Table 2. Several 5-bit square-loop tags have been designed and simulated to verify that the model is working correctly (Fig. 2). A method for increasing bit-carrying capacity of the data encoding circuit is utilized. The capacity can be increased further by adding more resonant elements to the exterior of the nested loops. The formulated technique essentially increases the information-bearing capacity without a noticeable increase in the physical footprint. The square chipless RFID tags are worked within frequencies from 8 to 17 GHz. The Table 2 Design specification of the tag

Design tool

Advanced design system (ADS)

No. of square resonator

5

Size of the tag

15 × 15 mm2 (2.25 cm2 )

Substrate

Rogers RO4003C having a thickness of 1.524 mm

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Fig. 2 Square designed 5-bit unit on a Rogers RO4003C slab with a metallic ground

Fig. 3 Simulation results of the square tag

RCS (Cartesian) (0 0 20)(Abs) [pw]/abs,dB…

dB(s.m)

Frequency / GHz

Table 3 Design specification of the tag

0 -20 0 -40 -60 -80 -100 -120 -140

5

10

15

20

25

Design tool

Advanced design system (ADS)

No. of square resonator

8

Size of the tag

12 × 12 mm2 (1.44 cm2 )

Substrate

Rogers RO4003C having a thickness of 1.524 mm

Bit capacity(bit/cm2 )

5.55 (bit/cm2 )

capacity-enhanced tag is depicted in Fig. 5 and dimensions are specified in Table 3 (Fig. 4).

3 Simulation Results The simulated antenna geometry is shown in Fig. 3 several 5-bit square-loop tags have been designed and simulated to verify that the model is working correctly. The thin structure performs profound absorptions of the impinging signal at a several

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Fig. 4 Square designed 8-bit unit on a Rogers RO4003C slab with a metallic ground

Fig. 5 Simulation results of the square tag 8-bit

RCS (Cartesian) (0 0 20)(Abs)…

Frequency/GHZ

0

dB(s.m)

0

5

10

15

20

25

-50 -100 -150

resonant frequencies identified with the loop resonators. The pronounced dip in the radar cross-sectional (RCS) response, representing to a ‘1’ data bit. Figure 6 illustrates tag performance when resonant material is copper or aluminum with the same sizes of the tag, there is different in response. Note that the frequency curve is more stable and profound absorptions when using aluminum instead of copper. The event that one of the printed loops in the unit cell is removed, the corresponding absorption peak disappears from the reflected signal, that giving the chance of encoding a wanted bit arrangement in Fig. 7. Four tags with various codes of “10111,” “11101,” “10011,”and “10101” are given, and good agreement is accomplished indicating the validity of the proposed model of RCS. The formulated chipless RFID tag is investigated for its RCS performance at different angles of oblique incidence. The investigation establishes the extent to which the proposed tag exhibits angular stability when illuminated in a slanted orientation. This feature may be necessary to develop realistic applications where a stable tag position cannot be guaranteed in Fig. 8. The tag demonstrates stable oblique angular performance up to 60°, enabling the tag to operate in a slanted orientation. In any case, the average RCS magnitude slightly decreases with an increase in angle of incidence.

Efficient Design for Square RFID Tag

431 RCS (Cartesian) (0 0 20)(Abs)…

dB(s.m)

Frequency / GHz 0 -50 0 -100 -150 -200 -250 -300

5

10

15

20

25

a) Simulation results of the square tag (Copper) 5-bit unit 11111

dB(s.m)

RCS (Cartesian) (0 0 20)(Abs)… 0 -20 0 -40 -60 -80 -100 -120 -140

Frequency / GHz 5

10

15

20

25

b) Simulation results of the square tag (Aluminum) 5-bit unit 11111 Fig. 6 Comparisons of RCS results of tags with different materials

Registered results for electromagnetic descriptors of the proposed chipless information encoding circuit are covered in this part. Table 4 appears a comparison of the proposed tag with other shaped resonators. The proposed tag encodes a maximum of 8 data bits while occupying a physical footprint of 1.44 cm2 . The resulting bit density is 5.55 bit/cm2 . Furthermore, the formulated tag is operated at a variety of incident angles. In experiments, the resonance frequency is the most important factor of RFID tags. As shown in previous figures, the proposed square tag prototype with their respective electromagnetic response obtained through CST® MWS® is shown in Fig. 3 The RCS response of silver, copper, and aluminum demonstrates distinct resonances, copper’s response less than other metals, using the same amount of nested elements. As shown in Fig. 4, tags with typical code of 11111 are fabricated on Rogers RO4003C substrate. The electromagnetic response with random sequences 10111, 11101, 10011, and 10101 are shown in Fig. 7. The capacity increased further by adding more resonant elements to becomes 8 data bits such as in Fig. 5, while occupying a footprint of 1.44 cm2 . The resulting bit density is 5.55 bit/cm2 . Furthermore, the formulated tag is operated at a variety of incident angles, as shown in Fig. 8. Although the structural infirmity of realized tag introduced through the fabrication process that causes a slight shift in resonant frequencies is observed, all resonances are unmistakably discernible with good absorption levels. Therefore, the proposed

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Fig. 7 Comparisons of RCS results of tags with different codes. a “10111,” b “11101,” c “10011,” and d “10101” dB(s.m)

RCS (Cartesian) (0 0 20)(Abs)…

0 -20 0 -40 -60 -80 -100 -120 -140

frequency /GHZ 5

10

15

20

25

(a) 10111 RCS (Cartesian) (0 0 20)(Abs)…

dB(s.m)

Frequency/GHZ 0 -20 0 -40 -60 -80 -100 -120 -140

5

10

15

20

25

(b) 11101 RCS (Cartesian) (0 0 20)(Abs)…

dB(s.m)

frqency/GHZ 0 -20 0 -40 -60 -80 -100 -120 -140

5

10

15

20

25

(c) 10011

dB(s.m)

RCS (Cartesian) (0 0 20)(Abs)…

0 -20 0 -40 -60 -80 -100 -120 -140

frequency/GHZ 5

10

(d) 10101

15

20

25

Efficient Design for Square RFID Tag

433

Fig. 8 Angular stability and RCS for configurations

Table 4 Comparison with other shaped resonators Resonator shape

Bit density (bit/cm2 )

No. of bits

Tag size (cm2 )

Square resonator (RogersRO4003C substrate)

5.55

8

1.44

Rectangular [13]

0.55

05

9.00

Circular [14]

3.80

09

2.36

Triangular [13]

1.21

10

8.25

tag needs not to be repeated to achieve satisfactory absorption at resonant frequencies resulting in a minimized design. Furthermore, no fake peaks are observed in either experimental results, validating 1:1 between resonators and data bits. Table 4 demonstrated comparison with other shaped resonators.

4 Conclusion The main essence to transmit and receive the information in system of radio frequency identification (RFID) is the antenna requiring distance for reading of the data, so the design of the antenna apparatus is important right now and the design and simulation of the antenna are given utilizing CST STUDIO. A primary function will concentrate on studying its design and to store multiple data bits within a small physical tag footprint. Square RFID tag A compact and scalable model for chipless RFID tag is presented. The overall tag design has been realized using Rogers RO4003C laminate and its performance was examined through electromagnetic simulations. The comparison is performed by the RCS response of the same tag dimensions obtained for three metals (silver, aluminum, and copper). The result for using the aluminum and silver in Large RCS has been

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obtained. Noted that, the silver suffers from drawback environmental concerns, therefore, can replaced by the aluminum, without effect to the response, where the response of aluminu is close to that of silver, and also less expensive.

References 1. Jia XL, Feng QY, Ma CZ (2010) An efficient anti-collision protocol for RFID tag identification. IEEE Commun Lett 14(11):1014–1016 2. Shan H, Peterson J, Hathorn S, Mohammadi S (2018) The RFID connection: RFID technology for sensing and the Internet of Things. IEEE Microw Mag 19(7):63–79 3. Tikhov Y, Won JH (2004) Impedance-matching arrangement for microwave transponder operating over plurality of bent installations of antenna. Electron Lett 40:574–575 4. Ustundag A (2012) The value of RFID: benefits vs. costs, p 182 5. Khan UH et al (2016) Novel chipless displacement sensor circuit using spurline resonator. IEICE Electron Exp 13:p1008 6. Soboll P et al (2017) A multitude of RFID tags: a broadband design for stackable applications. IEEE Microw Mag 18:107 7. He Y et al (2016) UHF RFID tag with slot antenna integrated into blister medicine package. IEEE Antennas Wireless Propag Lett 15:956 8. Islam MA, Karmakar NC (2012) A novel compact printable dual-polarized chipless RFID system. IEEE Trans Microw Theory Techn 60:2142 9. Costa F et al (2013) A chipless RFID based on multiresonant high impedance surfaces. IEEE Trans Microw Theory Techn 61:146 10. Rauf S et al (2017) Triangular loop resonator based compact chipless RFID tag. IEICE Electron Exp 14:20161262 11. Martinez M, Weide DVD (2014) Compact slot-based chipless RFID tag. In: IEEE RFID technology and applications conference (RFID-TA), p 233 12. Cichos S, Haberland J, Reichl H (2002) Performance analysis of polymer based antenna-coils for RFID. In: International IEEE conference on polymers and adhesives in microelectronics and photonics, pp 120–124 13. Ahsan K, Shah H, Kingston P (2010) RFID applications: an introductory and exploratory study. IJCSI Int J Comput Sci Issues 7(1), No. 3 14. Nikitin PV, Lam S, Rao KVS (2005) Low cost silver ink RFID tag antennas. In: IEEE antenna and propagation society international symposium, vol 2B, pp 353–356 15. Yang L, Basat SS, Tentzeris MM (2006) Design and development of novel inductively coupled RFID antennas. In: IEEE antennas and propagation society international symposium, pp 1035– 1038

Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm B. V. Santhosh Krishna, J. Jijin Godwin, S. Tharanee Shree, B. Sreenidhi, and T. Abinaya Abstract White blood cell cancer is additionally mentioned as leukemia could also be a really perilous disease. To this day, the tactic of recognizing white blood cell cancer or leukemia remains done conventionally, which if done by different doctors, can cause a difference within the diagnosis. To retort these problems, a computerassisted method is proposed during which leukemia is detected from microscopic images employing a mixture of SVM and KNN. Initially, preprocessing is performed to arrange the image for processing. Later support vector machine (SVM) and Knearest neighbor are used for classification. The proposed algorithm classifies healthy and cancerous cells into one of the four types such as acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML). Additionally, the counting of the infected cells is also performed. Keywords Leukemia · Preprocessing · Segmentation · Support vector machine · Watershed algorithm · Blood cell classification

1 Introduction RBC, WBC, and platelets are the three primary blood components. Most cancer cells start in the body, but leukemia is the cancer type which in the cells of the blood begins and grows [1]. Leukemia, lymphoma, and myeloma are the three cardinal types of blood cancer. Leukemia may be a quite common sort of cancer and ranks 10th among the foremost common sorts of cancers round the globe. Children below 15 years aged and adults above 55 years aged are at a bigger risk of falling prey to the present deadly sort of cancer. Former information of its causes, risk factors, and symptoms can save B. V. Santhosh Krishna (B) Senior Assistant Professor, New Horizon College of Engineering, Bengaluru, India e-mail: [email protected] J. Jijin Godwin · S. Tharanee Shree · B. Sreenidhi · T. Abinaya Electronics and Communication Engineering, Velammal Institute of Technology, Chennai 601204, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_35

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many lives. Leukemia may be a sort of cancer that happens in your blood and blood making tissues or systemalymphaticum. Most of the time, leukemia leads to a rapid increase of white blood cells. The excess production of WBC’s crowds your blood and leaves less than enough space for other important elements like red blood cells and platelets. The red blood cells take the most important share within the production process. Leukemia occurs when the blood cells producing organs of your body start malfunctioning. The disadvantage of the prevailing method is that the fuzzy c means technique does not segment the WBC cells alone, and the correctness is additionally less in comparison to the proposed method and therefore the number of WBC is not calculated because the count of WBC is additionally significant for doctors reference.

2 Related Works Puttamadegowa et al. proposed a way that combines the FCM and snake algorithm into different image conversion techniques in search of leukocyte segmentation and counting advancements. The machine was able to achieve a precision percentage of 96 [1]. It is done by assigning a certain value and considering that all the others are binary image noise [2]. Vasuki Shankar et al. introduced an automatic lymphoblast detection and counting method using zack algorithms in MATLAB with an average accuracy of over 90% [3, 4]. This device also guarantees high speed, precision, and early disease detection [5]. The major disadvantage of watershed transformation is over-segmentation, as discussed in ‘Acute Leukemia Bone Marrow Images White Blood Cell Segmentation’ [4]. In Biji G, Dr. S. Hariharan’s ‘An Efficient Peripheral Blood Smear Image Analysis Technique for Leukemia Detection,’ WBC detection is conducted using a diffused expectation maximization (DEM) algorithm using electromagnetisms such as optimization detector (EMO). Detection by EMO in a precise manner, robustness, and stability at high performance [6].

3 Proposed Method 3.1 Input Image Read and view an image input. Reading an image in a workspace using image function. It is characterized as the process of obtaining an image from a source.

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3.2 Preprocessing Stage Preprocessing is a general term for the operations of images at the lowest abstraction level. The objective of preprocessing technique is to improve image data. It is possible to eliminate unnecessary distortions or enhance other picture characteristics that are required for further treatment. (a)

(b)

Resizing the input image: All images generated are resized to the same dimensions. If the particular size does not offer the same aspect ratio as the image input, the output image should be distorted. Image Enhancement: Improved image is the digital image correction method to ensure that the effects are more suitable for display or further study of images. In order to recognize important features, we can, for example, remove noise, sharpen, or picture brightness.

3.3 Image Segmentation Image segmentation is a process in which the image can be partitioned into multiple segments. It is also used to classify an image into several parts according to the feature of the image. The classification depends on the value of pixels. Image segmentation includes three steps the following [7]. • Color space conversions • Morphological operations

3.4 Color Space Conversions The first step is color image conversion. It is the process of translating a color representation from one base to another. In this process, we are using RGB to CMYK color conversion model. After this conversion, we are selecting the Y-color image. The above image is further segmented by using gray converted Y-color image [8]. Then adjusting the gray converted Y-color image to precisely identify the white blood cells. Black-and-white image conversion is applied to detect only the white blood cells. Since the black image does not process the detected white blood cells further process, the complement image is taken for further morphological operations.

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3.5 Morphological Operations Morphology is a method used to remove various image components for better analysis and representation of the forms and regions. This is a way to transform the data into the collection of functions [8]. It is a collection of processing images that process images according to shapes. Features such as form, color, and texture characteristics may be omitted. • Bordered corrected image • Morphological operated image • Morphological filled image

3.6 Watershed Transformation Watershed is a transformation in grayscale images. The main goal of this technique is to segment the image, typically when two regions of interest are close to each other [9]. The gray rates are the same or the same. If the regions are of the same object to obtain perfected segmentation, areas with different level grays should be combined in this case.

3.7 Feature Extraction Here the features of an image such as color features, texture, and statistical features are extracted. Color features—Mean color values. Geometric features—Radius, perimeter, area, etc. Texture features—Entropy, energy, correlation, homogeneity, etc. Statistical features—Variance, skewness, mean, gradient matrix, etc. The relation between actual area and convex area of the hull is known as solidity. That measure will be defined as, Solidity =

Area Convex Area

The measure to which a shape is compact characterize compactness or circularity. This measure is defined as, Compactness =

Perimeter2 Area

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3.8 Classification Classification of the image means the extraction from multiband raster image of information groups. The obtained raster from image classification can be used to produce thematic maps. The classification can be performed and achieved by Image Classification toolbar. There are many algorithm for classification and some classification algorithm that is given below, • SVM • KNN

3.9 SVM SVM is the algorithm used for high-precision classification. It is an algorithm for guided learning. SVM is a classifier in which a hyperplane can separate a group of binary-labeled vectors of training data. A datapoint is viewed as a m-dimensional vector in support vector machine. There are many hyperplanes that could classify the data. The objective of support vector machine is to find best hyperplane that represents larger distance between two modules. In a linear hyperplane, many hyperplanes can exist. The main function of this stage is to categorize the cancer cells and find out different types of leukemia. It first processes and distinguishes the unhealthy cells from the healthy cells. If the cell is unhealthy, it will further categorize those nonhealthy cells into four types [7] by microscopic blood image analysis. If the cell is classified as a non-healthy cell, then it will also give the count of white blood cells present [10, 11].

3.10 KNN In the image processing technique, the KNN is used for the classification. KNN is a nonparametric method. It is used to accurately classify the image and works on binary class. The consequence is a class member. The subject matter is accepted in the general class of his KNN by plurality votes of his neighbors. Here, k is an integer of decent value and of no value. If k = 1, the object is simply allocated to the next class. This is a direct classifier where objects are separated according to the nearest class. KNN consists of numerous attributes that are easier to classify. KNN is a simplified algorithm to store all existing cases and to identify new cases on the basis of a similarity measure [12–17].

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4 Experimental Results and Discussion The input image is given to the system. The processing takes place as described in Fig. 1. The system will give a subsequent image as a result. The input picture the device receives is shown in Fig. 2. Preprocessing is then done to improve image data eliminate unwanted irregularities or enhance a picture shown in Fig. 3. Then the conversion of color to space is applied. Conversion to CMYK is used here. Taking the yellow color pattern shown in Fig. 4 used for further analysis. Features can be extracted using the segmentation of watersheds shown in Fig. 5. It also reveals how filtering by making white blood cells darker will strengthen segmentation. The segmented image which is shown in Fig. 6 is obtained. Figure 7

Fig. 1 Flow diagram of the proposed method

Detection of Leukemia and Its Types Using Combination … Fig. 2 Input image

Fig. 3 Preprocessed image

Fig. 4 CMYK color converted image

441

442 Fig. 5 Feature extracted

Fig. 6 Segmented image

Fig. 7 Final output

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Fig. 8 Count of the white blood cells

shows the final output which classifies the cancer category. Figure 8 indicates white blood cell counts.

5 Conclusion Efforts were made to identify and count leukemia and its form from microscopic images of the blood using techniques for image processing which is presented in this study. Preprocessing was done to extract noise from the frames and to detect lymphocytes from the picture is segmented. The watershed transform is used to isolate clustered lymphocytes after extracting shape and color; SVM is used for the standard and explosive cell classification and KNN for the leucemia-type classification observed. In future, we will develop this program to identify different subtypes of AML and other diseases linked to blood.

References 1. Raje C, Rangole J (2014) Detection of leukemia in microscopic images using image processing. In: International conference on communication and signal processing, pp 255–259, Apr 2014 2. Puttamadegowa J, Prasannakumar S (2016) White blood cell segmentation using fuzzy C means and snake. In: 2016 International conference on computation system and information technology for sustainable solutions (CSITSS), Bangalore 3. Gatc J, Maspiyanti F (2016) Red blood cell and white blood cell classification using double thresholding and BLOB analysis. In: 2016 Fourth international conference on information and communication technologies (ICoICT) 4. Deshmukh P, Jadhav CR (2015) A survey of detection of Leukemia using white blood cell segmentation. Int J Trends Eng Res 294–298 5. Nee LH, Mashor MY, Hassen R (2012) White blood cell segmentation for acute leukemia bone marrow images. J Med Imaging Health Inf 2:278–284 6. Shafique S, Tehsin S (2018) Computer-aided acute lymphoblastic leukemia diagnosis system based on image analysis. Comput Math Methods Med 2018:1–11 7. Shankar V, Deshpande MM, Chaitra N, Aditi S (2016) Automatic detection of acute lymphoblastic leukemia using image processing. In: IEEE international conference on advances in computer applications (ICACA) 8. Parvaresh H, Sajedi H, Rahimi SA (2018) Leukemia diagnosis using image processing and computational intelligence. In: 22nd IEEE international conference on intelligent engineering systems 9. Shen T, Wang Y (2018) Medical image segmentation based on improved watershed algorithm. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEA)

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10. Kumarapandian S (2018) Melanoma classification using multiwavelet transform and support vector machine. Int J MC Square Sci Res 10(3):01–07 11. Karthik S, Annapoorani V, Dineshkumar S (2016) Recognition and tracking of moving object in underwater sonar images. Int J MC Square Sci Res 8(1):93–98 12. Sen NB, Mathew M (2016) Automated AML detection from complete blood smear image using KNN classifier. Int J Adv Res Electr Electron Instrum Eng 5(7) 13. Prabu S, Lakshmanan M, Noor MV (2019) A multimodal authentication for biometric recognition system using intelligent hybrid fusion techniques. J Med Syst 43:249 14. Ramesh GP, Parasuraman S (2019) Design and implementation of u-shape microstrip patch antenna for bio-medical application. Int J Adv Sci Technol 28(12):364–374 15. Hemanth Kumar G, Ramesh GP (2019) Reducing power feasting and extend network life time of IOT devices through localization. Int J Adv Sci Technol 28(12):297–305 16. Vasudevan V, Balaji K (2018) Performance of Cuk-KY converter fed multilevel inverter for hybrid sources. Indonesian J Electr Eng Comput Sci 10(2):436–445 17. Rebinth A, Mohan Kumar S (2019) A deep learning approach to computer aided glaucoma diagnosis. In: IEEE international conference on recent advances in energy-efficient computing and computation at St. Xaviers catholic college of engineering, Nagercoil on 7th and 8th Mar 2019

Prediction Communication Time and Data Size Based-Bluetooth in Mobile Crowdsensing for IoT Abbas M. Ali Al-muqarm and Furkan Rabee

Abstract Smartphones today are playing important role in our daily life. The reason behind that is they contain many embedded sensors such as camera, microphone, GPS, etc., in addition to wireless communication technologies such as Bluetooth and Wi-Fi. The deployment of sensors in large areas with limited resources is difficult and as well as the high cost, for this reason the mobile crowdsensing (MCS) scenario appeared, which assumes that people are sensing the environment through their smart phones and sharing this data with others, where there are problems of zone with unavailable coverage of Internet and the energy consumption in smartphones. In this paper, we proposed a protocol to manage the process of forwarding data in the environment of MCS based on the Bluetooth technology, in the forwarding process between the participants until they reach to the participant who has the Internet to be uploading data to the server via Wi-Fi and based on the Internet of Things (IoT) protocols known as CoAP and MQTT, and also we presented models to estimate the direct communication time between users in different situations in terms of velocity, acceleration, distance, and direction with constant acceleration of each node during a direct communication to predict the amount of data that could be transferred during the specified time and measurement of throughput, delay from end-to-end, success and failure cases when transferring data to the server in real time and energy consumption. We used a CupCarbon simulator which used for smart city and IoT also used MATLAB to simulation the mathematical models. Keywords Internet of Things (IoT) · Mobile crowdsensing · (MCS) · Bluetooth · Energy consumption · Mobile application

A. M. A. Al-muqarm (B) · F. Rabee Computer Science Department, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq e-mail: [email protected] F. Rabee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_36

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1 Introduction In recent times, smartphones have spread tremendously, and almost everyone has owned it [1]. Smartphones are equipped with many embedded sensors such as a camera, microphone, Gps, etc. [2–4]. It also contains wireless communication technologies such as Wi-Fi that is used with or without the Internet and Bluetooth which is used to send and receive various data such as pictures, audio, and video files, etc., without the need for Internet. Smartphones are one of the important elements of Internet of Things (IoT) these days, as users can share data with each other, these phones can communicate with other devices via applications (API) or directly over the Internet [5]. The most common technologies of communication in the Internet stack do not take into consideration the limitations of the Internet of Things devices. Wi-Fi is not energy efficient, does not support a large number of end-to-end devices and does not cover large areas. The appropriate alternative is Bluetooth 5 and 802.15 4. Bluetooth 5 is the latest version of Bluetooth 4.2, and the current Internet network uses hypertext transfer protocol (HTTP) in the OSI model, but the HTTP protocol consumes a lot of resources, so it is necessary to design protocols for IoT devices with limited resources, and the most common protocols in the IoT are the constrained application protocol (CoAP) and message queuing telemetry transport (MQTT) [6], where can be used Wi-Fi when user needs to upload data to the center. Scenario of collecting data from the environment and sharing it among users through smartphones is called mobile crowdsensing (MCS), where every user uses his smartphone to share any type of data with others, such as sharing pictures of beautiful areas that user had visited it and recommending others to visit, areas of congestion to avoid it, pictures of accidents, etc. Most of the people in the area of MCS do not have Internet continuously in their smart phones to share the data, here appeared the need to use technology that does not depend on the Internet such as Bluetooth. The process of data sharing between human-to-human (H2H), vehicle-to-human (V2H), human-tovehicle (H2V), or vehicle-to-vehicle (V2V) is a time-bound process, which means that the process of participation depends mainly on the time possible to stay in the coverage area during the movement to send and receive data between the participants user. Contributions of this paper have four keys: 1.

2.

3. 4.

Design a protocol called Bluetooth subscribe (BSS) to run in the MCS area, where data sharing is allowed for more than one hop depending on Bluetooth and in zone without Internet. Modeling the time of persons who stay in direct communication and in variety situations, taken into account velocity and acceleration as well as direction by the proposed mathematical equations. Determine the amount of data that can be transferring during this period time depending on our proposed protocol. Predict the time required for data access to the server based on the Internet availability or unavailability, number of hops, and the size of the data.

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We organized the remainder of this paper as in following: Section 2 presents the research related to our work. Section 3 presents a background and notation about Internet of Things and mobile crowdsensing. Section 4 presents the wireless communication technologies (Bluetooth and Wi-Fi). Section 5 presents the proposed protocol (BSS) design. Section 6 presents our models to estimate communication time. Section 7 calculates the data packet delivery time over our proposed protocol (BSS). Section 8 presents the performance analysis of the mathematical models and BSS. Section 9 presents a comparison with other studies. Section 10 presents the conclusion.

2 Related Work MCS is already a large research area, where there is a need to transfer data between the participants and manage them until they reach to the server in the case of the available or unavailable of the Internet and calculate direct communication time between participants, the Fiandrino and et al. [7] presented a simulation platform for mobile crowdsensing called CrowdSenSim, where CrowdSenSim is designed for urban environments and smart cities and can be its applicability (CrowdSenSim) in a smart street lighting. Silva and et al. [8] proposed a platform to estimating the amount of carbon dioxide depending on readings of sensors in vehicles to monitoring the pollution of vehicular, where crowdsensing techniques used to extract data from vehicles in real time and stored data locally on the devices. Abbas and Yoon [9] provide solutions for energy conserving in using diverse wireless radio access technologies for IoT connectivity such as the third generation partnership project (3GPP) machine type communications, IEEE 802.11ah, Bluetooth low energy (BLE), and Z-Wave. Mikhaylov and Ter-vonen [10] provide a mechanism to transmission data via Bluetooth for several hops, where the first mechanism is to detect and establish contacts with the near nodes and to discover the way to the remote nodes, either the second machine processes the storage of data that was transferred on the intermediate nodes and destination to the target, in their test used only four nodes. In [11], authors proposed a movement-prediction-based routing (MOPR), the proposed algorithm allows the routing protocol to avoid breaking or failure of the link by the moving nodes during the data transmission process, the algorithm determines the possible routes to the destination and then selects the most stable route depending on the node moving toward to the destination, and the algorithm uses the motion information of the vehicle to determine the best route. In this paper, Taleb et al. [12] proposed the scheme ROMSGP to determine a more stable route, and the proposed scheme works to group the vehicles in groups according to their direction, where the most stable path is determined by selecting the link that has the longest time for the link to expire but, in the proposed scheme, the authors did not take into consideration if there were no vehicles moving in the same direction of the group in [13]. The authors presented a protocol (parallel three-way

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handshaking) to manage the path determination between users where they assumed the division of participants into two types, the first type are users who collect data from MCS area and their names (collector). The second type is responsible for uploading the data that they obtained from the first type to the server and called them up loader. Namboordiri and Gao [14] proposed a protocol to prediction-based routing (PBR), determines a stable route on highway and use the velocity and location to predict failures of route also predicts the route lifetime to determines new routes before the old rout is break, where the researchers assumed that the vehicles in the link period were going at a constant velocity. Abbas and Fan [15] proposed a clusteringbased reliable low-latency multipath routing framework depend on technique of ant colony optimization to compute the best routes, while the vehicles are communicated between them according to end-to-end latency, reliability, energy consumption, and throughput, but did not specify the stable path and did not take into account the change in speed during communication between vehicles. In the paper [16], schemes designed for selecting the stable route that has the longest lifetime, and they are taking into account the change in speed of vehicles during communication also calculated the delivery time of packet before the sending data, and the authors presented equations to calculate the time of communication between the vehicles if they are going in parallel line and did not take into consideration the movement of vehicles in different directions. In our previous work [17], we presented a new framework called efficient power consumption (EPC—MCS) based on use of one of the IoT protocols such CoAP; in (EPC—MCS), we divided the MCS area into two areas: global mobile node and local mobile node where the volunteers in the global area are collecting data by using their smartphone and sending data to the local area, where a local node uploads the data to the cloud via a Wi-Fi or a 3G connection with a piggyback.

3 Background and Notation 3.1 Internet of Things (IoT) Internet of Things is the next technology revolution, IoT is the shortened term, the first Internet of Things term is used by Kevin Ashton in 1999 [18], the Internet of Things can communicate between things (such as refrigerator, cars, TV, air conditioner, doors, cameras and those things in the smart home) [19], and the Internet of Things can allow to communicate between human-to-machine (H2M), human-tohuman (H2H) intervention from the people [20]. These things may be independent sensors or embedded, as in smartphones, where they can be used to sensing the environment (temperature, humidity, light, fire, and noise) where these sensors can be tracked via the Internet. Internet of Things uses different communication technologies, such as radio-frequency identification (RFID), Bluetooth, near field communication (NFC), and wireless networks (Wi-Fi and ZigBee) to connect things between them, and on the other hand, there are many non-physical protocols that are used

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to integrate with the communication technologies mentioned above for the same purpose, such as message queue telemetry transport (MQTT), constrained application protocol (CoAP), extensible messaging and presence protocol (XMPP), etc. [21]. IoT usually contains sensors that suffer from limited resources such as power, processing, memory, and bandwidth; these sensors work on battery and deployed in large areas, where the process of recharging or replacing the battery is very difficult [22].

3.2 Mobile Crowdsensing (MCS) The term of mobile crowdsensing (MCS) was coined by Ganti et al. [23]. MCS model is used by users who use a smartphone as a new direction in the development of the Internet of Things. The volunteers rely on collecting data from the environment by taking advantage of smartphone features such as camera, GPS, etc. A typical MCS scenario involves users carrying their smartphones with applications running in the background to continuously collect sensor readings, either from built in sensors or wearable’s, such as data acquisition activity requires minimal user involvement, and it is named as opportunistic sensing in literature, in contrast to participatory sensing, which requires active user involvement to create sensor readings. Millions of mobile devices or more such as android phones, iPads, and iPhones are deployed in large areas like in smart cites and carried by people. MCS is successful in the expansion of the area of individual sensors to the community (a group of people) and the participation of people in the perception of different manifestations and collective reporting [24]. The advantage of MCS is to reduce the cost of deploying sensors that are usually fixed and cannot be easily replaced, solves the problem of the coverage range, where it covers large areas of participants’ movement, measure, and map phenomena of common interest [25]. Monitoring of environmental noise pollution in urban areas [26].

4 Wireless Communication Technologies 4.1 Bluetooth It is known today that all smartphones include Bluetooth technology that developed from 20 years ago, which today has become one of the most important technologies in which the Internet of Things depends on it [27]. Bluetooth is a wireless technology with high speed and low power designed to connect many type of devises such as phones, laptops, etc. [28] Bluetooth is a short range technology and range from 10 to 100 m, where it uses the IEEE 802.15.1 standard and it is capable of transferring picture, audio, and video [29]. Bluetooth

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Fig. 1 Time for one period in Bluetooth 4.2

v4.2 has the same data rate with Bluetooth 4.0 and 4.1, and it is 1 Mb/s, but has different payload that equal to 251 byte and time of one period is 2500 µs as shown in Fig. 1 [30]. Due to its ease of use and low-power consumption, Bluetooth technology is used in many devices and networks, and the BLE 4.0 supports only peer to peer (P2P) and star topology, but does not support multi-hop [31]. The latest version of Bluetooth 4.2 has provided features that have made BLE a powerful candidate among all low-power communication technologies in IoT area [32]. BLE technology can communicate with 20 devices with a range of 77 m [33]. Power consumption of classic Bluetooth, Bluetooth low energy (BLE), and Wi-Fi is 215 mW, 10 mW, and 835 mW, respectively [13, 34, 35], where Bluetooth and BLE consume less than WiFi. Bluetooth is a fairly low-energy technique [36]. The use of Bluetooth 4.0 in its standard form is not effective in the area of MCS because it does not support sending data and receiving it for more than one hop, and this is the limitation in Bluetooth [31].

4.2 Wi-Fi Technology Principle Wi-Fi is a shortened form to the full name wireless fidelity [37]. It depends on the IEEE 802.11 standard [36]. Most current technologies such as smart phones, laptops, and tablets are compatible with Wi-Fi [33]. Wi-Fi allows smart devices to exchange data without using a router in some ad hoc configurations [38]. Wi-Fi provides a maximum data rate of 54 Mb/s, where Wi-Fi devices such as smart phones and laptops can send and receive data between them at a distance of about 300 feet with the possibility of access to the Internet [39]. Wi-Fi has a high transmission time with large payload data sizes [40]. It is consumed much power [41]. And large amount of power needed, so this problem with IoT devices works on batteries [36]. The power consumption is an issue for Wi-Fi technology [33].

5 Protocol Design In this part of the paper, we proposed protocol for MCS, we called it Bluetooth subscription (BSS), and we designed a BSS protocol to work in the application layer to solve the limitations of Bluetooth which is not supporting the sending and receiving

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data for more than hop, energy consumption, and areas where there is no Internet in addition to managing the task of sharing data in the area of MCS between mobile nodes (persons or vehicles).

5.1 Protocol Header This part shows the format of our proposed protocol (BSS), where the BSS format is shown in Fig. 2. Application ID: When the subscriber downloads the application into his smartphone online for the first time only, the application will receive a unique ID address. The phone sends the Mac address to the server database to ensure that the subscriber will receive the same ID in the future when the application is deleted and re-downloaded again. TTL: Time to live, the time to destroy data or delete it automatically from the subscriber’s phone if the data exceeded the specific time and did not send to the server within this period. Number of next packet: The application divides data into packets and sends them; this field is required for the receiving to know how many packets they will be receiving. Sequence of packet: Sequence of the packets. Check sum: A checksum can be defined as an error detection method, where used to determine the integrity of data and know if a part of it has been lost. Optional: for any future additions. Maximum Packet Size: The maximum payload of BSS protocol is 240 bytes. Gps and Time: The data are split into packets, after the last packet is sent by the application; one packet is sent containing the location of the sending phone and the time, to be later collected by the server. This process is important to know the time and location of the data. 2 byte

1 byte

App. ID

TTL

Fig. 2 BSS protocol format

2 byte 2 byte No. of Sequence of next packet packet Maximum Packet Size 240 byte

2 byte

2 byte

Check sum

Optional

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5.2 Protocol Communication Control This subsection presented the working mechanism of the BSS protocol through the design of an algorithm to control on the process of communication between the participants in the area of MCS and the process of uploading data to the server, when the availability of the Internet based on Wi-Fi or unavailable based on Bluetooth, where the most important factors taken into account are two limitations as follows: energy consumption and unavailable of Internet. Algorithm 1 illustrates the process of establishing communication between two users; Algorithm 2 illustrates the process of uploading data to the server. Notations: S_m: The sender mobile. R_m: The receiver mobile. E_p: Energy of smartphone. Algorithm 1 Establishing Communication between Two Users

Algorithm 2 The Process of Uploading Data to the Server

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When the mobile receives a message from another mobile which checks if the application ID of mobile from the subscriber list and checks the availability of the Internet, if there is an Internet connection, it works on the IoT stack to upload the packets to the server by using CoAP or MQTT protocol over Wi-Fi or 3G as in Fig. 3, or if there is no Internet connection, it uses the BSS to forward packets to the nearby mobile, such as in Fig. 4. Figure 5 shows the BSS area and mechanism of work. In transport layer, we propose a protocol called UDP-M to be compatible with the BSS protocol and with a maximum payload of 240 bytes. Fig. 3 IoT stack (online)

Fig. 4 BSS protocol stack (offline)

Application

COAP/ MQTT

Transport

TCP/UDP

Network

IPv6/ IPv4

Adaption

6LoWPAN

Data link

IEEE 802.11

Physical

IEEE 802.11

Application layer

BSS

Transport layer

UDP - M

Data link

IEEE 802.15.1

Physical

IEEE 802.15.1

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Fig. 5 BSS in MCS area

To estimate the time of vehicles or people to stay in direct communication when moving at different velocities, acceleration and different directions, also the size of the data that can be transferred during this time, the proposed models for these as in the following Sect. 5.

6 Mathematical Models for Direct Communication Time Analysis When dealing with MCS area, where there are people or vehicles mobility, so it is important to calculate the time of mobile nodes to stay in direct communication, we considered into account that people or vehicles are moving in different situations in terms of velocity, acceleration, distance, and direction. Figure 6 displays the visualize of movement for two mobile nodes.

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Fig. 6 Visualize of movement for two mobile nodes

Let (X m , Y m ), V m , and Am are the position, velocity, and acceleration of the mobile m at moment (t 0 ), respectively. (X n , Y n ), V n , and An are the position, velocity, and acceleration of the mobile n at moment (t_0), respectively. (X m  , Y m  ) and (X n  , Y n  ) are the position of mobile m and n at moment (t 1 ), respectively, d is the distance between two mobile (The maximum range of Bluetooth). Lemma 1 When two mobiles move, there will be a satisfying time for them to stay in direct communication over Bluetooth; thus, there is efficient amount of data that will be exchange between them taking into account, in this case, the same acceleration for m and n. Prove 1 We assume the mobile m and n have the same acceleration, in other words, the acceleration is same during the communication period, and the time for mobile m and n to stay in direct communication is formulated as following: 

 1 X m , Ym − (X m , Ym ) = Amt 2 + V mt 2 

   1 X n , Yn − X n , Yn = Ant 2 + V nt 2

(1) (2)

So from (1) and (2) can be written Eq. (3) as: 1 (Am − An )t 2 + (Vm − Vn )t + d − d  = 0 2

(3)

where d = (X m , Ym ) − (X n , Yn ), d  = (X m  , Ym  ) − (X n  , Yn  ). Now, calculate the time to staying in direct communication when same acceleration for mobile m and n as following:    d − d  t= (Vm − Vn )

(4)

where V m = Vn and d  equal to zero because it is the meeting point of the two mobiles.

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Lemma 2 Even if two mobiles move with different acceleration, so there will be a sufficient time to transfer data within the direct communication. Prove 2 We assume mobile m and n have different acceleration and velocity in two cases. Calculate the delta of Eq. (3) for case 1 and case 2 as in following:  = (Vm − Vn )2 − 2(Am − An ) ∗ d

(5)

Case 1 If [(V m > V n ) and (Am > An )] or [(V m < V n ) and (Am < An )]: The maximum time to stay mobile m and n in direct communication, calculate by the following equation: √ −|Vm − Vn | +  t= |Am − An |

(6)

Case 2 If [(V m > V n ) and (Am < An )] or [(V m < V n ) and (Am > An )], where, there are two subcases: Case 2.1 When one mobile node leaves the coverage area of the other nodes before the speeds of them become equal, the maximum time to staying mobile m and n in direct communication is calculated by the following equation: √ |Vm − Vn | +  t= |Am − An |

(7)

Case 2.2 We assume same speed for mobile m and mobile n, in this case calculate the time to stay in direct communication to transfer amount of data between them as following: The time to stay mobile m and n in direct communication is t + t  , where t = t 1 − t 0 time when the speed of one is equal or inferior to the other. t  = t 2 − t 1 time when the speed of one is exceed than of the other. t=

|Vm − Vn | |Am − An |

1 (Am − An )t 2 + d − d  = 0 2

(8) (9)

Prediction Communication Time and Data Size …

√ 

t =

457

−2(Am − An ) ∗ d |Am − An |

(10)

Hence (Vm − Vn ) t +t = + |Am − An | 

√ −2(Am − An ) ∗ d |Am − An |

(11)

To more clarify: < Speed of n eq. (3.15)

If speed of m

When dealing with the different movement directions and made an angle between them, we presented the following equations to calculate the time in different angles and in condition 0◦ < ∅ < 90◦ . Figure 7 shows the movement and angle formed between mobile m and n. For mobile m  1 Amt 2 + V mt cos ∅ 2   1 2 Amt + V mt sin ∅ − Ym ) = 2 

(X m  − X m ) =

(12)

(Ym 

(13)

Fig. 7 Movement and angle formed between mobile m and n.

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For mobile n  1 Ant 2 + V nt cos ∅ 2   1 Ant 2 + V nt sin ∅ (Yn  _Yn ) = 2 

(X n  − X n ) =

(14) (15)

So from (12) to (15) we get the equations: 

   1 1 Amt 2 + V mt cos ∅ + Amt 2 + V mt sin ∅ 2 2 (16)     1 1 Ant 2 + V nt cos ∅ + Ant 2 + V nt sin ∅ (17) (X n  , Yn  ) − (X n − Yn ) = 2 2 (X m  , Ym  ) − (X m , Ym ) =

From (16) and (17), we represented the time t, during which the distance between vehicles m and n will be |X m  − X n  | on the X-axis and |Y m  − Y n  | on the Y-axis, by the following equation 

 1 2 (Am − An)t + (V m − V n)t cos ∅ 2   1 2 (Am − An )t + (Vm − Vn )t sin ∅ + k − k  = 0 + 2

(18)

where k = (Xm , Ym ) − (Xn , Yn ), k = (Xm , Ym ) − (Xn , Yn ). Lemma 3 In case the two mobiles were not moving in a parallel way, it could calculate an efficient time to transfer data when they move with the same acceleration. Prove 3 We assume that the mobile m and n have same acceleration, in this case, can calculate the time to stay mobile m and n in direct communication as following: t=

k − k (Vm − Vn ) ∗ (cos ∅ + sin ∅)

(19)

where V m = V n . Lemma 4 There are nodes moving in the MCS environment with different velocity and acceleration, so that the time they stay in direct communication determines the size of the data to be transferred. Prove 4 We assume mobile m and n have different acceleration in two cases. Calculate the delta of Eq. (23) for case 1 and case 2 as in following:  = [(Vm − Vn ) ∗ (cos ∅ + sin ∅)]2 − 2(Am − An ) ∗ (cos ∅ + sin ∅) ∗ (k − k  ) (20)

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Case 1 If [(V m > V n ) and (Am > An )] or [(V m < V n ) and (Am < An )]: The maximum time to stay mobile m and n in direct communication is calculated by the following equation: √ −|Vm − Vn | ∗ (cos ∅ + sin ∅) +  t= |Am − An | ∗ (cos ∅ + sin ∅)

(21)

Case 2 If [(V m > V n ) and (Am < An )] or [(V m < V n ) and (Am > An )], where, there are two subcases: Case 2.1 When one mobile node leaves the coverage area of the other nodes before the speeds of them become equal, the maximum time to stay mobile m and n in direct communication is calculated by the following equation: t=

√ |Vm − Vn | ∗ (cos ∅ + sin ∅) +  |Am − An | ∗ (cos ∅ + sin ∅)

(22)

Case 2.2 The two mobiles stay in direct communication when they move with the same speed and when they move at a specific angle where there is a period of time to transfer the amount of data between them. In this case, we calculate the time as follows: t = t 1 − t 0 time when the speed of one is equal or inferior to the other. t  = t 2 − t 1 time when the speed of one is exceeded than of the other. The time to stay mobile m and n in direct communication is t + t  . |Vm − Vn |∗(cos ∅ + sin ∅) |Am − An |∗(cos ∅ + sin ∅)     1 1 (Am − An )t 2 cos ∅ + (Am − An )t 2 sin ∅ + k  − k  = 0 2 2 t=

(23) (24)

where k  = (X m  , Ym  ) − (X n  , Yn  )). Hence √ −2(Am − An ) ∗ (cos ∅ + sin ∅) ∗ (k  − k  )  (25) t = (Am − An ) ∗ (cos ∅ + sin ∅) √ |Vm − Vn | ∗ (cos ∅ + sin ∅) −2(Am − An ) ∗ (cos ∅ + sin ∅) ∗ (k  − k  ) + t + t = |Am − An | ∗ (cos ∅ + sin ∅) |Am − An | ∗ (cos ∅ + sin ∅) (26)

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7 Data Packet Delivery Time Over BSS To model time of data packet delivery, we calculate the total time to delivery packet from node to another depending on following equations [16]: Tdelay =

p.size b.rate

(27)

where Tdelay is the transmission time between two nodes, p.size is the size of packet, andb.rate is the bit rate in medium. Pdelay =

d prop p

(28)

where Pdelay the propagation delay, d is the distance between two mobile nodes, and props is the propagation speed in medium. Ttotal = Tdelay + Pdelay

(29)

Let Ttotal is the time consumed to send packet, from node1 to node2. To calculate the number of packets, which can be send in Ttotal , we presented the following equations: Number of packets =

Ttotal time for one packet

(30)

where time for one packet: the time needed to transmit 1 packet over BSS protocol. Latency between two nodes = Number of packets ∗ 0.0025 s

(31)

where 0.0025 s is the time in second needed to transmit one packet over Bluetooth. To estimate the time, which required to upload data to the server over the Internet, we use the following equation: Upload Tim =

Data size IN.s

(32)

To estimate the end-to-end time (from a mobile node to server), the following equations are proposed: Total Latency = (Latency between two nodes * No. hops) +

n−1 i=1

waiting time + upload time

(33)

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where total latency is the end-to-end delay, and latency between two nodes is the time to send data between two nodes. No. hops is the number of hops from first sender until it reaches to server, n is a number of hops, and waiting time: the time between two hops where the packets remain at user before sending to other nodes (note: if sending to node with Internet over one hop, there will be no waiting time). Total time throughput = (Latency between two nodes ∗ No. hope) + upload time

(34)

where total time throughput is the time to calculate throughput (without waiting time) [42]. Throughput = data size/Total time throughput

(35)

8 Performance Analysis of the Mathematical Models and BSS In this section, we implemented the all proposed equations in Sects. 6 and 7 by using MATLAB. After implementing we got on (108,000) value, each one represents time from different situations such as the change in velocity and same acceleration or vice versa, or both are variable and add to the movement at different directions; from 108,000, we took the (1125) worst cases, and then we took the average time which is 0.6818 s. Figure 8 shows the minimum time and samples. Figure 9 shows the average time. Bluetooth needs 380 µs for one period and has the total packets size (header and payload) is (265 byte), where 251 is the maximum payload size [30] and the time consumed by Bluetooth version 4.2 to send one packet is calculated according to Eq. (29) as follows: Ttotal (265 byte ∗ 8 bit)/(1 Mb/s) + (10 m)/(300, 000, 000 m/s) = 2120 µs + 380 µs = 2500 µs where 265 bytes is the maximum packet size of Bluetooth 4.2 with header, 1 Mb/s is the bit rate of Bluetooth 4.2, 10 m is the average distance between two nodes, and 3 × 〖10〗8 m/s is the propagation speed of light. To calculate the number of packets sent in this average time, we depend on Eq. (30) as follows: (0.6818 s)/(0.0025 s) = [272.72] = 272 packets, this means that a maximum of 272 packets can be send in 0.6818 s to another mobile node before the connection is interrupted, where 0.0025 s is the time that needed to send one packet.

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Fig. 8 Minimum time and samples

Fig. 9 Average or mean time

A. M. A. Al-muqarm and F. Rabee

yes

yes

Yes ( two areas)

Abbas and Fan, (2018) ,[15]

Mohamed Nabil et al, (2019)[16]

Furkan Rabee, (2018) [43]

online /offline

offline

offline

online (gateway)

no

online /offline

yes

Namboordiri and Gao, (2007), [14]

offline

Our proposal

yes

Taleb and et al, (2007 ),[12]

offline

offline

yes

Hamid. Menouar and et al, (2007),[11]

offline - online

Kalyani Sahoo and no et al, (2019) [44]

Routing mechanism (Source –destination)

Researcher

Comparison between our proposal and other studies

Yes (accurate)

yes

Yes

no

no

Yes (not accurate)

no

no

Wireless technology yes/no

Yes

not

not

Considered

not

not

Yes ( not accurate )

Yes ( not accurate )

Considered or not

Modeled

not

not

Modeled

not

not

not

not

modeled or not

Data packet delivery time

yes

yes

Yes

no

Yes (Not accurate)

no

no

no

Power consumption

yes

no

no

no

no

no

no

no

App.

Yes , in( x-axis and y-axis) ( different direction)

Not calculate

Not calculate

Yes , only in x-axis ( parallel direction)

Not calculate

Not calculate

Not calculate

Not calculate

direct communication time

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9 Comparison Between our Proposal and other Studies This section comparison between the proposed mathematical models and BSS with other studies as explained in Table 1.

10 Conclusion We can conclude that our protocol is designed as a solution to the problem of unavailable Internet in MCS area, in addition to taking into account the limited energy in the smartphones of volunteers through the design of a protocol that works in the application layer for the Bluetooth protocol to support the process of transmission and receiving data for more than one hop, this is not supported by traditional Bluetooth, models have been presented to estimate the time of the participants staying in direct communication and predict the amount of data that can be transferred during this time, the results show that our protocol can work in areas without Internet and solve the problem of the inability of Bluetooth to communicate for more than one hop, where it has an acceptable average delay, high throughput in addition to low-power consumption.

References 1. Wang L, Zhang D, Yan Z, Xiong H, Xie B (2015) EffSense: a novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading. IEEE Trans Syst Man Cybern Syst 45(12):1549–1563 2. Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150 3. Habibzadeh H, Qin Z, Soyata T, Kantarci B (2017) Large-scale distributed dedicated- and non-dedicated smart city sensing systems. IEEE Sens J 17:7649–7658 4. Habibzadeh H, Boggio-Dandry A, Qin Z, Soyata T, Kantarci B, Mouftah HT (2018) Soft sensing in smart cities: handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Commun Mag 56:78–86 5. Yaqoob I, Hashem IAT, Ahmed A, Kazmi SA, Hong CS (2019) Internet of Things forensics: recent advances, taxonomy, requirements, and open challenges. Futur Gener Comput Syst 92:265–275 6. da Cruz MA, Rodrigues JJP, Al-Muhtadi J, Korotaev VV, de Albuquerque VHC (2018) A reference model for Internet of Things middleware. IEEE Internet Things J 5(2):871–883 7. Fiandrino C, Capponi A, Cacciatore G, Kliazovich D, Sorger U, Bouvry P, Giordano S (2017) Crowdsensim: a simulation platform for mobile crowdsensing in realistic urban environments. IEEE Access 5:3490–3503 8. Silva M, Signoretti G, Oliveira J, Silva I, Costa D (2019) A Crowdsensing platform for monitoring of vehicular emissions: a smart city perspective. Fut Internet 11(1):13 9. Abbas Z, Yoon W (2015) A survey on energy conserving mechanisms for the Internet of Things: wireless networking aspects. Sensors 15(10):24818–24847 10. Mikhaylov K, Tervonen J (2013) Multihop data transfer service for bluetooth low energy. In: 2013 13th international conference on ITS telecommunications (ITST). IEEE, pp 319–324

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11. Menouar H, Lenardi M, Filali F (2005) A movement prediction based routing protocol for vehicle-to-vehicle communications. In: Proceedings of V2VCOM, San Diego, CA, USA 12. Taleb T, Sakhaee E, Jamalipour A, Hashimoto K, Kato N, Nemoto Y (2007) A stable routing protocol to support ITS services in VANET networks. IEEE Trans Veh Technol 56(6):3337– 3347 13. Rabee F, Al-Haboobi A, Nsaif MR (2019) Parallel three-way handshaking route in mobile crowd sensing (PT-MCS). J Eng Appl Sci 14(10):3200–3209 14. Namboordiri V, Gao L (2007) Prediction-based routing for vehicular ad hoc networks. IEEE Trans Veh Technol 56(4):2332–2345 15. Abbas F, Fan P (2018) Clustering-based reliable low-latency routing scheme using ACO method for vehicular networks. Veh Commun 12:66–74 16. Nabil M, Hajami A, Haqiq A (2019) Predicting the route of the longest lifetime and the data packet delivery time between two vehicles in VANET. Mobile Inf Syst 17. Ali Al-muqarm AM, Rabee F (2019) IoT technologies for mobile crowd sensing in smart cities. J Commun 14(8):745–757 18. Ashton K (2009) That ‘Internet of Things thing. RFID J 22(7):97–114 19. Rose K, Eldridge S, Chapin L (2015) The Internet of Things: an overview 20. Aggarwal R, Lal Das M (2012) RFID security in the context of Internet of Things. In: First international conference on security of Internet of Things, Kerala, pp 17–19, 51–56 21. Silva BN, Khan M, Han K (2017) Internet of Things: a comprehensive review of enabling technologies, architecture, and challenges. IETE Techn Rev 1–16 22. Wang Z, Zhang L, Zheng Z, Wang J (2018) Energy balancing RPL protocol with multipath for wireless sensor networks. Peer-To-Peer Netw Appl 11(5):1085–1100 23. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49:32–39 24. Zhang D, Wang L, Xiong H, Guo B (2014) 4W1H in mobile crowd sensing. IEEE Commun Mag 52(8):42–48 25. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39 26. Rana RK, Chou CT, Kanhere SS, Bulusu N, Hu W (2010) Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks. ACM, pp 105–116 27. Collotta M, Pau G, Talty T, Tonguz OK (2018) Bluetooth 5: A concrete step forward twoard the IoT. IEEE Commun Mag 56(7):125–131 28. Sarkar SK, Basavaraju TG, Puttamadappa C (2016) Ad hoc mobile wireless networks: principles, protocols, and applications. CRC Press 29. Madakam S, Ramaswamy R, Tripathi S (2015) Internet of Things (IoT): a literature review. J Comput Commun 3(05):164 30. Bluetooth, last visited: 20197/07/10. https://www.bluetooth.com/blog/exploring-bluetooth-5how-fast-can-it-be/ 31. Skoˇcir B (2018) Multi-hop communication in Bluetooth Low Energy ad-hoc wireless sensor network. Informacije MIDEM 48(2):85–96 32. Raza S, Misra P, He Z, Voigt T (2015) Bluetooth smart: an enabling technology for the Internet of Things. In 2015 IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 155–162 33. Thomas D, Wilkie E, Irvine J (2016) Comparison of power consumption of Wi-Fi inbuilt Internet of Things device with bluetooth low energy. Intl J Comput Electr Autom Control Inf Eng 10(10):1837–1840 34. Jawad H, Nordin R, Gharghan S, Jawad A, Ismail M (2017) Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors 17(8):1781 35. Qadir Z, Al-Turjman F, Khan MA, Nesimoglu T (2018) ZIGBEE based time and energy efficient smart parking system using IOT. In: 2018 18th Mediterranean microwave symposium (MMS). IEEE, pp 295–298 36. Reiter G (2014) Wireless connectivity for the Internet of Things. Europe 433, 868 MHz

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37. Ding J, Li TR, Chen XL (2018) The application of Wifi technology in smart home. J Phys Conf Ser 1061(1):012010. IOP Publishing 38. Lamkimel M, Naja N, Jamali A, Yahyaoui A (2018) The Internet of Things: overview of the essential elements and the new enabling technology 6LoWPAN. In: 2018 IEEE international conference on technology management, operations and decisions (ICTMOD). IEEE, pp 142– 147 39. Singh SM, Advocate ER (2016) Broadband over power lines a white paper. State of New Jersey, Division of the Ratepayer Advocate, NJ 40. Gray J, Banhazi TM, Kist AA (2017) Wireless data management system for environmental monitoring in livestock buildings. Inf Process Agric 4(1):1–17 41. Ojha T, Misra S, Raghuwanshi NS (2015) Wireless sensor networks for agriculture: The stateof-the-art in practice and future challenges. Comput Electron Agric 118:66–84 42. Charles AJ, Kalavathi P (2018) QoS measurement of RPL using Cooja simulator and Wireshark network analyser

JOJO—A Social Media Application with a Live Map Interface to Advance Social Security Abhishek Pughazhendhi, Sri Balaji Muruganandam, and Sridevi B.

Abstract There are a lot of women’s security applications similar to this which are available on the Internet, having the facility which is limited only to send alert messages to a set of people or to the nearest police station which will not be effective if the person receiving the alert message is not available to respond. JOJO gives the power to its every registered user to help the person who is under abuse, attack, ill-treatment (women, transgender and even men) or whosoever in danger. The application can be used in two ways. It can be used by the person who is in danger to send alert messages, and it can be used by the people who have always wanted to help but did not get the opportunity or have enough details. This application can be triggered by using the hardware device which could either be connected with the user’s mobile through Bluetooth or a wired network, and it works in coordination with the application. The application is triggered immediately either through the hardware or by shaking the phone vigorously. The application has a map in which the location of every user who has installed this app will be displayed in real time using a Marker, and when the victim triggers the app, the colour of the marker changes, and continuous vibrations are triggered in every available user’s mobile until they open the notification. The app redirects to Google Maps which provides the route to reach the victim. The main objective of JOJO is to provide the power of helping a victim to the general public as it increases the chance of the victim being saved exponentially. As the number of users increases, the chances of a victim being saved increase tremendously. Apart from this, JOJO is a full-scale social media application with a lot of engaging features such as the “Best Guardian” and Chat Box. Keywords Social media application · Live map interface · Victim protection · Large-scale audience · Social security

A. Pughazhendhi (B) · S. B. Muruganandam · Sridevi B. Department of Electronics and Communication, Velammal Institute of Technology, Panchetti, Chennai, India Sridevi B. e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_37

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1 Introduction JOJO is a social media application which focuses on connecting people who need help (abuse, harassment and any form of violence) with the people who have always wanted to help. Social medias like Facebook, Instagram and Twitter have billions of users all around the world [1]. They are one of the biggest inventions which humankind has come up with. They have made communication and interaction between people easier than none other invention before. But they often tend to fall on the entertainment and recreational side [2]. People tend to use these mobile applications solely for the purpose of recreation. They exchange messages, post pictures, share comments and express their views [3]. Social media have proven a point that people around the world have always been willing to connect with each other until they have a common interest [4]. Another interesting point which we can never deny is that people crave for attention. Every comment, every picture, every post and opinion which a user shares here are to gain attention. People love when other people notice what they do. People love when they are appreciated for their work. Companies have been profiting from these basic human behaviours for centuries [5]. But this following which social media has can be put to good use.

2 Motivation There are numerous crimes happening around the world. National Crime Records Bureau (NCRB) has mentioned in its report from 2018 that around 80 murders, 289 kidnappings and 91 rapes happen around India each day. Most of these crimes happen very close to our neighbourhood, and we might not even have a clue. In 2017, Delhi was ranked number one in crime rate. Every 1050 people among 100,000 were affected. If the rest of the unaffected population was made aware of the crime at the instant it happened, they could have helped. Instead of relying on the government and the police department who could not keep an eye out on the entire population of a city or a country, JOJO strives to give the power of helping other people to people themselves. This increases the probability of a person being saved [6]. There are a lot of security applications available. But they all concentrate on sending alert messages to a selected number of contacts or to the nearest police station. If this method of approach was successful, we would not be seeing the crime rate increasing day by day. According to RAINN, a renowned American nonprofit anti-sexual assault organization, only 230 out of 1000 sexual assaults are reported to the police, and only five among them get charged. When it comes to robbery, only 619 out of 1000 get reported to the police, and only 20 of them are incarcerated. Some crimes cannot even be traced back to the perpetrator unless he/she is not red handed. People do not report cases because the process of doing so is tedious. Instead, if there is a system to instill feat among the convicts that their crimes will not go unnoticed, the crime rate could go down. Instead of focusing on the aftermath of a crime, JOJO

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hopes to prevent it from ever happening by taking it to the attention of a huge number of people /users.

3 Existing Solutions 3.1 Available Mobile Applications There is an overload of mobile applications available out there labelled as “Women’s Security Applications” [7, 8]. They all serve a common purpose. There are several ways through which a victim can activate them. But the method of activation primarily involves vibrating your phones above a particular threshold or pressing the home button over a fixed number of times. Once the app is activated, it then sends out alert messages either to a limited number of contacts / friends or to a nearby police station [7, 8]. The alert message contains the victim’s location which can be used to reach the location of the victim to help [7]. This approach has a very less rate of success since only a limited number of people are involved (Fig. 1). The social media applications (Eg: Facebook and Instagram) serve no purpose other than recreation. Posting a help request might even take hours to get a reply. Fig. 1 Available/existing solution

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3.2 Toll-Free/Dedicated Helplines Various governments around the world have dedicated helplines which the people can use to call for help when they find themselves in a dangerous situation. United States is known for its 911 emergency service. USA has one of the well-organized emergency action departments. But still, there are a lot of crimes which go unsolved. This is because the crime happens at a very short notice [9]. And it gets difficult to trace the criminals after that.

4 Proposed Methdology/Approach JOJO focuses on giving the chance to save other people to the people themselves and not just to the officials. JOJO is a social media application just like Facebook and Instagram. People can chat, post pictures and have fun. But JOJO has one major addition to it [10]. Every JOJO user is connected to network of other users who are ready to help each other when the need arises. A user first has to install the application in his/her mobile phones. The user then has to allow JOJO to access certain features of the mobile phones such as the contacts, location and Internet. Once all these permissions are granted, the user has to sign up. In the sign-up process, the important credentials of the user will be saved. The mobile number and the email ID of every user will be verified. Once this is done, you become a verified JOJO user. JOJO has a map interface. You can either choose you location marker to be visible right from the start or you can make it visible only when you call for help. You can continue being a regular user by posting photos or chatting with your friends. Whenever a fellow user needs help, he/she would activate the ‘Call for Help’/Alert feature of the application. The method of activation is fully customizable, and you can do this when you sign up to the application. Immediately, when a call for help is registered, the application is going to send alert messages to every user in the nearest vicinity [3]. This is a major advantage which JOJO has. Apart from this, you can also choose to have the alert messages sent to your friends and family and also to the nearest police station [11]. Every nearest user will receive a message which has a map link. It will show them the shortest route through which they can reach you. Unlike other conventional methods, this exponentially increases the chances of a victim being saved since there a lot of people involved in this situation. There are other additional features to motivate and appreciate the people who has the highest number of help rate [12]. The popularity which one can get out of a social media application would also act as a major driving force in motivating people to save each (Fig. 2).

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Fig. 2 Proposed methodology

5 Working 5.1 Sign-up Process JOJO has a straight forward registration/sign-up procedure. Every user has to enter his/her mobile number and Email ID. They will be verified using a one time password (OTP), respectively. Once the OTP verification is done, the users must enter their profile information which includes username, gender, date of birth, address, etc. You will also need to upload your photo with your face clearly visible. The application then requests permission to access your mobile phone’s location, calls and contacts. Once you allow all the required permissions, you will be included into the ‘Guardian Network’. Your location marker will be displayed on the map. You can choose your location marker’s visibility by turning it on only in the cases of danger and emergencies. You will be on standby until you need help or any of your nearest users call out for help. You can access all the features of the application which includes chat box, picture gallery, news feed, etc. You have to select the method of how you would like to activate the application’s major feature which is to send alert messages to all the nearby users when you find yourselves in danger. You can either choose to shake your phone vigorously or press your home button. The number of times your home button needs to be pressed and the vibration intensity is completely customizable. Your user status will be labelled as “Safe” (Fig. 3).

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Fig. 3 Block diagram of sign-up and verification

5.2 Operation—Victim Side A user remains in standby unless he/she finds themselves in danger. In situations like these, the alert feature of JOJO can be activated either by shaking your mobile phone over the selected threshold or by pressing the home button for the selected number of times. You can also activate the application by connecting your mobile to a Bluetooth device (Example: smart watch). Once you manage to activate the application, your user status will change from “Safe” to “Under Danger”. Now regardless of your wish, your location will now be available in the JOJO’s map network and will be marked using a red marker [13]. Every available JOJO user within a close radius who is currently logged in will be receiving an alert message [3]. This message will have all your details including your photo for identification. This message will also have a map link which will direct them to your location using the shortest route. The guardian can either choose to help or decline which is their personal choice. The application will continue to send alert messages to other JOJO users until a group of people chooses to help [14]. Once you receive help/saved from the situation, your status will be changed from “Under Danger” to “Safe”. Your live location will no more be available to other users unless you choose otherwise. You can now choose to provide a good review about the guardian who helped you. This will also provide them scores/points (Fig. 4).

5.3 Operation—Guardian Side The users who are currently labelled as safe are referred to as “Guardians”. Once a user asks for help, every available user within a given radius would receive an alert message. The guardian can either choose to accept or decline the message based on his/her availability. The message will have a map link which will direct the guardian to the location of the victim in the shortest route possible [3]. Once the guardian reaches the location, he/she can identify the victim using the picture and the information provided in their profile. After helping, the guardian can request the

JOJO—A Social Media Application with a Live Map … Fig. 4 Flow chart—operation in the victim end

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victim to provide a review which will help him/her to climb over the scoreboard. Being a social media, JOJO focuses on encouraging other users to save the victims through the scoreboard system. The guardian who helps the greatest number of people within a given period will receive benefits and coupons. They will be visible to all the other users in the newsfeed providing them popularity. Over a period of time, this will motivate more people to join the network and improve the chances of a person being saved (Fig. 5).

6 Application Interface The major focus is to make the application as interesting as possible. Saving people is the major goal, but there are a ton of other features included in this application making it a completely fun social media tool to use. Figure 6 shows you the simulation of how the login screen and the permission tab would look. The application can be installed by following a few easy steps. You can sync JOJO with any of your google or Facebook account. Once you complete the login/sign-up process, you will get access to every feature available in this application. You will be redirected to the newsfeed which is very similar to what you see in other social media. You can see the posts which other users post and you can post some on your own. You can have a conversation with the friends you make. You will also get access to the map page which is the major purpose of this application. Figure 7 shows a simulation of how the map page would look. Every available JOJO user will be connected to this map network. From here, you can easily identify the users who are safe and the ones who need help. You will automatically receive an alert message if a user near your vicinity asks for help. But you can also choose to help far away victims in your own interest. The application will generate revenue only from advertisements. Therefore, the app will not entertain any paid features. Every feature included in this application will be described in the following paragraphs.

7 Features Included If saving people sounded boring, there are a lot of other features which improve your experience. The following points cover all the features which will be included in this mobile application.

JOJO—A Social Media Application with a Live Map … Fig. 5 Flow chart—operation in the guardian end

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Fig. 6 Start-up screen

Fig. 7 Map interface

7.1 Periodic Data Analysis The application keeps track of all the users who are using this application. It tracks the number of crimes in a region by keeping a track on the number of users who have asked for help [1, 2]. Over a period of time, the total number of users who were in danger, the city or the street which has the greatest number of crimes, the people who have helped the highest number of people and a lot of other important statistics will be calculated [1]. The information from the server will be collected and visualized into essential graphs and information. Apart from the statistics, this will be used to improve the user experience. Areas where the highest number of crimes happen will be marked as “Hot Zones” in the map. These are the areas where the guardians can expect the most number of crimes to happen. This will add a game-like feel to the mobile application. With every victim a guardian saves, he earns more points and climbs up the chart and the more popular he/she can become. These reports can also help the respective Police Departments tighten the security in the most vulnerable areas. This can reduce the number of crimes exponentially (Figs. 8 and 9).

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Fig. 8 Graph—location versus number of crimes

Fig. 9 Periodic analysis of the number of crimes

7.2 Frequent Rewards and Recognition JOJO is very similar to its existing counterparts when it comes to its social media features. You can post pictures, chat with the friends you make and voice out your opinions. The more people you save, the more popular you become. Your profile will automatically pop-up on the newsfeed to let other users know. Your points will also raise in the scoreboard. You can redeem these points as coupons to buy products online. This will even motivate the people who has no social involvement to help, at least to gain popularity. Even the most negative and toxic people might want to help others at least for the sake of these rewards and the popularity.

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7.3 Best Guardian The number one user on the scoreboard will be named as the best guardian. The greater the number of people you save the higher your points will get. The guardian with the greatest number of points will hold this position. You will continue to retain the title until another user gains more points on the score board.

7.4 #MeToo Feature People who were victims of domestic abuse or any form of violence can voice out their opinions here. Your opinions will empower people who are currently facing them. People who lost their faith in the justice system can call out for help from the people themselves. This would pave a path to stop domestic violence happening in our neighbourhood which we have no idea about.

7.5 Privacy The profile information and the live location will be safeguarded under all circumstances. The location of a user cannot be viewed by the others until he/she chooses to do so [2]. A user can individually change their user settings and decide whether his/her location and profile information should be made visible or not.

References 1. Cao L (2012) Social security and social welfare data mining: an overview. IEEE Tran Syst Man Cybernet Part C (Appl Rev) 42(6):837–853 2. Tsikerdekis M, Zeadally S (2014) Multiple account identity deception detection in social media using nonverbal behavior. IEEE Trans Inf Forensics Secur 9(8):1311–1321 3. Kim JT, Lee JH, Lee HK, Paik EH (2010) Design and implementation of the location-based personalized social media service. In: 2010 Fifth international conference on internet and web applications and services, pp 116–121. IEEE 4. Monisha M, Mohan PS (2017) A novel IOT based approach to establish an ultra-low power self security system. In: 2017 International conference on innovations in information, embedded and communication systems (ICIIECS). IEEE, pp 1–6, Mar 2017 5. Castillo-Cara M, Mondragón-Ruíz G, Huaranga-Junco E, Antúnez EA, Orozco-Barbosa L (2019) SAVIA: Smart city citizen security application based on fog computing architecture. IEEE Lat Am Trans 17(07):1171–1179 6. Ruman MR, Badhon JK, Saha S (2019) Safety assistant and harassment prevention for women. In: 2019 5th International conference on advances in electrical engineering (ICAEE). IEEE, pp 346–350, Sept 2019

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7. Akash SA, Al-Zihad M, Adhikary T, Razzaque MA, Sharmin A (2016) Hearme: a smart mobile application for mitigating women harassment. In: 2016 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE). IEEE, pp. 87–90, Dec 2016 8. Yarrabothu RS, Thota B (2015) Abhaya: an Android App for the safety of women. In: 2015 annual IEEE India conference (INDICON). IEEE, pp 1–4, Dec 2015 9. Babu VH, Balaji K (2020) Survey on modular multilevel inverter based on various switching modules for harmonic elimination. In: Intelligent computing in engineering. Springer, Singapore, pp 451–458 10. Kumanan T (2020) Link quality and energy-aware metric-based routing strategy in WSNS. In: Intelligent computing in engineering. Springer, Singapore, pp 533–539 11. Manikandan G, Anand M (2020) Radix-2/4 FFT Multiplierless architecture using MBSLS in OFDM applications. In: Intelligent computing in engineering. Springer, Singapore, pp 553–559 12. Sundaram A, Ramesh GP (2020) Investigation of solar based SL-QZSI Fed sensorless control of BLDC motor. In: Intelligent computing in engineering. Springer, Singapore, pp 779–787 13. Snehalatha N, Shiny Angel TS, Amudha S (2016) Remote display access using remote frame buffer and IO streaming. Int J MC Square Sci Res 8(1):23–40 14. Rajesh D (2016) Ch panel based routing scheme for mobile wireless sensor network. Int J MC Square Sci Res 8(1):183–198

Investigation the Receiver Complexity of a Three-Dimensional OCDMA System Based on Different Codes Rasim Azeez Kadhim and Suhad Shakir Jaber

Abstract In this paper, the receivers of three-dimensional optical code division multiple access (3-D OCDMA) systems based on a 3-D perfect difference (3-D PD) code and a 3-D Diluted Perfect Difference/Multi-Diagonal (3-D DPD/MD) code are investigated. The main problem of using 3-D PD code in OCDMA system is the complexity of the receiver where the required number of the Fiber Brag gratings (FBGs) increases with the spectral code length. Also, the number of optical delay lines (ODLs) rises based on temporal code length. The 3-D DPD/MD code which was proposed by the author, to reduce the receiver complexity. The comparison results prove that the 3-D DPD/MD code has lower receiver complexity than the 3-D PD code. The required number of FBGs and ODLs of a 3-D DPD/MD code is constant with the code length because it depends on the basic code length not on the extended length. While it is proportional with the code length for a 3-D PD code. Hence, the 3-D DPD/MD code can reduce the receiver complexity to less than 25% of the 3-D PD system receiver according to the code length.

1 Introduction OCDMA technology has inspired big interest because of the demand increasing for higher data-rates and data security. Three resources are available for spreading the OCDMA codes represented by the wavelength (spectral), time (temporal) and spatial (optical fiber). In the 1-D OCDMA system where single resource is used, the code length increases with the number of subscribers where the accommodation of large number of subscribers needs very large code sequence [1–8]. The 2-D codes by investment of two resources simultaneously were developed to overcome the drawback of 1-D codes [9–13]. Moreover, the 3-D code has been introduced for R. A. Kadhim (B) College of Information Technology, University of Babylon, Hilla, Iraq e-mail: [email protected] S. S. Jaber Computer Center, University of Babylon, Hilla, Iraq © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_38

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further increasing in the code set size [14, 15]. The OCDMA system receiver has single or multiple stages according to the number of dimensions that have been utilized. Hence, the 3-D OCDMA system requires three stages to implement the receiver where each stage is used to detect the optical pulses in a certain dimension. This paper focuses on the receiver complexity of a 3-D OCDMA system based on the 3-D PD and 3-D DPD/MD codes. The organization of this paper is: The receiver construction of a 3-D PD code is presented in Sect. 2. Section 3 presents the receiver structure of the 3-D DPD/MD code system. Section 4 contains the comparison results and discussions. The last section is the conclusions of this work.

2 The 3-D PD Code Receiver Construction The construction of the 3-D PD code depened on the PD code [14]. Let X , Y and Z are three PD codes where X = {x0 , x1 , . . . , x M−1 }, Y = {y0 , y1 , . . . , y N −1 } and Z = {z 0 , z 1 , . . . , z P−1 } with the code lengths M = k12 − k1 + 1, N = k22 −k2 + 1 and P = k32 −k3 + 1 where k1 , k2 and k3 are the code weights of X , Y and Z , respectively. Precisely, X , Y and Z , represent the code of spectral, temporal and spatial domains, respectively. Figure 1 shows the receiver structure, in which it consists of an integrator, four balanced detectors, four correlators and two combiners. The correlators 1 and 3 consist of w2 delay lines while the correlators 2 and 4 consist of N − w2 delay lines based on the temporal code sequence Y and Y respectively. The number of optical delay lines (ODL) is expressed:   1 number of corelators × temporal code length 2   1 ×4× N ODL1 = 2 ODL1 =

(1)

Each balanced detector comprises two circulators, two sets of FBGs and two photodiodes. The number of FBGs depends on the spectral code sequence X and can be calculated as follows: FBG1 = 2 × number of balance detectors × spectral code weight FBG1 = 2 × 4 × k1

(2)

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Fig. 1 Receiver structure of the 3-D PD code system [14]

3 The 3-D DPD/MD Code Construction The construction of the 3-D DPD/MD codes depeneds on the MD code, and the DPD codes that constructed by applying the dilution method on the PD code [15]. A two sets of DPD codes are used for spectral and time domains. Let X and Y are two PD codes where X = {x0 , x1 , . . . , x M−1 } and Y = {y0 , y1 , . . . , y N −1 } with the code lengths M = w12 − w1 + 1, N = w22 −w2 + 1 where w1 and w2 are the weights of codes X and Y , respectively. The diluted sequences of X and Y are X D and Y D that can be constructed by inserting m − 1 and n − 1 zeros after each entry of X and Y , where m and n are the extending factors of X and Y , respectively [12]. The DPD

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codes have the same weights as the original PD codes but the lengths and the sizes are M D = m M, and N D = n N . Then, the groups of codes X De and Y D f for spectral and temporal domains will be generated from X D and Y D by repeated M D − 1, and N D − 1 shifting to the right, respectively. In addition, let Z l = zl,0 , zl,1 , . . . , zl,P−1 is a set of MD code uses for the spatial domain with code length of P = PZ w3 , where PZ and w3 are the code size and weight, respectively. The receiver structure is illustrated in Fig. 2, which comprises one combiner, two correlator, two balanced detectors and an integrator. The correlator 1 consists of w2 delay lines and the correlator 2 consist of N D /n − w2 delay lines based on the code sequence Y D f and Y D f respectively. The number of optical delay lines (ODL) is expressed as:   1 number of corelators × temporal code length/n 2   1 ND ODL2 = ×2× 2 n ODL2 =

(3)

The balanced detector consists of four circulators, four sets of FBGs, two photodiodes and an integrator. The set of FBGs depends on X De and X De . The FBGs number can be calculated as follows:

Fig. 2 Receiver structure of the 3-D DPD/MD code system [15]

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FBG2 = number of balance detectors × spectral code length/m MD FBG2 = 2 × m

(4)

4 Comparison Results The comparison between the 3-D PD/MD code and the 3-D PD code in terms of number of the required FBGs and optical delay lines are shown in Figs. 3 and 4. Figure 3 presents the relationship of the number of FBGs with the spectral code length according to Eqs. 1 and 3. The basic spectral code length of a 3-D DPD/MD code is 3. For the 3-D PD code, the number of FBGs rapidly increases with the increasing of the length of the spectral code due to the necessity of increasing the weight of the spectral code with the code length. While, the FBGs number of the 3-D DPD/MD code is fixed at a certain value because the increasing of the spectral code length doesn’t need to increase the weight. Moreover, Fig. 4 illustrates the relation between the temporal code length and the number of optical delay lines when the basic temporal code length of a 3-D DPD/MD code is 3. Obviously, the number of optical delay lines of a 3-D DPD/MD code is fixed because it depends on the basic temporal code length which is fixed. But, the number of optical delay lines of a 3-D PD code is linearly proportional with the length of temporal code. Hence, the 3-D DPD/MD code achieves much more reduction in the required number of FBGs 100 3-D PD code 3-D DPD/MD code

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Fig. 4 The number of optical delay lines versus the temporal code length of 3-D DPD/MD and 3-D PD codes

and ODLs to implement the receiver especially when the system uses large code length. Also, the number of FBG2 is divided by FBG1 and ODL2 by ODL1 to show how much reduction in complexity. In Fig. 5, the relationship between the spectral 0.5 0.45 0.4

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Fig. 6 The ODL2/ODL1 versus the temporal code length

code length and FBG2/FBG1 is drawn. Clearly, The FBG2/FBG1 decreases with the increasing of the spectral code length in which the required number of FBGs based on the 3-D DPD/MD code is reduced to less than 25% compared to the 3-D DP code. In the same manner, Fig. 6 illustrates the relation between the ODL2/ODL1 and the temporal code length where the increasing of temporal code length leads to decreasing the ODL2/ODL1. This figure shows that the using of 3-D DPD/MD code reduces the number of ODLs under 20% in comparison to the using of the 3-D PD code.

5 Conclusion The receiver complexity of an OCDMA systems is one of the important issues that degrades the performance because it increases the required power, the cost and the receiver size. In this Paper, the receiver of the 3-D DPD/MD code were compared with the 3-D PD code. The comparison results reveal that the construction of the OCDMA system based on the 3-D DPD/MD code is simpler than the system of the 3-D PD code in which the receiver complexity was reduced to under 25%. Hence, the 3-D DPD/MD code is more reliable in practical implementation.

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References 1. Chung FRK, Salehi J, Wei VK (1989) Optical orthogonal codes: design, analysis and applications. IEEE Trans Inf Theory 35(3):595–604 2. Yang G-C, Kwong WC (1995) Performance analysis of optical CDMA with prime codes. Electron Lett 31(7):569–570 3. Weng CS, Wu J (2001) Perfect difference codes for synchronous fiber-optic CDMA communication systems. J Light Technol 19(2):186–194. https://doi.org/10.1109/50.917875 4. Zaccarin D, Kavehrad M (1993) An optical CDMA system based on spectral encoding of LED. Photonics Technol Lett IEEE 5(4):479–482 5. Kavehrad M, Zaccarin D (1995) Optical code-division-multiplexed systems based on spectral encoding of noncoherent sources. Light Technol J 13(3):534–545 6. Wei Z, Shalaby HMH, Ghafouri-Shiraz H (2001) Modified quadratic congruence codes for fiber Bragg-grating-based spectral-amplitude-coding optical CDMA systems. Light Technol J 19(9):1274–1281 7. Wei Z, Ghafouri-Shiraz H (2002) Unipolar codes with ideal in-phase cross-correlation for spectral amplitude-coding optical CDMA systems. Commun IEEE Trans 50(8):1209–1212 8. Wei Z, Ghafouri-Shiraz H (2002) Codes for spectral-amplitude-coding optical CDMA systems. J Light Technol 20(8):1284 9. Yang C-C, Huang J-F (2003) Two-dimensional M-matrices coding in spatial/frequency optical CDMA networks. Photon Technol Lett IEEE 15(1):168–170 10. Lin CH, Wu J, Yang CL (2005) Noncoherent spatial/spectral optical CDMA system with twodimensional perfect difference codes. J Light Technol 23(12):3966–3980. https://doi.org/10. 1109/JLT.2005.859407 11. Yin H, Ma L, Li H, Zhu L (2009) A new family of 2D wavelength/time codes with large cardinality for incoherent spectral amplitude coding OCDMA networks and analysis of its performance. Photon Netw Commun 19(2):204–211. https://doi.org/10.1007/s11107-0090225-7 12. Yeh B-C, Lin C-H, Yang C-L, Wu J (2009) Noncoherent spectral/spatial optical CDMA system using 2-D diluted perfect difference codes. J Light Technol 27(13):2420–2432 13. Kadhim RA, Fadhil HA, Aljunid SA, Razalli MS (2014) A new two dimensional spectral/spatial multi-diagonal code for noncoherent optical code division multiple access (OCDMA) systems. Opt Commun 329. https://doi.org/10.1016/j.optcom.2014.04.082 14. Yeh B, Lin C, Wu J, Fellow L (2009) Noncoherent spectral/time/spatial optical CDMA system using 3-D perfect difference codes. J Lightwave Technol 27(6):744–759 15. Kadhim RA, Fadhil HA, Aljunid SA, Razalli MS (2015) Development of a three dimensional code based on diluted perfect difference and multi-diagonal codes for OCDMA systems. Int J Appl Eng Res 10(18)

Health Cloud—Health Care as a Service D. Muthukumaran, K. Umapathy, and S. Omkumar

Abstract The healthcare sector has moved from computerized procedures and virtual medical treatment since digital technologies entered into the world of health care to more than just Hospital Information Systems (HIS), Electronic Medical Records (EMR). Through the developments of computer technologies, health care in all forms of industries becomes rapidly online, collaboratively, more patientcentered and data-driven. This vast volume of data produced and the numerous healthcare facilities accessible to patients cannot be protected by conventional technical technology in the healthcare sector. Cloud Storage is an increasingly growing phenomenon involving many providers all delivered via a pay-as-you-go format on demand over the Internet. It aims to speed up the delivery of apps and rising costs. Online infrastructure will play a key role in controlling the movement toward digital data development and medical services everywhere possible. Cloud infrastructure will also provide a major difference to reducing the expense of change of health care, maximizing services and generating a new age in healthcare creativity. The report quickly addresses some of the problems that the insurance sector faces with digital technology. This outlines a framework designed to deliver different cloudbased health resources. The article also describes the distribution of a service that is offered within the mentioned framework. Keywords Health care as a service · Health care · Cloud computing · Image processing · Cloud services

1 Introduction Over the last decade, the volume of digital data collected by health services has dramatically risen with the advancements of information and communications technology. The PACS storage requirements are observed to be growing by 20–40% per year and combined PACS storage requirements will triple every four to five D. Muthukumaran (B) · K. Umapathy · S. Omkumar Department of ECE, SCSVMV, Kanchipuram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_39

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years. PACS storage requirements are estimated to be rising. Over the last 15 years, the amount of pictures requested by doctors has increased. Around 1995 and 2004, the number of MRI units decreased by more than 50%. The number of MRI units decreased by 50% [1]. Because rich health care and patient data lead to better and educated treatment for patients, it is not prosaic to take charge of such details. The created data must be preserved and retained for many years, depending for instance on the country’s medical regulations. HIPAA allows healthcare facilities to preserve the documentation of patient services for six years upon discharge [2]. Health Insurance Portability and Accountability Act (HIPAA) This increasing volume of data cannot be handled over the longer term with the minimal network setup available in a hospital. However, routine data retention, recovery and access procedures for incidents are included in the Clinical Information Protection Guidelines as an emergency procedure [3]. The burden of hospital IT departments can be increased by these stringent needs. Health abundance of knowledge generated is a daunting challenge for healthcare institutions. The exchange of patient evidence and its openness from everywhere is another obstacle the health sector is confronting [4]. Legislative proposals as part of the American Recovery and Reinvestment Act, such as the Health Information Technology for Economic and Clinical Health Act (HITECH), encourage the advancement of the sharing of health information (HIE) to facilitate collaboration efforts between healthcare agencies. By the exchange of knowledge, the collaboration of disparate provider systems, these partnership initiatives will help minimize duplication of medical data, increase reliability and quality of treatment as well as enhance operational performance of the hospital systems that underwent merger. The existing PACS systems, however, produce siloed data and can only be exchanged between related PACS systems, respectively. Each modality in a hospital system could have its own PACS system and no guarantee is made that these systems could be mutually compatible [5]. Except in one company or agency, knowledge is challenging to exchange if several PACS have been purchased from different vendors. Thanks to the absence of fast and safe computer exposure from beyond the corporate barrier, conventional hospital IT structures become much tougher to exchange knowledge through healthcare networks. The obstacle also resides in the reality that patient information is accessible anywhere. With a legacy of hospital IT framework, it will take a significant effort by IT management to ensure smooth access to medical data through a range of devices such as personal computers, tablets and cell phones [6]. The use of cloud infrastructure technology in a healthcare system will easily address all the challenges listed in this segment. Cloud infrastructure is a paradigm for allowing the availability of universal, accessible on-demand network access to a common pool of infrastructure services that are set up in setup (e.g., networks, servers, storage, software and service). The article discusses an effort to create a healthcare network, Health Cloud, which will leverage the power of the web, explains the various healthcare processes involved in this framework and how this program makes use of a cloud infrastructure. Detailed details regarding the application of the service provided (image processing) [7].

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2 Related Work Within this paper, we would present the idea of the e-Health Cloud, where many of its stakeholders will be outlined. They would also propose the development of an eHealth ecosystem and explain a number of problems that threaten the implementation of the e-Health Cloud [8]. In specific they do not discuss patient network protection, a critical concern for the general security of e-health platforms and refer out many gaps of existing e-health implementations and standards. In order to fill this void, we have an architecture for protection in e-health infrastructure to create privacy [1] domains. Our approach provides consumer application protection and integrates it effectively with network security principles. Implementation of a cloud network for the collection, uploading and retrieval through Cloud Computing of electronic health info. The mobile app is built with the Android Software of Google and manages [9] patient health information and medical photos (DICOM help and JPEG2000 code). Amazon’s S3 cloud provider has tested the built framework. The latest understanding of cloud storage technologies as a significant landmark in the growth of medical storage. Given their immense promise, there are weaknesses in our perception of how people evaluate and plan to fight transition in the cloud. The research creates an interactive model, which combines technology recognition and the status [10] quo view, to clarify the purpose and expectation for healthcare practitioners to utilize the medical cloud service. This chapter addresses the challenges of privacy of health data and retains the conditions of privacy. In relation to the advantages of cloud infrastructure in health care, model delivery of cloud computing is often explored from a [11] healthcare network viewpoint. In addition, other approaches that have been introduced lately are addressed in depth to reduce the privacy issues and satisfy privacy requirements. The report discusses the benefits and disadvantages of utilizing health services across the cloud. The debates about the numerous healthcare clouds and frameworks that the healthcare sector should adopt are also implemented. It [12] also demonstrates software and leading SaaS technology vendors to healthcare professionals. Why businesses have used SaaS with a reliable corporate wellness platform. The key aim of the special issue and a short outline is included in this article. The condition in which EMRs are adopted is then known. Emerging technology innovations that have a significant effect on the [13] healthcare system are then introduced. This involves health sensing for the gathering of patient data, medical data processing and usage for precise diagnosis and prediction. The next subject is cloud infrastructure, which can provide healthcare resources flexible and cost-effective. This research provides an important model to illustrate the purpose and desire of patients to avoid the usage of health cloud resources. A field study in Taiwan has been undertaken to gather patient data, and the data has been analyzed using a systemic equation model. The findings indicate that patient aversion to the usage of the health cloud [14] stems from sucking prices, reluctance, perceived interest, change prices and vulnerability. Quality expectancy, commitment expectancy, social effect and ease of use indicate that the desire of the user to utilize the health cloud is strongly

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and specifically influenced. The creation of club facilities in the healthcare sector not only promotes an exchange of patient paper information between hospitals and clinics. Furthermore, [15] the transition to the digital system alleviates the daunting challenges of network maintenance by health institutions. However, it can still include significant risks to the protection of the patient’s records on third-party Web sites.

3 Proposed Method Health Cloud operates on a service-oriented model that provides a variety of healthcare practices. The software may be provided on premises, e.g., from a business network or a cloud platform. Both on premise or in cloud, such providers should be able to access their cloud resources, irrespective of where the providers are hosted. The tools provided include medical data collection and indexing, imaging, production of papers, charts and pattern analysis. Both such systems provide identification and access management capabilities and are able to leverage the current identification and security framework of a facility, such as LDAP, Active Directory or another program.

3.1 Storage Archival and Indexing Services The healthcare records are usually housed in complex database facilities or PACS. Small storages are required for independent PACS units. New and changing ways create complex images that have enormous storage needs, and legacy systems will be short of meeting them in the long run, with the improvements in medical imaging technology. The efficiency of data recovery from a standalone PACS may also be deteriorating as the size of the data increases. Cloud PACS provides the chance to solve storage capacity constraint with a flexible storage system dependent on requests. Through indexing cloud safety results, the retrieval of results will be optimized. Health Cloud can store data into popular cloud vendors like blob storage for Windows Azure; the Simple Storage Service of the Amazon Web Service and can be expanded easily to support any cloud vendor. It can also store relationship data in either SQL Azure or Amazon RDS. The processed data is indexed to maximize the recovery. Data recovery and archiving can be programmed conveniently and can be done at specified intervals. The program is also a choice for geo-replication of medical data as an evacuation response in cases of disasters. The service indexes the data that is stored in cloud and this helps in a faster retrieval of data. The service covers healthcare requirements, such as the HIPAA Health Information Technology Act (HITECH) and the Federal Information Security Management Act (FISMA), by means of the combination of security measures. The Service provides for the implementation of healthcare regulatory requirements. Health data

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protection is ensured by anonymization of patient records with the usage of thirdparty encryption software and services. The secure usage of Cloud protection technology assures the health of medical data at rest while processed in cloud. For their services, several cloud vendors are dedicated to implementing specific medical practices. In order to create administrative controls of records, the service has access control features such as authentication and authorization. The program also promotes simple data exchange between different hospital organizations and different patient groups. In order to maintain safe data exchange, the company utilizes a range of encryption and tokenization methods such as OpenSSL and SAML.

3.2 Image Processing Services Healthcare systems are generally used only for the retrieval of images. These photos are taken from a mode and are manually analyzed using numerous algorithms to determine the patient’s medical condition. This method is highly calibrated and the effects of the calculation are required to return within a very short time. Many healthcare providers have their own proprietary algorithms, and clinics are heavily charged for licenses, which significantly increase operational costs. The computerintensive cloud algorithms as utilities are an invaluable approach. Those facilities can instead be used on a pay-per-use basis for every health program or hospital. That often reduces the expense of providing high-end computer-driven healthcare devices and the requisite facilities.

3.3 Reporting Services Reporting systems offer a simple method for exchanging patient records from various agencies in a community system and across separate providers of health care. This leads to significantly enhancing patient safety, growing medical data duplication, lowering treatment costs and increased coordination between healthcare professionals. Pay for usage may be used with the monitoring process (Fig. 1).

3.4 Charting and Trend Analysis of Healthcare Data Data are vast and diverse that are gathered every day in a hospital. The diagrams and research tools lead to aggregating this data and to interpreting it in different ways. Such programs help to track a patient’s safety, increase medication efficacy, and appropriate medical care actions in sensitive circumstances. The analytical service provides an array of knowledge that can be used for many applications, such as scientific studies, preventive health treatment, enhanced public health practices, and

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Fig. 1 Overall structure of health cloud

so on. This form of research involves computational processing and on-demand flexible computer services are supported by cloud infrastructure.

4 Result and Discussion The image treatment facility of Health Cloud enables numerous image treatment operations on every Cloud-Speicher medical file. This is a cloud computing operation. C++ software is designed utilizing the image processing framework. The services are run on a cloud virtual machine instance from Microsoft Windows Server 2008 R2. Azure Storage blob storage resources allow the user to download images. The company also maintains a store for many algorithms for image processing. The service indexes and caches the images processed for quicker replies. It ensures secure transfer of data to its customers by means of a contact protocol TCP/HTTPS and has encryption mechanisms such as SSL. The program facilitates various identity security methods such as username/password and SAML tokens. The management of the cloud image processing software ensures its stability and the cloud architecture will be utilized to scale up to demand levels (Fig. 2).

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Fig. 2 Structure of image processing services

The code is created with C++, but the language or methodology of programming does not restrict the customer’s capacity to send orders to the server in the form of a plain text or JSON. The user apps for these services may be accessible from different devices including a Mac, a laptop, a handheld telephone, etc. The user generates a command string with the name of the image file and the picture frames, the processing method, the protected token/X509 certificate for safe communications as demanded by the picture processor. This command is provided to the server to search whether the picture file is local by searching for the indexed entries. The picture is taken off the azure storage blob if it is not contained in the local cache. For the processing procedure of the image and the required algorithm, the client must define and process the frame number submitted. For confidentiality purposes, the resultant picture frame is anonymized. The frame is then compressed and authenticated by a

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waste-free technique and then sent over the TCP/HTTPS channel as a JSON (BSON) [16] binary tube. The clients are presented with the binary JSON, the text is decrypted, the picture frame decompressed and decompressed and the users displayed the file. The consumer program has various operations like pan, zoom in, zoom out, etc.

5 Conclusion Health Cloud offers many services that are safe, efficient and economical for the management and processing of medical data through cloud infrastructure. They use the advantages of cloud storage, such as applications on demand, scaling, access management and payment on request. Cloud infrastructure will lead to addressing many of the emerging medical data problems confronting the healthcare industry. Cloud storage will help handle an increasing medical data volume, exchange information effectively across healthcare systems and reduce the operating costs of the business. Cloud infrastructure will allow the healthcare industry to balance its IT priorities on a real-time basis rather than on a simulated presumption. Health Cloud is a revolutionary way to high operating and risk reduction risks in emerging countries and provides quality healthcare services. The privacy of health records is involved, though, because some regulatory do not wish to retain patient details on public cloud storage systems.

References 1. Löhr H, Sadeghi AR, Winandy M (2010) Securing the e-health cloud. In: Proceedings of the 1st acm international health informatics symposium, pp 220–229 2. Aldossary F (2017) Health observation system using cloud computing. Int J MC Square Sci Res 9(4):08–16 3. Babu VH, Balaji K (2020) Survey on modular multilevel inverter based on various switching modules for harmonic elimination. In: Intelligent computing in engineering. Springer, Singapore, pp 451–458 4. Kumanan T (2020) Link quality and energy-aware metric-based routing strategy in WSNS. In: Intelligent computing in engineering. Springer, Singapore, pp 533–539 5. Manikandan G, Anand M (2020) Radix-2/4 FFT multiplierless architecture using MBSLS in OFDM applications. In: Intelligent computing in engineering. Springer, Singapore, pp 553–559 6. Sundaram A, Ramesh GP (2020) Investigation of solar based SL-QZSI fed sensorless control of BLDC motor. In: Intelligent computing in engineering. Springer, Singapore, pp 779–787 7. Ramesh GP, Kumar NM (2020) Design of RZF antenna for ECG monitoring using IoT. Multimed Tools Appl 79:4011–4026 8. AbuKhousa E, Mohamed N, Al-Jaroodi J (2012) e-Health cloud: opportunities and challenges. Fut Internet 4(3):621–645 9. Doukas C, Pliakas T, Maglogiannis I (2010) Mobile healthcare information management utilizing cloud computing and android OS. In: 2010 annual international conference of the IEEE engineering in medicine and biology. IEEE, pp 1037–1040 10. Hsieh PJ (2015) Healthcare professionals’ use of health clouds: integrating technology acceptance and status quo bias perspectives. Int J Med Inf 84(7):512–523

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11. Abbas A, Khan SU (2015) E-health cloud: privacy concerns and mitigation strategies. In: Medical data privacy handbook. Springer, Cham, pp 389–421 12. Chauhan R, Kumar A (2013) Cloud computing for improved healthcare: techniques, potential and challenges. In: 2013 e-health and bioengineering conference (EHB). IEEE, pp 1–4 13. Yang JJ, Li J, Mulder J, Wang Y, Chen S, Wu H, Wang Q, Pan H (2015) Emerging information technologies for enhanced healthcare. Comput Ind 69:3–11 14. Hsieh PJ (2016) An empirical investigation of patients’ acceptance and resistance toward the health cloud: the dual factor perspective. Comput Hum Behav 63:959–969 15. Abbas A, Khan SU (2014) A review on the state-of-the-art privacy-preserving approaches in the e-health clouds. IEEE J Biomed Health Inf 18(4):1431–1441 16. Khoa TA, Nhu LMB, Son HH, Trong NM, Phuc CH, Phuong NTH, Duc DNM (2020) Designing efficient smart home management with IoT smart lighting: a case study. Wirel Commun Mob Comput

Information Gain-Based Enhanced Classification Techniques Enas Fadhil Abdullah, Alyaa Abdulhussein Lafta, and Suad A. Alasadi

Abstract The success of data mining tasks depends on the selection of an accurate classification algorithm. To evaluate the efficacy of a classification algorithm of interest, classification accuracy and algorithm run time are used. Trialing with several algorithms can raise the cost of the data mining operation. In this paper, information gain that is feature selection algorithm is used to improve the classification accuracy and reduce run time of classification technique. The paper uses classification techniques such as part rule, sequential minimal optimization, naïve Bayes, and random forest for a medical dataset. Dataset used in the paper is regular about physical activity, which is vital to maintain or even improve a person’s health. In this paper, the procedure has been built based on record the accuracy and run time of the classification algorithms before and after using the information_gain algorithm. As a result, the accuracy after the information_gain algorithm was the highest for the part rule algorithm and the best run time to implement the naïve Bayes algorithm. Keywords Physical activity · Feature selection · Information gain · Part rule · Classification

E. F. Abdullah (B) Department of Computer, Faculty of Education for Girls, University of Kufa, Najaf, Iraq e-mail: [email protected] A. A. Lafta Department of Communication, Engineering Technical College of Al-Najaf, Al-Furat Al-Awsat Technical University, Najaf, Iraq e-mail: [email protected] S. A. Alasadi Department of Information Network, College of Information Technology, University of Babylon, Babil, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_40

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1 Introduction To solve practical problems in engineering, economics, sociology, environmental science and many other fields, the uncertain data need to identify. The growth of data and present databases goes above the ability of individuals to study this data, which makes both essential and an opportunity to extract knowledge from databases. The databases in medical contain hug information about patients and history of their diseases. New medical knowledge extracted from relationships and patterns within this data [1]. The detection (mining) of hidden but valuable knowledge from massive databases is the core step of data mining, which results in. Data mining is the nontrivial extraction of implicit previously unknown and potentially useful information about data [2]. Data mining technology provides a user-oriented approach to novel and unseen patterns in the data. To improve the quality of service, healthcare administrators use the discovered knowledge. The medical practitioners use the explored knowledge to decrease the number of opposing medicine effects and suggest less expensive medicinally alternatives. Human physical activity recognition has been getting growing attention in recent years. Human behavior and its classification are important in physiotherapy, medicine, behavioral sciences, etc. [3]. A body-worn sensor, an inexpensive, effective, and feasible, is called an accelerometer, which has been often used in daily physical activity classification [4, 5]. Physical activity recognition has many applications in fields, such as rehabilitation, healthy aging, active living, and medical monitoring. The medical research literature for many years ago has widely used the positive effects of physical activity on health [6]. Janssen and Leblanc [7] travel around the assistances of physical activity for children in school-aged and youngster in the early phases of physical human development. Merglen et al. [8] displayed the benefits of weekly sports workout for young wellbeing. The researchers in Biddle and Asare [9] studied the association between lack of physical activity and mental illnesses. The activity recognition system has been built by [10]. To perform as local optimization, they use genetic algorithms of the feature set for each person and assessing activity recognition systems by a leave-one-subject-out (LOSO) cross-validation method. The researcher uses three different machine learning techniques for comparison, and these methods are random forest, C4.5, naive Bayes.

2 Data Mining An important stage of knowledge discovery is data mining. In latest years, it has involved a great transact of interest in the information industry. The data mining method involves an iterative order of data cleaning, data selection, data mining pattern recognition, data integration, and knowledge presentation. In essentials, data mining may accomplish class description, classification, clustering, association,

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time series analysis, and prediction. Data mining differs from traditional data analysis which is discovery-driven. It is a new interdisciplinary area, closely associated with inductive logic programming, statistics, data warehousing, neural networks, and machine learning. Data mining can be classified into two learning methods (supervised learning and unsupervised learning). The distinction depends on how the learner classifies the data, it is unsupervised if the classes are unknown else it is supervised learning if the classification takes under supervision. In unsupervised learning (clustering), the classes are not predetermined, and the inputs are indefinite; at the beginning, the model is not available with input data, based on the statistical properties or similarities among the values clusters which are formed. In supervised learning (classification), the classes are determined; the inputs are either presumed or well-known at the start [11].

3 Classification Classification learning called supervised since it runs below supervision by providing the real class for every training sample. The play or do not play judgment, type of iris, the acceptability of the labor contract, and the lens recommendation are examples of some outcome. The class is the outcome of the example. The classification learning is successfully evaluated by trying out the model explanation that is learned on an free set of trial data for which the correct classifications are known but not made available to the machine [12]. Classification is the method of defining a model that labels and separates data classes or concepts, to use the model to guess the class of data whose class label is unidentified. The resultant model centered on the analysis of a set of teaching data (i.e., data items whose class label is known). The classification model can be represented in many forms, for example, decision trees, rule-based (IF–THEN) rules, and mathematical formulae. In the rule-based method, the model is symbolized as a set of IF–THEN rules. Depending on how rules are used the classification rules are formed for classification. Two methods are used for building classification rules: the direct method that directly extracts rules from data such as one R and RIPPER and the indirect method where the rule is extracted from other classification models such as C4.5 rules and decision tree [13]. A decision tree is a flowchart similar to tree organization, anywhere node denotes a test on a feature value, each branch represents a result of the test, and tree leaves denote classes or class distributions. Decision trees can simply derive some set of rules. As an example, the son will like the movie or not to the classifier. The decision tree will start constructing the rules with the types your son likes as nodes and the objects like or not as the leaf nodes. By following the path from the root node to the leaf node, a rule is constructed. There are several methods built on decision tree such as random forest algorithm (RF) [13], as shown in Fig. 1

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Fig. 1 RF example based on decision tree

4 Classification Techniques One of the used data mining technique that is a supervised predictive is classification that organizes data into categories. Generally, it contains two phases training and testing of classifies data. Training data is used to train the classification model with the presence of a target (class) and then to construct the training patterns using one of the classification techniques. To classify unknown data, the training patterns could be used in the testing phase. The common examples of classification are neural network and decision tree. For instance, a decision tree may be used to classify patient disease using pre-trained patient symptoms based on information gain. Decision tree considers a good classifier in predicting the type of patient disease using information gain, gain ratio [14]. The classification techniques evaluated by its run time. Run time is the overall time from when an algorithm starts to finish. There are several ways available to record the run time of an algorithm. The actual execution time relies on the configurations of the computer and the algorithm on which it is implemented including CPU speed, the number of CPU cores, and memory capacity. Run time is relatively small in testing operation while training time may be used to show the difference of execution between two algorithms [15].

4.1 Part Technique The part technique is rules classification. Find the rules from a partial tree [12] constructed using the J4.8 classifier technique and combine the divide-and-conquer with separate-and-conquer strategy of rule learning steps of part algorithm as follows:

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Construct a partial decision tree on the existing set of examples and then form a rule of the decision tree such that the rule made from a leaf with the largest coverage. After building the rule discard the decision tree and remove the instances covered by the rule.

4.2 Sequential minimal optimization (SMO) Sequential minimal optimization (SMO) is an algorithm for resolving the quadratic programming (QP) problem that rises during the training of support vector machines (SVM). SMO algorithm in 1998 has generated a lot of exhilaration in the SVM community, and it is usually used for preparation support vector machines. It was developed by John Platt in 1998 at Microsoft Research. The Karush–Kuhn–Tucker conditions are the first derivative tests (first-order necessary conditions) that used to satisfy some regularity conditions to solve nonlinear programming to be optimal. SOM algorithm uses the parameters (α i and α j ) the goal is to optimize both parameters. Finally, (b) parameter is changed based on the new α’s. This procedure is repeated until the α’s converges [16]. The full SMO algorithm is devoted to heuristics for selecting which α i and α j to improve or maximize the objective function as much as possible. For large data sets, this is dire for the speed of the algorithm, since there are n (n − 1) potential ranges for α i and α j , and some will result in much less enhancement than others. Basically iterate whole α i , (i = 1, 2, … n). If α i does not achieve the Karush–Kuhn–Tucker (K) conditions. To select the threshold (b) that is satisfied with the KKT conditions after optimizing αi and αj depends on Eq. (1) [16]:

b=

⎧ ⎨

b1 if 0 7, it comes under basic, if pH < 7,

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it is acidic, and if pH = 7, it is neutral. There are several examples that can be used to determine the pH value. By using this device, we can get all the details regarding the level of the water, and also the pH value is determined. • With the help of this project, many villagers will be able to drink the pure water free from germs and other impurities. • Moreover, the transportation problem is also solved; V-tank can be used at villages and mountain regions. • It is even used in farming and agricultural purposes. • It is used to sprinkle water to fields to reduce man’s work. • The purification can also be done by natural process.

5 Conclusion • By using this, we can supply pure water to the villagers and also be able to reduce the waterborne diseases. V-tank can be used at villages and mountain regions. • It is even used in farming and agricultural purposes. It is used to sprinkle water to fields to reduce man’s work. • The purification can also be done by natural process. • The purity of the water level can be extended to more natural adsorbents which is quite efficient.

6 Future Enhancement • The purity of the water level can be extended more and also use more natural adsorbents which is quite efficient. • The transportation can be made with less expense and more advanced.

References 1. Ravichandran S (2017) Cloud connected smart gas cylinder platform senses LPG gas leakage using IOT application. Int J MC Square Sci Res 9(1):324–330 2. Prasad NCS, Ramesh GP (2020) Analysis of digital FIR filter using RLS and FT-RLS. In: Intelligent computing in engineering, pp 79–586 3. Swarnalatha A, Manikandan M (2020) Intravascular ultrasound image classification using wavelet energy features and random forest classifier. In: Intelligent computing in engineering, pp 803–810 4. Khan S (2016) Wireless sensor network based water well management system for precision agriculture. In: 2016 26th international telecommunication networks and applications conference (ITNAC), pp 44–46 5. Ntambi F, Kruger CP, Silva BJ, Hancke GP (2015) Design of a water management system. In: AFRICON, pp 1–5

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6. Narendran S, Pradeep P, Ramesh MV (2017) An internet of things (IoT) based sustainable water management. In: 2017 IEEE global humanitarian technology conference (GHTC), pp 1–6 7. Sammaneh H, Al-Jabi M (2019)“IoT-enabled adaptive smart water distribution management system. In: 2019 international conference on promising electronic technologies (ICPET), pp 40–44 8. Kavitha S (2019) Review paper on smart water management system. Int J Opt Sci 5(2):31–35 9. Shrenika RM, Chikmath SS, Ravi Kumar AV, Divyashree YV, Swamy RK (2017) Non-contact water level monitoring system implemented using labview and arduino. In: 2017 international conference on recent advances in electronics and communication technology (ICRAECT), pp 306–309 10. Jin H, Feng L, Liang R, Xing S (2011) Design of urban and rural water resources information management system based on Delphi. In: 2011 second international conference on mechanic automation and control engineering, pp 7284–7287 11. Arun M, Sugirtharani AJ, Carolina JMP, Teresa AC https://doi.org/10.1109/ICACCS.2019.872 8529. IEEE, India 12. Sachio S, Noertjahyana A, Lim R (2018) IoT based water level control system. In: 2018 3rd technology innovation management and engineering science international conference (TIMESiCON), pp 1–5 13. Redwan F, Rafid S, Abrar AH, Pathik BB (2019) An exploratory approach to monitor the quality of supply-water through IoT technology. In: 2019 international conference on automation, computational and technology management (ICACTM), pp 137–142

Water Body Detection of Haditha Dam Lake from Satellite Imagery Using Image Processing Techniques Rabah N. Farhan and Sawsan Abdulaali Arif

Abstract Water is an important source of human life, especially in Iraq, where it has two strategic rivers, the Tigris and the Euphrates. It is important to monitor these rivers and lakes and to detect the changes in morphology and water body associated with the climate and the amount of rain falling. In this research, aerial and satellite images were analyzed using image processing algorithms to monitor Haditha Lake in western of Iraq From 2016 to 2019. The image dataset was collected using Sentinel-2 satellite. Image analysis on a multi-spectral image containing 13 bands was accomplished using k-mean clustering method. The normalized difference water index was extracted from Band 3 and Band 8 of the multi-spectral image of the Sentinel-2. The proposed algorithm used to estimate the amount of water fluctuation during this period was successfully and accurately determine the water body of Haditha Dam for the time span. Keywords Water detection · Lake · Image processing · Water fluctuation · Satellite images

1 Introduction The Haditha Dam project is located 7 km north-west of the modern city and is of length (8250) m. Concrete dam cuts down the river is (500) m approximately, and up (75) m, while the remaining parts are from the earthy pile and the lengths (3300) m on the right bank of the river and (5500) m on the left bank and the dam contains six outlets (water gates) each gate has length about (17.5) m. The dam also contains a hydroelectric station with a capacity of (666) MW. The surface area of the Haditha water lake is about (500) km2 at the normal and storage level. The main purpose R. N. Farhan (B) Renewable Energy Research Center, University Of Anbar, Ramadi, Iraq e-mail: [email protected] S. A. Arif Ministry of Water Resources, Baghdad, Iraq © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_42

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for which the dam was established are the formation of a water tank to control the Euphrates River after entering Iraqi territory prevent flood hazards and regulate drainage. Hydroelectric power generation with a capacity of 660 MW of electricity [1]. In this research, image processing algorithms were used to detect and determine the water content of a Haditha Dam from 2015 to 2019 using satellite images of the Sentinel satellite images. K-mean clustering algorithm was used for segmentation the dam images. Figure 1 shows the location of Haditha Dam.

Fig. 1 Haditha Dam in western of Iraq

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Table 1 Spectral bands for the Sentinel-2 sensors Sentinel-2 bands

Sentinel-2A Central wavelength (nm)

Sentinel-2B Bandwidth (nm)

Central wavelength (nm)

Bandwidth (nm)

Spatial resolution (m)

Band 1—Coastal aerosol

442.7

21

442.2

21

60

Band 2—Blue

492.4

66

492.1

66

10

Band 3—Green

559.8

36

559.0

36

10

Band 4—Red

664.6

31

664.9

31

10

Band 5—Vegetation red edge

704.1

15

703.8

16

20

Band 6—Vegetation red edge

740.5

15

739.1

15

20

Band 7—Vegetation red edge

782.8

20

779.7

20

20

Band 8—NIR

832.8

106

832.9

106

10

Band 8A—Narrow NIR

864.7

21

864.0

22

20

Band 9—Water vapor

945.1

20

943.2

21

60

Band 10—SWIR—Cirrus

1373.5

31

1376.9

30

60

Band 11—SWIR

1613.7

91

1610.4

94

20

Band 12—SWIR

2202.4

175

2185.7

185

20

2 Remote Sensing Using Sentinel-2 Satellite Sentinel-2 is an Earth observation satellite which continuously obtains remote sensing technology digital image in ground and seawater [2]. Two satellites (Sentinel2A and Sentinel-2B) were assigned a wide variety of facilities and technologies, such as farm monitoring, disaster response, categorization of forest cover or groundwater. Table 1 shows the Sentinel-2 multi-band sensors [3] (Fig. 2).

3 Literatures Survey The problem of study and analyses of various surface earth phenomena’s using satellite images were examined by many researchers during the last decades. In [4], the dust storms detection was studied using satellite infrared images. In [5], at this study, the topic of the rise of the lake of Mexico level using the satellite image was studied. In [6], detection of water quality using simulated satellite data and semi-empirical algorithms in Finland was investigated. In [7], the exploration of a precise and fast

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Fig. 2 Sentinel-2 satellite in action

way of monitoring water chemical and biochemical quality in the reservoirs of Shenzhen, China was investigated. In [8], modeling the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and OLI images was explored. In [9], a deep learning approach of satellite imagery was investigated. In [10], a lake classification techniques were studied using Landsat 8 captured images. In [11], land cover and crop type classification from satellite imagery using machine learning techniques was investigated. In [12], a comprehensive review of using satellite imagery in remote sensing was accomplished. In [1, 13], the authors highlight the role of the geological conditions on Mosul and Haditha Dams, Iraq.

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3.1 K-nearest Neighbor for Satellite Image Segmentation Estimation and detection of the quantity of water in the dam lakes involve techniques to split the water in the dam bodies of water from the sides of the lake and the background image of the satellite picture captured. Due to its simplicity and efficiency, the algorithm entitled ‘k-nearest neighbor’ [14] was chosen for segmentation task. Clustering is a method to divide a set of data into a specific number of groups. • Calculates the k centroid • Choosing each point to the cluster which has nearest centroid from the particular data point. In this case, Euclidean distance used as a tool to find the splitting distance. Assume an image with resolution of x * y, and the image has to be cluster into k number of cluster. Let I(x, y) be an input pixels to be cluster and C k be the cluster centers. The algorithm is shown below: 1. 2.

Initialization of cluster score k as the cluster center. For each pixel of an image, calculate the Euclidean distance d, between the center and each pixel of an image using the relation given below: D(I) = |I(x, y)| − Ck

3. 4.

Allocate all pixels on distance D to the closest location. Recalculate new pixel coordinates of the center using the Eq. (2). Ck =

1 I (x, y) k y∈C x∈C k

5. 6.

(1)

(2)

k

Repeat the process until it satisfies the tolerance or error value Upgrade cluster pixels to new image.

4 Proposed Algorithm for Haditha Dam Water Body Detection 4.1 Satellite Imagery Preparation The satellite images of Haditha Lake were collected from [15]. These images were arranged according to the date it captured (from 2015 to 2019). The image bands collected were all the 11 bands. Figure 3 shows Haditha Dam from this data source.

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Fig. 3 Thermal image of Haditha Lake taken by Sentinel-2 satellite

4.2 Normalized Difference Water Index The NDWI index is most appropriate for water body mapping. The surface water has high absorbability and low emission spanning from visible to infrared wavelengths. The index uses the green and near-infrared image bands based on this phenomenon. NDWI is calculated as [16]: NDWI =

pGreen − PNIR pGreen + PNIR

(3)

where PGreen is TOA reflectance value of the green band, PNIR is the TOA reflectance of the NIR. The freely available Sentinel-2 level-1C dataset is already a standard product of TOA reflectance. The NDWI adopted for our work: NDWI(10 m) =

p3 − P8 p3 + P8

(4)

Note that both Band 3 and Band 8 of Sentinel-2 have a geospatial resolution of 10 m, and thus, the calculated NDWI in Eq. (2). Figure 4 shows the NDWI of Haditha Dam image.

4.3 Satellite Imagery Histogram Equalization (HE) This phase of preprocessing of images usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. The general form of HE is given by [14]: 

cdf(v) − cdfmin × (L − 1) h(v) = round (M × N ) − cdfmin

 (5)

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Fig. 4 Normalized difference water index of Haditha Lake taken by Sentinel-2 satellite

where cdf is cumulative distribution function given by: CDF(i) =

i 

px ( j)

(6)

j=0

In this case, Px represents the probability of an occurrence of a pixel j of level i in the image. Figure 5 shows the histogram equalization HE of the Haditha Dam.

Fig. 5 Histogram equalization of Haditha Lake taken by Sentinel-2 satellite

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Fig. 6 K-mean clustering of Haditha Lake taken by Sentinel-2 satellite

4.4 K-mean Clustering In this stage of proposed algorithm, the k-mean clustering algorithm discussed previously applied for the resultant image. Figure 6 shows the resultant image.

4.5 Binarization and Area Estimation Direct binarization operation is applied to the image for accurate area estimation. From the Sentinel specification, it is found that the image resolution is (see Table 1) approximately 10 m for Band 3 and Band 8. Choosing control point on the map, it is found that every pixel in image equal 50 m in the map image of Sentinel-2. Figure 7 shows the block diagram of the proposed algorithm.

5 Results and Discussions The proposed system was built using MATLAB 2018. Haditha Lake imagery was taken for the years from 2016 to 2019 (due to luck of Sentinel-2 project). Figure 8 shows the system under work.

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Satellite Imagery Preparation.

Image Preprocessing

NDWI Histogram Equalization (HE)

Binarization

K-mean Clustering Y

N

If Cluster

Non Water

Compute Area of W t

END Fig. 7 Proposed system flowchart of Haditha Lake body area detection

The system was successfully clustering satellite images into water regions and non-water regions converting water regions into white areas and non-water into black areas (binarization). Figure 9 shows the clustering of the Sentinel-2 imagery for Haditha Dam Lake for the years from (2016 to 2019). Peak-to-signal noise ratio (PSNR) is the proportion between maximum attainable powers and the corrupting noise that influence likeness of image. It is used to measure the quality of the output image. This quality measurement function was used to measure the PSNR for each image after k-mean clustering. PSNR is given by [16]: ⎤

⎡ ⎢ PSNR = 10 · log1 0⎢ ⎣

max(r (x, y))2 n x −1 n y −1

[(r (x,y))] 1 0 0 n x n y n x −1 n y −1 [r (x,y)−t(x,y)]2 0

0

2

⎥ ⎥ ⎦

(7)

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Fig. 8 Proposed system Haditha Lake body area detection

where r (x, y) is the input image and t (x, y) is the segmented image. Figure 10a, b shows the PSNR for satellite image of 2016 and 2019, respectively. Water area to non-water regions clustering was shown in Fig. 11 using our algorithm.

6 Conclusions Our proposed system successfully clusters the water regions from non-water. The lack of satellite image of sentinel-2 forces us to limit the period span to just 4 years. Through analysis of the spatial images, it is clear to us that there is a difference in the water reservoir of the modern dam and clear oscillation during this short period of time. Other sources and satellite images can be used for extended periods of time to observe the growth of the water size of dams in Iraq and their association with certain climatic events in future.

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a

b

c

d

Fig. 9 Clustering water regions using binarization: a Haditha Lake 2016, b Haditha Lake 2017, c Haditha Lake 2018, d Haditha Lake 2019

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Fig. 10 PSNR for the image of 2016 (a) and 2019 (b)

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Fig. 11 Area proportion for water to non-water regions of Haditha imagery during 2016 to 2019 (b)

Reference 1. Sissakian VK, Adamo N, Al-Ansari N, Knutsson S, Laue J, Elagely MJJES, Engineering G (2018) A comparative study of mosul and haditha dams, Iraq. Geol Cond 8(2):35–52 2. Malenovský Z, Rott H, Cihlar J, Schaepman ME, García-Santos G, Fernandes R, Berger MJRSE (2012) Sentinels for science: potential of sentinel-1,-2, and-3 missions for scientific observations of ocean, cryosphere, and land 120:91–101 3. MultiSpectral Instrument (MSI) Overview. In: https://earth.esa.int/web/sentinel/technical-gui des/sentinel-2-msi/msi-instrument 4. Shenk WE, Curran RJJMWR (1974) The detection of dust storms over land and water with satellite visible and infrared measurements. Mexico. J Phys Oceanogr 102(12):830–837 5. Stumpf HGJJoPO (1975) Satellite detection of upwelling in the Gulf of Tehuantepec, Mexico. J Phys Oceanogr 5(2):383–388 6. Härmä P, Vepsäläinen J, Hannonen T, Pyhälahti T, Kämäri J, Kallio K, Eloheimo K, Koponen SJSotTE (2001) Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland 268(1–3):107–121 7. Wang Y, Xia H, Fu J, Sheng GJSotTE (2004) Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data 328(1–3):195–206 8. Rokni K, Ahmad A, Selamat A, Hazini SJRs (2014) Water feature extraction and change detection using multitemporal Landsat imagery 6(5):4173–4189 9. Basu S, et al (2015) Deepsat: a learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems 10. Bhardwaj A, Singh MK, Joshi P, Singh S, Sam L, Gupta R, Kumar RJIJoAEO, Geoinformation (2015) A lake detection algorithm (LDA) using Landsat 8 data: a comparative approach in glacial environment, vol 38, pp 150–163 11. Kussul N, Lavreniuk M, Skakun S, Shelestov AJIG, Letters RS (2017) Deep learning classification of land cover and crop types using remote sensing data 14(5):778–782 12. Rana H, Neeru NJAiCS, Technology (2017) Water detection using satellite images obtained through remote sensing 10(6):1923–1940

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13. Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In Proceedings of the 4th international conference on advances in pattern recognition and digital techniques pp 137–143 14. Burney SA, Tariq HJIJoCA (2014) K-means cluster analysis for image segmentation, vol 96, no 4 15. The Sentinel Online technical website. In: https://sentinel.esa.int/web/sentinel/about-sentinelonline 16. Du Y, Zhang Y, Ling F, Wang Q, Li W, Li XJRS (2016) Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band, vol 8, no 4, pp 354

Identity-Based Data Outsourcing with Comprehensive Auditing in Cloud-Based Healthcare Applications K. Rakesh, B. Krishna Teja, M. Venkata Akhil, and T. Ramesh

Abstract Portable cloud computing (MCC) permits versatile clients to have onrequest access to cloud administrations. The versatile cloud model in which the data concerning the patients’ records is broke down, and it can separate proposals. Since the human services applications want progressively more calculation and correspondence assets, these need access to more information inside and beyond an association as well. In compact appropriated registering, a fine-grained level access control of multi-server cloud data is a pre-basic for the successful execution of end customers’ applications. In our proposed system, another arrangement that gives a combined technique of fine-grained gets the opportunity to control over cloud-based multiserver data nearby a provably secure versatile customer affirmation segment for the Healthcare. The proposed paradigm is, as far as we can see, first one to search for finegrained information in a multifaceted, dispersed computer situation over different cloud servers. Keywords Fine-grained level access control of information · User authentication · Attribute-based encryption

K. Rakesh (B) · B. Krishna Teja · M. Venkata Akhil · T. Ramesh Department of Computer Science and Engineering, RMK Engineering College, Chennai, India e-mail: [email protected] B. Krishna Teja e-mail: [email protected] M. Venkata Akhil e-mail: [email protected] T. Ramesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_43

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1 Introduction Distributed computing might be an unbelievably essential area in different applications. Tawalbeh et al. suggested a modular cloud architecture that breaks down information about medical data and allows for ideas to be segregated. Given that applicants for social services are increasingly requiring more measurement and communication, and these require access to a lot of information both within and outside a relationship. The bleeding edge of the cornerstone of social services must be applied in a fancy range and in the opportunity to speak inside and through authoritative boundaries. Healthcare industry licences to improve resilience in a wide variety of viewpoints, including the production, improve the assembly processes and in the type of markets, increase both item efficiency and profitability. Given the crude work carried out by the Main Policy Attribute-Based Encryption (KP-ABE), several late-fashioned proposals are introduced to control the cloud protection networks and the social insurance network for different applications: the remote sensors organize (WSN). The stable and mutual authentication between scalable clients and cloud servers is the most crucial thing when customers are reaching cloud information through a transparent and unsafe channel. If you are aware of the customer’s asset convincing position, the framework of this authentication process must be sufficiently lighter. A brief description of the confirmation element of multiple distributed computing reveals that the arrangement of complex fine grain information is not managed over various cloud service information in a striking analysis job. All these associated grains come to regulate schemes because they were meant for a solitary server state. Any CSj cloud server checks the validity of the portable client before granting access to own knowledge characteristics. The trustworthy customer MUi may obtain access to server CSj databases if its access benefit or approval is necessary [1].

2 Literature Review In [2]. Attribute-Based Encryption for Fine-Grained Access Control of Encrypted Data: Because technically complex data are exchanged and treated through the Internet with untouchable targets, data set in these areas should be scrambled. The downside to storing data is that, once it is completed, it would be exchanged expressly at a raw-grained basis, that is to say, handing the secret key to another social set. We use an encryption technique, known as Key Policy Attribute-Based Encryption (KPABE), for finely grained sharing of data. In our cryptosystem, ciphertexts are characterized by collections of qualities and private keys that bind to control systems that a consumer may scrap. We demonstrate our importance in exchanging survey log information and encryption of correspondence. Our progress improves privacy key structures that support hierarchical identity-based encryption (HIBE).

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In [3]. On the Design of Provably Secure Lightweight Remote User Authentication Scheme for Mobile Cloud Computing Services: The protected and knowledgeable convention of mobile distributed computers to validate consumers is a key issue owing to the exchanging of information using the Internet between end customers and mobile telephones. Shared checking of a portable customer and cloud professional organization is necessary to access every cloud management. Whatever the situation, the existence of the cellular phones’ asset requisites would further test this enterprise. In this paper, we suggest another consumer affirmation that is safe and lightweight, adaptable to a portable dissemination, with a cryptographical hash, bitwise XOR and coated extractor limitations [4]. Via casual security analysis and detailed safety review using the abnormal prophet model, the proposed conspiracy has been seen to safeguard against major aloof and dynamic attacks and to offer customers namelessness. We also allow a systematic safety review for the planned plot by Pro Verif 1.93 recreation. We have also given clarification that our suggested plot is based on the logic of Burrows-Abadi-Needham (BAN). Since the conspiracy suggested does not misuse any mandatory asset cryptosystem, in contrast to current similar proposals, it has the lowest computing cost. Furthermore, in comparison with current plans for which it has less communication fee, the suggested plot does not have enrolment focus in the validation procedure [5]. In [6]. Cloudlet-based Mobile Cloud Computing for Healthcare Applications: In many areas of our lives, sophisticated cell phones are used to shop on the Internet and create and share various documents. However, these devices are subjected to various drawbacks, including short lifespan batteries and restricted storage and handling. New technology in portable cloud computing will help to solve these barriers. The discharge process limits the use of force and recovers portable storage by carrying out immense cloud assignments [7, 8]. Mobile phones are connected to cloud expert organizations using 3 G or LTE developments that have a range of difficulties including reduced communication capability, expense and lack of activity. We also implemented a robust and stable decentralized portable computing model depending on the Cloudlet idea where cellular phones consumers can genuinely communicate with cloud properties using less costly innovations, such as Wi-Fi. Only if the management is not available in the cloud is the user aligned with the cloud initiative. In several applications where protection and expertise are needed, the proposed cloud-based platform can be used. It appears to be used to maintain and decompose health data of patients for well-being purposes. The after results of our model replication suggest that it is more effective and accurate than the other versatile models, which do not use the cloud [9, 10]. In [11]. A Precise Reward Scheme Achieving Anonymity and Traceability for Crowd computing in Public Clouds: While giving namelessness at terminal gadgets (e.g. Android and iOS gadgets) is gainful for clients (e.g. devouring administrations and assets without the danger of being followed), important partners (e.g. suppliers and governments) may require contingent namelessness for charging reason or to find unscrupulous or bargained customer gadgets [12]. In a distributed computing arrangement where an enormous number of calculation errands are submitted to the open cloud, the open cloud server

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may experience calculation top, and consequently, reactions to the terminal gadgets might be postponed. Along these lines, in this paper, we plan the main exact prize plan accomplishing both namelessness and discernibility for crowd computing in broad daylight mists. We at that point demonstrate the security of the proposed conspire in the irregular prophet model and exhibit the reasonableness of the plan utilizing recreations.

3 System Framework The proposed savvy human services system comprises of a few segments. One part contains patients and specialists, another segment involves edge registering, but then another segment covers distributed computing. The patients can live in their homes in a city, while the specialists and parental figures can be situated at any assigned medical clinics and centres. They do not have to communicate up close and personal; be that as it may, they do associate using web applications or versatile applications. Any brilliant gadgets or the Internet of Things (IoT) can catch the pictures or information from the patients and transfer them through a web application or a versatile application. While transferring, the gadgets or the IoT can implant watermarks in the information [13]. To interface and synchronize with the central cloud, two administration frameworks are living on the edge processing: an edge figuring stage applications the executives’ framework and an edge registering to facilitate foundation the board framework. The edge registering stage applications the board framework oversees correspondence applications, the remote system and the radio access organize. Also, it furnishes communication with the applications the board framework (in the cloud) and the foundation as assistance (IaaS) [14]. The dissemination director disperses the work to various servers to limit the heap on every server. The edge registering and distributed computing together give lowinactivity correspondence, elite figure and ongoing yield [15, 16].

4 Proposed System The multi-cloud server considers three fundamental entities: 1. 2. 3.

Confidential Core of Registration. A set of S1 users and S2 clients for the cloud. An outside consumer or a lawfully registered user can attempt to attack the device through a number of security attacks, which are considered to be an opponent.

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The validation technique retains a strategic gap from device cost-effective encryption activities to render battery-restricted mobile phones and a shrewd card easy to use. In comparison with the plan currently in operation, the new plot does not have the RC in the authentication process. It has furthermore low communications costs. Attribute-Based Encryption (ABE) ABE is a type of open key encryption in which a customer’s secrecy key and the ciphertext depend on attributes (e.g. the country in which they reside or the kind of membership they have). The unravelling of a ciphertext in such a scheme is possible only if the operation of the customer key complies with the characteristics of the ciphertext. A crucial aspect of security in property-based encryption is the aversion to conspiracy: an adversary with several keys will be able to access information if, in any situation, one key reward arrives. One of the two kinds of trait-based encryption plans: Key-approach characteristicbased encryption (KP-ABE). In KP-ABE, clients’ mystery keys are produced dependent on an entrance tree that characterizes the extent of the benefits of the concerned client, and information is scrambled over a lot of qualities. ABE is used to create keys for the user. Arrangement: This is a randomized calculation that accepts a security parameter as the main info. It creates the open parameters PK and an ace key MK likewise called Attribute Key. Encryption: This is a calculation that takes as information—an entrance structure ‘A’, for example a lot of properties and the open parameters PK. For this situation, the yield is figure content E. Key Generation: This is a spontaneous computation which takes the input structure A, the ace key MK and the opened parameters PK as details. The performance is an unscrewing key or Key AK attribute. Decryption: This calculation takes as information, the figure content E that was scrambled under access structure A, the unscrambling key AK and the open key PK. The yield is message M.

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Advanced Encryption Standard AES typically performs the entire of its estimates on bytes instead of bits. The 128 bits of a plaintext block are thus handled as 16 bytes by AES. The 16 bytes are arranged as a net in 4 parts and 4 columns.

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Encrypting data with AES and ABE

5 Results and Discussions

Registering the cloud server Before deployment of cloud servers CSj, RC stores necessary information like: 1. 2. 3. 4.

Server ID The Master Secret Key Cyclic Group and Generators in respective servers.

File Upload

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• Uploading the file. • After registration, the RC can upload the file with access policy. • If any cloud server trait is happy with get to strategy, they can get to the record by utilizing the crypto key. • Else, we should re check the entrance arrangement and the document ought to be transferred with the legitimate access approach.

Mobile User Register • Registration by the user. • By selecting own accreditations, MUi registers in the RC through a protected channel. RC chooses the parameters for the user server and loads details required on its smart user identity.

Mobile User Login • Logging into the user’s account.

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• MUi enables sophisticated card criteria to login to the desired cloud service CSj and sends its own credentials. The separate credential is not used to connect to various cloud storage viaMUi.

View File

6 Conclusion The fine grain level access control of multi-tenant cloud storage is not a flexible, disseminated statistic. Executing fine-grained data is an open research challenge which can be managed in a multi-cloud server condition. In this paper, we organize a structure that provides a single framework of distilled goods that can position a provenly stable scalable customer acceptance section over cloud-based multi-server results. In order to allow battery-restricted mobile phones and asset-powered glossy cards for all purposes, the validation technique holds away from computing expensive cryptographic operations. By picking their own certifications, MUi registers in RC

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via a secure channel. By picking its own accreditations, MUi registers to RC through a secure channel.

References 1. Shermin Shamsudheen A (2019) Smart agriculture using IoT. Int J MC Square Sci Res 11(4) 2. Tawalbeh LA, Bakhader W, Mehmood R, Song H (2016) Cloudlet-based mobile cloud computing for healthcare applications. In: IEEE global communications conference (GLOBECOM’16), Washington, DC, USA, pp 1–6 3. Roy S, Chatterjee S, Das AK, Chattopadhyay S, Kumar N, Vasilakos AV (2017) On the design of provably secure lightweight remote user authentication scheme for mobile cloud computing services. IEEE Access 5(1):25808–25825 4. Yu S, Ren K, Lou W (2011) FDAC: toward fine-grained distributed data access control in wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(4):673–686 5. Goyal V, Pandey O, Sahai A, Waters B (2006) Attribute-based encryption for fine-grained access control of encrypted data. In: Proceedings of the 13th ACM conference on computer and communications security (CCS’06), Alexandria, Virginia, USA, pp 89–98 6. Wang H, He D, Sun Y, Kumar N, Choo K-KR (2018) PAT: A precise reward scheme achieving anonymity and traceability for crowd computing in public clouds. Futur Gener Comput Syst 79:262–270 7. Umapathy K, Sridevi T, Navya Sri M, Anuragh R (2020) Real time intruder surveillance system. Int J Sci Technol Res (IJSTR) 9(03):5833–5637. ISSN 2277-8616 8. Vijayabaskar V, Rajendran V (2011) Analysis and modeling of wind dependence of ambient noise in shallow water of Arabian sea. Eur J Sci Res 50:28–34(SJR IF: 0.21) 9. Ratchinsky K (2016) Cloud today and tomorrow: why hospitals are tripling the use of cloud services. Available at https://www.healthcareitnews.com/blog/cloud-today-and-tomorr owwhy-hospitals-are-tripling-use-cloud-services. Accessed on November 2017. 10. Cloud Computing: Building a New Foundation for Healthcare (2011). Available at https:// www-05.ibm.com/de/healthcare/literature/cloudnew-foundation-for-hv.pdf. Accessed on Nov 2017 11. Wu L, Chen B, Choo K-KR, He D (2018) Efficient and secure searchable encryption protocol for cloud-based Internet of Things. J Parallel Distrib Comput 111:152–161 12. He D, Kumar N, Wang H, Wang L, Choo K-KR (2017) Privacy preserving certificateless provable data possession scheme for big data storage on cloud. Appl Math Comput 314:31–43 13. Kalpana AV, Rukmani Devi S, Indira N (2018) An efficient localization for smart defense node connection based node position tracking and identification in wireless sensor networks. J Web Eng 17(6):2452–2471 14. Kalpana AV, Rukmani Devi S, Indira N (2019) A unique 3D localization in wireless sensor networks using adaptive stochastic algorithm. Appl Math Infor Sci 13(4):236–242 15. Indira N, Rukmanidevi S, Kalpana AV (2020) Light weight proactive padding based crypto security system in distributed cloud environment. Int J Comput Intell Syst 13(1):36–43 16. Kalpana AV, Rukmani Devi S, Indira N (2020) Robust 3D localization using cubical geometry in wireless sensor networks. Solid State Technol 63(6):18846–18874.

Edge Technology Enabled IOT Blockchain-Based Health Monitoring for Chronically Sick Patients Munisamy Shyamala Devi, P. Swathi, M. Nitesh Kumar Sah, Ayesha Jahangir, and Shubham Santhosh Upadhyay

Abstract The Internet of things is a trending technology that is integrated with diverse real-time applications to form a interdisciplinary research outcomes. Internet of things can be implemented in all the real-time business and commercial market to attract the customers and to evolve itself as the future of Internet. Similar to Internet of things, blockchain is also emerging field that can be integrated to provide openness and security to each sensors in the applications. Monitoring and servicing the sensors that are connected in IoT framework still remain unsolved problem. With this background, this paper attempts to integrate Internet of things and blockchain together in designing the proposed framework for IoT blockchain-based health monitoring system. Illegal users are not allowed to perform any fault transaction inside the blockchain network with its ability to involve in smart contract and consensus, thereby increasing the security between the doctors and the chronically ill patients. This system is designed by keeping immovable aged patients in mind who are affected by chronical diseases which need on time treatment and continuous observation by the doctor. The proof of concept for the proposed IoT blockchain-based health monitoring system with Ethereum private blockchain network under a genesis block is designed in this paper to analyze the performance. Keywords Blockchain node · Consensus · Ledger · Internet of things M. Shyamala Devi (B) · P. Swathi · M. Nitesh Kumar Sah · A. Jahangir · S. S. Upadhyay Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India e-mail: [email protected] P. Swathi e-mail: [email protected] M. Nitesh Kumar Sah e-mail: [email protected] A. Jahangir e-mail: [email protected] S. S. Upadhyay e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_44

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1 Literature Review and Shortcomings The popular consensus algorithms used in blockchain are discussed in the context of e-health where the blockchain platforms are reviewed for their appropriateness in IoTbased e-healthcare. IoT-based blockchain framework for accessing and managing e-healthcare EHR data that is highly more trusted, secure, transparent, and efficient [1]. IoT-based smart home is designed with the required components [2]. The future direction of the development of power systems and a new block chain transaction framework is put forward in consideration of problems existing in current energy market. A power trading framework based on the blockchain technology in the distributed electricity market is proposed [3]. Particular branch of symmetric key encryption, called ARX algorithms, to encrypt the data for blockchain is used. Lightweight ring signature technology which allows a signer to sign data in an anonymous way [4]. New architecture is proposed for arbitrating roles and permissions in IoT. The new architecture is a fully distributed access control system for IoT based on blockchain technology [5]. Coherent and comprehensive picture is shaped on fundamental working principles of blockchains and how blockchain-based systems achieve the characteristics of decentralization, security, and auditability [6]. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain enabled IoT [7]. To establish the relationship between IoT and BC for device credibility verification, framework with layers is proposed to intersect, and self-organization blockchain structures [8]. Hyperledger fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity for each food package [9].

2 Introduction 2.1 Blockchain Blockchain data structure consists of Previous Block Header, Timestamp, Nonce, and Merkle Root Hash. A blockchain is a data structure that is used to create a digital ledger of transactions and is shared with the distributed network of nodes. Each node user on the network performs manipulation on the distributed ledger in a secure way without the need for a central authority. It also maintains the agreement and copyright details of the data in the distributed ledger and the block chain process. The Internet of things make the devices to be controlled by the end devices like computer or mobile. The information about the things are captured by the sensors, and they are accessed by the remote devices that are connected with the devices through Internet gateways.

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3 IoT Blockchain-Based Health Monitoring System The nodes involved in the design architecture of the IoT blockchain-based health monitoring system are shown in Fig. 1. The nodes involved in this IoT blockchain architecture receive the information from the respective sensors that are connected to chronically ill patients. Each node in blockchain network acts as a miner. Each blockchain node maintains the local copy of blockchain with all the approved transactions. The transactions that are involved in each body sensors node are accessing, storing, and monitoring the sensor data. If any deviations happens in the sensor data, it is immediately notified to the doctors smartphone node and the change in the sensor data is also updated in the distributed ledger. And any treatment recommendations from the doctor is also updated in the distributed ledger. This ensures that all the authorities of the hospital have the data transparency in the treatment that is carried for any patients in the hospital. The use case diagram of the blood pressure node is shown in Fig. 2. Here let us see the implementation procedure of the blood pressure sensor node, and the operation of the nodes are as follows. 1. 2. 3.

Store Blood pressure Details Transaction Access the Blood pressure details Transaction Monitor the Blood pressure Status Transaction.

Temperature Sensor Node Ledger

Pulse Rate Sensor Node Ledger

Blood Pressure Sensor Node Ledger

Patient Smartphone Node IoT BlockChain Based Health Monitoring System

Ledger

Doctor Smartphone Node

Glucometer Sensor Node Ledger

Ledger

Respiration Sensor Node Ledger

EMG Sensor Node Ledger

Fig. 1 Nodes in the IoT blockchain-based health monitoring system

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Blood Pressure Sensor Node

Blood Pressure Control Node

BlockChain Ledger Node

[1] Sensor instantly updates the Blood pressure details in its sensors. [3] Policy threshold pressure checking smart contract operation is performed with the help of blockchain doctor manager.

[2] Extract the systolic and diastolic pressure of blood pressure Sensor

[4] Consensus raised for storing the pressure details to blockchain nodes [5] Accept to storing the pressure blockchain nodes

details to

[6] Store hash[Previous block], timestamp, Nonce, Merkle Root, hash[current block], pressure details

[7] Add this block with existing block

[8] Update the distributed ledger with the current blockchain [9] Broadcast the updated blockchain ledger to all the nodes in the blockchain

distributed

Fig. 2 Use case diagram of blood pressure details transaction in IoT blockchain-based health monitoring system

3.1 Algorithm Design for Blood Pressure Sensor Node Algorithm 1: Blood Pressure Control Process [1] [2] [3] [4] [5] [6] [7]

Installation of the Pressure nodes permanently inside body either as wearable or submersible inside the body. Blood pressure Sensor continuously updates the systolic and diastolic pressure details sensed from the body through the sensors. Extract systolic, diastolic pressure of Blood pressure Sensor for time interval Automatic systolic, diastolic pressure details extraction if it deviates from the threshold pressure levels Receive systolic, diastolic pressure details from Sensor through Internet. Blood pressure details allowed to store in distributed ledger for data analytics. Blood pressure processed data is retrieved by Blood pressure Control Node.

Algorithm 2: Store Blood pressure Details Transaction [1] [2] [3]

Get the Blood pressure details of chronically ill patients who are in treatment Policy checking smart contract operation is performed to store in blockchain Consensus raised for storing the pressure details from other blockchain nodes

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[4] [5] [6] [7] [8]

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Allow the Blood pressure details to be updated in the blockchain Store hash[Previous block], timestamp, Nonce, Merkle Root, hash[current block], Blood pressure details. Add this new block with the existing block. Update the distributed ledger with the current blockchain. Broadcast the updated blockchain ledger to all nodes in the blockchain.

4 Implementation and Performance Evaluation The Proof of Concept for the proposed IoT blockchain-based health monitoring system with Ethereum private blockchain network under a genesis block is designed in this paper. The throughput of the management hub is evaluated that affects the latency of the operations of the blockchain and is shown in “Fig. 3 and 4”. IoT devices are implemented through LibCoAP Library which is the C implementation of CoAP. The LibCoAP code was modified to automatically generate a public/private key per device which identifies the IoT devices uniquely. To test the performance of this system, the benchmark tool CoAPBench is used in this paper.

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5 Conclusion This paper attempts to explore the features of the blockchain and Internet of things. The challenges of this proposed architecture IoT blockchain-based health monitoring system are that the theoretical verification is yet to be evaluated. In this paper, we have proposed design architecture, namely IoT blockchain-based health monitoring system and designed the framework of working process model which enhances the performance of security and data transparency. The nodes involved in the IoT blockchain-based health monitoring system are clearly depicted, and the use case diagram is elaborated for the blood pressure sensor node. Also, the transaction algorithm details for the blood pressure sensor nodes are explained with all required details. Further, the same system can be improved by applying various consensus algorithms for the same to predict the performance parameters. The future work of this paper is to add and upgrade the sensors used to predict the clinical parameters of the patients. The security of the sensors is also planned to implement based on the context-based clinical information.

References 1. Yang C, Delcher C, Shenkman E et al (2018) Machine learning approaches for predicting high cost high need patient expenditures in health care. Bio Med EngOnLine 17:131 2. Ray PP, Dash D, Salah K, Kumar N (2018) Blockchain for IoT-based healthcare: background, consensus, platforms, and use cases. IEEE Syst J. https://doi.org/10.1109/JSYST.2020.2963840 3. Madaan L, Kumar A, Bhushan B (2019) Working principle, application areas and challenges for blockchain technology. In: IEEE 9th ınternational conference on communication systems and network technologies, Gwalior, India, pp 254–259 4. Cheng S, Zeng B, Huang YZ (2017) Research on application model of blockchain technology in distributed electricity market. https://doi.org/10.1088/1755-1315/93/1/012065 5. Dwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. J Sens 19:326. https://doi.org/10.3390/s19020326 6. Novo O (2018) Blockchain meets IoT: an architecture for scalable access management in IoT. IEEE J Internet Thıngs Class Fıles 14:8 7. AliM S, Vecchio M, Pincheira M, Dolui K, Antonelli F, Rehmani MH (2019) Applications of blockchains in the ınternet of things: a comprehensive survey. IEEE Commun Surv Tutor 21(2):1676–1717 8. Khari M, Garg AK, Gandomi AH, Gupta R, Patan R, Balusamy (2019) Securing data in Internet of Things using cryptography and steganography techniques. IEEE Trans Syst, Man, Cybern Syst 50(1):73–80 9. Qu C, Tao M, Zhang J, Hong X, Yuan R (2018) Blockchain based credibility verification method for IoT entities. Secur Commun Netw

Computer Vision-Based Framework for Anomaly Detection Rashmi Chaudhary and Manoj Kumar

Abstract Computer vision involves analysis and processing of images. The feature that makes computer vision a choice for research is the use of high dimensional data for generating image information. Object tracking and trajectory modelling are the two main vital applications of computer vision. In today’s scenario, million and billions of cameras are installed only in India, which is a potential source of data that can be used for extracting useful information. While tracking the movement of objects in a crowded scenario, it is of utmost importance to efficiently detect groups. By doing so one can easily maintain the crowd interaction and at the same time can detect the anomalies. In this paper, various relevant methods for anomaly detection have been reviewed. After studying those methodologies a framework for small group detection into a crowded scene has also been proposed in which with the help of morphological operations the groups are decided. The implementation of the proposed framework is to be seen as ongoing research work. Keywords Action recognition · Computer vision · Video processing · Real-time processing

1 Introduction Computer vision is an artificial intelligence field which helps in training the computer to interpret and understand the visual world by using machine learning techniques. Video surveillance [1] is one of the fields of computer vision. It has been a wide area for anomalous behaviour detection and activity recognition [2]. The frames extracted from the videos have been analysed with the help of motion-oriented techniques and methods. Time being an entity of third dimension has been introduced R. Chaudhary University School of Information Communication and Technology, GGSIPU, Delhi, India M. Kumar (B) Netaji Subhas University of Technology East Campus (Formerly AIACT&R Geeta Colony), Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_45

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while processing of the frames. Over the past few years, video surveillance has been a very important part of everyday life. Almost everywhere cameras have been installed to monitor human-based activity. The development of strong and efficient surveillance methods was an inspiration for video processing. It is used in variety of areas nowadays as in human action recognition, traffic detection, entry point checking in sensitive areas, crowd behaviour and overcrowd estimation, abnormality recognition, automatic number plate recognition, augmented reality and industrial quality inspection etc. In surveillance applications, the action is triggered with the prompt of any anomaly detected and the information is sent to the authority for preventive action against the person. With the advent of computer vision embedded with the deep learning, it is now very much possible to tackle down the problems with such anomalies. It can be done by analysing the human activity and behaviour. In this paper, several methods and approaches have been reviewed for human action tracking and anomaly detection namely; human model-based methods, template matching methods and local feature extraction methods. A summarised literature review is shown in a tabular form and also a framework is being proposed for anomaly detection. Further, the paper is composed as follows: Section 2 has the literature survey in summary and tabled format. Total 14 research papers are included in the table for literature survey. Section 3 is about the learning’s from the surveyed literature. The proposed framework is mentioned in Sect. 4 with the help of a block diagram. Section 5 is the conclusion for this paper. Future scope and the perspective of this work are also mentioned in Sect. 5 with conclusion.

2 Literature Survey The available literature on human action detection related to anomalies can be categorised in different aspects. One such method is human model-based methods. With the help of information related to body parts and movements, these methods recognise actions. It can be the use of moving light display (MLD) on the human joints to trace the actions in better form [3]. Furthermore, template matching can also be used to analyse body part in turn to track the activity. In some of works, a layered approach is used to localise body parts and detect actions. In general, there is a problem of high dimensional space for all the model-based approaches. There are also methods proposed to deal with this problem to extract an efficient human pose, background substitution has been proposed [4]. Reduction in dimensionality has also been proposed by uniformly distributing the rotations [5]. The second method is appearance-based technique which needs not to take into consideration the localisation of body components. A region of interest is used to present the required human activity. With the help of tracking or background subtraction one can find the ROI [6]. Very less computation is used by this method to identify any of the human activity. It is very useful in complex environment where it is nearly impossible to

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focus on any body part of human to track. Silhouette information is also used in several proposed methods. Extraction of silhouette is done on the different background substitution methods [7]. Another method for this is local feature method. According to this to get information about an image, local distinctive points can be very helpful. Scale Invariant Feature Transform (SIFT) converts any imaged into collection of different feature vectors. Finally, the approach used on them is called as staged filtering. Similar concepts are adapted by several feature-based techniques to process video [8]. Spatial temporal feature detector has also been proposed for the purpose of activity recognition [9]. In [10] various motion-based shape information has been applied on parts of video frames. Finally, a motion-based feature descriptor has been computed. In other work, three-dimensional human possess has been used to recognise human action. They have used non-negative matrix factorisation to find out the shape information. In [11] a GAIT energy template has been proposed to analyse the walking behaviour of any person. There is another category of these methods that uses fusion of optical flow to extract action. Optical flow has been used to detect information from ROI by applying flow-based difference between pixels of sequential frames. A robust method of event classification on the basis of dense points was introduced by Wang et al. [8] which used optical flow to detect motion in the video sequence. Using this approach to classify events, an improved version of dense trajectory was proposed by Wang et al. [12] in which they have incorporated boundary information of motion area (Table 1).

3 Lessons Learnt After going through the relevant papers, it is observed that there are few research proposals, which focused on medium and large crowd density. Few of them have actually performed morphological operations on the input data (frames). Moreover, these morphological operations are time-consuming procedures, which demand for fast implementation of morphological operations using binary image. Alternatively for fast morphological operations, arbitrary structuring elements method can also be used. This will have lower or almost equal complexity but better computing time than most of such methods. Object detection methods are not used widely. YOLO and VIBE (visual background extractor) can give better result in the form of accuracy.

4 Proposed Framework The proposed framework makes use of several computer vision-based techniques to detect groups and no groups in a crowded scene. The framework consists of the camera feed in the form of a video, which is converted to frames. These frames are used to identify the people by extracting the foreground from its background

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image and make use of morphological image processing to remove any texture and noise imperfections in the detected foreground. After this, a morphological image is obtained on which continuous boundary points are detected in the form of contours. Contour boxes though obtained are computed to detect the groups on the basis of area and threshold distance between them. The threshold area identifies groups if a large area is bounded by the contour computation process. The strong grounded Table 1 A summarised review of relevant research papers for anomaly detection S. No. Title

Authors

Details

Year Limitations and future extensions

1

A Survey of Applications and Human Motion Recognition with Microsoft Kinect [1]

Roanna Lun, Cleveland State University, Wenbing Zhao, Cleveland State University

Research on 2015 Only Kinect Motion sensor-based recognition methods were using data provided which are collected by not feasible in Kinect sensors. real-time systems Wide classification of human motion recognition is provided

2

A Survey on Zhong Zhang, Vision-Based fall Christopher Conly, Detection [2] Vassilis Athitsos. University of Texas

Collection of systems and algorithms to detect a fall status for any elderly person or for any one

2015 Dataset-based details are not provided effectively

3

A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector [3]

Debapratim Das Dawn, Soharab Hossain Shaikh University of Calcutta, Kolkata

Details on STIP-based methods to detect human action and behaviour Also discusses few datasets (public) on which the methods are applied

2016 STIP-based method can be combined with other method for betterment of it

4

Learning Temporal Regularity in Video Sequences [4]

Mahmudul Hasan, Amit K. Roy Chowdhury University of Maryland, College Park

A generative 2016 Temporal Pattern are model is not sufficient to discussed for extract information motion patterns of various activities as it is quite like eating food etc. difficult to get patterns from the video itself (continued)

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Table 1 (continued) S. No. Title

Authors

Details

Year Limitations and future extensions

5

Transition-Aware Human Activity Recognition Using Smartphones [5]

Jorge-L. Reyes-Ortiz, Luca Oneto, Albert Samá. University of Genova

This is about 2016 Neural network can transition-based be utilised to activity enhance recognition effectiveness of system. With system the help of probabilistic output of SVM prediction

6

A Review on Human Activity Recognition Using Vision-Based Method [6]

Shugang Zhang, Zhiqiang Wei. Ocean University China

This paper reviews various activity representation and classification methods

7

A survey of depth and inertial sensor fusion for human action recognition [7]

Chen Chen, Roozbeh Jafari University of Texas at Dallas, Richardson

It depicts use of 2017 Energy efficient vision and protocols for sensor inertial sensors nodes can be tried together for apart from the vision human action theory recognition efficiently

8

Deep Learning in Visual Computing and Signal Processing [8]

Danfeng Xie, Lei Zhang, and Li Bai. Temple University, Philadelphia

Applied deep learning methods for scenarios like anomaly detection on road, fault diagnosis and human activity

2017 Provide a pathway to the usage of DNN in various applications, however mostly pre trained models were used

9

Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications [9]

Abdulsalam Yassine, Shailendra Singh. Lakehead University, Thunder Bay, Canada

This paper is working on human activity patterns for health care monitoring using big data

2017 This can be extended for multiple application for betterment of daily living

10

Review of Fall Detection Techniques: A Data Availability Perspective [10]

Shehroz S. Khan, Jesse Hoey. University of Waterloo

Review of all 2017 Provided details and the types of drawback of sensors and available dataset feature however extraction image-based datasets methods used in were not explore to prevent fall widely

2017 Study for morphological-based operations can be appended

(continued)

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Authors

Details

Year Limitations and future extensions

11

Vision-based action recognition of construction workers using dense trajectories [11]

Jun Yang, Ziyan Wu. Northwestern Polytechnical University, China

Activity of construction workers is monitored to improve the productivity in terms of cost and labour

2017 It is a high computational approaches which require high configuration system hence not suitable for real-time systems

12

An Efficient Vision-based Group Detection Framework in Crowded scene [12]

Monika Pandey, Shivam Singhal

A video-based 2018 Abnormal Activity framework is can be tracked once proposed to group is detected identify a group of people from the whole standing crowd

13

What Actions are Needed for Understanding Human Actions in Videos [13]

Gunnar A. Sigurdsson, OlgaRussakovsky, Abhinav Gupta. Carnegie Mellon Univ

Human activity understanding is analysed. Analysis of dataset and metrics is done

14

An Overview of B. Ravi Kiran, Deep Dilip Mathew Learning-Based Thomas, Methods for Ranjith Unsupervised Parakkal. and Uncanny Semi-Supervised Vision Anomaly Solutions, Detection in Bangalore Videos [14]

2017 Spatio-temporal analysis including various time frame can be done

Learning-based 2018 Pre- trained models methods are with parameters can discussed for be used for various video anomaly application detection and categorised on the basis of type and model of the algorithm

feature of our approach is the threshold distance that estimates the distance between the consecutive contours and detects the crowd density. Also thresholding feature checks every individual with their detecting neighbours if they belong to the same clustering group (Fig. 1).

5 Conclusion and Future Scope for Extension In this work, we have presented a comprehensive survey on anomaly detection and human activity detection and a framework is proposed for the group detection in crowd. The complete study was bifurcated into group of methods. The focus was on

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Fig. 1 Proposed framework

human model-based methods, template modelling, layered approaches, appearancebased techniques and local feature extraction methods. Here, motion recognition methods have been reviewed successfully. Deep learning and machine learning techniques are found to be more suitable and efficient for activity recognition. For better recognition accuracy, the feature set and the parameters for the model must be chosen very carefully. Several authors have focused on crowed behaviour analysis, and still, it is a challenging area where many issues need to be addressed like enhancement in accuracy, identification of suitable algorithms for anomaly detection, handling of occlusion, accuracy enhancement in low camera quality, suitable feature extraction which can support both spatial and temporal aspect of videos etc.

References 1. Hasan M, Choi J, Neumann J, Chowdhury AKR, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733–742 2. Lun R, Zhao W (2015) A survey of applications and human motion recognition with microsoftkinect. Int J Pattern Recognit Artif Intell 29(05):1555008 3. Xu H, Tian Q, Wang Z, Wu J (2015) A survey on aggregating methods for action recognition with dense trajectories. Multim Tools Appl 75(10):5701–5717 4. Sigal L, Isard M, Haussecker H, Black M (2011) Loose-limbed People: estimating 3D human pose and motion using non-parametric belief propagation. Int J Comput Vision 98(1):15–48 5. Burenius M, Sullivan J, Carlsson S (2013) 3d pictorial structures for multiple view articulated pose estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Portland, Oregon, pp 3618–3625 6. Polana R, Nelson R (1994) Low level recognition of human motion (or how to get your man without finding his body parts). In: Motion of non-rigid and articulated objects, Austin, Texas, pp 77–82 7. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21 8. Laptev I (2005) On space-time interest points. Int J Comput Vision 64(2–3):107–123 9. Dollar P, Rabaud V, Cottrell G, Belongie S (2005) Behavior recognition via sparse spatiotemporal features. In: IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, Beijing, pp 65–72

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10. Zhang Z, Hu Y, Chan S, Chia L (2008) Motion context: a new representation for human action recognition. In: European conference on computer vision, Marseille, France. Springer, Berlin, pp 817–829 11. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322 12. Wang H, Kläser A, Schmid C, Liu C (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vision 103(1):60–79 13. Zhang Z, Conly C, Athitsos V (2015) A survey on vision-based fall detection. In: Proceedings of the 8th ACM international conference on pervasive technologies related to assistive environments, pp 1–7 14. Dawn DD, Shaikh SH (2016) A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. Vis Comput 32(3):289–306

Novel Method for Relative Automobile Maintenance Index for Smart Cities Navin Sridhar and Kamalnath Venkateswaran

Abstract The automobile industry is by far one of the world’s largest economic sectors by revenue and a primary mode of transportation for many developed and developing economies. In another side, smart vehicles are an indispensable part of smart cities. People invest in automobiles to promote personal well-being and facilitate ease of convenience of lives to commute from one place to another. Apart from having sufficient financial capital to get an automobile, there are various factors to consider before buying like price, quality, cost of ownership, and reliability. However, when looking out for retaining the vehicle for long-term use, the maintenance cost of ownership of the automobile is an essential factor. A consumer needs to consider general service cost, accidental service cost, repair/replacement cost, insurance, and other imperative costs such as fuel consumption and labor. By considering these factors, a novel method named normalized maintenance index is proposed to compute a relative vehicle maintenance index which estimates an authentic and precise maintenance index score based on relative data. The index score is calculated based on the relative cost of different vehicles available in the current market based on engine type, range of kilometers, and vehicle model. The resulting maintenance index score of each model of car available in the market can reflect the consumer or the original equipment manufacturer to gain an in-depth analysis of the vehicle along with insights on its performance cost on each factor. This paper provides an index of the vehicle model, analyzing its potential and limitations and underlining its possible future applications for smart cities, providing new insights toward the definition, assessment, and maintenance of future smart vehicles. Keywords Maintenance index · Vehicle sustainability · Reliability index · Vehicle maintenance · Smart cities · Smart vehicles

Navin Sridhar (B) · Kamalnath Venkateswaran Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Anna University, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_46

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1 Introduction Smart cities have been drawing attention of researchers as seen in recent intensive studies. In association with this fact, this situation is expected to continue in future works. Smart vehicles are an indispensable part of smart cities. Scientists have been researching vehicles and transportation in order to reach safe and reliable mobility. The automobile industry is the fastest-growing sector globally; its dynamic growth phases make it one of the leading industries at a global level. It plays a vital role in the development of the worldwide economy due to the high revenues and inflated client demands. The automobile industry helps foster the country’s economic development; thus, it is recognized widely as a serious economic sector. Today, the global automobile industry is concerned with growing consumer demands for efficient maintenance costs while retaining a vehicle. When choosing the right vehicle, there are various factors to consider. Be it a new vehicle or a used vehicle, certain factors are essential while choosing the right vehicle: price, quality, cost of ownership, and reliability, the price corresponds to the value or price of the vehicle at which it is sold in the current market. Quality refers to the mechanical, electronic, and technological components and accessories that build the vehicle. Cost of ownership corresponds to costs, including fuel, scheduled maintenance, insurance, labor, and depreciation value of the car. It is a mandatory cost to every vehicle and varies peculiarly even from one vehicle model to another. Everyone who owns a vehicle goes through repair or replacement of parts as the vehicle ages or any accidents occur, the cost demanded for such corresponds to the vehicle’s reliability. The factors quality, cost of ownership, and reliability associate with the maintenance factor of the vehicle. It varies from model to model of the vehicle and is relative to all the vehicles present in the current market. It is complex to consider the factors quality, cost of ownership, and reliability associated corresponding to every vehicle before opting since these data can only be obtained after the vehicle starts to perform in the current market. Since this is one of the essential factors before purchasing the vehicle, there is much demand among the public relating to the automobile industry. Many researchers have focused on studying and obtaining insights regarding maintenance performance. Some researchers applied the analytical hierarchy process (AHP) to study the automobile market [1–3]. AHP, however, has some constraints, such as this method requires expert assistance to give scores for each considered property of an automobile in measuring performance. This makes the obtained result subjective and not efficient to operate. To overcome the constraints from the results obtained from the AHP method, data envelopment analysis (DEA) is employed by certain researchers [4–9]. DEA developed by Charnes et al. [10] is a popular nonparametric efficiency evaluation method. It evaluates relative efficiencies of decision-making units (DMUs) with their multiple inputs and outputs [11]. However, while employing the DEA approach, research leads to two main constraints. The first one is that the automobile market has not been studied. The prior DEA based researches suggestions cannot give effective implications for the automobile market. The second and the most important one is the maintenance

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cost for the reliability of the automobile has been ignored in the maintenance evaluation [12]. According to the Automotive Maintenance and Repair Trade Association, the quantitative relation of the total value of all automobile components to the value of a finished automobile is nearly twelve times. Therefore, the maintenance cost is a significant factor in the automobile purchase decision for a consumer. In this paper, the proposed novel method named normalized maintenance index based on max–min approach [13, 14] is employed to compute the maintenance index of automotive vehicles while considering the maintenance cost based on the factors that affect the quality, cost of ownership, and the reliability of the vehicle, namely general service index (GSI), accidental service index (ASI), repair/replace index (RRI), insurance index (INI), and imperative index (IMI). By adopting the method, the composite index is computed, which is a method of combining several variables or indicators to reflect overall assessment [15–17]. Each factor’s indices are summed up to provide the composite index named relative maintenance index (RMI) that implies a vehicle with lower RMI will have lower maintenance cost that helps the consumers and OEMs know the relative maintenance performance cost of a specific automobile model.

2 Methodology The proposed method, normalized maintenance index, is employed to normalize the indicators and construct indices ranging between 0 and 1 by determining the upper bound (maximum value) from the inputs, and the lower bound (minimum value) is considered as a zero in all the calculations since the cost is always positive. The normalized output is further scaled by multiplying with the respective factor’s weight. The factors include general service, accidental service, repair/replacement due to wear and tear, insurance, and other imperative factors such as fuel consumption, and labor. Each factor’s calculated indices are summed up to provide the composite index named relative maintenance index (RMI). The RMI of an automobile is based on the cumulative relative index value of general service index (GSI), accidental service index (ASI), repair/replace index (RRI), insurance index (INI), and imperative index (IMI). The dataset, vehicle maintenance record [18], is curated from the service records of vehicles obtained through expert consultation from the respective field, and the official OEMs Web site is used for the proposed method of computing relative automobile maintenance index. The dataset consists of general mandatory service data, service record data, data of a list of the cost of components of the vehicle, fuel consumption data, labor, and insurance cost data of six models of the vehicle used. The weightage of each factor of RMI is based on insights obtained from the experts of the respective field.

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2.1 General Service Index (GSI) General service refers to the cost of the automobile components that are repaired/ replaced in the general maintenance cycle that is recommended by the original equipment manufacturers (OEMs). General service index (GSI) is the ratio of the sum of the cost of total services to the maximum cost of total services among all automobiles. GSI is computed for each automobile and is relatively valued on a scale of 3. Lower the GSI indicates lower general service maintenance cost of a specific automobile. The general service maintenance cost and components that are to be serviced at regular intervals are identified, data up to eight general services are collected through records available from OEMs Web site, and sample data from dataset [18] are given in Table 1. Thus, the GSI for each automobile is computed as follows: Generalmax = max

Cn

(1)

Cn /Generalmax × 3

(2)

1 0.” Various mechanisms satisfy the definition of DP, and these mechanisms are known as differentially private mechanisms. In the category of perturbation-based techniques for DP, Laplace and Gaussian mechanisms are examples that add noise from the Laplace and Gaussian distributions respectively. ε is considered to be the privacy parameter that governs the level of privacy that can be achieved by DP in order to protect sensitive information.

2.2 Sensitivity [1] “The Sensitivity of a mechanism M is given by S = max ||M(D1 )−M(D2 )||1 D1,D2

(2)

where ||D1 – D2 ||1 = 1.” Sensitivity denotes a single data value having maximum possible magnitude such that this data value represents the difference between two datasets both consisting of data values of the same nature. Along with ε, sensitivity is the other parameter that determines the amount of noise generated in Laplace and Gaussian mechanisms.

2.3 Utility Metrics [5] “The accuracy of inferences obtainable from data altered by a mechanism as compared to original data is known as Utility of that mechanism.” There are various absolute percentage error metrics defined to measure utility like Mean Absolute Percentage Error (MAPE) [6], Symmetric Mean Absolute Percentage Error (SMAPE) [6], and Modified Mean Absolute Percentage Error (MMAPE) [7].

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They can be mathematically given as    (xi − xi )    MAPE = μ   xi    (x − x )  i   i SMAPE = μ  xi +xi    2    xi − xi   MMAPE = μ  xi + 1 

(3)

(4)

(5)

where μ denotes the average/mean function, xi denotes the original data value, and −

xi denotes the perturbed data value.

2.4 Utility and Privacy Privacy and utility are inversely related, and there is always a trade-off between them. Highly private data is less useful, and highly useful data is less private. The goal is to find an equilibrium condition where the perturbed data achieves a benchmark level of privacy but at the same time can be used to obtain useful inferences.

3 Related Work In this section, the existing local data aggregation scheme is explained. This is followed by a discussion on research work associated with the healthcare domain by employing DP techniques to maintain the privacy of personal health information. Finally, research done in order to discover exploits by identifying security vulnerabilities in fitness trackers is highlighted. In the existing PPEA scheme [3], LDP has been achieved via adding random noise to the collected data values using either the Laplace or the Gaussian mechanism. The effectiveness of this technique has been validated by measuring utility metrics MAPE, SMAPE, and MMAPE for varying values of ε and the number of endpoints using two existing datasets, the smart meter dataset, and the stock ticker dataset. The demonstration of this scheme has been done using the summation operation as the aggregation function. Privacy-preserving aggregation of personal health data streams [8] has been achieved by developing a novel mechanism for personal health data streams characterized as temporal data collected at fixed intervals leveraging LDP. This technique has used the Laplace mechanism to achieve LDP. It uses an existing PAMAP2

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physical activity monitoring dataset consisting of health data obtained from wearable devices for analysis. Another technique, mSieve [9], has achieved Differential Behavioral Privacy for time series of mobile sensor data. It has obtained results using a consolidated dataset consisting of 660 h of ECG, respiration, and activity data collected from 43 participants via physiological chest bands, inertial wrist bands, and GPS-enabled smartphones. Both these schemes do not achieve privacy guarantees using DP for data collected in real time. Re-DPoctor [10] is a real-time health data releasing scheme with w-day DP where the privacy of health data collected from any consecutive w days is preserved. Its performance has been evaluated by experimenting on captured heart rates from wearable devices attached to a hospital patient for a period of three months. Though, this scheme only collects real-time heart rate data from a single endpoint whereas our proposed extension to PPEA does so from multiple endpoints simultaneously. Research has also been carried out to expose hardware attack vectors [11] that enable circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware used in its fitness trackers, leading to spoofing of valid encrypted fitness data of individuals. Work has also been done to exploit the pairing vulnerability in BLE [12] for healthcare devices used in medical telemetry applications. This work has demonstrated exploiting the device pairing process of Fitbit Flex. SensCrypt [13] is a protocol devised for secure data storage and communication in affordable and lightweight personal fitness trackers. Associated research has also presented a case of reverse engineering and identifying security vulnerabilities in two such trackers, Fitbit Ultra and Gammon Forerunner 610. Though, this technique only protects private health data at rest and is not designed for safeguarding private’ health data in transit. [14] has discussed how to extract live data from Xiaomi’s fitness tracker Amazfit Bip among numerous other trackers, by using the same BLEbased protocol mechanism that allows extraction of live data from Mi Band 2, another tracker manufactured by Xiaomi. However, there exists no DP based solution that can effectively assist in privatizing streaming health data collected from fitness trackers. So, we hereby propose to extend PPEA, which follows a highly secure local DP, and make it suitable for health data from fitness trackers. Reference [15] demonstrates a series of vulnerabilities present in the Fitbit ecosystem by using techniques like protocol analysis, software decompiling, and static and dynamic embedded code analysis to reverse engineer communication symantics, leading to a loss of user privacy.

4 System Architecture Design The system architecture design shown in Fig. 1 consists of four components, the sensor and the compute node, which together form an IoT edge device, a partial aggregator, and finally, a server. The PPEA algorithm is divided into four stages, and each stage is associated with one of these components.

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Fig. 1 System architecture design

1. 2.

Real-Time Data Acquisition—Real-time heart rate values are collected from the two sensors and are passed onto the respective compute nodes. DP Noise Addition—This stage consists of three sub-tasks which execute serially on the compute nodes, operating on the obtained heart rate values from the respective sensors. (a) (b)

(c)

Noise Addition before Splitting—Each heart rate value is noised using the Laplace or the Gaussian mechanism. Splitting—The noised heart rate values are split into unique random fractions with the number of splits depending upon the number of partial aggregators. These partial aggregators are required to achieve aggregator obliviousness as explained in [3]. The scheme considers two partial aggregators to be sufficient to satisfy this property. Noise Addition after Splitting—A large random noise is added to every split of every noised heart rate value. These splits are then sent over to the two partial aggregators.

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4.

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Partial Aggregation—The two partial aggregators calculate partial sums of the splits they receive and then send these over to the server which acts as the final aggregator. Final Aggregation—The server performs the final average aggregation operation.

5 Experimental Setup One of the sensors is Xiaomi’s Mi Band 2 fitness tracker, and the other one is an XD58C pulse sensor. Both the compute nodes are Arduino UNO boards. Since an Arduino UNO board does not have an in-built capability to communicate with an external device using BLE, an ESP32 Wi-Fi Bluetooth development board is used as an intermediary between the Mi Band 2 and the Arduino UNO. The ESP32 collects real-time heart rate values in beats per minute (bpm) from the Mi Band 2 via BLE and then sends them over to the Arduino UNO. The ESP32 is considered to be part of the sensor itself. The same Arduino UNO boards that act as the compute nodes also provide the functionality of partial aggregators. The server runs on a standard laptop device. The setup can be seen in Fig. 2.

Fig. 2 Experimental setup

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6 Implementation The code is divided into three parts. The main algorithm proposed by the scheme runs on the Arduino UNO boards and has been implemented in Arduino’s programming language. Since these Arduino UNO boards are also simultaneously acting as the partial aggregators, sending splits from one board to the other is implemented using serial communication. Calculation of the partial sums is also implemented in the same code running on these boards. The server has been implemented using Python 3.5 and is responsible for acquiring the partial sums and calculating the average of noised heart rate values obtained from the sensors. The ESP32 code establishes a BLE connection with the Mi Band 2 and then performs real-time heart rate acquisition from it. This code has been implemented in C++. The Arduino has an in-built pseudo-random number generator which only generates random integers following the uniform distribution. In order to generate a random floating-point number, a random integer over a large range is generated and is then divided by the length of the range. There exists no out-of-the-box library for the Arduino programming language which enables the generation of random numbers following the Laplace and the Gaussian distributions. The fact that the difference between two independent random numbers following the Exponential distribution gives a random number following the Laplace distribution [16] has been used. The Box–Muller method [17] has been used to generate random numbers following the Gaussian distribution.

7 Results The utility metrics for PPEA for both Laplace and Gaussian mechanisms have been compared with a central method of aggregation which also uses both Laplace and Gaussian mechanisms for perturbation via noise addition. In this central method, all the data values are directly aggregated at the final aggregator without the presence of intermediate partial aggregators and then post the average operation having been performed by the final aggregator, and cumulative noise is added to this average value. The results for utility metrics as well as execution times have been obtained for varying ε values from 0.01 to 2 and for varying number of sensors from 5 to 90 with a step of 5. The sensitivity has been considered to be 220 since this is the maximum heart rate that can be recorded for a human. The results have been obtained using one fitness tracker and one pulse sensor that mimics the presence of another fitness tracker. The number of sensors is varied by acquiring multiple heart rate values repeatedly from these two sensors themselves. This setup mimics the presence of multiple sensors. Streaming real-time heart rate values from the set of sensors results in the computation of the average heart rate before noise perturbation to vary across iterations.

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To obtain a more accurate average heart rate value, the averages calculated across a certain number of iterations have again been averaged.

7.1 Utility Metrics Utility metrics MAPE, SMAPE, and MMAPE have been measured to validate the effectiveness of the PPEA algorithm. MAPE and SMAPE results for varying values of ε and number of sensors have been shown. MMAPE results have not been shown since they are similar to the MAPE results. By increasing the value of ε and keeping the number of sensors equal to 90, the privacy utility trade-off can be observed in Figs. 3 and 4. As ε increases, privacy decreases, and the utility increases, indicated by decreasing MAPE and SMAPE values, and vice-versa. Also, the central method using both the Laplace and Gaussian mechanisms (Central-L and Central-G) has better utility as compared to the local method using the same mechanisms (PPEA-L and PPEA-G), since the aggregate amount of noise added in the central method is less as compared to the local method. By increasing the number of sensors and keeping the value of ε equal to 0.1, it can be observed in Figs. 5 and 6 that the utility increases. An increase in the utility with an increasing number of sensors is highly desirable since a network of sensors in an IoT ecosystem will typically have a large number of sensors.

Fig. 3 MAPE for varying values of ε

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Fig. 4 SMAPE for varying values of ε

Fig. 5 MAPE for varying number of sensors

7.2 Memory Consumption Overall memory consumption on a single resource-constrained Arduino UNO board has been measured to analyze the feasibility of implementing the PPEA algorithm on a processing device such as this one.

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Fig. 6 SMAPE for varying number of sensors

• Program storage space = 12% (3950 bytes out of a maximum of 32,256 bytes) • Global variables’ dynamic memory usage = 9% (200 bytes out of a maximum of 2048 bytes, which leaves 1848 bytes for local variables) The above memory usage is for the algorithm running on data values collected only from a single sensor and consisting of only the three sub-tasks as part of the DP noise addition stage that are executed on the resource-constrained compute node as part of the IoT edge device, along with the implementation requirements for the generation of random numbers following the Laplace or the Gaussian distribution. As the memory utilization is minimal, most of the memory remains available and free to be used by the device to perform other tasks.

7.3 Execution Time Total execution time using both the Laplace and the Gaussian mechanisms with respect to varying values of ε, comprising sub-tasks of the algorithm’s DP noise addition stage running on a single resource-constrained Arduino UNO board for data collection from a single sensor, has been measured to analyze the implementation feasibility. With an increase in the value of ε, the total execution time without the use of the PPEA scheme remains almost constant on the millisecond time scale, lying within a range of 0.008 ms and having an upper bound of approximately 0.076 ms. For PPEA-L and PPEA-G, it again remains fairly constant, lying in a range of 0.04 ms

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Fig. 7 Total execution time for varying ε

with an upper bound of approximately 1.385 ms, which can be concluded to be affordable, given the level of privacy achieved. This can be observed in Fig. 7.

7.4 Power and Energy Consumption With the ATmega328p microprocessor on the Arduino UNO board running at 16 MHz in active mode and the board powered via a 5 V USB cable, the average current consumption of the board is 45–50 mA. The maximum current consumption of the board goes up to 145–150 mA if a DC voltage supply is used. Accordingly, the average power consumption in the former case is 0.225– 0.25 mW. The power consumption on Arduino with a constant clock frequency and a fixed voltage source remains constant and independent of the workload running on the board. Considering the total execution time required for performing the DP noise perturbation computation to be 1.385 ms, the additional energy consumption on the edge device is nominal and equal to 311.625–346.25 mJ, as compared to the edge device directly sending the obtained sensor values to the aggregator, without applying the DP technique.

8 Conclusion and Future Scope In this work, we have provided a proof of concept for effectively employing the PPEA scheme in a network of IoT edge devices focusing on personal health devices like fitness trackers, to establish privacy guarantees against the release of personal health

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information like periodic heart rate values of individuals. We have also described and addressed the challenges posed by the IoT devices for implementing such randomization schemes, particularly because of the presence of resource-constrained environments leading to the lack of in-built out-of-the-box provisions. The work presented in this paper can be extended to a large-scale network comprising multiple IoT edge devices from the healthcare domain. We have focused on privatizing personal health data acquired from a fitness tracker. However, this work could be extended to achieve the privacy guarantees on data acquired from various other healthcare devices that monitor the personal health parameters of individuals.

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16. Samuel K, Tomasz K, Krzysztof P (2001) The laplace distribution and generalizations: a revisit with applications to communications, economics, engineering and finance. Birkhauser. pp 23 (Proposition 2.2.2, Equation 2.2.8). ISBN 9780817641665 17. Box G, Muller M (1958) A note on the generation of random normal deviates. Ann Math Statist 29(2):610–611. https://doi.org/10.1214/aoms/1177706645

Performance Analysis of Duobinary and CSRZ Modulation Formats on Self-Phase Modulation Effect in Optical Communication Network Using Fiber Bragg Grating (FBG) TusharKant Panda, A. B. Mukesh Kumar Behera, Guriya Kumari, and Swadhin Polei Abstract With advancement in the field of atomic scale fabrication and increase in demand for high data traffic, optical communication has become the backbone of modern-day communication. It offers low attenuation, high capacity and low power consumption in a long-haul communication environment. Though optical communication has a lot of advantages, it also gets affected by the detrimental effects like dispersion and nonlinearities. This article presents a detailed analysis on the impact of duobinary and carrier suppressed return to zero (CSRZ) modulation formats on unfavorable nonlinear self-phase modulation. Here, simulations are carried out by using optisystem software, and the performance of the design is measured in terms of bit error rate (BER) and Q factor. Furthermore, fiber Bragg grating (FBG) is used in the receiver end to combat the dispersion caused due the pulse spreading. Finally, a comparison is made between the performances of both the modulation formats. Keywords Fiber nonlinearities · Duobinary modulation · CSRZ modulation · M-z modulator · Bit error rate · Q factor

1 Introduction Fiber nonlinearities are considered as one of the major detrimental effects of communication networks. Nonlinear impacts in optical fibers basically occur because of the variation in the refractive index with respect to the intensity of the signal and also due to inelastic scattering mechanisms [1, 2]. The power dependence of refractive index produces Kerr effect [3]. The Kerr-nonlinearity can produce effects like four wave mixing (FWM), self-phase modulation (SPM) and cross phase modulation (CPM) contingent on the sort of information signal applied [4, 5]. In this paper, the impacts of self-phase modulation are considered. Self-phase modulation can influence the phase of the signals that result in spectral broadening of pulses and can lead to dispersion T. Panda · A. B. Mukesh Kumar Behera (B) · G. Kumari · S. Polei Department of Electronics and Communication Engineering, GIET University, Gunupur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_51

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[6]. Recently, nonlinear effects in optical fibers have generated a lot of interest in the mind of researchers due to the challenges that optical communication faces because of this. The modulation formats used in the optical communication link also play a vital role while determining the efficiency of the network. The most widely recognized modulation format in optical networks is on–off-keying (OOK), which uses either non-return-to-zero (NRZ) or return-to-zero (RZ) forms [7, 8]. NRZ modulation format is preferred primarily due to its cost effectiveness, relative simplicity of generation and the signal bandwidth that requires only half of the bandwidth used for RZ modulation format. Terrestrial optical networks that employ wavelength division multiplexing (WDM), where cost is an essential factor, typically employ NRZ modulation. RZ modulation however offers better immunity to fiber nonlinear effects [9]. Nonlinear effects coming about because of single channel propagation evident mainly through self-phase modulation, where each channel modifies its own phase. This can result in chromatic dispersion [10, 11]. If the system is operated beyond the threshold power level, it can also affect the system as it makes SPM grow which eventually leads to chromatic dispersion. Akira Suda et al. in their paper investigated the impacts of nonlinear chirp on self-phase modulation [12]. Wuth et al. investigated the effect of self-phase modulation on duobinary and single sideband modulation formats and have also compared the receiver sensitivity using Q factor and bit error rate with respect to input power and fiber length [13]. Selvendran et al. investigated the effect of duobinary and CSRZ modulation formats on self-phase modulation. A dispersion compensating fiber (DCF) of length 20 km is used to combat the effect of dispersion produced due to self-phase modulation. The performance of the link is calculated with variation of input power and a varying data rate in terms of quality factor and bit error rate [14]. In this paper, we investigate the impact of duobinary and CSRZ modulation formats on self-phase modulation. To combat the effect of pulse broadening produced due to SPM, we have used fiber Bragg grating (FBG) as dispersion compensator. Furthermore, the receiver sensitivity is analyzed in terms of Q factor and bit error rate (BER) with variable input power of the laser, and the performance is measured also by varying the datarate. A comparison is also made between the performances of both the modulation formats. This paper is organized as follows, Sect. 2 represents the simulation setup for both the modulation formats, Sect. 3 is the result analysis, and the Sect. 4 is the conclusion.

2 Simulation Setup To analyze the performance of the system, simulations are carried out using optisystem 14 software. The corresponding simulation setups are shown in figure. Figure 1 represents the simulation setup for the transmission link using duobinary modulation format. A pseudorandom bit sequence generator is used in the transmitter

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Fig. 1 Simulation setup with duobinary modulation

side that generates a pulse train of 1s and 0s which is fed into the input of a duobinary modulator that modulates the signal with a continuous wave laser operated at a frequency of 193.1 THz [15]. To combat the effect of the dispersion accumulated in the channel, an FBG is used at the receiver end cantered around the same frequency as the laser. The signal is then retrieved using a photodetector followed by a low pass filter. Finally, the output is analyzed using a BER analyzer that determines performance parameters like BER, Q factor and eye height. Similarly, Fig. 2 represents the simulation setup implementing CSRZ modulation format [16].

Fig. 2 Optical communication setup with CSRZ modulation

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Figure 3 shows the duobinary subsystem where signal is first generated with an NRZ pulse generator by using a pseudorandom bit sequence generator as a precoder. The generator drives the first Mach-Zhender modulator (MZM) and then concatenates this modulator with a second modulator that is driven by a sinusoidal electrical signal. The duobinary precoder is composed of an ex-or gate with a delayed feedback path. In order to obtain the recursive decoding in the receiver, the precoder is used. Figure 4 shows the CSRZ subsystem where a CSRZ signal is generated in a very similar way to the RZ format. However, the frequency of the sinusoidal electrical

Fig. 3 Duobinary subsystem

Fig. 4 CSRZ subsystem

Performance Analysis of Duobinary and CSRZ Modulation Formats … Table 1 Fiber parameters

Table 2 Simulation parameters taken in optisystem

625

Parameters

Values (SMF)

Reference wavelength

1550 nm

Fiber length

50 km

Dispersion

17 ps/nm/km

Dispersion slope

0.075 ps/nm2 /km

Effective area

80 μm2

Simulation parameters

Values

Bit rate

5–30 Gbps

Number of samples

8192

Samples per bit

64

Sequence length

128

signal applied in the second MZM has half of the bit rate. The second MZM is biased in a way to provide alternating optical phases between 0 and π for the neighboring time slots. The phase of bits ‘1’s is alternating with a difference of 180 degrees. This phase difference causes the elimination of the carrier at 193.1 THz. The values of the various parameters of the fiber are tabulated in Table 1, and Table 2 represents the values of various simulation parameters.

3 Results and Discussions In this simulation, evaluation is done for both the modulation formats in terms of performance parameters like BER and Q factor. Simulations are carried out for various values of input power ranging from 0 to 30 dbm. The data rate of the system is also varied from 5 to 30 gbps, and the corresponding results thus obtained in terms of Q factor and BER are listed in following tables. While Table 3 represents results of duobinary modulation, Table 4 represents results obtained using CSRZ modulation format. It can be observed from Table 3 that results obtained in duobinary modulation are pretty much similar irrespective of the input power variation. However, better results are obtained at 5 gbps, 10gbps, 20gbps and 30gbps for i/p power of 25 dbm, 20 dbm, 20 dbm and 15 dbm, respectively. The eye diagrams of the corresponding values are given below in Figs. 6, 7, 8 and 9, respectively. Similarly, a graph is plotted showing the variation of Q factor with respect to input power for various data rate and is shown in Fig. 5. The data enlisted in Table 4 suggests that the results obtained in CSRZ modulation are quite better than the results obtained in duobinary modulation. The best results are obtained at 5 gbps, 10gbps, 20gbps and 30gbps for i/p power of 30 dbm, 25 dbm,

0

1.981

2.02965

2.03982

2.04508

2.72923

2.71914

5

10

15

20

25

30

2.14801

2.15081

2.15809

2.15457

2.14025

2.0857

0

2.67567

2.06759

1.99024

1.95565

1.94705

1.93906

0

2.04028

2.00849

2.00826

2.18254

2.18116

1.90938

0

0.00263563

0.00313185

0.0196407

0.0199201

0.0204691

0.0231517

1

5 gbps

30 gbps

BER at different data rates 20 gbps

5 gbps

10 gbps

Q factor at different data rates

0

POWER (dbm)

Table 3 Measured Q factor and BER for the different input power and data rate in duobinary modulation

0.00281509

0.0146783

0.0143639

0.0145124

0.0150698

0.0173746

1

10 gbps

0.00371063

0.0191488

0.0231435

0.025075

0.0255681

0.0260371

1

20 gbps

0.00781128

0.0189126

0.0188214

0.013176

0.0129418

0.0251165

1

30 gbps

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7.3116

7.75929

7.93031

8.02655

8.02867

8.70087

8.69016

5

10

15

20

25

30

6.42023

6.77246

6.64843

6.58457

6.55854

6.49485

6.22659

2.39513

2.44875

2.19988

2.99189

2.98719

2.96624

2.91464

0

2.58052

2.58628

2.59796

2.62259

2.67028

2.76846

1.80E−18

1.63E−18

1.11E−16

4.98E−16

1.09E−15

4.26E−15

1.32E−13

5 gbps

30 gbps

BER at different data rates 20 gbps

5 gbps

10 gbps

Q factor at different data rates

0

POWER (dbm)

Table 4 Measured Q factor & BER for the different input power and data rate in CSRZ modulation

6.41E−11

6.33E−12

1.47E−11

2.26E−11

2.69E−11

4.12E−11

2.37E−10

10 gbps

0.000104291

0.00563156

0.0114437

0.00137712

0.00139405

0.00147872

0.00169804

20 gbps

1

0.00380678

0.00376245

0.00367113

0.00346751

0.00309331

0.00241946

30 gbps

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Q-Factor

2.5 2 1.5 1 0.5 0 0 dbm

5 dbm

10 dbm

15 dbm

20 dbm

25 dbm

30 dbm

Bit Rates 5 gbps

10 gbps

20 gbps

30 gbps

Fig. 5 Variation of Q factor with respect to input power at various data rate for duobinary modulation

Fig. 6 Eye diagram of duobinary system at 5 Gbps and 25 dBm

25 dbm and 0 dbm, respectively. The graph showing the variation of Q factor with respect to input power at various data rate and eye diagrams for the corresponding values is given below in Figs. 10, 11, 12, 13 and 14, respectively.

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Fig. 7 Eye diagram of duobinary system at 10 Gbps and 20 dBm

4 Conclusion In this paper, the effect of SPM on duobinary and CSRZ modulation formats has been analyzed and simulated using optisystem 14 software. The simulation results show that with higher datarate and high input power CSRZ modulation performs better as compared to duobinary modulation. Whereas due to fiber nonlinearities, the performance of duobinary modulation is restricted. The higher value of Q factor and less BER supports our claim of the proposed CSRZ system to be a better candidate for combating the effect of SPM at higher datarate. This work can be further extended by using a hybrid dispersion compensation module for effective dispersion compensation.

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Fig. 8 Eye diagram of duobinary system at 20 Gbps and 20 dBm

Fig. 9 Eye diagram of duobinary system at 30 Gbps and 15 dBm

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Q-Factor

10 8 6 4 2 0

0 dbm

5 dbm

10 dbm

15 dbm

20 dbm

25 dbm

30 dbm

Bit Rates 5 gbps

10 gbps

20 gbps

30 gbps

Fig. 10 Variation of Q factor with respect to input power at various data rate in CSRZ modulation

Fig. 11 Eye diagram of CSRZ system at 5 Gbps and 30 dBm

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Fig. 12 Eye diagram of CSRZ system at 10 Gbps and 25 dBm

Fig. 13 Eye diagram of CSRZ system at 20 Gbps and 15 dBm

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Fig. 14 Eye diagram of CSRZ system at 30 Gbps and 0 dBm

References 1. Gall HL, Jamet JP (1971) Theory of the elastic and ınelastic scattering of light by magnetic crystals. In: First-order processes, vol 46, ıssue. 2. Wiley Online Library 2. Santhanam P, Prober DE (1984) Inelastic electron scattering mechanisms in clean aluminum films. Res Gate, Phys Rev 29(6) 3. Hoffmann MC, Brandt NC, Hwang HY, Yeh KL, Nelson KA (2009) Terahertz Kerr effect. AIP Appl Phys Lett 95(23) 4. Sihval NK, Garg AK (2015) Simulation and analysis of self phase modulation fiber non linearity. J Multidisc Eng Sci Technol (JMEST) 2(6). ISSN: 3159-0040 5. Agrawal GP (2019) Cross-phase modulation. In: Nonlinear fiber optics, 6th edn, Chap 7, pp 245–295 6. Panada TK, Patra KC, Barapanda NK, Rao MM, Mishra P (2016) Comparison of dispersion compensation performance using fiber bragg grating and dispersion compensating fiber of a standard fiber optic link. Int J Concept Comput Inf Technol 4(4) 7. Mendez AJ, Morookian JM (2002) Return-to-zero (RZ) modulation of multifrequency lasers (MFLs) for application to optical CDMA. Int Soc Opt Eng 4653 8. Liang H, Li W, Linze N, Chen L (2010) Comparison of return-to-zero and non-return-tozero coded pulses. In: 9th ınternational conference on optical communications and networks (ICOCN 2010), IEEE Xplore 9. Bobrovs V, Porins J, Ivanovs G (2007) Influence of nonlinear optical effects on the NRZ and RZ modulation signals in WDM systems. Electron Electr Eng Technol 4(76):55–58 10. Udayakumar R, Khanaa V, Saravanan T (2013) Chromatic dispersion compensation in optical fiber communication system and its simulation. Indian J Sci Technol (IJST) 616(33959)

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11. Rostami A, Oskouei MS (2006) Investigation of chromatic dispersion and pulse broadening factor of two new multi-clad optical fibers. Int J Comput Sci Netw Secur (IJCSNS) 6(8) 12. Suda A, Takeda T (2012) Effects of nonlinear chirp on the self-phase modulation of ultra short optical pulse. Appl Sci 2(2):549–557 13. Shake I, Takara H, Kawanishi S (2003) Simple Q factor monitoring for BER estimation using opened eye diagrams captured by high-speed asynchronous electrooptical sampling. IEEE Photon Technol Lett 15(4) 14. Yahya CB (2005) Analysis of optical performance monitoring methods based on Q factor and bit error rate estimation. In: Second IFIP ınternational conference on wireless and optical communications networks (WOCN 2005). Published by IEEE Xplore 15. Jadon U, Nain H, Mishra V (2016) Analysis of duo-binary modulation scheme in single channel on the basis of BER. In: International conference on recent trends in electronics, ınformation and communication technology (RTEICT 2016). IEEE Xplore 16. Selvendran S, Avaninathan S, Kadarkarai EM (2018) Investigation on the ınfluence of duobinary and CSRZ modulation formats on self phase modulation effect in optical communication network. Int J Sci Res Phys Appl Sci (IJSRPAS 2018) 6(4):17–22

COVID-19 Patient Health Management System Using IoT P. Ramchandar Rao, Ch. Rajendra Prasad, Sridevi Chitti, Shyamsunder Merugu, and J. Tarun Kumar

Abstract In the pandemic situations, the physicians will never have the direct contact with the patients. Hence, a remote health management device is developed in this paper using ESP32 and ThingSpeak cloud application. If a person is suspected of having COVID-19, he has to contact primary health centre of his nearest place where the developed device is already been located. The person has to place the above device in contact with his body to measure temperature, heartbeat, oxygen level and cough. The same device will have the feature to measure room temperature and humidity. Various sensors are used to detect the above parameters. The sensed values will be taken up by ESP32. The data from ESP32 is stored in cloud through ThingSpeak application. The physician can take over the details from cloud and diagnose whether the person is suffering from COVID-19 or not. In this paper, the diagnostic information followed by the physician is the body temperature which is >37.8 °C, heartbeat is >100, oxygen level is 116 db. If these measures are satisfied then the buzzer sounds which alerts the consulting physician. Keywords ESP32 · Temperature sensors · Pulse sensor · Vibration sensor · Pulse oximeter · ThingSpeak · Buzzer · Display

P. Ramchandar Rao Department of ECE, Center for Embedded & IoT, SR University, Warangal, Telangana 506371, India Ch. Rajendra Prasad (B) · S. Chitti · J. Tarun Kumar Department of ECE, School of Engineering, SR University, Warangal, Telangana 506371, India e-mail: [email protected] S. Chitti e-mail: [email protected] S. Merugu Department of ECE, Sumathi Reddy Institute of Technology for Women, Hasanparthy, Telangana 506371, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_52

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1 Introduction In any advancement that advances the human race in terms of technology, health is still a major concern. The current corona virus attack that destroyed the world economy, the value of health care has become an example. In those places where the epidemic is spread, tracking such patients using remote health monitoring technology is often a safer option. The method of remote patient monitoring enables patient assessment beyond conventional medical settings, which extends access to human being property offices at cheap price [1]. Main aim of development of this project is smart health monitoring and alert, by using sensors are attached to the patient and in case of any problems, uses the Internet to alert their loved ones. The aim of designing monitoring systems is to minimize healthcare costs by minimizing visits, hospitalizations and diagnostic testing procedures to physician offices [2]. Both of our bodies use temperature and pulse recognition to explore well-being comprehension. A sensors output is connected to ESP32 to monitor the patient condition, and liquid crystal display also has the ability to swap alarms remotely. If the system senses any abrupt changes in the perception of heartbeat, body temperature and body vibrations due to cough the system alert the doctor to the position of patients under IOT and also display parameters of the patient’s vibrations, heartbeat and temperature live on the site or in screen. SMS-based patient prosperous screening and IOT-based patient monitoring system, there is a significant capacity. Different clients may view subtle parts of the patient flourishing in the IOT-based framework [3]. The explanation seems to be that when going through a page or URL, the information should be checked. Global System for Mobile Communications-based patient monitoring, the prosperous results are sent using GSM via SMS, where medicinal ability will not be in nearer to the citizens in most rural areas [4]. Normally, individuals ignore some kind of minor health conditions that are indicated by changes in vital elements such as body temperature in the early stages, heartbeat, etc. When the health problem has been elevated to a serious position and people life is threatened, they need health checkup; it may affect needless wasting of their income. It is often particularly taken into account when such epidemics are distributed in a region where it is difficult for doctors to reach them. In order to prevent the spread of disease, a feasible approach to save many lives will be to offer sensors which are connected to people those who are tracked from remote area [5]. In this paper, the device is developed by assembling all five sensors in a board. In the literature, the symptoms of the disease are not measurable. In this work, the symptomatic values are displayed to the doctor. However, the device is unable to recognize the asymptomatic COVID-19 patients. Section 2 of this paper describes the proposed method and explains the experimental setup which includes block diagram and schematic diagram, and Section 3 is about the working and implementation of project. In Sect. 4, this paper shows the ThingSpeak and experimental results.The World Health Organization released some symptoms of corona virus [6] (Table 1).

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Table 1 Difference between corona virus and other diseases symptoms Symptoms

Corona virus

Cold

Flu

Fever (≥37.8 °C)

Common

Rare

Common

Cough

Common (Usually dry & continuous)

Mild

Common

Shortness of breath

Sometimes

No

No

Headache

Sometimes

Rate

Common

Sore throat

Sometimes

Common

Sometimes

Runny/stuffy nose

Rate

Common

Sometimes

Sneezing

No

Common

No

Diarrhoea

Rare

No

Sometimes

2 Proposed System The block diagram of the proposed scheme is shown in Fig. 1. To measure the temperature, heartbeat, oxygen, cough the patient, the sensors are connected into the patient’s body. There is one more sensor DHT11 to measure the humidity and room temperature where the patient is residing. These sensors’ outputs are connected to a ESP32 that reads the values of all five sensors. Then those measured values are uploaded into IoT cloud with the help of ThingSpeak channel. The doctor then accesses the values from the base station at some other location. Thus, the doctor will determine the condition of the patient on the basis of the temperature, heartbeat, oxygen level, vibration values and the room sensor values, depending on the values necessary steps are taken.

Fig. 1 ESP32 pinout diagram

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As per World Health Organization report, the COVID-19 patient symptoms are commonly fever, cold/cough, shortness of breath. According to common symptoms test, we proposed this system. In this system, doctors are no need to directly interact the patient and there is chance to decrease the spreading of virus [7]. ESP32 Control Unit: The ESP32 module is a cost-effective, low power control system on a on-chip microcontroller with incorporated Wi-Fi module in it and dual mode Bluetooth [8]. The source code is stored in the on-chip memory available on the ESP32. This block can be considered as an interface between the programmer and the user. So, it is considered as the heart of the block. In ESP32, the operating voltage range is from 2.2 to 3.6 V. The voltage value at 3.3 V under regular function the ESP32 thing will control the chip. The ESP32 module is highly integrated with builtin antenna switches, filters, RF Balun, and controlling amplifier, noiseless receiver amplifier and power managing modules. Features of ESP32: Processors: CPU is operating at 160 or 240 MHz and 32-bit LX6 performing at up to 600 DMIPS of microprocessor of Xtensa single-core or dual-core co-processor of ultra low power (ULP). The static RAM (SRAM) memory is 520 KB, connectivity type is wireless, the Wi-Fi version type is 802.11 b/g/n, and the Bluetooth version is v4.2 BR/EDR and BLE [9]. LM35 Temperature Sensor: The LM35 is linear monolithic temperature sensors. It is a 3-terminal sensor used to measure surrounding temperature ranging from − 55 to 150 °C. For every one degree raise in temperature, the output voltage of sensor is 10 mV. The output of sensor is linearly calibrating degree centigrade. LM35 is a temperature measuring device having an analog the output voltage is proportional to the temperature. It provides output voltage in centigrade [10]. It does not need any exterior calibration circuitry, and sensitivity of LM35 is 10 mV/°C. LM35 gives temperature output which is more precise than thermistor output. The pins of LM35 device are shown in Fig. 2. In general, human body temperature is 36–37.5 °C is normal, >37.5 °C is high and below 95 to 99 is very healthy, 100 or oxygen level is 116 db the buzzer will give the emergency siren and alert the doctor or take care. The entire data is uploading to cloud server (Fig. 10).

4 Experimental Results Figure displays full prototype of health monitoring device along with the sensors. The sensors measured data is on the LCD monitor which is shown in Figs. 11 and 12; the cloud values and graphs are displayed on mobiles when doctor interfaces the cloud server. Using the IoT application platform, registered users can access this data from the cloud. The patient’s sensor values are shown in the application, as shown in Fig. 12. Based on these obtained values, the patient’s condition is diagnosed by doctor. The medicines can be administered, and necessary action can be recommended even from a distance by the doctor.

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Start

Temperature Sensor

Pulse Sensor

t>37.8

Hbt>100

Yes

Yes

Vibrator Sensor

Oxygen Sensor

t>35 & h>30%

O2116d b Yes

Temp & Humidity Sensor

Yes

Yes

Buzzer ON, Liquid Crystal Display and Upload into Cloud

Fig. 10 Flowchart of proposed system

5 Conclusion The IoT-based system is the possible solutions for any isolated patient track in the area of health monitoring in particular. This makes it easier to protect the individual wealth constraint data within cloud, to minimize stay in the hospital for traditional exams, and most importantly to track condition of patient and diagnose disease at any distance by any doctor. A health monitoring framework based on IoT was established in this paper. The device uses sensors, which are also displayed on an LCD, to monitor human body temperature, heartbeat rate, oxygen level, cough level and room humidity and temperature. By the technology of wireless communication, these sensor values are then sent to a IoT cloud hospital database system. Such information is then collected with the IoT platform on an approved personal smart phone and diagnosis the patient.

6 Future Work The symptoms like taste in the tongue and brain activity are also the indications of COVID-19 so that these parameters must be considered in the future work in the development of the device. mqtt protocol is suggested to mark the checklist which is already saved in database.

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(a) Temperature

(b) Heartbeat

(c) Vibration sensor Fig. 11 ThingSpeak application graphs. a Temperature of patient, b heartbeat of patient, c cough level

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Fig. 12 Patient health condition displayed on LCD

References 1. Joyia GJ, Liaqat RM, Farooq A et al (2017) Internet of medical things (IOMT): applications, benefits and future challenges in healthcare domain. J Commun 12 (4) 2. Banka S, Madan I et al (2018) Smart healthcare monitoring using IoT. Int J Appl Eng Res 13(15):11984–11989 3. Perumal K, Manohar M (2017) A survey on ınternet of things: case studies, applications, and future directions. In: Internet of things: novel advances and envisioned applications. Springer International Publishing, pp 281–297 4. Riazulislam SM et al (2015) The ınternet of things for health care: a comprehensive survey. In: IEEE access 5. Rizwan P, Suresh K (2017) Design and development of low investment smart hospital using Internet of things through innovative approaches. Biomed Res 28(11) 6. Di Gennaro F, Pizzol D, Marotta C, Antunes M, Racalbuto V, Veronese N, Smith L (2020) Coronavirus diseases (COVID-19) current status and future perspectives: a narrative review. Int J Environ Res Public Health 17(8):2690 7. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 8. Almotiri SH, Khan MA et al (2016) Mobile health (m- health) system in the context of IOT. In: IEEE 4th ınternational conference on future ınternet of things and cloud workshops (FiCloudW), pp 39–42 9. Darshan KR et al (2015) A comprehensive review on usage of internet of things (IoT) in healthcare system. In: Proceedings of ınternational conference on emerging research in electronics, computer science and technology 10. Pravalika V, Rajendra Prasad C (2019) Internet of things based home monitoring and device control using Esp32. Int J Recent Technol Eng 8(1 Special Issue 4):58–62 11. Ramchandar Rao P, Srinivas S, Ramesh E (2019) A report on designing of wireless sensor networks for IoT applications. Int J Eng Adv Technol 8(6 Special Issue 3):2005–2009. https:// doi.org/https://doi.org/10.35940/ijeat.F1236.0986S319 12. Deepak N, Rajendra Prasad C, Sanjay Kumar S (2018) Patient health monitoring using IOT. Int J Innov Technol Explor Eng 8(2):454–457 13. Sanjay Kumar S, Ramchandar Rao P, Rajendra Prasad C (2019) Internet of things based pollution tracking and alerting system. Int J Innov Technol Explor Eng 8(8):2242–2245 14. Prasad CR, Bojja P (2020) The energy-aware hybrid routing protocol in WBBSNs for IoT framework. Int J Adv Sci Technol 29(4):1020–1028 15. Ramchandar Rao P, Merugu S et al (2020) Automated grain repository using IOT. J Mech Continua Math Sci 304–312

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16. Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun Syst 70(4):617–634 17. Khari M, Garg AK, Gandomi AH, Gupta R, Patan R, Balusamy B (2019) Securing data in Internet of Things (IoT) using cryptography and steganography techniques. IEEE Trans Syst Man Cybern Syst 50(1):73–80 18. Balas VE, Jha S, Khari M, Kumar R (eds) (2019) Internet of things in biomedical engineering. Academic Press 19. Khari M (2018) Wireless sensor networks: a technical survey. In: Handbook of research on network forensics and analysis techniques. IGI Global, pp 1–18 20. Internet of Things (IoT) https://www.statista.com/statistics/471264/iot-numberof-connecteddevices-worldwide/ 21. Chavan P, More P et al (2016) ECG—remote patient monitoring using cloud computing. Imperial J Interdisc Res 2(2)

Implementation of Artificial Neural Network for Image Recognition Using Chinese Traffic Sign Image Dataset Manisha Vashisht

and Brijesh Kumar

Abstract In the last few decades, the increasing potential of Information technology revolutionized data and information management, in particular, the data acquisition, data processing, and predictions. The effort has been truly interdisciplinary, where, image processing techniques, and artificial intelligence (AI) based model implementation have played their roles. The latest technology innovations have enabled the researchers to execute computational experiments which would have never been possible if these would have been tried using the conventional methods. Traffic signage detection is considered one of the most researched subjects in area of computer vision and image processing. So far, there has been limited research conducted on this subject using the Chinese Traffic Sign research database (TSRD) and also the results obtained are not so encouraging. This paper provides innovative approach of using HSV (Hue, Saturation, Value) base image transformation approach on TSRD dataset and implementing artificial neural network for image detection purposes. This research is conducted on a pilot of 10 image categories taken from TSRD and the results have found to be highly encouraging. The results observed on small dataset have encouraged to extend research covering the entire image dataset. Keywords Object recognition · Object detection · Machine learning · Image processing · Artificial intelligence · Computer Vision · Traffic Signage · Traffic Sign Recognition (TSR) · MATLAB

1 Introduction With ever-increasing population and growing traffic on roads, it has become increasingly important to follow rules while driving. National Highway Traffic Safety Administration (NHTSA) data survey points out that human mistakes are reason behind 94% of road accidents. A survey conducted in the year 2017 provided alarming M. Vashisht (B) · B. Kumar Manav Rachna International Institute of Research and Studies, Faridabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_53

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results describing that 52,274 drivers were involved in 34,247 road accidents, in which 37,133 people lost their lives. The data from these findings makes it all the more important for scientists across the globe to contribute to scientific ways using the latest technologies to help in reduction of such accidents in future [1]. It has been a global practice to place the traffic signage across the roads to help the drivers take timely actions to prevent accidents. The signages are placed on roads in such a manner that these are easily visible to drivers in all weather conditions [2]. This research paper has used public dataset of Chinese traffic sign research databases [3]. The TSRD database has 6164 images containing 58 sign categories, which are further divided into two sub-databases as training database and testing database. The training database has got 4170 images and testing database has 1994 images, which are annotated using sign and category coordinates. Further, we have used HSV color spaces for image transformation [4–8]. Hue refers to the color’s angle, saturation tracks the quantity of the color and Value denotes the brightness. HSV is known to be a cylindrical color model where colors are mixed and then are shown within the cylindrical bounds, making it easier for human beings to understand. This has been one of the reasons for researchers considering hue-based color features in areas addressing traffic signage detection problems [10–14]. The challenge with respect to identify the correct image from the image dataset requires the mathematical model to learn from the patterns and identify the correct image. In past, ANN has been successfully used as a technique for solving problems where the solution needs model to learn from the available patterns [15–20]. For image recognition, we have used ANN technique on the 10 image categories dataset identified from Chinese TSRD. The paper is organized as below: • Section 2 covers the literature review, describing work done in the similar area by researchers in past. • Section 3 describes the high-level black diagram and the ANN framework • Section 4 provides the results of ANN model, and • Section 5 concludes with guidance on the future scope of work. Next, we provide insights from the past research work done in the subject area of research.

2 Literature Review Viola-Jones [21] introduced the concept of real-time detection of human face while utilizing the method of window made to slide and multi-scale feature extraction. Histogram of Oriented Gradients (HOG) technique was used by Dalal and Triggs [22] during 2005 for successfully detecting the moving pedestrian people.

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Gao et al. [11] provided an argument in the year 2006 claiming when both shape and color feature techniques of extraction are used together, the output obtained has better recognition accuracy for images captured under different weather conditions. In the year 2007, Broggi et al. [23] proposed an innovative traffic signage system based on neural networks classification algorithm. Color segmentation and RGB color space were considered for recognizing the shape of traffic sign. The results showed improvement in the image detection accuracy. Later in the year 2008, Mingqiang et al. [8] provided a detailed comparative analysis of various techniques of extracting features from images considering multiple parameters including shape signatures and transform domains. In the year 2014, Benco et al. [9] provided a detailed study on the gray-level cooccurrence matrix (GLCM) which was used for statistical texture features extraction. The technique was further optimized for extracting probability matrices from the color images for finalizing the texture classification. In 2015, Qian et al. [7] made use of the Chinese traffic sign dataset for detection and recognition by applying deep convolutional neural network (CNN) which is known to produce high performance with regard to detection rate and recognition accuracy. The results were found to be encouraging to pursue further research in the same area. In the year 2018, Singh et al. [24] used 50,000 images from German traffic sign recognition benchmark (GTSRB) and proposed a neural network-based scientific model that resulted in accuracy of around 97% on the test image dataset. In 2019, Tabernik and Skoˇcaj [25] used 200 traffic sign categories using convolution neural network (CNN) technique for detecting traffic signs and obtained mean average precision (map) of 95%. In 2019, Jain et al. [26] used genetic algorithm-based technique on Chinese image database using CNN model, and achieved accuracy of 99.16%. In 2019, Sapijaszko et al. [27], proposed three-layer feedforward multilayer perceptron on TSRD dataset and achieved an image recognition accuracy of 96%. In the year 2019, Wang and Guo [28] have proposed an efficient CNN-based approach that follows the YOLO model. The authors have introduced normalized batches along with the region proposal network to optimize the model. The results showed improvement in the traffic sign detection speed and reduction in the hardware compute of the infrastructure requirement.

3 Proposed Framework for Model Design For purpose of developing traffic signage detection framework, the high-level architecture diagram is shown in Fig. 1. The 10 image categories from the Chinese TSRD have been taken as an input. Feature extraction has been implemented using HSV model and ANN has been used for image recognition. The framework considers the images from the dataset as inputs which undergoes feature extraction process and then ANN classifier algorithms provided the image recognition results. MATLAB tool provides access to Neural Network Toolbox which has been used to design, develop, train and validate an ANN-based model. By default,

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Fig. 1 High-level block diagram

the number of hidden layers is set to 10. This number can be changed, as per the network optimization requirements. Hidden layers help in optimizing the model results. Levenberg–Marquardt algorithm is used for training the network considering its lesser memory requirement, better optimization capability and fast speed. Figure 2 shows the neural network and displays algorithms used for data division and training. Figure 3a depicts visualization of errors between target values and predicted values after training a feedforward neural network. Figure 3b shows variation in gradient coefficient with respect to number of epochs. It can be seen that gradient value goes on decreasing with increase in number of epochs. The total numbers of epochs used are 7. Training state represents the current progress/status of the training at a specific time while training is in progress.

4 Results The regression value depicts a closer relationship with respect to object recognition results obtained from 10 image categories of Chinese TSRD. The R-value denotes the

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Fig. 2 ANN configuration

correlation that exists between outputs and targets. Figure 4 represents the regression relationship and defines the prediction reliability of the network designed. Overall test results on our proposed framework conducted on considered datasets, has Rvalue of 1 on training data, 0.98 on validation data and 0.97 on testing and 0.99 on the combined data. The value of R denotes a closer relationship and prediction capability. The results obtained are better and comparable to the past researches done on the same dataset [26, 27].

5 Conclusion Results from our experiments recommend that the proposed framework based on ANN has provided encouraging results. The result further suggests that model has good quality of fit and capability of prediction. The conclusion is built on experiments conducted on 10 image categories of Chinese traffic signage image dataset

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Fig. 4 Regression results

and application of HSV and artificial neural networks for image recognition. The regression results are limited to the 10 categories of the TSRD image dataset. For future, it is recommended to extend the results to the entire TSRD image dataset and extend comparison using other machine learning algorithms.

References 1. Traffic Safety Facts, Research Note, NHTSA’s National Center for Statistics and Analysis, April 2019 2. Congress IR (2012) Code of practice for road signs 3. National Nature Science Foundation of China (NSFC) (n.d.). Database Home. (online) www. nlpr.ia.ac.cn. Available at: https://www.nlpr.ia.ac.cn/pal/trafficdata/index.html. Accessed 6 Oct 2020

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4. Wang G, Ren G, Quan T (2013) A traffic sign detection method with high accuracy and efficiency. In: Proceedings of the 2nd international conference on computer science and electronics engineering. Atlantis Press 5. Zhang J, Huang M, Jin X, Li X (2017) A real-time chinese traffic sign detection algorithm based on modified YOLOv2. Algorithms 10(4):127 6. Kaplan K, Kurtul C, Ak˙In HL (2012) Real-time traffic sign detection and classification method for intelligent vehicles. In: 2012 IEEE international conference on vehicular electronics and safety (ICVES 2012). IEEE, pp 448–453 7. Qian R, Zhang B, Yue Y, Wang Z, Coenen F (2015) Robust Chinese traffic sign detection and recognition with deep convolutional neural network. In: 2015 11th international conference on natural computation (ICNC). IEEE, pp 791–796 8. Mingqiang Y, Kidiyo K, Joseph R (2008) A survey of shape feature extraction techniques. Patt Recogn 15(7):43–90 9. Benco M, Hudec R, Kamencay P, Zachariasova M, Matuska S (2014) An advanced approach to extraction of colour texture features based on GLCM. Int J Adv Rob Syst 11(7):104 10. Warsi F, Khanam R, Kamya S, Suárez-Araujo CP (2019) An efficient 3D color-texture feature and neural network technique for melanoma detection. Informatics in Medicine Unlocked, 17, p 100176 11. Gao XW, Podladchikova L, Shaposhnikov D, Hong K, Shevtsova N (2006) Recognition of traffic signs based on their colour and shape features extracted using human vision models. J Vis Commun Image Represent 17(4):675–685 12. Gupta S, Trivedi MC (2016) Hand skin classification from other skin objects using multidirection 3D color-texture feature and cascaded neural network classifier. In: Proceedings of international conference on ICT for sustainable development. Springer, Singapore, pp 523–534 13. Singh S, Gupta SC (2016) Human object detection by HoG, HoB, HoC and BO features. In: 2016 fourth international conference on parallel, distributed and grid computing (PDGC). IEEE, pp 742–746 14. Del Alamo CJL, Pérez LJF, Calla LAR, Lovón WRR (2013) A novel approach for image feature extraction using HSV model color and niters wavelets. In: 2013 XXXIX Latin American computing conference (CLEI). IEEE, pp 1–7 15. Munakata T (1998) Fundamentals of the new artificial intelligence, vol 2. Springer, Heidelberg, p 43 16. Vashisht V, Lal M, Sureshchandar GS (2015) A framework for software defect prediction using neural networks. J Softw Eng Appl 8(08):384 17. Vashisht V, Lal M, Sureshchandar GS (2016) Defect prediction framework using neural networks for software enhancement projects. J Adv Math Comput Sci, pp 1–12 18. Vashisht V, Measuring and analyzing the impact of implementing sub process monitoring and defect prediction model in the software development life cycle. shodhganga.inflibnet.ac.in 19. Vashisht V, Kamya S, Vashisht M (2020) Defect prediction framework using neural networks for business intelligence technology based projects. In: 2020 international conference on computer science, engineering and applications (ICCSEA). IEEE, pp 1–5 20. Boetticher G, Srinivas K, Eichmann DA (1992) A neural net-based approach to software metrics 21. Viola P, Jones M (2001) Robust real-time face detection. In: Null. IEEE, p 747 22. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 886–893 23. Broggi A, Cerri P, Medici P, Porta PP, Ghisio G (2007) Real time road signs recognition. In: 2007 IEEE intelligent vehicles symposium. IEEE, pp 981–986 24. Singh M, Pandey MK, Malik L (2018) Traffic sign detection and recognition for autonomous vehicles. Int J Adv Res Ideas Innov Technol (IJARIIT) 4(2):1666–1670 25. Tabernik D, Skoˇcaj D (2019) Deep learning for large-scale traffic-sign detection and recognition. IEEE Trans Intell Transp Syst 21(4):1427–1440 26. Jain A, Mishra A, Shukla A et al (2019) A novel genetically optimized convolutional neural network for traffic sign recognition: a new benchmark on Belgium and Chinese traffic sign datasets. Neural Process Lett 50:3019–3043. https://doi.org/10.1007/s11063-019-09991-x

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27. Sapijaszko G, Alobaidi T, Mikhael WB (2019) Traffic sign recognition based on multilayer perceptron using DWT and DCT. In: 2019 IEEE 62nd international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 440–443 28. Wang Z, Guo H (2019) Research on traffic sign detection based on convolutional neural network. In: Proceedings of the 12th international symposium on visual information communication and interaction, pp 1–5

Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor Ajit Kumar Sahoo and Siba K. Udgata

Abstract Temperature plays a vital role in determining the environmental conditions. Non-contact ultrasonic sensors use time of flight (ToF), which depends on the speed of sound in the measurement medium. The medium of propagation influences the speed of sound, and in the air medium, it is highly affected by temperature, humidity, and other gases present in the medium. Ambient temperature can be estimated using the speed of sound, time of flight, and the object’s distance from the ultrasonic sensor with proper compensation of humidity effect. The ultrasonic temperature measurement system determines the average temperature of the medium based on the changes in ultrasonic sound speed in the medium of travel. This paper proposes a non-contact ultrasonic sensor-based ambient temperature estimation system using two machine learning approaches: multiple linear regression (MLR) and support vector machine (SVM) regression. A low-cost 40 kHz ultrasonic transducer (HC-SR04) is used for the experiment to determine the temperature of the medium. The proposed ultrasonic sensor-based temperature estimation system is preferable in confined spaces such as room, boiler, tank, and other industrial applications where the temperature needs to be measured in a non-contact manner. To validate the proposed system’s accuracy, experiments are conducted in different environmental conditions with temperature ranging from 22 to 45 ◦ C and relative humidity ranging from 30 to 85%. Experimental results indicate that in the proposed measurement system, the temperature estimation error is bounded by ±0.4 ◦ C. Keywords Ultrasonic sensor · Machine learning · Time of flight · Temperature estimation · Speed of sound

A. K. Sahoo · S. K. Udgata (B) School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, India e-mail: [email protected] A. K. Sahoo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_54

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1 Introduction Non-contact ultrasonic measurements are a cost-effective solution for many applications such as industrial, medical, and scientific research [4]. The most common sensors used for air temperature measurement are single-point temperature measurements and are usually called sensor by contact. These sensors must contact the air and use their radiant energy to change their physical characteristics to measure temperature. These sensors usually do not respond instantly, and their measurement range is limited [12]. A non-contact ultrasonic temperature measurement technique is highly desirable for instantaneous ambient temperature measurement. The ultrasonic sensor measures the distance based on the ToF method. The speed of sound in the air can be calculated from the ToF and the distance as given in Eq. (5). The ultrasonic sensor can estimate the average ambient temperature from the change in sound speed, using the relationship between temperature and speed of ultrasonic sound, as described in Eq. (4). However, the speed of sound also depends on the humidity in addition to temperature. The estimated ambient temperature is higher than the actual temperature because the sound speed in humid air is faster than that of dry air. Single-point temperature measurement will not give expected accuracy due to the gradient of temperature and humidity in the measurement medium. Most of the existing works on ultrasonic temperature measurement is based on signal processing techniques, and experiments are performed in a controlled environment and low measurement ranges. This paper presents a machine learning approach to accurately estimate the air temperature using a low-cost ultrasonic sensor with different ultrasonic measurement ranges and environmental conditions with temperature ranges from 22 to 45 ◦ C, relative humidity ranges from 30 to 80%. In the proposed method, the model is trained using the speed of sound and relative humidity, and then the sensor node can estimate the ambient temperature more accurately. The main contributions of this study over existing works are summarized below: • Non-contact temperature measurement based on machine learning approaches without modification of hardware. • The proposed system measures the highly accurate temperature of the measurement medium, which is not possible in single-point temperature sensors. • Long-range temperature estimation and faster response time (less than 100 ms compared to standard temperature sensor response time 2 s). The remainder of this paper is structured as follows. Section 2 reviews the existing works related to ultrasonic sensor temperature measurements. Principles behind the ultrasonic-based measurement are discussed in Sect. 3. The theory behind how the velocity of sound is affected by temperature and humidity is explained in Sect. 4. The proposed machine learning method is described in Sect. 5. System implementation and experimental procedures are covered in Sect. 6. In Sect. 7, results and discussion are presented, and conclusions with the future scope are discussed in Sect. 8.

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2 Related Works Temperature measurements play an important role in various applications. In the past several decades, ultrasonic temperature measurement has been evolving as a new temperature measurement technology for environments. The techniques of temperature measurement using ultrasound in the air include the time of flight technique [2, 5, 6, 12]. In [3], the authors proposed a non-intrusive method for the temperature measurement of stored biomass based on acoustic sensing techniques. The accuracy of temperature measurement of stored biomass is with a maximum error of 1.5 ◦ C under all test conditions. Tsai et al. [12] proposed an ultrasonic air temperature measurement system with self-correction for humidity without humidity correction, it is accurate to ±0.4◦ C, with humidity correction, and it is correct to ±0.3 ◦ C from 0 to 80 ◦ C. A new microcomputer-based air temperature measurement system using the ultrasonic time of flight technique is presented in [5]. The experiment conducted with temperature ranging from 0 to 80 ◦ C, relative humidity range from 20 to 90% RH, and the distance considered is 50–200 mm. The standard uncertainty of the temperature measurement is approximately 0.39 ◦ C. Liao et al. [7] proposed an ultrasonic temperature sensor system to measure the temperature of an air conditioner (AC) in an automobile with an accuracy of ±0.4 ◦ C with temperature ranges from 0 to 80 ◦ C and distance of 100 cm with a response time within 100 ms. Motegi et al. [9] demonstrated an acoustic technique for simultaneous measurement of air temperature and humidity in moist air. The accuracy of measurement within 0.5 K and the temperature is 293–308 K, and relative humidity (RH) is 50–90% RH. Sahoo et al. [11] proposed an improved neural network algorithm to improve the accuracy of the ultrasonic measurement system with compensation temperature and relative humidity. Rochhi et al. [10] presented an analytical method based on ultrasonic signal reconstruction to improve the accuracy of the ultrasonic measurement method. Existing ultrasonic temperature measurement methods discussed above are based on ultrasonic signal processing techniques, limited measurement ranges, and most of the experiments performed in controlled environments. Since there is a gradient of temperature and humidity in the measurement medium, so a single-point measurement will not give accurate temperature measurement. Standard sensors usually do not respond instantly; thus they are not ideal for dynamic tracking measurements with fast changing temperature. Therefore, in this work, we propose machine learning algorithms to increase measurement accuracy. The experiment was performed at different distances ranging from 100 to 400 cm and in different environmental conditions to test and validate the proposed model.

3 Basic Principle of Ultrasonic Measurement Ultrasonic sensors are a reliable and cost-effective solution to many non-contact measurement applications. It operates based on the principle of measurement of the ToF. ToF is the round trip time of the emitted signal and its return after getting

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Reflected signal

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Fig. 1 Working principle of ultrasonic-based measurement system

reflected by an object. The ultrasonic sensor converts electrical energy into acoustic waves and vice versa. The acoustic wave signal is an ultrasonic wave traveling at a speed of sound c with a frequency above 20 kHz. A 40 kHz ultrasonic sensor HC-SR04 is used in this experiment. This sensor has a wide range of non-contact distance measurement capabilities from 2 to 400 cm. The ultrasonic transmitter (Tx) transmits ultrasonic wave pulses toward the object, and the receiver (Rx) receives an echo signal reflected from the object, as shown in Fig. 1. A microcontroller unit (MCU) is used for communication with an ultrasonic sensor. To begin measuring the distance, the MCU sends a trigger signal to the ultrasonic sensor. A signal of +5V (HIGH) is sent over the trigger pin for around 10 µs to trigger the sensor. The ultrasonic sensor generates eight 40 kHz ultrasonic waves, and the echo pin goes high until the wave returned after reflected from the object. The total transit time of an ultrasonic wave transmitted to and reflected from the object is used to determine the distance between the ultrasonic sensor and the object. The distance d from the sensor to the object is c × ToF (1) d= 2

4 Theory The speed of sound wave traveling in a medium strictly depends on the medium properties [1]. For an ideal gas, the speed of sound can be expressed as  c=

γ RT M

(2)

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where c is the speed of sound, γ is the specific heat ratio, R is the universal gas constant (R = 8.314 J/(mol K) ), T is the absolute temperature, and M is the molecular mass of the gas. For air, γ = 1.40 and M = 0.02896 kg/mol. The propagation speed of ultrasonic wave at 0 ◦ C is 331.45 ± 0.05 m/s. For every ◦ 1 C increase in temperature, the speed of sound increases 0.607 m/s. Graphs of Eq. (3) shown in Fig. 2a represent the correlation between speed of sound and temperature. When the temperature is known, the formula for calculating sound velocity is (3) c = 331.45 + 0.607 ∗ Tc m/s where Tc is the temperature in degree Celsius. The temperature can be estimated from the speed of sound using Eq. (4). Tc =

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(4)

From Eq. (2), it is observed that the speed of sound increases with increase in temperature. It is evident from Eq. (1) that ToF depends on the speed of sound. As speed of sound increases, the ToF decreases (Eq. 3). c=

2×d ToF

(5)

Therefore, ToF increases with decrease in speed of sound and the medium temperature. An increase in relative humidity in the air increases the speed of sound by a small amount. The combined effect of temperature and relative humidity on speed of sound is shown in Fig. 2b.

5 Proposed Method 5.1 Multiple Linear Regression (MLR) Multiple linear regression (MLR) technique is used to formulate the complex input– output relationship. The main objective of MLR is to find out an approximation linear function between a set of explanatory (independent) variables and the response (dependent) variable. y = β0 + β1 x1 + · · · + βi xi + · · · + βm xm + 

(6)

where y is the dependent variable, xi is the ith independent variable, βi is the polynomial coefficients of xi , m is the number of independent variables, and  is the model’s error term or residuals. In this experiment, the temperature is chosen as the dependent variable, while relative humidity and speed of sound are chosen as an independent variable.

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5.2 Support Vector Machine (SVM) Regression SVM is a supervised machine learning method for classification and regression [8, 13]. It is based on nonlinear statistical technique to transform input space into feature spaces of higher dimensionality. SVM regression is an extension of the SVM, for predicting numerical values. Instead of generating hyperplane, a different function is derived on the basis of training data to predict numerical values. Given a set of training data points (x1 , y1 ), (x2 , y2 ), . . . , (xi , yi ), . . . , (xn , yn ) ⊂ X × R , X is the input vector space, xi is the input vector, yi is the observed output value which corresponds to input vector xi , and n is the number of samples. The regression function can be represented as follows: f (w1 , w2 , . . . , wn , b) = y = w, x + b + 

(7)

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(8)

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The kernel function plays a pivotal role in the SVM performance evaluation. We used radial basis function (RBF) kernel function in the SVM model for its better generalization ability compared with other kernel functions.

5.3 Performance Evaluation Metrics The performances of MLR and SVM regression were examined based on the estimation of ambient temperature. Five standard statistical performance evaluation metrics, root mean-squared error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R 2 ) were calculated on test data samples.

6 System Implementation The experimental setup of the ultrasonic temperature measurement system is shown in Fig. 3, which comprises of a 40 kHz ultrasonic sensor, a temperature and humidity sensor, a bluetooth module, and arduino microcontroller. Bluetooth module is used to communicate the measurement result to the personal computer to examine the measurement result and further processing. Specifications of all these components are listed in Table 1. The ultrasonic sensor consists of two transducers, one for emitting sound pulses at 40 kHz frequency and the other is to detect the sound wave (echo signal) reflected from the target object surface. The operating distance range of the ultrasonic sensor is 2–400 cm. For testing, the ultrasonic transducer was fixed at some height on a wall, and time of flight values were collected at four different distances: 100, 200, 300, and 400 cm. Experiments were performed at various temperature and relative humidity

Fig. 3 Snapshot of experimental setup of ultrasonic temperature measurement

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Table 1 Components used in this experiment and its specifications Components Specifications Microcontroller Ultrasonic sensor Temperature and humidity sensor Bluetooth module

Arduino nano (ATmega328) HC-SR04 DHT22 (AM2302) HC-05

levels, with temperature ranging from 22 to 45 ◦ C and relative humidity ranging from 30% RH to 85% RH. Nearly, 11,000 data samples are collected consisting of ToF, temperature, and relative humidity for this experiment at four different distances: 100, 200, 300, and 400 cm.

7 Results and Discussion This section presents the numerical results of the experiments performed with the MLR and SVM regression models. The entire dataset consisting of 11,000 samples is randomly divided into a training dataset (8250 data points; 75% of the total data) and the test dataset (2750 data points; 25% of the total data). Each training data point consists of three variables (temperature (t), relative humidity (h), and speed of sound (s)). The raw data of both the training set and testing set are normalized. The SVM regression model parameters are optimized using cross-validations. To evaluate the performance and generalization capabilities of MLR and SVM regression models, both the models are used to estimate the temperature on the test dataset. The results of MLR and SVM model are shown in Figs. 4 and 5, respectively. Statistical evaluation parameters (RMSE, MSE, MAE , MAPE, and R 2 ) of MLR and SVM model on the test data are listed in Tables 2 and 3, respectively. In the case of MLR, Fig. 4a–d, the histogram of residual values shows the frequency of residual values. Most residual errors are concentrated around zero, revealing a good fit of the model to the observed data. But in the case of SVM, most of the residual errors are dispersed more in comparison with MLR, as shown in Fig. 5 a–d. Similar observations can be seen in Fig. 4e–h of MLR and Fig. 5e–h of SVM model. The coefficients of determination (R 2 ) values of both models indicate that MLR performs better than SVM regression. It is thus seen that the MLR model outperforms the SVM model.

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Fig. 5 Performance plots of SVM regression, a–d histogram of residuals and e–h actual temperature versus predicted temperature Table 2 Performance (RMSE, MSE, MAE, and MAPE and R 2 ) of MLR Measurement RMSE MSE MAE MAPE ranges (cm) 100 200 300 400

0.1605 0.1619 0.1511 0.1590

0.0257 0.0262 0.0228 0.0253

0.1406 0.1332 0.1244 0.1317

0.0043 0.0040 0.0038 0.0039

R-square 0.9993 0.9994 0.9994 0.9994

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Table 3 SVM regression model performance (RMSE, MSE, MAE, MAPE, R 2 ) Measurement RMSE MSE MAE MAPE R-square ranges (cm) 100 200 300 400

0.3257 0.3187 0.3733 0.3256

0.1061 0.1016 0.1394 0.1060

0.2616 0.2590 0.3043 0.2776

0.0082 0.0081 0.0099 0.0089

0.9973 0.9976 0.9969 0.9975

8 Conclusions and Future Scope The machine learning-based ultrasonic temperature measurement system can accurately measure the average ambient temperature with a reasonable accuracy bounded by a maximum of ±0.4 ◦ C under the experimental conditions. The instant response time of the system is 100 ms, which is much less than the response time of standard temperature sensors. Especially in an environment with fluctuations in temperature and humidity levels, the proposed system can accurately measure the temperature. The results indicate that the MLR technique outperforms the SVM method. This proposed system’s main advantages are non-contact measurement, ease of implementation, longer ranges of measurement, software enhanced high resolution in measurements, and faster response time without any upgradation of hardware. The accuracy of the measurement depends on the surface of the object from which the ultrasonic signal is reflected and the angle of the ultrasonic sensor. Ultrasonic waves depend on temperature and humidity, and other parameters and gases present in the environment. In the future, we propose to study the estimation of relative humidity in consideration of other environmental parameters using the ultrasonic sensor. We also propose to study different other machine learning algorithms for improving the accuracy of the proposed system.

References 1. Bohn DA (1988) Environmental effects on the speed of sound. J Audio Eng Soc 36(4):223–231 (1988). http://www.aes.org/e-lib/browse.cfm?elib=5156 2. Dobosz M, Sciuba M (2020) Ultrasonic measurement of air temperature along the axis of a laser beam during interferometric measurement of length. Measure Sci Technol 31(4):045202 (2020). https://doi.org/10.1088/1361-6501/ab491b 3. Guo M, Yan Y, Hu Y, Lu G, Zhang J (2018) Temperature measurement of stored biomass using low-frequency acoustic waves and correlation signal processing techniques. Fuel 227:89–98. https://doi.org/10.1016/j.fuel.2018.04.062 4. Hauptmann P, Hoppe N, Püttmer A (2002) Application of ultrasonic sensors in the process industry. Measure Sci Technol 13(8):R73–R83. https://doi.org/10.1088/0957-0233/13/8/201 5. Huang YS, Huang YP, Huang KN, Young MS (2007) An accurate air temperature measurement system based on an envelope pulsed ultrasonic time-of-flight technique. Rev Sci Instrum 78(11):115102. https://doi.org/10.1063/1.2804115

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6. Jia R, Xiong Q, Wang L, Wang K, Shen X, Liang S, Shi X (2016) Study of ultrasonic thermometry based on ultrasonic time-of-flight measurement. AIP Adv 6(3):035006. https://doi. org/10.1063/1.4943676 7. Liao TL, Tsai WY, Huang CF (2003) A new ultrasonic temperature measurement system for air conditioners in automobiles. Meas Sci Technol 15(2):413–419. https://doi.org/10.1088/09570233/15/2/014 8. Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55(1):169–186. https://doi.org/10.1016/S0925-2312(03)00431-4 9. Motegi T, Mizutani K, Wakatsuki N (2013) Simultaneous measurement of air temperature and humidity based on sound velocity and attenuation using ultrasonic probe. Japn J Appl Phys 52(7S):07HC05. https://doi.org/10.7567/jjap.52.07hc05 10. Rocchi A, Santecchia E, Ciciulla F, Mengucci P, Barucca G (2019) Characterization and optimization of level measurement by an ultrasonic sensor system. IEEE Sens J 19(8):3077–3084. https://doi.org/10.1109/JSEN.2018.2890568 11. Sahoo AK, Udgata SK (2020) A novel ANN-based adaptive ultrasonic measurement system for accurate water level monitoring. IEEE Trans Instrum Meas 69(6):3359–3369. https://doi. org/10.1109/TIM.2019.2939932 12. Tsai WY, Chen HC, Liao TL (2005) An ultrasonic air temperature measurement system with self-correction function for humidity. Meas Sci Technol 16(2):548–555. https://doi.org/10. 1088/0957-0233/16/2/030 13. Vapnik V, Golowich SE, Smola A (1996) Support vector method for function approximation, regression estimation and signal processing. In: Proceedings of the 9th international conference on neural information processing systems. NIPS’96, MIT Press, Cambridge, pp 281–287

Design and Implementation of a Computerized Library Management System Using GUI Alaa Hussein Ali Al-Obaidi

Abstract The Library Management System could be a utility to help the library man for dealing with references media center in an academic institution and college. The framework would give fundamental ordering of highlights to include/update data, include/renew references, and oversee review with special concerning a frameworks dependent to the customer’s advertising for necessities and requirements. Library is an authoritative structure that is an ordinary the board mill knowledge network “MIS” that has progression incorporates with installation as well backing for tail-deadline database as well faced-deadline utilization improvement perspectives. Concerning past needs a formulation for information flexibility as well genuineness to that heavy information insurance with extraordinary media centers. Concerning last demands with utilization totally commonsense, simple for utilized, continuously. Since international network with “World Wide Web” broadened very abstinence, propelled media centers have develop into an interesting issue. Since 1992, a huge amount of studies have been done and a couple of achievements have been made. This area is an investigation of these examinations. We at first look at organizing propelled libraries, including importance of modernized libraries, structure essentials for cutting-edge media centers, investigate manners concerned to electronic media centers, as well designing for cutting-edge media centers. By then an undertaking, “Digital Library Initiative,” is introduced as illustration for realizing propelled media centers. Keywords Management system · Data security · Management information systems

1 Introduction Considering “World Wide Web,” entry toward International network is result to some portion of regular constantly existence. Endless people search the Internet reliably. A consistently expanding number of people need to look recorded arrangements. A. H. A. Al-Obaidi (B) Babylon University-College of Fine Arts, Babylon, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_55

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Regardless, the business development for glancing through tremendous collections, made in the US authority bolstered assessment adventures since 1960s, there is no more differed a ton. Another irritated for data recuperation development that pushed using here open recognition for the network like a fundamental establishment since 1990s [1]. Large number of publics acknowledge utilizing internet network, with structures have the central establishment of customary usual presence. In order to obtain the required effect for exact occur, regardless, the writing by hand on Net should be helped previous giving minor passing for one who reinforces really fruitful investigations [1]. A wide scope about varieties should be recorded as well looked suitably, consisting the above mentioned of little systems also colossal requests, to precise as well easygoing correspondences, concerning content, picture as well visual storage facilities, with these through vernaculars with social orders. Every overall sense new development is relied upon for backing the advanced chase as well requesting convenience which known as “modernized libraries.” Essentially, the inspiration driving propelled media centers that to convey a profitable also convincing interest to the network. Nevertheless, for an actual electronic media center, looking isn’t adequate. The rule actions for customers could request for five arrangements: finding as well picking through significant origins, recuperating data structure, deciphering the recouped, arraigning refined through data nearby, also offering outcomes to others. The mentioned activities considered unsouring, however, are confined and entranced [2]. Actually, no fixed configuration for cutting-edge media center has been available. Additionally, with moving period, one finds progressively increasingly about cutting-edge media centers, the configuration propels. Referring to data the administrators opinion, propelled media centers considered as structures which solidify the device for automated preparing, accumulating as well correspondence, the substance, also programming expected for repeating, copy, also expand all organizations of social event, reviewing, discovering as well spreading data presented from ordinary media centers reliant with research and various subjects. Concerning customer side, propelled media centers are structures which outfit a system of customers with judicious passing toward a colossal, sifted through vault of information and data. When organizing and executing propelled libraries, there are a couple of perspectives expected to be mindful [3]: 1.

2.

3.

Interoperability: the way that to associate composite as well self-governing digitally designed media centers for supporting customers with a consistent scene for different assets. Illustration of items and storehouses: illustrate digitally structured items with combinations for simplifies utilization with systems like obligations which reinforce assigned investigation with comeback also contribute basis to powerful inter-workability. Gathering administration and configuration: mixing data supplies with the organization that handled combinations, good administrations, award as well regulation, non-material also interactive media data occupation, formulation, repository, guiding as well healing.

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Customer interfaces and human–computer strategy: customer nature design, demonstration of data, impression as well exploration for huge data combinations, connection with data handling/investigation equipment, versatility for differentiations to customer facility also system transmission capacity. Commerciality, communal as well lawful cases: suitable administration, commercial figures to the utilization for computerized data, as well fees devises used for supplying here commercial figures, customer comicality.

From the year 1992, computerized library institutions developed like an examination zone, with organized job harmony. Several accomplishment was formed, particularly within portrayal of items as well vaults, assortment association, and UIs. What’s more, a great deal of computerized library systems were invented, begun with great nations. USA was the pioneer for advanced libraries investigation territory. “National Science Foundation (NSF),” “Advanced Research Projects Agency (ARPA),” as well “National Aeronautics and Space Administration (NASA)” mutually financed the computerized libraries investigation venture named “Digital Library Initiative (DLI).” This system has separated for two stages, those were named “NLI I” and “NLI II,” individually. “NLI I” started since 1994 and then finished during 1998, with total cost of 25M US$. It concentrated on significantly propelling the way to gather, save, also arrange data in computerized structures, as well creating it accessible with easy to understand methods of looking, recovery also preparing within correspondence systems [4]. There are six colleges took part for here activity. These colleges are “Carnegie Mellon University,” “Stanford University,” “University of California at Berkley,” “University of California at Santa Barbara,” “University of Illinois at Urbana-Champaign,” and “University of Michigan.” Every college concentrated to a particular territory. “Carnegie Mellon University” concentrated to an intelligent direct advanced visual media center framework [5, 6]. There are 24 activities endorsed, in light of “NLI I,” “NLI II” would accentuate on man-focused exploration, substance as well assortments stationed examination, framework focused exploration, advancement of computerized libraries proving ground for innovation testing, exhibition and approval, and as model assets for specialized and non-specialized area networks and will design proving grounds and applications for undergrad training [4].

2 Architecture of Digitally Designed Libraries Before starting with putting design for digitally designed media centers, priory request is to justify some enquiries: 1.

Defining “Digital Library”? In any way they can distinguish among data stored in the “World Wide Web”? Number of there libraries and the technique utilized to in-home the attaching link? Number of customers they could handle [5]?

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The type of the will be the groundwork utilized? The type of text applied? The kind of is the interconnection among “Digital Library” with academic effects administration counting publisher issues? The type of computation?

The most difficult inquiry in application is the 3rd statement mentioned above. In spite of the fact that measurements for conventional library, for example, accuracy and review can be straightforwardly relevant to certain parts of advanced media center as well were generally acknowledged, the computerized libraries are substantially much mind boggling also here were significantly much to be thought of. “Measurements are required to manage issues, for example, the appropriated idea of the advanced library, the significance of UIs to the framework, and the requirement for frameworks ways to deal with manage heterogeneity among the different parts and substance of the computerized library” [5]. Here is a gathering taking a shot at here in case, named “D-Lib Working Group on Digital Library Metrics.” Four sorts of exploration issues predominantly available in computerized media centers: “interoperability,” depiction for articles with vaults, assortment the board and association, and UIs and human-PC connection.

2.1 Digital Libraries Interpretation Prior introducing “Digital Libraries” meaning, a definition of some fundamental assumptions must be proceed: 1. 2. 3.

They are un surrounded, homogeneous combination of data. Providing extra divergence in data as well servicing producers. Introducing various applications more than searching.

A notification to the last point ought to be considered particularly. As appeared in Fig. 1, the principle exercises of clients can be grouped into five classes: finding and choosing among significant sources, recovering data from them, deciphering what was recovered, dealing with the sifted through data locally, and imparting results to other people. These exercises are not really successive, yet are rehashed as well entrance [2, 7]. Clients could access unreservedly utilizing hover to complete the specified job. All with all, clients shall be engaged with different assignments simultaneously. They should move to and fro among these errands and among the five regions of action. They have to discover, break down, and comprehend data of fluctuating sorts. They have to re-sort out the data to utilize it in numerous specific circumstances, and to control it in a joint effort with associates of various foundations and focal point of intrigue. Actually, in this case no specific meaning of “Digital Libraries.” The explanation includes when investigation advanced with much knowledge of digitally designed libraries is a matter for future research. Among these segments, to set up regular plans with the defining of advanced articles, as well for the connecting of the mentioned plans for conventions with theme

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Fig. 1 Digital library users main activities

moving in, “metadata,” as well item kind orders has considered the greatest critical request. Putting plans concerning computerized items which permit worldwide interesting resource is the reason to encouraging asset participating, connections, as well interaction with advanced media center frameworks as well to encouraging increasing of computerized media center models. Further basic necessity was an open basic crypto-structure foundation, along with the improvement for an arrangement to basic workers as well the meaning of principles with conventions. It was important for helping computerized media center demands with territories, for example, guarantee also confirmation, protection, corrects the executives, with installments of the utilization for licensed innovation [6]. Simply after these issues are tended to, is it feasible for business distributers and other data providers to make a lot of high-esteem copyrighted data extensively accessible to computerized library clients. This thusly will limit the advancement of exploration models and might be a twisting component in investigations of client conduct.

2.2 Digital Libraries Inquire Cases In the domain of digital libraries, there are five basic inquire cases in, also see Fig. 2. These cases summarized as (i) “interoperability,” (ii) “description of objects and repositories,” (iii) “collection management and organization,” (iv) “user interfaces and human–computer interaction,” as well (v) “economic, social, and legal” cases.

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Fig. 2 Block diagram of digital library system

1.

“Interoperability” Most specialized “interoperability” inquire include convention structure which bolsters in wide scope of cooperation schemes, between storehouse conventions, appropriated search conventions and advancements (counting the capacity to look through composite bibliography along part of degree of acceptable organization), as well item exchange conventions [3]. For different administrations gave via computerized library systems should be “interoperable.” Available “Internet” conventions considered clearly insufficient to the application. Modern conventions as well frameworks have been required. Here brings about a topic for whereby will convey “Design and Implementation of Digital Libraries 421” model frameworks also whereby will form arrangements among cutting-edge abilities with universality of connection. Dealing with such logical inconsistency would make a basic impact through the advancement of computerized library systems.

2.

Objects Explanation and Archive Portrayal for articles as well stores are important for giving clients a lucid perspective on data in different computerized libraries. Articles and stores must be depicted in a reliable design to encourage appropriated investigation with recovery to various origins. “Interoperability” pointed to degree of profound denotations have demand advancements in depiction just like recovery, items trade, with article recovery conventions [7]. Affairs will incorporate an explanation as well utilization of “metadata” with its catch in turn calculation against items, figured utilization for depictions to articles, alliance as well reconciliation of composite stores among different denotation, bunching as well programmed various leveled association of data, and calculations for programmed rating, positioning and assessment of data quality, type, and different properties [8]. Information portrayal and trade, the explanation as well exchange of theory of data setting, also in proper jobs for man curators with topic master within computerized libraries setting have likewise significant.

3.

Administration Assortment and Coordination In this case, focal issues have approaches with techniques to consolidating data assets about that organized within oversaw assortments, corrects the board, installment, also supervised. The connection among cloning also reserving for data with assortment the executives in a disseminated situation, the power and nature of substance in advanced libraries, guaranteeing and recognizing the qualities of substance, improved help of literary data and backing of non-printed and sight and sound data catch, association, stockpiling, and recovery all call for research. The protection of advanced substance for extensive stretches of time, over

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numerous age of equipment and programming innovations and guidelines is fundamental in the making of powerful computerized media centers and need cautious assessment.

4.

Customer Interfaces and Man–Computer Communication With parcel of affairs by UIs also man-PC association, there are focal issues. here affairs consist of: showing data, representation as well route for enormous data assortments, with connections toward data control/investigation devices, the utilization of progressively advanced models of client conduct and needs in long-haul communications with computerized libraries, increasingly extensive comprehension of client needs, destinations, and conduct in utilizing advanced library frameworks, and adjusting to varieties in the abilities of client workstations and system associations in introducing suitable UIs.

5.

Commercial, Communal, and Lawful Cases

Computerized library systems not consisting of just mechanical builds; they operate inside an affluent legitimate, communal, as well monetary setting, which still accomplishing just toward a degree so to achieve the more extensive requirements. Board rights, financial samples to be utilized for computerized data, as well charging frameworks and help those monetary samples have been required [8]. Client protection and complex approach issues concerning assortment advancement and the executives, and safeguarding and filing are additionally required. Providing library training is very useful for tackling the above-mentioned issues. More readily comprehension is required for the social setting of advanced archives, including creation, possession, the demonstration of distribution, renditions, legitimacy, and uprightness.

2.3 Digital Libraries Data Architecture The architecture for data show in Digital Libraries will be mentioned in this part. Two procedures for the architecture are provided first, and then examining the heart of the system to provide the framework with the suitable procedure. This concept will be illustrated in the below block diagram, Fig. 3 [9–11].

Architecture for Informaon in Digital Libraries

Architecture of Digital Library Systems

User Interfaces

Repository

Handle System

Fig. 3 Structural diagram of a digital library system

Digital Objects

Seaerch System

Digital Meta Data

Digital Meterial

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3 The Proposed DLMS System applicability testing of digital library administration, requiring some form of survey for the project aims and contents. Absolutely, this could not be essentially concerning too small scale projects, especially when the region of the project is simply absorbed.

3.1 The Analysis and System Methodology In bigger activities, the possibility might be done yet in a casual sense, either in light of the fact that there is no an ideal opportunity for a conventional report or in light of the fact that the undertaking is a need and should be implemented in any matter. Unavoidably here record is an alternate report against the investigation specifically as causes for simple comprehensibility. With artificially calling attention to the primary standards subtleties it permits one to continually allude to the different areas of the examination itself; thus it might be utilized as a clever file to the advanced library report with complex code. At the point when a plausibility study is completed, there are four principle territories of thought [12]: • • • •

“Technical”—the possibility of implementing project from technical side. “Financial”—the financial competence of the project. “Organizational”—the compatibility of the system with available actions. “Ethical”—social acceptability of the new system presence.

The above-mentioned points are very important to be satisfied in order to maintaining smooth implementation for the digital library system. This can be applied with the utmost care followed in designing, analyzing, and examining the performance of the digital library system.

3.2 “Structured System Analysis and Design Methodology (SSADM)” Here is a lot of norms produced concerning frameworks examination as well employment structure. “SSADM” utilizes a blend of texture with outlines all through an entire activity pattern of a framework structure, from the underlying plan thought to the real natural plan of the employment. “SSADM” strategy includes succession utilization for investigation, recording as well configuration assignments worried about the accompanying stages [13]. There are seven main stages or phases to satisfy the “SSADM” Phase 0: “Feasibility study” Stage 1: “Investigation of the current environment”

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Stage 2: “Business system options” Stage 3: “Requirements specification” Stage 4: “Technical system options” Stage 5: “Logical design” Stage 6: “Physical design”.

3.3 Digital Library Design Features In web-based advanced library planning, the particulars are essential. Suggested framework will be assembled utilizing MatLab17b software code as well will be overseen via the organization presented by foundation, among information provided from the “Library Systems Office (LSO).” “Electronic Digital Library Management System” with “LSO” facility, alongside assigned delegates from chosen assortment suppliers (such that, a faculty part against the foundation) providing support benefits, as well the bibliography in any case will openly be difficult to reach. The framework will utilize commented on URLs to give approval information to the program. Clients will have the option to utilize a secret phrase and client character explicit to be produced by this framework; moreover, new clients must make a record with the goal that they can login [9]. What’s more, the client/customer inputs inquiry of the necessary pursuit toward the “search and peruse” entry page, the framework plays out an activity, as well produces a yield as for the information question. The client transmits with a question toward the computerized library database, at that point the inquiry has been deciphered, handled so that necessary yield/outcomes has been transmitted to the clients page assimilation. Here empowers the client either customer for having an easy pass toward every examination and subjects accessible available on establishments electronic computerized library the board framework [14, 15].

3.4 Subsystem Access to Web A primary motivation behind here framework has to permit clients for transferring as well viewing investigation topics also alter library assets by means of the Internet. This web interface will give the clients an office to see the library materials situated in the far off capacity. Clients will be given offices to transfer gained research material (administrator), look for specific textbooks within the assortment (understudies). Some literary depiction will be provided which has coordinated through the portrayal for every textbook with that coordinating ones will be shown. So, clients will have the option for getting towards the data among utilizing for an Internet program. When there is no chance to make the information contains a guide data, the information transferred to “PDF” form, “TXT” and so forth [16].

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Fig. 4 “Web-based digital library management system” block diagram illustration

Aquision Storage Managment Metadata Managment Search and Browse Publicaon Managment

3.5 Data Base This is in charging of storing data information and messages in the system. This utility has been divided with two sections: “collection” as well “user” “database.” 1.

“Collection Database”: “Database” assortment consists of every discretely exist of web-based computerized media center the board framework assortments, entered and kept up by administrator faculty. “Database” assortment consists of every pertinent data concerning an assortment of all motivations behind herein utilization, containing: accessory collection (privately imagined, yet maybe got from listing data), label collection (which classified either provided via distributer), origin collection (such as habitually distributer, however containing either patrons), type collection, etc. [13, 15, 17–20].

2.

The “user database”:

“User database” consists of each required data concerning the customer or user dealing with herein utilization reasons, these data encountered of: “User name” (defined with distinct windows with the arrangement of first name, last name, etc.), “E-mail” location, work description (when available) and so forth. A block diagram of the suggested “Web Based Digital Library Management System” has been illustrated with Fig. 4.

4 System Implementation and Simulation Results Framework usage has been drunkard utilizing MatLab17b programming language. During the advancement life cycle, electronic computerized library the board framework (DLMS) has been executed on Windows working frameworks. In framework execution stage, the recently evolved framework is conveyed to the clients, association after legitimate and sufficient testing. Framework usage envelops arrangement for phases with every one of herein phases were basic for an effective execution to

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each framework. Execution: A usage phase is done in the accompanying angles of implementation: a b c d

“Home page interface.” “Input/login interface.” “Acquisition/Upload interface.” “Repository interface.”

The equipment prerequisite of this structure has been cultivated by means of a PC framework with least prerequisites as given for this design: Pentium IV PC framework either “IBM” perfect, with processor speed of 2 GHZ, 2 GB RAM memory capacity, 15 GB free Hard circle aria to an establishment for a vital programming. Huge-space hard disk is required to deal with database that contains the library information and “metadata,” “Internet/intranet” network equipment, and “Security Implementation Hardware,” for example, “firewall.” The electronic flowchart of the DLMS has been represented in Fig. 5. Fig. 5 DLMS web-based flowchart

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Fig. 6 DLMS importing operation command

The overall DLMS has been programmed using graphical user interface (GUI) package provided by the MatLab20 programming language. The GUI has the ability to facilitate searching process to the user by viewing command windows with the required task. The library information of the names of references books and their categories have been listed and saved in an excel sheets. These codes data will be entered to the DLMS program using importing command which will provide a command window in which all the tables manipulations can be done. Figure 6 illustrates the DLMS importing operation. When these tables are imported to the program, their codes will be separated to individual tables and could be arranged in any manner with all the statistical utilities abilities. These statistical utilities will much facilitate the access to the names of the library books according to their categories, that is, scientific, humanist, literature, portraits, articles, journals, textbooks, thesis, projects, newspapers, etc. and so on. The GUI design will make the used entering the name code of the reference, then entering the category code, and by pushing one bottom the information of the location of the searched book will appear in the location code field. This process is demonstrated in Fig. 7. The statistical analysis of the importing library data has been shown in Fig. 8 which introducing various graphical functioning and analysis. The GUI package has been programmed using MatLab17b m.files programming language, and the overall DLMS has been put in a web-based access package.

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Fig. 7 GUI command window of the DLMS

5 Conclusions and Future Work Software updating undergoes formed very important for either living period to adjust to the modern as well simpler innovation particularly in the examination foundation lately. In the scholastic setting conveyance, research is the leader. Library simple exploration has transformed into what can be conveyed to distant frameworks in customers/clients homes, workplaces, lodgings, etc. This task gob is pointed toward designing the establishment library accessible to understudies or customers at a tick of mouse in individual territories of decision. It empowers understudies to approach most recent learning offices, for example, articles, diaries, reading material, postulation, ventures, papers, and so forth with their PC frameworks without experiencing the thorough advances and routine in the customary foundation libraries. The new contribution in this research, is the utilization of the GUI package with the MatLab17b programming language. This utility will provide statistical operations in wide range to the DLMS stored data in which permitting a perfect access abilities to the library information. On the other hand, MatLab20 programming functions will enhancing the web influence of the data information between the client/student and the university.

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Fig. 8 Statistical analysis of the importing library data

References 1. Ian H, Witten McNab RJ, Stefan JB, Bainbridge D (2000) Greenstone: a comprehensive opensource digital library software system. In: Proceedings of the 5’b ACM conference on digital libraries 2. Abiteboul S (1997) Querying semi-structured data. In: Proceedings of the 6th international conference on database theory 3. Yahia SA, Botev C, Jayavel (2004) TeXQuery: a fulltext search extension to Xquery. In: Proceedings of the 13’h conference on world wide web 4. Brian F, Cooper, Neal Sample, Franklin MJ, Hjaltasonl GR, Shadmonl M (2001) A fast index for semistructured data. In: Proceedings of the 27’h VLDB conference 5. Dirk BHE, Williams JZ (2002) Efficient phrase querying with an auxiliary index. In: Proceedings of the 25d’ annual international ACM SIGIR conference on research and development in information retrieval

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6. Dongwook S, Hyuncheol J, Honglan J (1998) BUS: an effective indexing and retrieval scheme in structured documents. In: Proceedings of the third ACM conference on digital libraries 7. Smith L (2018) What Postgre SQL has over other open source SQL databases: Part I. Retrieved from compose. In: https://www.compose.com/articles/what-postgresql-has-over other-opensource-sql-databases 8. Bainbridge D, Thompson J, Witten IH (2003) Assembling and enriching digital library collections. In: Proceedings of the 3rd ACM/IEEE-CS joint conference on digital libraries (JCDL) 9. Study Tonight (2018) Normalization of database. Retrieved from studytonight. In: https://www. studytonight.com/dbms/database-normalization.php 10. Smart Draw (2018) Entity relationship diagram. In: https://www.smartdraw.com/entity-relati onship-diagram 11. Kumar A, Saigal R, Chavez R, Schwertner N (2004) Architecting an extensible digital repository. In: Proceedings of the 2004 joint ACM/IEEE conference on digital libraries (JCDL) 12. Team PC (2017) 7 key features online shoppers demand from an online store. Retrieved from PinnacleCart 13. Yan Y, Jianzhong L (2003) Cluster-based data allocation method of web servers in digital library. Comp Eng Appl 39: 38–41 14. Guilin L, Jianzhong L, Yan Y (2003) Information extraction from PDF using plug-in. Comp. Appl. 23:110–112 15. Yan Y, Jianzhong L (2005) Interest-based recommendation in digital library. J Comp Sci 1:40– 46 16. Xianrui G, Jianzhong L, Yan Y (2002) Parallel document data loading algorithm in digital library. Comp Sci 29:104–106 17. Meirui R, Jianzhong L, Yan Y (2002) The implementation of an automated text categorization system based on Naive Bayes method. Comp Sci 29:285–287 18. Yan Y, Jianzhong L (2004) Topology-based paper recommendation in digital library. J Comp Res Develop (in press). IJISET Int J Innov Sci Eng Technol 2(3). www.ijiset.com ISSN 23487968 478 19. Brian F, Cooper, Neal Sample, Gfsli MJ, Hjaltasonl R, Franklin MS (2001) 20. Tochukwu C, Nwachukwu-nwokeafor KC, Henrieta U (2015) Designing a web based digital library management system for institutions and colleges. Computer Engineering Department Michael Okpara University of Agriculture, Umudike, IJISET Int J Innov Sci Eng Technol 2(3). www.ijiset.com ISSN 2348-7968

Deep Things-Net: A Novel Approach for Glitch Detection in IoT Devices Using Deep Iterative Learning Thirumurugan Shanmugam, Jamal Abdullah Al-Zahli, and S. Radha Rammohan

Abstract Emergence of IoT in current epoch is fast growing enabling the complex systems easily configured by the Internet on the go. The massive accumulation of data and transactions makes the system complex in processing and creates a way to data glitches. To simply this complex system, deep learning-based highly secure network is created to detect the forbidden glitches occurring on the IoT devices. Deep IoT network is modeled using deep CNN and deep belief network for improved predictive performance. The massive collection of sequenced operations is considered as surveys of the IoT devices, and hence, the formulated predictive model considers the response rate of the presented database as reference. Finally, an adaptive and iterative nature of prediction algorithm is structured with novelty in design architecture. Keywords Deep Internet of Things · Deep learning · Malware detection · Big data analysis · Cyber security · Machine learning

1 Introduction Internet of Things occupies the maximum part or time of our life nowadays, in which accumulation of massive amount of data is formulating every day which leads the challenges provided for the research peoples on big data analysis and analytics. Because of the ease of use and flexibility, lots of data have been coming on the server which cannot be simply handled by the system; hence, an efficient optimization algorithm is required to do the analytics. The frequent usage of the data which also may leads to hacking very prominent. Many kinds of cyber security treats are detected every day by which the forbidden malware is detected and corrected by the

T. Shanmugam (B) · J. A. Al-Zahli Department of Information Technology, University of Technology and Applied Sciences, Suhar, Sultanate of Oman S. Radha Rammohan Dr. M.G.R Educational and Research Institute, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_56

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IoT devices. Such systems may or may not aware of the hacking mechanisms which adopted into the system files. IoT devices are vulnerable in seaports, shopping malls, public places, educational centers, promotional activities, entertainment centers, professional places, and service organizations. The fast and frequent connectivity enhances the consumers connect to the devices at ease, and by the way, transmission of data has been done in a fraction of time. The minute time gap, which allows the system resilience, is the time, where the glitch producing malwares enter the system administrative module and read the temporary file for preparing the hacking mechanism. This research is keener on analyzing the parameter which is more impacted on generating the forbidden glitch at IoT devices during the sequenced consumer operation. The types of IoT attacks are Web attacks, data injection attacks, spoofing of DNS, session hijack, phishing, denial of service, protocol attack, and man in the middle attacks. Some of the glitching attacks are sometimes harmful of the data logger is not active at the instant. The remaining part is detailed literature survey and then elaborately presented the statement of problem. Results were discussed in detail and, finally, conclusion and future enhancement. The major work presented in this paper is • Deep learning-based highly secure network which is created and detected the forbidden glitches occurring on the IoT devices. • New structured model is proposed which is deep learning Boltzmann machinebased belief network and deep convolution neural network that is clubbed together to form a hybrid model of deep things. • Comparative study analysis based on precision, F1 score, and recall done for Live IoT platform.

2 Literature Survey Mois et al. [1] stated that the enlargement of a cyber-physical organization observes the conservation surroundings or the ambient illnesses in covered universes at isolated localities. C. A. Alves et al., 2015 stated in their research [2] that wireless sensor networks (WSNWs) and Internet of Things (IoT) have been used to create numerous eHealth classifications, which permit a surgeon (or caregiver) to observe a persevering (or elderly) incessantly and real time, each nearby or distantly, at minor cost and actuality less indiscreet in the repetitive than the outmoded checking equipment. M. T. Lazarescu, [2013], stated in their research [3] that Internet of Things (IoT) affords a virtual view, via the Internet etiquette, to a massive variety of real-life applications, formulating from a motor vehicle, to a consumer object, smart building, and to trees in forest. Its demand is the pervasive comprehensive access to the position and location of any interesting applications. In 2017, Bisio et al. [4] depicted that some of the critical part of IoT device is continuous monitoring. Rehabilitation and elderly monitoring for active mature can assistance from things-net (IoT) aptitudes

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in individual for in-home behaviors. In his research the gesture detection, two kinds of functions he focused, one is the activity recognition scenario, and another is the movement recognition system. Real-time IoT for hazard monitoring and analysis system for reliable networks, evaluated by Paul and Sarath [5] one of his research conclusion ended with the following findings, that incidents due to business deathtrap are communal leading to loss of life, healthiness, or property. An intelligent attempt is used to develop through the industrial hazardous system monitoring which is applicable with things-net platform. In his current research work, he implemented the real time, hazardous monitoring system through Raspberry Pi. It uses various MQTT sensors, heterogeneous sensor modules, etc. N. Vemishetty et al. [2015] highlighted in [6] that a novel smart transmission technique with seamless handoff apparatus to accomplish pervasive connectivity using many on-chip radios steamrolling inaccessible health intensive care applications. The research work is more unique and universally connected with multiple parameters. The adaptive performance is measured and simulated. Manju et al. [7] proposed elliptic Galois cryptography protocol. In that protocol cryptography technique was adopted. Vimal et al. [8] proposed hybrid model mitigates the jamming and enhance the security parameters. IoT applications and its challenges were elaborately reviewed in the view of sensing [9]. Also, pros and cons, availability, reliability of IoT, and the correlation between IoT and Internet of Computing (IoC) were discussed in detail. In the conclusion, handshaking model ensures the IoT and IoC provide smart life in future.

3 Problem Statement The present research is focused on deep learning of Internet of Things and their security treat platforms. In most of the applications of IoT devices, the malware detection is enabled already. In some systems, the post occurrence issues due to malwares and intermediate hacking mechanisms create forbidden glitches in the IoT devices which leads to temporary dead lock condition of the connected network. The research work is focused on providing an efficient algorithm which will detect the malware and avoid the dead lock condition of the IoT devices through machine learning system through predictive models.

4 Methodology The methodology adopted here is the machine learning-based predictive model which is more adaptive to the changing environment and iterative in design since the proposed hybrid combination of deep belief network and deep convolution neural network provides such a clear depiction on predictive structure.

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4.1 Deep Belief Network DBNW or deep belief network is probabilistic in nature composed of restricted deep learning Boltzmann machine. Deep belief networks (DeepBN) always make more inter-connections and an intra-connection which contains the XOR-based auto decoders inside the model at every layers of the network. The hidden layers are limited, the information about the configuration is fetched from the code source, and the hidden layer detects the correlation level present in the data. As per the theory, the DeepBN are pre-modeled adaptive greedy learning scheme only in which the layers are getting connected at every primitive sequence without leaving any layer, all the greedy combinations are compared and analyzed. By learning the deep intense of the data present in the hidden layers, the predictive nature of the proposed DeepBN increases rapidly and consistently. DeepBN machines are normally apt for new structure which needs more fine-tuning. The fine-tuning of the algorithm is achieved by back propagation model and wake and sleep-WS algorithm.

4.2 Deep Convolution Neural Network. Convolutional neural networks are more accurate and repeated windowing technique, which we introduced here, performs highly reliable model of DeepCNN structure with updated weights of the IoT device sequence as inputs. Most of the CNN models are almost same only the variation in hidden layer selection, the prominent adaptive convolution algorithm which is a standard and efficient algorithm in theory. CNN is basically an arithmetic operation called convolute or merging two different set of data with same sequence of sizes. The feature extraction is done by applying the convolution filter maps. The convolution map or the feature map by the convolution filter is kept moving in a sliding phase. The convolution applies at every feature vector; henceforth, the convolution model is flexible and more accurate. The general structure of CNN model is depicted below. 1.

2.

Design Parameters Filter size = 3 × 3 Filter count = min 32 − max 1024 Stride = default 1 Padding layer = Padding. System Design The design block diagram shows the overall functional methodology implemented for the detection of glitches in the IoT devices through predictive approach. The proposed model is implemented using MATLAB tool, and the results are simulated. MATLAB is a highly reliable and standard scientific computing tool which enables the neural network toolbox for designing the both deep belief network and deep convolution neural network to form a hybrid deep things-net architecture (Fig. 1).

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Fig. 1 IoT Glitch detector block diagram

3.

Design Description See Fig. 1.

4.

Data preprocessing The massive data flow in IoT devices is normally handled systematically, using a highly secure encryption scheme that can be mutually understood by transmitter and receiver. The data is preprocessed initially by reading the raw data from the IoT device, converting the data into readable format, resizing the data, and separating the attributes in a predefined format. The formatted data contains the time stamp, data size, numerical, and special characters. The data preprocessing module extracts the text attributes. Normalization This module is used to normalize the data by applying it into neural networkbased pattern matching model in which the data is pipelined into a specific flow of pattern. The pattern is nothing but the predefined sequence of flow of IoT device communication. The specific pattern is altered dynamically by the system controls. The normalized data in the specific format would be the feature of the data. The feature is sequenced into a vector. Predictive model The feature extracted data is further transferred into a deep learning convolution neural networks-based predictive model. The creations of data base are also important here, and the data base contains the predefined model of collection of IoT device sequence information in the form of vectors. The sequences of

5.

6.

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devices act as bags of controls (BOC). The deep CNN model is iteratively training the database at every interval of time. The CNN model predictive the malware and glitches present in the IoT data by analyzing the flow pattern. The predictive measure is a form of iterative or respected task. Sequence Mapper

The sequence mapper acts as feature mapping model for the IoT device inputs in which the usual sequence of surveyed data is preprocessed and pattern extracted. The standard sequence of the IoT network is controlled by the master module; the forbidden entry of the hackers will extract the sequence data initially and permits their own sequence values to collapse the entire IoT device which leads to dead lock condition. Unfortunately, in many IoT applications, the occurrence of such malware glitch is very many unaware, and hence, highly accurate predictive models which run recursively is required.

5 Results and Discussions 1.

Data visualization, deep things-net configurator, and deep CNN training and testing. The three groups of data of real-time IoT devices class are given in Fig. 2. Figure 2 shows how data is normalized using SOM model NN. The training is batch weight, and input is 1, and layers are 100 and output also 100 (Fig. 3).

2.

Performance Measurement.

Formulas induced for calculating the performance of the deep CNN with deep BN model is achieved through the confusion matrix and evaluation of true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR), etc. Accuracy = TPR + TNR/(TPR + TNR + FPR + FNR)

(a) Data Visualization - IoT devices Fig. 2 Data visualization and data normalization

(1)

(b) Data Normalization - SOM Model NN

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Fig. 3 Deep CNN traning

Table 1 Comparative study analysis Data source

Features

Parameter

Feed forward

Back propagation

Proposed deep CNN_DBN

Live IoT platform

Timing sequences of IoT devices recorded

Accuracy

74%

67%

85%

F1 score

0.74

0.81

0.85

Precision

0.75

0.68

0.83

Recall

0.72

0.99

0.87

Precision = TPR/(TPR + FPR)

(2)

Recall = TPR/TPR + FNR

(3)

F1 Score = 2 ∗ (Precision ∗ Recall )/Precision + Recall

(4)

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Table 1 clearly depicts the existing way of analysis model and proposed deep learning Internet of Things network using deep convolution network and deep belief network using Boltzmann machine as a cascaded iterative model.

6 Conclusion A novel deep things network for Internet of Things for detecting the forbidden glitches present in the massive IoT networks is evaluated. The proposed model is designed using the statistical and neural network toolbox available in MATLAB simulator 2018 version, and the input data is collected live from the sample IoT network in real time, predicting the sequence of operations which is predetermined for certain period of time. The surveyed sequence of data is further feature extracted and normalized. Deep learning Boltzaman machine-based belief network and deep convolution neural network are clubbed together to form a hybrid model of deep things-net which is our newly structured model. The performance analysis for the same is evaluated using the accuracy Eq. (1), precision Eq. (2), F1 Score Eq. (4), and recall Eq. (3).

7 Future Scope The future work will focus on automated algorithm which will detect the malware and avoid the dead lock condition of the real-time IoT devices through machine learning. Acknowledgements This research was supported by the University of Technology and Applied Sciences Suhar, Oman. The authors would like also to thank the anonymous reviewers for their valuable feedbacks, which have contributed to the paper.

References 1. Mois G, Saanislaav T, Foolea S (2016) A cyber-physical system for environmental monitoring. IEEE Trans Instrum Measur 65(6) 2. Alves RCA, Gabriel LB, de Oliveira BT, Margi CB, dos Santos FCL (2015) Assisting physical (Hydro)therapy with wireless sensors networks. IEEE Internet Things J 2(2):113–120. https:// doi.org/10.1109/JIOT.2015.2394493 3. Lazarescu MT (2013) Design of a WSN platform for long-term environmental monitoring for IoT applications. IEEE J Emerg Sel Top Circ Syst 3(1):45–54. https://doi.org/10.1109/JETCAS. 2013.2243032 4. Bisio I, Delfino A, Lavagetto F, Sciarrone A (2017) Enabling IoT for in-home rehabilitation: accelerometer signals classification methods for activity and movement recognition. IEEE Internet Things J 4(1):135–146. https://doi.org/10.1109/JIOT.2016.2628938

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5. Paul S, Sarath TV (2018) End to end IoT based hazard monitoring system. In: 2018 international conference on inventive research in computing applications (ICIRCA), Coimbatore, pp 106–110. https://doi.org/https://doi.org/10.1109/ICIRCA.2018. 8597430 6. Vemishetty N, Jadhav P, Adapa B, Acharyya A, Pachamuthu R, Naik GR (2015) Affordable low complexity heart, brain monitoring methodology for remote health care. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC) Milan, pp 5082–5085. https://doi.org/https://doi.org/10.1109/EMBC.2015.7319534 7. Khari M, Garg AK, Gandomi AH, Gupta R, Patan R, Balusamy B (2019) Securing data in Internet of Things (IoT) using cryptography and steganography techniques. IEEE Trans Syst Man Cybern Syst 50(1):73–80 8. Vimal S, Khari M, Crespo RG, Kalaivani L, Dey N, Kaliappan M (2020) Energy enhancement using multiobjective ant colony optimization with double Q learning algorithm for IoT based cognitive radio networks. Comput Commun 154:481–490 9. Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun Syst 70(4):617–634

IoT-Based Smart Security System on a Door Lock Application Debabrata Dansana, Brojo Kishore Mishra, K. Sindhuja, and Subhashree Sahoo

Abstract The Internet of Things (IoT) is the term that describes the communication between physical object also known as the “things” with the interconnection network. Nowadays, IoT plays a major role in every aspects of lives, i.e., simply communicating with other devices to security of homes, and the role of IoT is seen everywhere. From smart cities to smart home along with medical system, researchers are keen on doing research in these fields. Whatever physical objects we can see it can be transformed into IoT devices with proper data connection and using some tools. It can be controlled and communicate over Internet. Smart bulb to smart dustbin, smart refrigerator, smart mirror, and so on these lists can never end. The keys to a smart home depend upon the security of the door. If the entrance of the home is secured, then the home will be more secure. Nowadays, everyone is living away from his/her home for most of the time because of work, academics, and so many things, but our most expensive and precious things are at home. So many recent security issues are happening from theft to unwanted entry of some persons. Senior citizen and children are there at home, and their security should not be hampered at all when the owner is absent in his home. From the concept of smart home, we a smart door unlocking system has been proposed that uses infrared sensors, arduino board to give alarm when any suspicious activity is going on. Although there are many researches have been done in this smart door unlock system, but our proposed method yields the best result with low cost. Here, user will notify about all the suspicious things when that person will absent also no unauthorized entry will be there, so it leads to better and more secure environment in his home. This paper revolves around the IoT security which mainly focuses on home automation. The proposed system is designed to help an individual secure his/her house from theft; if someone break the door or entered wrong password for the door, it directly informed to the concerned house owner. Home automation provides the automatic control over the appliances at home and secures the home from an intruder. D. Dansana (B) · B. K. Mishra · K. Sindhuja Department of Computer Science and Engineering, GIET University, Gunupur, India S. Sahoo Department of Computer Science and Engineering, Pondicherry University, Kalapet, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_57

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Keywords Smart door · Arduino · Password · Home automation · Arduino · Infrared ray (IR) sensors

1 Introduction These days, innovation is a fundamental piece of everybody’s lives. It impacts a few aspects of regular day to day existence and permits improved social cooperative energy, simple transportation, the ability of enjoying amusement, and media and aides in the progression in medication. The creation of a few gadgets like PDAs and PCs has made a few people dependent on innovation for interfacing with companions, store and recover data like pictures, recordings, archives, and music. The Internet is a typical interface that few gadgets use so as to make the day-by-day life of numerous individuals. Web has assumed a spearheading job in giving quick answers for different issues and has given the capacity and has associated all the remote spots which has added to critical decrease in cost and furthermore vitality utilization. Incident like theft is more common, what is uncommon that people are unaware of advanced systems of intrusion detection. Home mechanization or shrewd home is characterized as inception of innovation inside the home environmental factors to give simplicity and security to its occupants. The innovation of the Internet of Things is utilized to analyze and execute home mechanization. IoT likewise extraordinarily adds to supply the executives and recognition easily of control. The World Wide Web is extraordinarily utilized in home mechanization that gives choices by means of moderate utilization of vitality. Wireless home automation using IoT uses the appliances and the mobile device or the password modules to control the mishappenings at home. Also saves the energy and keeps the home secure. Installing different components is very difficult to maintain, so the proposed system can turn this difficulty into simple and possible. Arduino is the main part used in this system, and with it some sensors, buzzer is used in intrusion detection. Existing system does not have any buzzer system. It only checks for the password; if it is matches, then the door opens, and if not, it may get block or remains unopened. But our system uses the buzzer and informing the owner a wrong person is going to enter the house.

2 Literature Survey On the off chance that there is a message sent on the MQTT broker, at that point entryway lock will get the message and search for what request which has been gotten. The info time for the entryway to open is introduced to a certain time span in the event that the order is an open entryway [1]. The proposed approach is acknowledged utilizing Bluetooth innovation for the network, android-based advanced mobile phone app for the end client access, and client certifications [2]. The ZigBee module in smart door lock works as an interface between sensor centers and the control

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module. ZigBee module is actualized in the advanced locking system which goes about as the primary regulator for the general smart home system in the enterprise. The electronic lock is made out of a guideline processor, a ZigBee module, a lock regulator, a CDMA module, and a camera module [3]. This paper aims to propose a security door lock system based on Raspberry pi technology. Cameras, keypad, and pi-lids are being utilized to provide an alarming system [4]. In [5], a framework has structured that contains sensors to distinguish snag, contact, heat, smoke, and sound. The entire framework is constrained by a PIC microcontroller 16F76. This framework requires additional equipment segments like sensors and GSM modem [6]. The framework utilizes the ZigBee module in advanced entryway lock, and the entryway locks go about as a focal principle controller [7]. A framework has proposed by the author in paper [8] dependent on the RFID innovation which gives a touch LCD screen. Another framework comprises of a form in NFC capacities of a PDA [9]. Here, the author gives emphasis to the essential host has a period breaking point to answer whether to open the entryway or not [10]. The cell phone can get caution buzz when there is a potential gatecrasher and can bolt/open the entryway. The attractive sensor is additionally used to naturally bolt the entryway when it is shut [11–13]. For a smart room automation and localization system, a mobile robot can be used while overcoming challenges associated with localization, perception, cognition, and control while mobile robot can extract data from the sensor and will decide the trajectory position and how to act according to the environment [14]. Here, the author proposed a model which used digital signature for authentication and triple DES encryption standard for data transmission which are implemented using MD5 and RSA algorithm for the design of smart cities [15]. The reinforcement learning techniques and multi-objective ant colony optimization (MOACO) algorithms have been applied to deal with the accurate resource allocation between the end users. Here, swarm intelligence-based and reinforcement learning technique used here with neural coaching for task execution [16–18]. This research paper is organized as followed: In Sect. 1, an introduction is given; Sect. 2 deals with some literature survey. In Sect. 3 method, material and proposed models have been discussed as well as present experimental results and discussions with proper explanations. In Sect. 4, finally, we concluded the research work with future scope

3 Method and Material Some of the features of proposed system are digital code lock venture—is a straightforward electronic number lock framework or an electronic blend lock utilizing Arduino—which has a preset 4-digitsecret phrase put away inside the program. The framework gathers 4-digitclient input by using the password module, contrasts the client input and the preset secret key inside program, and if the client input and put away secret key matches, access will be allowed (by opening the entryway with the

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assistance of servo motor) and deactivating the IR sensor which is installed at the door. On the off chance that there is a confuse between client input and put away secret word, access will be denied (by not opening the shut door), and if wrong password is entered three times consecutively, then buzzer will ON, and if the person enters the room without entering the password, then also buzzer will ON, as the IR sensor is already in activation mode.

3.1 Components Required • Arduino Uno: It is an open hardware stage when users do his program on using it. • LCD Display: Liquid crystal display (LCD) is a display which surface is flat in nature it works on the concept of light modulating properties with the help of polarizer to produce light beam. • Keypad: allows an individual to easily enter numeric values into a computer • IR Sensor: An infrared sensor is an electronic gadget that measures infrared lighting from objects. It plays a vital role in security application. It senses the object from its surroundings and detects the motion of it. • Bread Board: A breadboard is a rectangular plastic board with a lot of minuscule openings or holes in it. These holes let the user effectively embed electronic parts to model (which means to fabricate and test an early form of) an electronic circuit • Buzzer: A bell or beeper is a sound flagging gadget • Servo Motor: A servo motor works on the closed-loop mechanism where it gives positional feedback. This motor is controlled by an outer electric signal. • Potentiometer: A potentiometer is a three-terminal resistor with a sliding or turning contact that frames a customizable voltage divider. It can be defined as an extension to resistor but it is having a third terminal on it where resistor has only two terminals. Here, the third terminals can be adjusted to give any fraction of voltage to the end of the resistor. • 220- Resistor: Resistor can be simply defined as a device which has the ability to resist the passage of current. Resistors are used to control the current flow adjusting the levels, and it is estimated in units of ohms (), • Connecting Wires: used to expand the terminating line or leg wires in an electric impacting circuit. • USB Cable: Used to connect Arduino and the system.

3.2 Flow Chart of Proposed Work A flow chart diagram of the smart security system on door lock application is represented in Fig 1; here, first user inputs the password from the keypad (‘I’ is a variable which is set to 0) and that is matched with the preset password; if it is matched, the

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Fig. 1 Flow chart of proposed work

LCD will display “Correct,” and the servo motor will rotate and door will be opened (IR sensor which is fitted near the door will be de-activated). If the user inputs the wrong password for the first time, then I will be incremented by 1, and if repeats, then I value will be incremented, and if i > 2, then BUZZER will be ON. If the user enters the room without entering the password (i.e., by breaking the lock or door), then IR sensor which is already in activated mode leads the BUZZER to be ON.

3.3 Implementation Custom key will collect the value from keypad using getkey() method and that value is stored in value[]. Count variable is used in order to count the number of attempts of the user to enter the password. The present value or the password is “98BC” and that is matched with the value[], and if matched door will be opened and LCD shows “correct,” serial monitor shows “Lock open” and door gets opened else serial monitor shows “password incorrect” and door remains closed. In this if k, i.e., count >2 OR data_ir is 1= i.e., IR sensor senses the presence (when password is Incorrect) then buzzer will turn ON. If the password is correct, flag value will be set to 1 else 0. If the person enters the room without entering password, flag will be 0, and IR sensor senses the presence then buzzer will be ON.

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3.4 Final Setup and Working Principle A block diagram of smart security system is given in Fig 2; here, we connected the keypad to the seven digital pins and LCD pins to the analog pins of the Arduino. Servo motor has three pins in which red is connected to +5v, brown is connected to ground, and the yellow is connected to pin no. 9 of Arduino. Buzzer’s one pin is connected to +5v, and other is connected to pin 11. IR sensor’s one pin is connected to +5v, and other is connected to pin 12.

Fig. 2 Block diagram of smart security system

Fig. 3 Digital code lock venture

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Digital code lock venture system is represented in Fig. 3, and user enters the password which is matched with the preset password in the Arduino, and if it matches, servo motor, which is connected to Arduino, will be activated, and the motor rotates thus opens the door. And if the password inputted is incorrect, then no action will take place, and if it is repeated three times, then Arduino activates the Buzzer and it will be ON. If the user enters the room without entering the password IR sensor, which is already in activation mode, senses the presence of object thus in turn buzzer will be ON (if password is correct, IR sensor will be deactivated).

3.5 Result in the Serial Monitor A demonstrating result on serial monitor is represented in Fig 4, while the user enters the password digit by digit is printed in the serial monitor and if the password matches, it will print “Lock Open,” and if the password is incorrect, it will display “Password Incorrect.” The user will also be getting the message in the LCD display while the user enters the password, and if matches, it will display as “Correct,” if it is wrong, it will display as “Enter Password.” Fig. 4 Demonstrating results on serial monitor

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4 Conclusion and Future Scope In today’s generation, everyone is dependent on Internet either directly or indirectly. This paper consists of two parts hardware and software. Software part defines the algorithm that we built for the intrusion detection. Hardware part describes how the system was built and the modules we used. The proposed system provides a mini computer-based home security system with a low cost and stable operating system. Main advantage is that it is fully automated, once installed does not require any kind of human interaction. In future works, it tends to be wanted to utilize the sustainable power source and subsequently can be remote. And it can also take the photos of the users who had inputted the wrong password and send it to the respective owner.

References 1. Aldawira CR et al (2019) Door security system for home monitoring based on ESP32. Procedia Comput Sci 157: 673–682 2. KumAri, VA, ArAin A, JiSKAni AA (2018) Keyless smart home: an application of home security and automation 3. Park YT, Sthapit P, Pyun J-Y (2009) Smart digital door lock for the home automation. In: TENCON 2009–2009 IEEE region 10 conference. IEEE 4. Hussein NA, Al Mansoori I (2017) Smart door system for home security using raspberry pi3. In: 2017 ınternational conference on computer and applications (ICCA). IEEEs 5. Khan SR, Al Mansur A, Kabir A, Jaman S, Chowdhury N (2012) Design and ımplementation of low cost home security system using GSM network. Int J Sci Eng Res 3:1 6. Kale MPV, Sharma SD (2014) Intelligent home security system using illumination sensitive background model. Int J Adv Eng Res Dev (IJAERD) 1 7. ElShafee A, Hamed KA (2012) Design and implementation of a WIFI based home automation system. World Acad Sci Eng Technol 68: 2177–2180 8. Park YT, Sthapit P, Pyun J-Y (2009) Smart digital door lock for the home automation. In: TENCON 2009- 2009 IEEE region 10 conference, pp 1–6 9. Hung C-H, Bai Y-W, Ren J-H (2015) Design and implementation of a door lock control based on a near field communication of a smartphone. In: 2015 IEEE ınternational conference on consumer electronics-Taiwan (ICCE-TW), pp 45–46 10. Gupta RK, Balamurugan S, Aroul K, Marimuthu R (2016 ) IoT based door entry system. Indian J Sci Technol 9:1–5 11. Prabaharan J, Swamy A, Sharma A, Bharath KN, Mundra PR, Mohammed KJ (2017) Wireless home automation and security system using MQTT protocol. In: 2017 2nd IEEE ınternational conference on recent trends ın electronics ınformation & communication technology; 2017; Bangalore, pp 2043–2045 12. Pandit V, Majgaonkar P, Meher P, Sapaliga S, Bojewar S (2017) Intelligent security lock. In: International conference on trends in electronics and ınformatics; 2017; Tirunelveli, pp 713–716 13. Dutta J, Wang Y, Maitra T, Islam SH, Rawal BS, Giri D (2018) ES3B: enhanced security system for smart building using IoT. In: 2018 IEEE ınternational conference on smart cloud (SmartCloud); New York, pp 158–165 14. Balas VE, Solanki VK, Kumar R, Khari M (eds) (2019) Internet of things and bigdata analytics for smart generation, vol 154. Springer, p 309

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15. Khari M, Kumar M, Vij S, Pandey P (2016) Smart cities: a secure datatransmission model. In: Proceedings of the second ınternational conference on ınformation andcommunication technology for competitive strategies, pp 1–5 16. Khari M, Garg AK, Gandomi AH, Gupta R, Patan R, Balusamy B (2019) Securing data in Internet of Things (IoT) using cryptography and steganography techniques. IEEE Trans Syst Man Cybern Syst 50(1):73–80 17. Vimal S, Khari M, Dey N, Crespo RG, Robinson YH (2020) Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Comput Commun 151:355–364 18. Vimal S, Khari M, Crespo RG, Kalaivani L, Dey N, Kaliappan M (2020) Energyenhancement using multiobjective ant colony optimization with double Q learning algorithmfor IoT based cognitive radio networks. Comput Commun 154:481–490

Analyzing Multidimensional Communication Lattice with Combined Cut-Through and Store-and-Forward Switching Node Dmitry A. Zaitsev , Tatiana R. Shmeleva , and Roman N. Guliak

Abstract A model of multidimensional communication lattice with combined cutthrough and store-and-forward switching node in the form of infinite Petri net has been constructed. The model implements a nondeterministic forwarding decision for both switching procedures that represents better adequacy to the real-life devices functioning in case of elementary tokens use without packet headers specification. An infinite system of linear algebraic Diophantine equations has been composed and solved in parametric form that allowed the p-invariance proof for any number of dimensions and any size of lattice, open hypercube, and hypertorus studied. For additional check, dedicated software, that generates models and p-invariants, has been developed. Invariants computed by system Tina coincide with parametric invariants. As a future work direction, an extended class of nets, combining infinite Petri nets and functional programming language Scheme is offered for performance evaluation of switching lattices. Keywords Torus interconnect · Switching lattice · Infinite petri net · Verification · Linear invariant

1 Introduction Interconnects of modern high performance computers represent multidimensional torus with 3D and 5D for IMB Blue Gene supercomputers and 6D for Fujitsu supercomputers K1 and Fugaku [1, 2], and the latest is the most powerful computer of the present time according to Top500 list. Verification of multidimensional communication lattice represents a significant task in demand [3] for symbolic techniques D. A. Zaitsev (B) · R. N. Guliak Odessa State Environmental University, Lvivska 15, Odessa 65016, Ukraine e-mail: [email protected] T. R. Shmeleva A.S. Popov Odessa National Academy of Telecommunications, Kuznechnaya, 1, Odessa 65029, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_58

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allowing conclusions for any number of dimensions and any size of lattice, general theory of lattices studied in [4, 5]. Infinite Petri net theory [6, 7] has been developed for symbolic (parametric) verification of systems obtained as a regular pattern repetition of one or a few basic components. Developed models of plain and multidimensional communication lattices [6– 9] have been based on either cut-through or store-and-forward principles of switch functioning, while recent real-life devices usually combine both principles. Moreover, we should model unconditional forwarding decision for both cases for obtaining useful models without considering the packet header information having a packed represented by an elementary token. Multidimensional lattices are also applied in systems biology [10, 11], nuclear physics [12, 13], and numerical algorithms [14]. Here, we use a combined cut-through and store-and-forward switching device model [15] to generalize two-dimensional lattice model [16] on multidimensional case. A novel notation system for ports and lattice composition is offered that allows us to simplify specifications. An open and torus lattices are studied. Linear invariants of the model are obtained in parametric form that allows us to conclude on boundedness, conservativeness, and consistency of a lattice of any size with any number of dimensions. The mentioned properties are considered as properties of an ideal networking protocol.

2 Detailed Model of a Switching Node In [16], a laconic state-of-art model of combined cut-through and store-and-forward switching device has been constructed. Here, we generalize this model on multidimensional case introducing a novel notation for convenient and concise composition of a multidimensional lattice. Comparing to notation of papers [6–8], the following changes have been adopted: - enumeration of both lattice nodes and node ports start from zero that yields brief composition of torus using modulo lattice size operation and uniformity; - directions within a chosen dimension are specified using numbers from the set {−1, 1} that allows computing an opposite direction using simple arithmetic negation, for example, for a direction denoted as n, the opposite direction is −n; moreover, when computing the neighbor node coordinate, the corresponding vector component is summed up with n. Let us consider a figure of switching node model [16] modified with the mentioned notation of ports as shown in Fig. 1. Note that the original model [16] uses simple clockwise enumeration of ports for the plain (2D) lattice composition. The device model vertices have the following meaning: transitions t f( j,n),( j  ,n  ) represent a nondeterministic forwarding decision with only one incoming arc from the port receiving tract pi j,n ; places p f ( j,n),( j  ,n  ) serve as a forwarding destination indicator; transition tc( j,n),( j  ,n  ) models cut-through switching—direct forwarding of a packet from the source port to the destination port; transition tb( j,n),( j  ,n  ) stores a packet within the

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Fig. 1 Switching device model, plain (2D) lattice case

corresponding section of the internal buffer; transition to j,n outputs a packet from a section of internal buffer. Specification of tb( j,n),( j  ,n  ) contains a read arc from the destination port buffer po j  ,n  which is represented by a loop (a pair of counter direction arcs with respect to place ( po j  ,n  ). The read arc allows us to represent the lower priority of buffering with regard to cut-through forwarding. The buffering is implemented only in the case, and the destination port is busy. When there are packets within a certain section of the internal buffer and a packet arrived from the same port, the choice between transmitting arrived packet or a packet from the buffer is implemented in nondeterministic way. It is the best possible solution that does not lead to cumbersome models, and it is justified by real-life procedures when we do not model priority classes of packets. With parametric expression (1), we generalize the switching device model on multidimensional case using a single parameter d that corresponds to the number of dimensions. Let us recollect [6, 7], that for the lattice composition for von Neumann neighborhood [17], we use ports situated on facets of a unit-size hypercube that represents a device in multidimensional space. In each dimension, we have a pair of

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opposite facets, one in the direction to −∞ and the other in the direction to +∞, and the first one denoted as −1 and the second one as 1. Thus, to specify a port, we need a pair ( j, n), where j specifies a dimension and n specifies a direction; note that this notation completely corresponds to a sparse vector of coordinate difference notation for a sparse vector having only one nonzero component (von Neumann neighborhood). ⎛⎛ ⎛

⎞⎞⎞ t f ( j,n),( j  ,n  ) : pi j,n → p f ( j,n),( j  ,n  ) ⎟ ⎜⎜ ⎝ tc ⎠,⎟ ( j,n),( j  ,n  ) : p f ( j,n),( j  ,n  ) , pol j  ,n  → po j  ,n  , pil j,n ) ⎜⎜ ⎟⎟ ⎟ ⎜⎜ tb ⎟ ⎜⎜ ⎟⎟ ( j,n),( j  ,n  ) : p f ( j,n),( j  ,n  ) , pbl, po j  ,n  → po j  ,n  , pb j  ,n  , pil j,n ,⎟ (1) ⎜⎜ ⎟   ⎜⎜ 0 ≤ j  ≤ d − 1, n  ∈ {−1, 1}, j  , n  = ( j, n) ⎟⎟ ⎜⎜ ⎟⎟ ⎝⎝ to j,n : pb j,n , pol j,n → po j,n , pbl ⎠⎠ 0 ≤ j ≤ d − 1, n ∈ {−1, 1} Here, we consider von Neumann neighborhood only though Moore, and generalized neighborhoods [17] can be studied as well. Let us remind that parametric expression (PE) [6–8] contains a header with the transition name separated by a colon symbol and then lists of its input and output places follow separated by an arrow symbol; the expression is accomplished with the index range specification. The initial marking is specified with a parametric sparse vector notation (2), where an asterisk symbol separates the marking and the place name.



  cpil ∗ pil j,n , cpol ∗ pol j,n , cpb ∗ pb j,n , 0 ≤ j ≤ d − 1, n ∈ {−1, 1} cpbl ∗ pbl

(2)

Usual value of the port buffer limitation constants cpil, cpol equals to 1—only a single packet is stored in the port buffer for each tract. For lattices without external inflow of packets (for instance, torus), we should consider non-empty sections of the internal buffer with cpb packets each to have some packets to transmit within the lattice. The total internal buffer size is specified by constant cpbl and is measured in the number of packets to store. The numbers of graphical elements of the node model given by (1) are calculated and represented by Table 1 in parametric form with regard to the parameter d that specifies the number of the lattice dimensions. Table 1 Number of graphical elements within the switching node model

Places

Transitions

Arcs

4 · d2 + 8 · d + 1

12 · d 2 − 4 · d

48 · d 2 − 16 · d

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3 Parametric Specification of Lattice Comparing to composition of lattices by merging contact places of nodes studied in [6–8], here we connect nodes by dedicated transitions which model unidirectional (simplex) channels of packet transmission [15, 16]. Thus, the notation of elements inside a node is unchanged for all the nodes of a multidimensional lattice, save each element is supplied with the upper index of the node within lattice, and enumeration starts from 0 to provide easy way of torus composition via mod k operation. Figure 2 illustrates composition of an open plain (2D) lattice. The general rule of connection (for von Neumann neighborhood): connected neighbor nodes have difference of absolute value equal to unit in exactly one coordinate; ports with the same number of dimension and opposite directions are connected. A multidimensional torus lattice, specified by PE (3), represents a matrix of indexed copies of the device model (1). The neighboring devices are connected by a pair of transitions: one transition for receiving (input) and the other transition for sending (output) of packets. Figure 3 illustrates connection of neighbor devices by a pair of dedicated transitions. Names of unidirectional (simplex) links tl ij,n are chosen with respect to the output device.

------------------------------------------------>X1 (0,-1) (0,-1) (0,-1) | | (1,-1) 0,0 (1,1) (1,-1) 0,1 (1,1) (1,-1) 0,2 (1,1) | | (0,1) (0,1) (0,1) | (0,-1) (0,-1) (0,-1) | | (1,-1) 1,0 (1,1) (1,-1) 1,1 (1,1) (1,-1) 1,2 (1,1) | (0,1) (0,1) (0,1) | (0,-1) (0,-1) (0,-1) | | (1,-1) 2,0 (1,1) (1,-1) 2,1 (1,1) (1,-1) 2,2 (1,1) v X0 (0,1) (0,1) (0,1) Fig. 2 Lattice composition scheme, plain (2D) open lattice case

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Fig. 3 Connection of neighbor nodes by dedicated transitions, names are chosen for 4D lattice

⎛⎛⎛⎛

t f (ij,n),( j  ,n  ) : pi ij,n → p f (ij,n),( j  ,n  )

⎞⎞⎞⎞

⎟⎟⎟⎟ ⎜⎜⎜⎜ i ⎟,⎟ ⎟ ⎟ ⎜ ⎜ ⎜ ⎜ tc j,n),( j  ,n  : p f ij,n),( j  ,n  , pol ij  ,n  → po ij  ,n  , pil ij,n ( ) ) ⎜⎜⎜⎝ ( ⎠⎟⎟⎟ ⎜⎜⎜ ⎟⎟ ⎜ ⎜ ⎜ tb(i j,n),( j  ,n  ) : p f (ij,n),( j  ,n  ) , pbl i , po ij  ,n  → po ij  ,n  , pb ij  ,n  , pil ij,n ⎟ ⎟⎟⎟ ⎜⎜⎜ ⎟⎟ ⎟   ⎟ ⎜⎜⎜ ⎟,⎟ ⎜ ⎜ ⎜ 0 ≤ j  ≤ d − 1, n  ∈ {−1, 1}, j  , n  = ( j, n) ⎟,⎟ ⎟ ⎜⎜⎜ ⎟ ⎟⎟⎟ ⎜ ⎜ ⎜ to i : pb i , pol i → po i , pbl i ⎟ ⎟ ⎟ ⎜ ⎜ ⎝ j,n j,n j,n j,n ⎠⎟ ⎜⎜ ⎟⎟ ⎜ ⎜ tl i : po i , pil nx(i, j,n) → pi nx(i, j,n) , pol i ⎟⎟ ⎜⎝ ⎠⎟ j,n j,n j,n j,−n j,−n ⎟ ⎜ ⎟ ⎜ 0 ≤ j ≤ d − 1, n ∈ {−1, 1} ⎠ ⎝ i = (i 0 , . . . , i d−1 ), 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1 (3) Function nx given by (4) specifies the next node index with respect to facet ( j, n) within multidimensional torus of size k. We suppose that modulo operation is extended concerning mapping negative numbers into non-negative numbers, in particular 0 − 1 mod k = k − 1. nx(i, j, n) = i ∗ , il∗ =



il∗ , l = j il∗ + n mod k, l = j

(4)

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Table 2 Number of graphical elements within a lattice Places   kd · 4 · d2 + 8 · d + 1

Transitions   k d · 12 · d 2 − 2 · d

Arcs   k d · 48 · d 2 − 8 · d

The lattice initial marking is specified by expression (5).   ⎞ ⎛  cpil ∗ pilij,n , cpol ∗ polij,n , cpb ∗ pb ij,n , 0 ≤ j ≤ d − 1, n ∈ {−1, 1} ⎜ ⎟ ,⎠ ⎝ cpbl ∗ pbl i i = (i 0 , . . . , i d−1 ), 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1 (5) Description of an open multidimensional lattice, studied in [16] for plain (2D) case, uses slightly modified expression (3) supplied with condition (6) for transitions tl ij,n specification that prevents connecting edge nodes; moreover, instead of torus neighbor function (4), hypercube neighbor function (7) is used; the initial marking specification has the same form (5).     i j , n = (1, −1), i j , n = (k − 1, 1) nx(i, j, n) = i ∗ , il∗ =



il∗ , l = j il∗ + n, l = j

(6)

(7)

The numbers of graphical elements of the multidimensional torus model given by (3) and (4) are calculated and represented by Table 2 in parametric form with regard to parameter d that specifies the number of the lattice dimensions and parameter k that is equal to the lattice size.

4 Computing Linear Invariants For computing p-invariants, we compose, directly on (5), an infinite system of linear Diophantine Eq. (8) that should be solved in non-negative numbers. Traditionally, unknowns are designated as x; for the notation compatibility, we preserved the place names as suffixes. Each equation represents a balance of incoming and outgoing arcs of the corresponding transition. Since all the arcs have multiplicity equal to unit, there are no other nonzero coefficients within the system, save from the set {−1, 1}.

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⎛⎛⎛⎛

⎞⎞⎞⎞ −x pi ij,n + x p f (ij,n), j  ,n  = 0 ( ) ⎟⎟⎟⎟ ⎜⎜⎜⎜ ⎟,⎟ ⎟ ⎟ ⎜ ⎜ ⎜ ⎜ −x p f (ij,n), j  ,n  − x pol ij  ,n  + x po ij  ,n  + x pil ij,n = 0 ⎠⎟⎟⎟ ( ) ⎜⎜⎜⎝ ⎜⎜⎜ ⎟⎟⎟ i i i i =0 i − x po i − x pbl + x po + x pb + x pil −x p f ⎜⎜⎜ ⎟⎟⎟  ,n  )  ,n   ,n   ,n  j,n j,n), j j j j ( ( ⎜⎜⎜ ⎟,⎟ ⎟ ⎜⎜⎜ ⎟⎟⎟ ⎜ ⎜ ⎜ 0 ≤ j  ≤ d − 1, n  ∈ {−1, 1},  j  , n    = ( j, n) ⎟ ⎟,⎟ ⎜⎜⎜ ⎟⎟⎟ ⎜⎜⎜ ⎟⎟⎟ i i i i ⎜ ⎜ ⎝ − x pb j,n − x pol j,n + x po j,n + x pbl = 0 ⎠⎟⎟ ⎜⎜ ⎟⎟ ⎜ ⎜ − x po i − x pil nx(i, j,n) + x pi nx(i, j,n) + x pol i = 0 ⎟⎟ ⎜⎝ ⎠⎟ j,−n j,−n j,n j,n ⎜ ⎟ ⎜ ⎟ ⎝ 0 ≤ j ≤ d − 1, n ∈ {−1, 1} ⎠   i = i 0 , . . . , i d−1 , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1

(8)

Obtained parametric solution of (8) for p-invariants has form (9). ⎛

 po ij,n , pol ij,n , ⎜  ⎜ 0 ≤ j ≤ d − 1, n ∈ {−1, 1} , i = i , . . . , i , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1 0  d−1 ⎜   ⎜ ⎜ pb ij,n , 0 ≤ j ≤ d − 1, n ∈ {−1, 1} , pbl i , ⎜   ⎜ i = i 0 , . . . , i d−1 , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − ⎜   1,  ⎜ i  ≤ d − 1, n  ∈ {−1, 1} , pi i , pil i ⎜ pf , 0 ≤ j  ,n  ) ⎜ j,n j,n , j j,n), ( (   ⎜ ⎜ i = i 0 , . . . , i d−1 , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1, ⎜ ⎜ 0 ≤ j ≤ d − 1, n ∈ {−1, 1} ⎜     ⎜ ⎜ ( pbl i , ( pil ij,n , pol ij,n , 0 ≤ j ≤ d − 1, n ∈ {−1, 1} , ⎜   ⎜ ⎜ i= i 0 , . . . , i d−1 , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1 )   ⎜ i i   ⎜ ( pb i , p f i  ,n  ) , 0 ≤ j ≤ d − 1, n ∈ {−1, 1} , pi j,n , po j,n , ⎜ j,n j j,n), ( (   ⎜ ⎝ i = i 0 , . . . , i d−1 , 0 ≤ il ≤ k − 1, 0 ≤ l ≤ d − 1 0 ≤ j ≤ d − 1, n ∈ {−1, 1}

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

(9)

Theorem 1 Each sparse vector specified by (9) is a solution of (8). We use a direct constructive proof technique, similar to presented in [6–8]. Each of five parametric solutions from sparse parametric matrix (9) is substituted into each of five parametric equations of infinite system (8) to obtain a correct equality (for any values of parameters). Theorem 2 Infinite Petri net specified by (3) and (5) is p-invariant for any number of dimensions and any lattice size. To prove the theorem, we compose, similar to [6–8], a sum of the fourth and fifth parametric solutions of sparse parametric matrix (8). The obtained parametric solution contains each place of the model. Thus, the net is p-invariant. Corollary Infinite Petri net specified by (3) and (5) is bounded and conservative for any number of dimensions and any lattice size. Boundedness and structural conservativeness are properties of p-invariant Petri nets according to [18]. The numbers of p-invariants of the multidimensional torus model given by (9) are calculated and represented by Table 3 in parametric form with regard to parameter

2·d

2

kd

L

· kd

Invariant 2

L

C

Invariant 1

2·d +1

C 2·d

L · kd

Invariant 3

Table 3 Number of p-invariants (L—lines, C—components)

2·d +1

C 1

L

Invariant 4 (4 · d

C + 1) · k d

1

L

Invariant 5 4 · (d + 1) · d · k 2

C

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d that specifies the number of the lattice dimensions and parameter k that is equal to the lattice size. Total number of p-invariants equals to (4 · d + 1) · k d + 2.

5 Generating Models and Invariants For computational experiments with models of various size and dimension, a dedicated software generator has been developed. It represents a modified hypertorus generator [19]; the modification concerns the node model and the lattice composition rules according to (3)–(5). The generator uses the logical Petri net specification format (.net) of modeling system Tina [20]. Besides, a dedicated software generator of p-invariants according to (9) has been developed. Based on comparisons of p-invariants computed by system Tina for generated models and p-invariants generated by a program, we conclude that they coincide. It acknowledges the hypothesis that expression (9) represents a basis of solutions of infinite system (8) that awaits its formal proof. To compare invariants, we use a program written in Scheme language [21] that allows convenient and laconic specification of algorithms on list structures.

6 Conclusions Multidimensional torus interconnect represents communication subsystems of modern supercomputers and clusters. In the present paper, a model of multidimensional communication lattice with combined cut-through and store-and-forward switching node in the form of infinite Petri net has been constructed. An infinite system of linear algebraic Diophantine equations has been composed and solved in parametric form that allowed the p-invariance proof for any number of dimensions and any size of lattice, open hypercube, and hypertorus studied. For additional check, dedicated software, that generates models and p-invariants, has been developed. Invariants computed by system Tina coincided with parametric invariants. As a future work direction, an extended class of nets, combining infinite Petri nets and functional programming language Scheme [21] is offered for performance evaluation of switching lattices. In colored Petri nets [22], which are connected with ordinary nets by the unfolding procedure [23], ML language [24] is applied.

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2. Dongarra J (2020) Report on the Fujitsu Fugaku System. Tech Report No. ICL-UT-20-06, University of Tennessee, Knoxville, Oak Ridge National Laboratory, University of Manchester, 22 June 2020 3. IGI-Global (2012) Grid and cloud computing: concepts, methodologies, tools and applications, 4 vol, Information Resources Management Association (USA), IGI-Global 4. Barmak JA (2011) Algebraic topology of finite topological spaces and applications. Springer Science & Business Media 5. Davey BA, Priestley HA (2002) Introduction to lattices and order. Cambridge University Press 6. Zaitsev DA, Zaitsev ID, Shmeleva TR (2017) Infinite petri nets: part 2, modeling triangular, hexagonal, hypercube and hypertorus structures. Compl Syst 26(4):341–371 7. Zaitsev DA, Zaitsev ID, Shmeleva TR (2017) Infinite petri nets: part 1, modeling square grid structures. Compl Syst 26(2):157–195 8. Zaitsev DA, Shmeleva TR, Groote JF (2019) Verification of hypertorus communication grids by infinite petri nets and process algebra. IEEE/CAA J Automat Sin 6(3):733–742 9. Zaitsev DA (2013) Verification of computing grids with special edge conditions by infinite petri nets. Autom Control Comput Sci 47(7):403–412 10. Giavitto J-L, Klaudel H, Pommereau F (2010) Qualitative modeling and analysis of regulations in multi-cellular systems using Petri nets and topological collections. In: Ciobanu G, Koutny M (eds) Membrane computing and biologically inspired process calculi (MeCBIC 2010), EPTCS vol 40, 162–177 11. Motro R (ed) (2009) An anthology of structural morphology. Word Scientific 12. Miyamoto K (2005) Plasma physics and controlled nuclear fusion. Springer 13. Zhang JX, Atkinson P, Goodchild MF (2014) Scale in spatial information and analysis. CRC Press, Taylor & Francis Group 14. Sanjay R, Sartaj S (2011) Hypercube algorithms: with application to image processing and pattern recognition. Springer 15. Zaitsev DA, Shmeleva TR, Retschitzegger W (2021) Spatial specification of grid structures by petri nets. In: Proceedings of 4th international conference on micro-electronics and telecommunication engineering (ICMETE 2020), 28–29 Sept 2020. Lecture Notes in Networks and Systems, vol 179 16. Shmeleva TR, Stetsenko IV (2021) Modeling unconditional forwarding decision within switching lattices, current trends in communication and information technologies. Petro Vorobiyenko et al. (Eds): Current Trends in Communication and Information Technologies, Springer 17. Zaitsev DA (2017) Generalized neighborhood for cellular automata. Theoretical Computer Science, 666: 21–35 18. Li ZW, Zhou MC (2010) Deadlock resolution in automated manufacturing systems. Springer 19. Zaitsev DA (2015) Generator of hypertorus petri net models. https://github.com/dazeorgacm/ htgen 20. Berthomieu B, Vernadat F (2006) Time petri nets analysis with TINA. In: Proceedings of 3rd international conference on the quantitative evaluation of systems (QEST 2006). IEEE Computer Society 21. Dybvig K (2009) The scheme programming language. The MIT Press 22. Jensen K, Kristensen LM (2009) Coloured petri nets: modelling and validation of concurrent systems. Springer 23. Kordon F, Linard A, Paviot-Adet E (2006) Optimized colored nets unfolding. In: Najm E et al (eds) FORTE 2006, LNCS vol 4229, 340–356 24. Ullman JD (1997) Elements of ML programming. Prentice-Hall

Implications of E- Learning in Education: An Analysis Deepanjali Mishra

Abstract Computer technology and multimedia have long been used in classes in the field of education. It was usually used by faculties of engineering and science because of its benefits. A series of study conducted have analyzed that using digital methodology of teaching can make a class much more interesting compared to the traditional methodology. There are so many learners with so many diversities and needs, e-learning could be one of the innovative techniques to impart education to them. It is not to be considered that only students need to study through e-learning, rather the teachers too need this technology to impart education which sums up that elearning is important technique for the students as well as teachers. Sometimes when a teacher needs to take a class through distance mode, he or she could easily conduct classes through digital classroom. Therefore, the paper would be an attempt to start with an introduction of the concept of multimedia, and how multimedia technology could be implemented to impart digital education to university students. Keywords Multimedia · E-learning · Education · Digitalization · Implications

1 Introduction The concept of education imparting has undergone sea changes. It is no longer restricted to classroom teaching where a teacher would come to the class and give lecture on a topic by using a chalk board for giving illustrations. With the advancement of technology, new methodology has started to develop, and students have been using them quite conveniently. Internet has been responsible for generating a new trend of education through various mediums like computerized electronic learning, online learning, and Internet learning. The courses are delivered online through Internet in order to facilitate the learners. They get an environment which is unconventional that makes learning more easier and comfortable. This course cannot be imparted through a CD or a DVD player. One of the approach is e-learning where learning does not D. Mishra (B) School of Humanities, KIIT University, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_59

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involve showing something on a DVD or a CD, but on the other hand, the sessions that are delivered online are much more interactive and a student is able to communicate with the professor who is delivering the class, with another student who is also a learner and from the other place in the world. The sessions can be delivered live where a participant can raise question through clicking the button and get the query answered where as in some cases, lectures are pre-recorded and shown to the students. But at the same time, a coordinator would be there who would be handling the sessions who is a professor or a faculty. He or she would be interacting with the participants and evaluating the performance. Verify assignments and conducting tests. This methodology of imparting education through e-learning is evolving as a growing mode which imparts education and has succeeded in becoming the most soughtafter method for many young learners. Fletcher [1], Kulik [2], Willett, Yamashita, and Anderson [3] have re-emphasized that a student can grasp more while giving education through technology-based learning instead of conventional methodology [4]. Brandon Hall has tried to explain that accessing knowledge which is imparted through digital mode is more informative and more intellectual in nature [5]. The processes are new and innovative. A variety of subjects could be possible through digital mode like English literature, language, history, science to name a few. It is a well-known fact that learners who are children and learners who are adults do not have same choices in methodology and requirement that is used in teaching. For instance, if there is any attempt to summarize a text, adult learners are expected to be more curious to understand the logic behind performing the activity, while a young learner would perform without asking any question. Adult learners are more practical in nature; therefore, they use to solve a problem through application by accessing their real-life experience and using it in their learning methodology, while a young learner is sensitive, not experienced, and hence, uses theoretical mode while solving problems. In case of an adult learner, it is the trial and error method and the satisfaction levels that is used in the process of learning. They not only learn through this process, but it helps to get themselves motivated through this methodology.

2 Objectives of the Study This paper would make an attempt to emphasis on understanding a broad about elearning, its role in e-learning in education, and the impact of e-learning making a comparative analysis between the conventional methods and digital methodology of education.

3 Concept of Digital Learning Digital learning is a technique of using technology as a medium to teach the students. Apart from this, digital education has many more usefulness to counter like online

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interaction with the students, taking a class with multi-users and so on. It has been proved to be beneficial for the teachers as well because of online teaching manuals, and other resources are very easy to access. It gives personal as well as professional education guides to the students. Gone are the days when the conventional methodology of teaching was followed in schools, colleges, and other professional colleges. Nowadays, it is easy to take classes by using various software and e-learning materials that have been launched online. These software easily automate a variety of the activities in the classroom. Education system is faced with severe downfalls and scarcities. There are lack of teachers, infrastructure, and so many shortfalls. A digital class room can be an effective alternative to these shortcomings. The digital classroom comprises of a combination of a smart class room, and e-learning technology is also known as a “technology-enabled” classroom. Here, the learner learns through interaction with the instructor and faculties which is fully supported through strategic use of information and communication technologies. The basic requirement of this e-learning process is Internet availability, cell phones, desktops, laptops, etc. Development of new devices take place every day with the latest versions in today’s world which is more digitalized and advanced. In the present scenario, India has become one of the top one of the top centers of education in the world. There are so many Indian universities who have students from all over the world. Indian education system provides the world class faculties and infrastructure along with rankings which is responsible for increasing the standard of education. India is equally equipped with latest technologies to compete with the world’s top ranking universities and cater to the needs of the students who achieve success in every field after attaining degrees from this country. Now coming to the young learners, who comprise of kids ranging from 5 years to adolescents in the age groups 3–17 years. In this techno savvy world, children learn rhymes, stories, and other topics of their interest through cartoons and animations. With the ongoing pandemic, schools have been resorting to online education system. The children are being taught their lessons through multimedia which is really interesting for them. They get to see animated films, cartoons, and games which makes them glued to the computers. Adolescent learners learn their lessons through online lectures that are imparted by the trainers and facilitators. The role of technology for instructional functions ought to apply educational strategies that harness the advantages of a particular technology used for learning [6, 7]. The event of content and learning environments should look on the far side perceptions of applying a typical formula for all content and begin applying strategies and technologies that employ bolster learning. Analysis scrutiny ancient lecture rooms strategies to online shows and discussion is showing few signs that technology ‘alone’ cannot turn out higher learning outcomes [8–10]. Educators constructing content for virtual learning environments ought to have operating data of the principles of psychological feature load theory. They need to be equipped with operating data to know, however, and once to use differing educational style to a numerous set of technologies that use transmission methodologies for instruction. Investigation the results of transmission on learning and performance needs a solid foundation in learning theory. A theoretically grounded investigation of transmission permits one

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to draw conclusions relative to the learner, instead of making an attempt the slippery slope of a media comparison [9]. Bishop and Cates effectively synthesize science theory and communications as a foundation for the investigation of the utilization of sound in transmission instruction. Another example of transmission investigations that area unit grounded in psychological feature theory includes the work of Richard Mayer [11, 7]. Mayer has based mostly the majority of his transmission work on associate in nursing integration of Sweller’s psychological feature load theory, Pavio’s dual-coding theory [9], and Baddeley’s memory model [12].

4 Types of Digital Learning There are various types of digital learning tools depending on the users, techniques, and context like e-learning resources, game learning, video learning, etc.

5 Applications of E-Learning 5.1 Use of E-Learning in Business Multimedia is widely used in business applications which are more convenient and easy. Some of them are business presentations, digital marketing, digital retailing, Web series marketing, etc. Oral presentation proves to be extremely useful in dayto-day life irrespective of the occupation of the users. They are not only helpful in teaching and learning, but it proves to be beneficial in imparting training, ecommerce and providing entertainment to the audience. Oral presentations provide a platforms to researchers to showcase their study through a wide range of multimedia. Oral presentations help the presenter to understand the queries of an employee while demonstrating a product. In various countries, it is easy to perform all the business transaction. Buyers can be influenced to shop for the product [13]

5.2 Educational Institutions Multimedia learning has been extensively used in education in the teaching and learning process. With the ongoing pandemic, COVID 19, the government has imposed lockdowns in various countries due to which it is not possible for the students and the teachers to come out of their homes. In this situation, e-learning is the only resort for continuing education in schools and educational institutions. It is not only the need of the hour but also has proved to be more effective for the students as well as the instructors. They can submit their assignments through virtual mode in

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virtual classrooms, and those assignments are instantly checked by their trainers. Most of the faculties encourage their learners and students to use online mode of education. This becomes easy to memorize their study materials and also reproduce them as and when required for stimulation of data. Moreover transmission of education through online mode can be used to facilitate in addressing the needs of the scholars with varied learning technologies [14]. Online teaching mode can be used to sensitize the needs of the learners by providing ample illustrations, case studies, and real-life examples. These are sensibly potential which may have more impact in understanding the topic through multimedia. For example, when the participants are made to form teams and assigned to make an oral presentation on any topic, it can lead to so many things like a student will learn the use of multimedia, it will generate more interest, and it will lead to an awareness about the events that are happening in day-to-day lives. It has proved to be more effective than the assignment which is given by the teachers based on writing an essay or a research paper. Apart from this, nowadays, mid-term and end-term examinations too are being conducted through online mode. Students are evaluated on the basis of tests that are conducted through online mode and are promoted on the basis of these tests. There are various software available which uploads the questions and answers which facilitates the teachers to evaluate the answer papers. It is easy and convenient as compared to the traditional mode of evaluation.

5.3 Home E-learning has been in turn employed in home for conducting adult learning program or for seizing distance education. Transmission learning may also facilitate the aspirants of competitive examinations to require up numerous online tests and acquire AN appraisal of themselves. Not solely that the scholars will study online by following directions from academics in a very transmission virtual schoolroom [15].

5.4 Public Places E-learning may also be conducted at public places through numerous transmission like TV and alternative electronic devices. A gaggle can be shaped, whereas sitting at one place, watch numerous online instructive programs that are informative and instructive moreover as entertaining.

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5.5 Virtual Learning: A supply of E-Learning Method Virtual learning is simply AN extension created by transmission. It applies imagination, sounds, and animations of basic transmission parts. As a result of it, needs from an individual stringing are navigation feedback; perhaps video game is AN multimedia in its fullest extension. It usually will not to describe a good form of applications usually related to immersive, extremely visual, and 3D environments. Simulation setting will be almost like the $64,000 world, and for instance in case of pilot or combat coaching, simulation, it can be considerably completely different from the truth as in video game games. This pertinence of virtual learning is planned to be somewhat sooner than the standard methodology of learning. On the opposite hand, virtual learning can be one among the simplest methodology in learning moreover as teaching method.

6 Disadvantages of Multimedia Learning Methodology Undoubtedly, there are various applications of e-learning in education and teaching and learning, yet it has its own disadvantages too. Lack of physical presence makes it difficult for the teacher to know if the student has understood the topic. Due to unstable network connection, it is difficult for the learners and trainers to participate in the class due to which certain important points are missed out. Apart from this, a learner from the remote areas is unable to attend the class and perform the assignments. Sometimes, the students put their cameras off and bunk the classes which becomes difficult for the teacher to make out if he or she is really present in the virtual class.

7 A Comparative Analysis of the E-Learning with the Standard Teaching In online classrooms, the learner is not directly interacting with the school. Thus, just in case of getting any queries, they will notice it tough to raise their online pedagog, as communication is usually impersonal in nature. However, these courses usually provide alternatives to access question like online forums, emails, and chat rooms. Though these alternatives will be useful for people to induce their queries answered, yet sometimes it fails. The learners usually suppose that interacting with a trainer live is one of the most effective processes to learn because it is interactive and permits for two-way communication. For such styles of individuals, synchronous online courses are going to be additional applicable. {another way|differently|in a completely different way|in our own way|otherwise} to accumulate information through an Internet medium is by looking on different search engines like Google, Bing, etc., though this helps by reducing the quantity of books

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one must browse, there could also be too several sources of data one must browse and opt for the relevant ones, which might lead to data overload. Traditional categories are additionally appropriate for young youngsters, teenagers, and young adolescents UN agency which are nonetheless a difficult work force. Regular group action in categories helps them act with alternative people of their own age, be higher disciplined, follow an everyday schedule, and improve their good condition and mental alertness. Schoolroom learning helps students and academics apprehend one another in a very higher manner. This permits academics to grasp the scholars and appraise their strengths and weaknesses higher, act as mentors, and guide students in their career potentialities. In a very ancient schoolroom, students will directly share their views and clarify their own queries with the teacher, therefore obtaining their queries answered quickly. Most of the time, books and schoolroom notes are terribly helpful for learning and spending exams. Understanding the question and answer pattern and with suggestions provided by knowledgeable academics, students will notice it additionally useful to find out than once victimization generalized online notes and suggestions accessible on the Web. Also, schoolroom learning is additional useful because of a nonstop interaction between students and academics, because it helps students to induce obviate their fears concerning exams, which might seldom happen with online steering. Lastly, interactions with smart academics facilitate and encourage students to realize higher marks. In case of standard learning methodology, the teacher interacts face to face, whereas in the case of online technique, the instructor needs to give deliberations through PowerPoint presentation mode.

8 Conclusion Therefore, one can conceptualize that e-learning involves the employment of digital tools for teaching and learning. It makes use of technological tools to modify learners study anytime and anyplace. It involves the coaching and delivery of data and motivates students to move with one another, still as exchange and respect completely different purpose of views. It eases communication and improves the relationships that sustain learning. Despite some challenges mentioned, the literature has sought after to elucidate the role of e-learning above all, and the way e-learning has created a powerful impact in teaching and learning. Its adoption in some establishments has enhanced school and learner’s access to data and has provided a fashionable setting for collaboration among students that have improved educational standards. The literature that explains the benefits and drawbacks of e-learning suggests the necessity for its implementation in educational activity for school, directors, and students to get pleasure from the total edges that escort its adoption and implementation.

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9 Scope of Further Research However, nowadays, looking into the perspectives of a highly competitive education market where students of humanities and social sciences have to compete with engineering students, the faculties have started using digital technology in literature classes. Paulo Freire has defined praxis as ‘reflection and action upon the world in order to change it’. Teaching literary criticism through multimedia would benefit the students in many ways. Teaching women’s gender, queer, and ethnic studies departments could be taken as a pedagogy for the students of literature. Professors in women’s gender studies could share content and approaches quickly.

References 1. Fletcher JD, Rockway MR (1986) Computer-based training in the military. In: Ellis JA (ed) Military contributions to instructional technology, pp 177–222, New York: Praeger 2. Kulik CLC, Kulik JA (1986–1987) Mastery testing and student learning: a meta-analysis. J Edu Technol Syst 15325345 3. Willett G (1985) A comparative evaluation of teaching methods in an introductory neuroscience course for physical therapy students. University of Nebraska Medical Center, p 125 4. Tavangarian D, Leypold M, Nölting K, Röser M (2011) Is e-learning the solution for individual learning? J e-learn 2009. 3. EC. Communication from the Commission: E-Learning—designing “Tejas at Niit” tomorrow’s education. European Commission, Brussels 5. Nagy A (2008) The impact of E-learning. In: Bruck PA, Buchholz A, Karssen Z, Zerfass A (eds) E-content: technologies and perspectives for the European Market. Springer-Verlag, Berlin, pp 79–96 5 (Bagui S (Reasons for increased learning using multimedia. J Educ Multimedia Hypermedia 7(1):3–18) 6. Hede T, Hede A (2010) Multimedia effects on learning: design implications of an integrated model. Paper presented at the ASET.) 7. Mayer RE et al (2003) e-Learning and the science of instruction: Proven guidelines for customers and designers of multimedia learning, San Francisco, CA: Pfeiffer 8. Beccue B, Vila J, Whitley L (2009) The effects of adding audio instructions to instructions to a multimedia computer based training environment 9. Solomon A (2015) Challenges and prospects of e-learning at the national open university of Nigeria. J Edu Learn 9(3) 10. Clark RC et al (2008) e-learning and the science of instructions, Wiley, pp 26–35 San Fransisco 11. Freund E (1987) The return of the reader: reader-response criticism. Methuen. Print, London 12. Gardner H (1983) Frames of mind: the theory of multiple intelligences. Basic Books Print, New York 13. Novak D (1998) Learning, creating, and using knowledge: concept maps as facilitative tools. Mahwah: L. Erlbaum Associates. Print 14. Blake R (2008) Brave new digital classroom: technology and foreign language learning. Georgetown University Press, Washington. Chickering AW, Gamson ZF (1987) Seven principles for good practice in undergraduate education. AAHE Bull 39:3–7. Halliday MAK, Hasan R (1976) Cohesion in English Longman London 15. Norris D, Bouchard J (1980) Observing children. Toronto Board of Education, Toronto 16. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC (2001) A taxonomy for learning, teaching, and assessing. New York, Longman

Behavior Analysis for Human by Facial Expression Recognition Using Deep Learning: A Cognitive Study Sudheer Babu Punuri, Sanjay Kumar Kuanar, and Tusar Kanti Mishra

Abstract With the change from laboratory controlled to challenging facial expression recognition (FER) in the wild and the recent success of deep learning techniques in different fields, deep neural networks have been increasingly leveraged for automated FER to learn discriminatory representations. Here, in this survey, we include a brief overview of deep FER literatures and provide insights into some essential issues. Firstly, we represent the existing datasets that are widely used for the purpose and then we define a deep FER system’s standard pipeline with the associated context information and suggestions for applicable executions for each level. We then present already existing novel deep neural networks (DNN) and related training approaches for the state-of-the-art deep FER techniques that are optimized on the basis of both static and dynamic image sequences. A competitive comparison of the experimental works is also presented along with an analysis of relevant problems and implementation scenarios. Lastly, an overview of the obstacles and appropriate opportunities in this area is presented. Keywords Facial expression recognition · Deep neural networks · Deep learning

1 Introduction Facial expressions are movements of various muscles supplied by the facial that are attached to and move the facial skin. Abundant research has been performed on automatic facial expression analysis because there are more practical uses in the fields S. Babu Punuri · S. K. Kuanar (B) GIET University, Gunupur, Odisha 765022, India e-mail: [email protected] S. Babu Punuri e-mail: [email protected] T. K. Mishra (B) Dept. of CSE, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3_60

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of commercial and academic research like consumer neuroscience and neuromarketing, media testing and advt agencies, psychological research, clinical psychology and psychotherapy, medical applications, software UI and Web site design, engineering or artificial social agents (avatars), driver fatigue surveillance, and many other human–computer interaction systems. The primary facial expressions, such as fear, surprise, anger, disgust, happiness, and sadness, have become essential part of learning emotions. Research progress for the facial emotion detection is with the help of binary decision tree [1] using convolutional neural network (CNN) [2], sentiment classification using neural network (NN) and hidden Markov models (HMM) [3], sentiment study in demonstration and auditory prompt [4], linking numerous main methods [5]. Though clean expression recognition primarily is based totally on images with visible faces, we can gain favorable results by combining different models right into a high-level framework. This provides balancing data and additionally improves robustness. To stay away from overfitting, deep neural networks (DNN) need a huge amount of data for training. Nevertheless, the facial expression (FE) datasets which are existing are not enough to train the famous neutral networks with deep architecture, which produce promising outcomes in tasks for object recognition. Goal of this review paper is to observe the human behavior analysis with the aid of the deep learning approaches.

2 The State-of-the-Art 2.1 Datasets We have enough labeled data to train a deep learning system for expression recognition with enough variations in the populations and environment. Here we present the openly accessible repositories that capture elementary facial expressions. These datasets are commonly used by the researchers in the field of FER. Table 1 illustrates an overview of such datasets.

2.2 Data Preprocessing In this section, we define three key steps which are typically used in an automated deep FER. They are preprocessing, deep learning of features and deep features classification. Preprocessing Methods—In unconstrained situations, variations (fluctuations) that are unrelated to facial expressions, like various illuminations, backgrounds area and head poses, are relatively common. Therefore, preprocessing is typically needed

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Table 1 Summary of the datasets for facial expressions. P = postured; N = natural Datasets

Image

Expressions

count (I/V/S)

Subject

State

Status

CK + [3]

593 I S

123

Laboratory

P&N

Seven elementary expressions plus contempt

MMI [4]

742 I and 2950 V

25

Laboratory

P

Seven elementary expressions

JAFFE [5]

219 I

10

Laboratory

P

Seven elementary expressions

TFD

112,334 I

–

Laboratory

P

Seven elementary expressions

FER-2013 [6]

35887 I

–

Web

P&N

Seven elementary expressions

AFEW 7.0 [7]

1889 V

–

Movie

P&N

Seven elementary expressions

SFEW 2.0 [8]

1766 I

–

Movie

P&N

Seven elementary expressions

Multi-PIE

755,770 I

339

Laboratory

P

Neutral, squint, surprised, disgust, scream and smile

BU-3DFE

2600 3D I

100

Laboratory

P

Seven elementary expressions

BU-4DFE

616 3D S

121

Laboratory

P

Seven elementary expressions

Oulu- CASIA

2880 S

80

Laboratory

P

Six elementary expressions without neutral

RaFD

1608 I

67

Laboratory

P

Seven elementary expressions plus contempt

KDEF

4980 I

70

Laboratory

P

Seven elementary expressions

EmotioNet

10 Lakh I

–

Internet

P&N

23 elementary expressions or compound expressions

RAF-DB [9, 10]

29,772 I

Internet

P&N

Seven elementary expressions and twelve compound expressions

AffectNet [11]

450,300 I

–

Internet

P&N

Seven elementary expressions

ExpW [12]

91,993 images

–

Internet

P&N

Seven elementary expressions (continued)

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Table 1 (continued) Datasets 4DFAB [2]

Image

Expressions

count (I/V/S)

Subject

State

Status

1.80 million 3 Dim faces

184

Lab

P&N

Seven elementary expression

to organize. Normalize the visual semantic perception expressed by the face before training the DNN to learn meaningful features. Alignment of the Face—This step is primarily used to identify the face and then eliminate background region. P Viola and M Jones is a method for face detection [16] which is a common and classically used application for face detection that is robust and quick to identify near frontal faces in terms of computation. Even though the face detection is the only critical method for enabling feature learning, further face alignment with localized landmark coordinates will therefore dramatically improve FER performance [17] (Fig. 1). Normalization of Face—Significant image changes may be introduced by differences in lighting and head poses and thus affect the FER results. Therefore, two traditional face normalization strategies are used to boost these fluctuations: normalization by illumination and normalization of pose. 1.

Normalization by Illumination: In different photos, illumination and contrast will differ, even from the same person with the same voice, particularly in uncontrolled environments, which may lead to broad intra-class fluctuations. For illumination normalization, other distinct algorithms named as isotropic diffusion (IS)-based normalization, Gaussian (DoG) gap, discrete cosine transformation (DCT) normalization and homomorphic filtering-based normalization can be used [18, 19].Normalization by Illumination: In different photos, illumination and contrast will differ, even from the same person with the same voice,

Fig. 1 Block diagram

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particularly in uncontrolled environments, which may lead to broad intra-class fluctuations. For illumination normalization, other distinct algorithms named as isotropic diffusion (IS)-based normalization, Gaussian (DoG) gap, discrete cosine transformation (DCT) normalization and homomorphic filtering-based normalization can be used [18, 19]. Normalization of Pose: Another common problem in unconstrained settings is the pose variation. Some experiments have used techniques of pose normalization to produce FER frontal facial views (e.g., [20, 21]), the most popular of which Hassner et al. [22] have suggested. In particular, a standardized 3D texture model for all faces will be built to estimate visible facial components after localizing facial landmarks. By projecting each input face image back to the reference coordinates scheme, the original frontalized face is then synthesized. Very recently, for frontal view synthesis, a number of GAN-based deep models were suggested and promising performances were recorded.

Augmentation of Data—In order to guarantee generalizability to a given recognition task, deep neural networks need adequate training data. However, most FER databases that are freely available do not have enough photos for training purposes. For deep FER, therefore, augmentation of data is a critical phase. Therefore for deep FER, data augmentation is a critical step. Methods of data augmentation can be categorized into two types: online data augmentation and offline data augmentation. At the training stage, the input samples are randomly cut from middle and from four corners of the picture and then horizontally rotated, which can result in a dataset that is ten times larger than the results of the initial training. Two standard forecast modes are followed in science: Only the face center patch is used to predict (e.g., [23, 24]). Deep Networks for Learning Features—Deep learning has recently become a common subject of research development and has reached the state-of-the-art outcomes for a wide set of applications [25]. Deep learning aims at capturing highlevel abstractions through hierarchical systems with multiple nonlinear transformations and representations.

2.3 Methodology An extensive amount of research has been carried out on expression recognition tasks on images without considering temporal information. CNN Loss Layer—In Cai et al. [26], island loss was formal in order to increase the distance between different class centers pairwise. In Li et al. [27], it will be formalized in order to extract the locally adjacent characteristics. Thus, for each word, the intraclass local clusters will become nearer. Together with the combination of this loss and softmax loss, training will significantly increase the discriminatory power of the features trained. Multi-task Network—Many existing FER networks concentrate on a task and learn features that respond to sentences without taking into account interactions with

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other latent variables. FER, however, is intertwined with different variables in the real. Reed et al. [28] have designed a higher order (Table 2). Boltzmann Machine (disBM)—The multiple coordinates for the related expression factors are learned by this machine. This disentangling device technique is such that units related to secret expression are invariant to the morphology of the face. Other authors have recommended that FER’s improved performance promises to work with other functions, such as localization of facial landmark and facial AU [29] detection, simultaneously on FER. Furthermore, several other works [30, 31] on multi-task learning for identity-invariant FER have been involved. Identity aware CNNs (IACNN) with two identical sub-CNNs are the methods introduced [32]. For features like expression discriminative, one stream uses expression loss sensitivity and the other stream uses identity loss sensitivity to learn identity-related identityinvariant FER characteristics. An approach named as multi-signal CNN (MSCNN) is suggested by the authors [33]. This approach is taught under the guidance of FER and face verification functions. Furthermore, there is another model called the CNN allin-one model [34]. This was implemented to solve a complex range of tasks for face analysis at the same time. Such tasks are facial recognition tasks, such as identifying smiles. Similarly, SmileNet suggests how it is possible to teach face identification along with smile recognition. For this technique, no form of pre-normalization step, including face detection and registration, is needed. Cascade Network—In order to construct deeper network, different modules for different tasks are joined sequentially in cascaded network, whereas the output from the first modules is given as input to the next modules. Several similar studies have suggested combinations of various systems to learn a function of hierarchy where variations of factors that are unrelated to speech can be slowly filtered out. Sequentially and independently, various networks or strategies for learning are most frequently combined, and each of them pays different and hierarchical tributes. To detect the facial expression area first, DBNs were trained [35]. Then a stacked auto-encoder classifies the parsed face components. A technique called multi-scale contractive convolutionary network (CCNET) was introduced [86]. In order to get local-translation-invariant (LTI) representations, this approach was introduced. A contractive auto-encoder was then built to hierarchically isolate the emotion-related variables with respect to identity and pose. In [4, 36] representations were studied primarily with the help of CNN architecture. To derive higher-level feature for FER, a multi-layer RBM is used. Generative Adversarial Networks (GANs)—Recently, in image synthesis, techniques based on GAN have been successfully used to produce impressive-realistic faces, numbers and a variety of other image types that are useful for improving data training and recognition tasks. In FER for pose-invariant and identity-invariant FER, many researchers suggest GAN-based models which are novel. Lai and Lai [37] suggested the GAN-based frontalization mechanism for pose-invariant FER, whereby the generator faces images of input while retaining the identity and expression features and distinguishes the real images from the front images generated. Zhang et al. [38] suggested a GAN-based model for multi-view FER which can produce images with various expressions under arbitrary poses. Yang et al. [39]

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Table 2 Analysis of different methodologies (TS: training set, VS: validation set, TeS: test set) Method used

Feature

Datasets

Data selection

Performance (%)

CK+

The initial frame and three frames from last

Seven classes: 93.80

Fine tune

The first frame and the last three frames

Six classes: (98.63) Eight classes: (96.83)

Loss layer

Three frames from last

Seven classes: 94.41 − 90.67

CNN and RBM CN CNN

The three frames from last Seven classes: 97.15 − 96.18 The three frames from last Seven classes: 95.39, − 95.53

MN

The three frames from last Six classes: 98.91 Loss layer FER 2013

TS: 28,909. VS: 3599. TeS: 3591

Test: 71.21

MN

FER 2014

TS: 28,909, VS: 3599. TeS: 3590

Validation and test: 67.25

FER 2015

TS: 28,909, VS: 3599, TeS: 3591

Test: 75.15

Loss layer FER 2016

TS: 28,909 VS: 3599 TeS: Test: 71.34 3592

NE

FER 2017

TS: 28,909 VS: 3599 TeS: Result: 73.75 3593

FER 2018

TS: 28,909 VS: 3599 TeS: Result: 76.25 3594

FER 2019

TS: 28,909 VS: 3,599 TeS: 3595

Result:75.44

MMI

The center three frames and the first frame

Seven classes: 74.76 (71.83)

MMI

Initial frame and Center 3 Seven classes: 75.86 frames

CNN and RBM CN

CNN

Loss layer MMI

The center three frames

Six classes: 78.47

MMI

The center three frames

Six classes: 78.54 (73.51)

SFEW 2.0 TS:925 VS:429 Fine tune

SFEW 2.1 TS: 895 VS:455

Validation: 51.15 Validation: 55.17 (46.7)

Loss layer SFEW 2.2 TS:998 VS:433, TeS:372

Validation: 54.21

NE

Validation: 51.76 Test: 54.57

TS:991 VS:433

Loss layer SFEW 2.3 TS:998446 TeS:392 MN

Validation: 52.53 test: 69.41

SFEW 2.4 TS:968, VS: 436, TeS:372 Validation: 50.99 Test: 54.32 (continued)

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Table 2 (continued) Method used

Feature

Datasets

NE

SFEW 2.5 TS: 998433 for validation Validation: 53.91 test: and 372 for testing 61.62

Data selection

Performance (%)

SFEW 2.6 TS: 998, VS:433 and TeS: Validation: 55.97 test: 372 61.30 Fine tune

TFD

4178 labeled images

Test: 88.91 (87.71)

MN

TFD

4278 labeled images

Validation: 87.8, test: 5.12

proposed a two-part identity-adaptive generation (IA-Gen) model for FER. Using cGANs, the upper component produces images of the same subject with distinct expressions. Chen et al. [40] introduced a privacy-preserving representation learning variational generative adversarial network (PPRL-VGAN) that combines VAE and GAN techniques in order to learn an identity-invariant representation that is specifically isolated from identity knowledge and generative for face image synthesis that preserves expression, for privacy-preserving representation learning. A deexpression residue learning (DeRL) technique was proposed by Yang et al. [41] to explore expressive content, which is filtered out during the de-expression process but still embedded in the generator. Then to minimize the effect of subject variations and optimize the FER efficiency, the model extracts this information directly from the generator. Mengyi et al. [4] presented a boosted DBN (BDBN) that performs iteratively feature representation, feature selection and classifier creation in a unified loop state instead of simply concatenating the outputs of distinct networks. In contrast to concatenation without input, this loopy method propagates the classification error backward to start the feature selection process alternately until convergence. Thus, during this iteration, FER’s discriminative capacity can be greatly enhanced, such as head posture, lighting and topic recognition (morphology of the face). In order to provide a solution to this type of problem, multi-task learning method is proposed to submit information from other relevant tasks and disassociate nuisance features. Linked Network—While implementing linked network, we need to consider two key factors. First factor is to consider the adequate network diversity to ensure complementarity. Second factor is to consider enough network diversity to ensure complementarity. First of all in the formation of various committees, different types of training data and different network parameters or architectures are taken into account. We can understand that various preprocessing methodologies like deformation and normalization generate different training data for diverse networks. For the second aspect, each member of the committee’s networks may be delegated at two levels: at the level of functionality and decision-making. The best-known technique for feature-level assemblies is to combine features learned from various networks [42].

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3 Challenges In addition to the most common expression classification tasks reviewed above, we also address some related issues in deep neural networks. 1.

2.

3.

FER in 3D on static and dynamic results—Two key problems are not solved, despite substantial advances made in 2D FER: changes in lighting and variations in pose [32]. Using 3D face shape models with depth details, which are naturally resilient to pose and lighting variations, 3D FER can capture subtle facial deformations. Depending on the distance from the depth camera, which offers important information on facial geometric relationships, the strength of facial pixels is recorded in depth images and videos. Datasets for facial expression—As the FER literature shifts its primary emphasis to the challenging environmental conditions in the wild, many researchers have devoted themselves to using deep learning techniques to resolve issues such as lighting variations, occlusions, non-frontal poses of head, bias of identification and low-intensity expressions recognition. The main problem facing deep FER networks is the lack of training data in terms of both quantity and accuracy, considering that FER is a data-driven operation and that it takes a significant amount of training data to train a sufficiently deep network to capture subtle expression-related deformation. Bias of the dataset and imbalanced distribution—Due to various conditions while collecting and the subjectivity of annotation, bias of data and inconsistent annotations are highly prevalent in various facial expression datasets.

4 Conclusion This paper presents a survey of various techniques and architectures of recognition of facial expression used to extract essential facial features. Detailed information with the appropriate information for various datasets used in recognition of facial expression was clarified. Current feature extraction methods are covered by contrast and recent challenges that will be beneficial for other researchers to solve established process problems and improve accuracy outcomes.

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Author Index

A Abdulhassan, Aladdin, 199 Abdullah, Alharith A., 91 Abdullah, Enas Fadhil, 499 Abdulmunem, Ashwan A., 333 AbdulNabi, Mohammed Ahmed, 369 Abedoun, Mohamed Salman, 397 Abinaya, T., 435 Abraham, Jibi, 607 Abutiheen, Zinah Abdulridha, 333 Ahamdi, Mahmood, 199 Alasadi, Suad A., 499 Al-Bayaty, Boshra F. Zopon, 313 Alhisnawi, Mohammad, 157, 199 Ali, Akbas Ezaldeen, 129 Aliesawi, Salah A., 353 Ali, Suhad A., 295 Aljaff, Sarkesh Khalid, 121 Al-muqarm, Abbas M. Ali, 445 Al Musawi, Ahmad F., 269 Al-Obaidi, Alaa Hussein Ali, 669 Al-Safi, Jehan Kadhim Shareef, 243 Alyas, Hussain H., 91 Al-Zahli, Jamal Abdullah, 685 Amrutha, K., 571 Arif, Sawsan Abdulaali, 519 Arpana, D., 61

B Babu, Preeja, 595 Babu Punuri, Sudheer, 725 Bala Nivetha, V., 109 Baqer, Naseem K., 141

C Chamundeeswari, G., 343 Chaudhary, Rashmi, 549 Chenchaiah, R., 175 Chitti, Sridevi, 635 Costa, Catarina, 21

D Dansana, Debabrata, 695 Dash, Artatrana Biswaprasan, 31 Das, Pradipta Kumar, 39 Digvijay, D., 175 Divya, R., 109 Drusya, K., 571

F Farhan, Rabah N., 519

G Gaata, Methaq Talib, 313 Gayathri, S., 109 Ghosh, Preetam, 269 Guliak, Roman N., 705

H Habib, Hamza B., 121 Hadi, Sheimaa A., 295 Hamza, Bashar J., 369 Harjan, Zahraa A., 333 Hmeed, Assef Raad, 353

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Kumar et al. (eds.), Next Generation of Internet of Things, Lecture Notes in Networks and Systems 201, https://doi.org/10.1007/978-981-16-0666-3

737

738 J Jaber, Suhad Shakir, 481 Jadaun, Shikhar, 13 Jahangir, Ayesha, 543 Jain, Tamanna, 1 Jasim, Wesam M., 353 Jasmine, K. S., 81 Jawad, Majid Jabbar, 295 Jijin Godwin, J., 435 Jose, S. Edwin, 385

K Kabat, Manas Ranjan, 39 Kadhim, Rasim Azeez, 481 Kaleli, Cihan, 243 Kalpana, A. V., 175, 185 Kamalnath Venkateswaran, 557 Kareem, Ameer Ali, 141 Karthikeyan, M. P., 513 Kaushik, Ashwani, 13 Kavitharani, K., 185 Khairi, Teaba W. A., 129 Khalaf, Rifaat Z., 121 Krishnan, Deepa, 595 Krishna Teja, B., 533 Kuanar, Sanjay Kumar, 725 Kumar, Brijesh, 647 Kumari, Guriya, 621 Kumar, Manoj, 549

L Lafta, Alyaa Abdulhussein, 499 Lilith Kumar, R., 513

M Manaa, Mehdi Ebady, 397 Manoharan, Geetha, 321 Mashkor, Alaa Abdalhussain, 141 Merugu, Shyamsunder, 635 Mishra, Biswaranjan, 31 Mishra, Brojo Kishore, 695 Mishra, Deepanjali, 717 Mishra, Tusar Kanti, 725 Mohammed, Daniah, 425 Mohialden, Yasmin Makki, 313 Mukesh Kumar Behera, A. B., 621 Munawara, A., 571 Muruganandam, Sri Balaji, 467 Muthu Kannan, P., 343 Muthukumaran, D., 489

Author Index N Nandhini, M., 185 Navin Sridhar, 557 Nimmagadda, Sailaja, 71 Nitesh Kumar Sah, M., 543 O Obaid, Ahmed J., 413 Omkumar, S., 489 P Paiva, Sara, 21 Panda, TusharKant, 621 Patwe, Sonali, 585 Pavithra, G. S., 109 Phansalkar, Shraddha, 585 Pilania, Urmila, 13 Polei, Swadhin, 621 Pragathi, K., 71 Prasanna Bharathi, S., 343 Pughazhendhi, Abhishek, 467 Puviarasi, R., 321 R Rabee, Furkan, 425, 445 Radha Rammohan, S., 685 Rajagopal, R., 385 Rajendra Prasad, Ch., 635 Raji, C. G., 571 Rakesh, K., 533 Ramalingam, Mritha, 321 Ramchandar Rao, P., 635 Ramesh, T., 533 S Saad, Wasan Kadhim, 369 Sahoo, Ajit Kumar, 657 Sahoo, Subhashree, 695 Sahu, Bandita, 39 Sai Nandan, A., 513 Sai Sumanth, B., 513 Sai Vignesh, C., 175 Samantray, Om Prakash, 51 Santhosh Krishna, B. V., 435 Sarthaja, 571 Selvi, S., 109 Shahani, Snehkumar, 607 Shanmugam, Thirumurugan, 685 Sharma, Sudhir Kumar, 1 Shelke, Srishti, 607 Shmeleva, Tatiana R., 705

Author Index Shubbar, Roaa, 199 Shyamala Devi, Munisamy, 543 Sindhuja, K., 695 Singh, Amar Nath, 31 Sivakumar, V. G., 289 Sreenidhi, B., 435 Sridevi, B., 467 Srinivasan, S., 343 Sunita, 1 Surapaneni, Ravi Kishan, 71 Swapna, H. R., 61 Swathi, P., 543 T Tarun Kumar, J., 635 Tharanee Shree, S., 435 Tripathy, Satya Narayan, 51 U Udgata, Siba K., 657

739 Umapathy, K., 489 Upadhyay, Shubham Santhosh, 543

V Vadivel, M., 289 Vaidya, Niramay, 607 Vanitha, L., 233 Vashisht, Manisha, 647 Venkata Akhil, M., 533 Venmathi, A. R., 233 Vijaya Baskar, V., 289 Vimal, S. P., 289 Vinish, A., 571 Vohra, Yatharth, 13

Z Zaitsev, Dmitry A., 705 Zaki, Rana M., 129