Innovative Mobile and Internet Services in Ubiquitous Computing : Proceedings of the 14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2020) [1st ed.] 9783030503987, 9783030503994

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Advances in Intelligent Systems and Computing 1195

Leonard Barolli Aneta Poniszewska-Maranda Hyunhee Park   Editors

Innovative Mobile and Internet Services in Ubiquitous Computing Proceedings of the 14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2020)

Advances in Intelligent Systems and Computing Volume 1195

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

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

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

Leonard Barolli Aneta Poniszewska-Maranda Hyunhee Park •

•

Editors

Innovative Mobile and Internet Services in Ubiquitous Computing Proceedings of the 14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2020)

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Editors Leonard Barolli Department of Information and Communication Engineering, Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka, Japan

Aneta Poniszewska-Maranda Institute of Information Technology Lodz University of Technology Łódź, Poland

Hyunhee Park Department of Information and Communications Engineering Myongji University, Yongin Seoul, Korea (Republic of)

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-3-030-50398-7 ISBN 978-3-030-50399-4 (eBook) https://doi.org/10.1007/978-3-030-50399-4 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Welcome Message of IMIS-2020 International Conference Organizers

Welcome to the 14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2020), which will be held from July 1 to July 3, 2020, Lodz University of Technology, Poland, in conjunction with the 14th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2020). This International Conference focuses on the challenges and solutions for Ubiquitous and Pervasive Computing (UPC) with an emphasis on innovative, mobile and Internet services. With the proliferation of wireless technologies and electronic devices, there is a fast growing interest in UPC. UPC enables to create a human-oriented computing environment where computer chips are embedded in everyday objects and interact with physical world. Through UPC, people can get online even while moving around, thus having almost permanent access to their preferred services. With a great potential to revolutionize our lives, UPC also poses new research challenges. The conference provides an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of UPC. For IMIS-2020, we received many paper submissions from all over the world. The papers included in the proceedings cover important aspects from UPC research domain. It is impossible to organize such a successful program without the help of many individuals. We would like to express our great appreciation to the authors of the submitted papers, the program committee members, who provided timely and significant reviews, and special session chairs for their great efforts. We are grateful to Honorary Co-Chairs: Prof. Makoto Takizawa, Hosei University, Japan; and Prof. Sławomir Wiak, Lodz University of Technology, Poland, for their advices and support.

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Welcome Message of IMIS-2020 International Conference Organizers

Finally, we would like to thank Web Administrator Co-Chairs and Local Arrangement Co-Chairs for their excellent and timely work. We hope that all of you enjoy IMIS-2020 and find this a productive opportunity to learn, exchange ideas and make new contacts.

IMIS-2020 International Conference Organizers IMIS-2020 General Co-chairs Aneta Poniszewska-Marańda Leonard Barolli

Lodz University of Technology, Poland Fukuoka Institute of Technology, Japan

IMIS-2020 Program Committee Co-chairs Adam Wojciechowski Hsing-Chung Chen Hyunhee Park

Lodz University of Technology, Poland Asia University, Taiwan Myongji University, Korea

IMIS-2020 Organizing Committee

Honorary Co-chairs Sławomir Wiak Makoto Takizawa

Lodz University of Technology, Poland Hosei University, Japan

General Co-chairs Aneta Poniszewska-Marańda Leonard Barolli

Lodz University of Technology, Poland Fukuoka Institute of Technology, Japan

Program Committee Co-chairs Adam Wojciechowski Hsing-Chung Chen Hyunhee Park

Lodz University of Technology, Poland Asia University, Taiwan Myongji University, Korea

Workshops Co-chairs Łukasz Chomątek Kangbin Yim Fang-Yie Leu

Lodz University of Technology, Poland SCH University, Korea Tunghai University, Taiwan

Advisory Committee Members Vincenzo Loia Arjan Durresi Kouichi Sakurai

University of Salerno, Italy IUPUI, USA Kyushu University, Japan

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IMIS-2020 Organizing Committee

Award Co-chairs Hae-Duck Joshua Jeong Tomoya Enokido Farookh Khadeer Hussain

Korean Bible University, Korea Rissho University, Japan University of Technology Sydney (UTS), Australia

International Liaison Co-chairs Witold Marańda Marek Ogiela Baojiang Cui Elis Kulla

Lodz University of Technology, Poland AGH Univ. of Science and Technology, Poland Beijing Univ. of Posts and Telecom, China Okayama University of Science, Japan

Publicity Co-chairs Krzysztof Stępień Hiroaki Kikuchi Zahoor Khan Keita Matsuo

Lodz University of Technology, Poland Meiji University, Japan Higher Colleges of Technology, UAE Fukuoka Institute of Technology, Japan

Finance Chair Makoto Ikeda

Fukuoka Institute of Technology, Japan

Local Arrangement Co-chairs Bartosz Wieczorek Tomasz Krym

Lodz University of Technology, Poland Lodz University of Technology, Poland

Web Administrators Donald Elmazi Miralda Cuka Kevin Bylykbashi

Fukuoka Institute of Technology, Japan Fukuoka Institute of Technology, Japan Fukuoka Institute of Technology, Japan

Track Areas and PC Members 1. Multimedia and Web Computing Track Co-chairs Chi-Yi Lin Tomoyuki Ishida

Tamkang University, Taiwan Fukuoka Institute of Technology, Japan

IMIS-2020 Organizing Committee

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PC Members Noriki Uchida Tetsuro Ogi Yasuo Ebara Hideo Miyachi Kaoru Sugita Akio Doi Chang-Hong Lin Chia-Mu Yu Ching-Ting Tu Shih-Hao Chang

Fukuoka Institute of Technology, Japan Keio University, Japan Kyoto University, Japan Tokyo City University, Japan Fukuoka Institute of Technology, Japan Iwate Prefectural University, Japan National Taiwan University of Science and Technology, Taiwan National Chung Hsing University, Taiwan National Chung Hsing University, Taiwan Tamkang University, Taiwan

2. Context and Location-aware Computing Track Co-chairs Massimo Ficco Jeng-Wei Lin

University of Campania Luigi Vanvitelli, Italy Tunghai University, Taiwan

PC Members Kandaraj Piamrat Kamal Singh Seunghyun Park Paolo Bellavista David Camacho Michal Choras Gianni D’Angelo Hung-Yu Kao Ray-I Chang Mu-Yen Chen Shian-Hua Lin Chun-Hsin Wu Sheng-Lung Peng

University of Nantes, France University of Jean Monnet, France Korea University, Korea University of Bologna, Italy Universidad Autónoma de Madrid, Spain University of Science and Technology, Poland University of Benevento, Italy National Cheng Kung University, Taiwan National Taiwan University, Taiwan National Taichung University of Science and Technology, Taiwan National Chi Nan University, Taiwan National University of Kaohsiung, Taiwan National Dong Hwa University, Taiwan

3. Data Management and Big Data Track Co-chairs Been-Chian Chien Akimitsu Kanzaki Wen-Yang Lin

National University of Tainan, Taiwan Shimane University, Japan National University of Kaohsiung, Taiwan

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IMIS-2020 Organizing Committee

PC Members Hideyuki Kawashima Tomoki Yoshihisa Pruet Boonma Masato Shirai Bao-Rong Chang Rung-Ching Chen Mong-Fong Horng Nik Bessis James Tan Kun-Ta Chuang Jerry Chun-Wei Lin

Keio University, Japan Osaka University, Japan Chiang Mai University, Thailand Shimane University, Japan National University of Kaohsiung, Taiwan Chaoyang University of Technology, Taiwan National Kaohsiung University of Applied Sciences, Taiwan Edge Hill University, UK SIM University, Singapore National Cheng Kung University, Taiwan Harbin Institute of Technology Shenzhen Graduate School, China

4. Security, Trust and Privacy Track Co-chairs Tianhan Gao Olivia Fachrunnisa

Northeastern University, China UNISSULA, Indonesia

PC Members Qingshan Li Zhenhua Tan Zhi Guan Nan Guo Xibin Zhao Cristina Alcaraz Massimo Cafaro Giuseppe Cattaneo Zhide Chen Clara Maria Richard Hill Dong Seong Kim Victor Malyshkin Barbara Masucci Arcangelo Castiglione Xiaofei Xing Mauro Iacono Joan Melià

Peking University, China Northeastern University, China Peking University, China Northeastern University, China Tsinghua University, China Universidad de Málaga, Spain University of Salento, Italy University of Salerno, Italy Fujian Normal University, China Colombini, University of Milan, Italy University of Derby, United Kingdom University of Canterbury, New Zealand Russian Academy of Sciences, Russia University of Salerno, Italy University of Salerno, Italy Guangzhou University, China Second University of Naples, Italy Universitat Oberta de Catalunya, Spain

IMIS-2020 Organizing Committee

Jordi Casas Jordi Herrera Antoni Martínez Francesc Sebé

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Universitat Universitat Universitat Universitat

Oberta de Catalunya, Spain Autònoma de Barcelona, Spain Rovira i Virgili, Spain de Lleida, Spain

5. Energy Aware and Pervasive Systems Track Co-chairs Chi Lin Elis Kulla

Dalian University of Technology, China Okayama University of Science, Japan

PC Members Jiankang Ren Qiang Lin Peng Chen Tomoya Enokido Makoto Takizawa Oda Tetsuya Admir Barolli Makoto Ikeda Keita Matsuo

Dalian University of Technology, China Dalian University of Technology, China Dalian University of Technology, China Rissho University, Japan Hosei University, Japan Okayama University of Science, Japan Aleksander Moisiu University of Durres, Albania Fukuoka Institute of Technology, Japan Fukuoka Institute of Technology, Japan

6. Track Modeling, Simulation and Performance Evaluation Track Co-chairs Tetsuya Shigeyasu Bhed Bahadur Bista Remy Dupas

Prefectural University of Hiroshima, Japan Iwate Prefectural University, Japan University of Bordeaux, France

PC Members Jiahong Wang Shigetomo Kimura Chotipat Pornavalai Danda B. Rawat Gongjun Yan Akio Koyama Sachin Shetty

Iwate Prefectural University, Japan University of Tsukuba, Japan King Mongkut’s Institute of Technology Ladkrabang, Thailand Howard University, USA University of Southern Indiana, USA Yamagata University, Japan Old Dominion University, USA

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IMIS-2020 Organizing Committee

7. Wireless and Mobile Networks Track Co-chairs Luigi Catuogno Hwamin Lee

University of Salerno, Italy Soonchunhyang University, Korea

PC Members Aniello Del Sorbo Clemente Galdi Stefano Turchi Ermelindo Mauriello Gianluca Roscigno Dae Won Lee Jong Hyuk Lee Sung Ho Chin Ji Su Park Jaehwa Chung

Orange Labs – Orange Innovation, UK University of Naples “Federico II”, Italy University of Florence, Italy Deloitte Spa, Italy University of Salerno, Italy Seokyoung University, Korea Samsung Electronics, Korea LG Electronics, Korea Korea University, Korea Korea National Open University, Korea

8. Intelligent Technologies and Applications Track Co-chairs Marek Ogiela Yong-Hwan Lee Jacek Kucharski

AGH University of Science and Technology, Poland Wonkwang University, Korea Technical University of Lodz, Poland

PC Members Gangman Yi Hoon Ko Urszula Ogiela Lidia Ogiela Libor Mesicek Rung-Ching Chen Mong-Fong Horng Bao-Rong Chang Shingo Otsuka Pruet Boonma Izwan Nizal Mohd Shaharanee

Gangneung-Wonju National University, Korea J. E. Purkinje University, Czech Republic AGH University of Science and Technology, Poland Pedagogical University of Cracow, Poland J. E. Purkinje University, Czech Republic Chaoyang University of Technology, Taiwan National Kaohsiung University of Applied Sciences, Taiwan National University of Kaohsiung, Taiwan Kanagawa Institute of Technology, Japan Chiang Mai University, Thailand University Utara, Malaysia

IMIS-2020 Organizing Committee

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9. Cloud Computing and Service-Oriented Applications Track Co-chairs Baojiang Ciu Neil Yen Flora Amato

Beijing University of Posts and Telecommunications, China The University of Aizu, Japan University of Naples “Frederico II”, Italy

PC Members Aniello Castiglione Ashiq Anjum Beniamino Di Martino Gang Wang Shaozhang Niu Jianxin Wang Jie Cheng Shaoyin Cheng Jingling Zhao Qing Liao Xiaohui Li Chunhong Liu Yan Zhang Baojiang Cui Hassan Althobaiti Bahjat Fakieh Jason Hung Frank Lai

University of Naples Parthenope, Italy University of Derby, UK University of Campania Luigi Vanvitelli, Italy Nankai University, China Beijing University of Posts and Telecommunications, China Beijing Forestry University, China Shandong University, China University of Science and Technology of China, China Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, China Wuhan University of Science and Technology, China Heinan Normal University, China Yan Hubei University, China Beijing University of Posts and Telecommunications, China Umm Al-Qura University, Saudi Arabia King Abdulaziz University, Saudi Arabia National Taichung University of Science and Technology, Taiwan University of Aizu, Japan

10. Ontology and Semantic Web Track Co-chairs Alba Amato Fong-Hao Liu Giovanni Cozzolino

Italian National Research Council, Italy National Defense University, Taiwan University of Naples Frederico II, Italy

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IMIS-2020 Organizing Committee

PC Members Flora Amato Claudia Di Napoli Salvatore Venticinque Marco Scialdone Wei-Tsong Lee Tin-Yu Wu Liang-Chu Chen Omar Khadeer Hussain Salem Alkhalaf Osama Alfarraj Thamer AlHussain Mukesh Prasad

University of Naples “Federico II”, Italy Italian National Research Center (CNR), Italy University of Campania Luigi Vanvitelli, Italy University of Campania Luigi Vanvitelli, Italy Tam-Kang University, Taiwan, ROC National Ilan University, Taiwan, ROC National Defense University, Taiwan, ROC University of New South Wales, Canberra, Australia Qassim University, Saudi Arabia King Saud University, Saudi Arabia Saudi Electronic University, Saudi Arabia University of Technology, Sydney, Australia

11. IoT, Crowdsourcing and Social Networking Track Co-chairs Sajal Mukhopadhyay Francesco Moscato

National Institute of Technology, Durgapur, India University of Campania Luigi Vanvitelli, Italy

PC Members Animesh Dutta Sujoy Saha Jaydeep Howlader Mansaf Alam Kashish Ara Shakil Donald Elmazi Elis Kulla Shinji Sakamoto

NIT Durgapur, India NIT Durgapur, India NIT Durgapur, India Jamia Millia Islamia, New Delhi, India Jamia Hamadard, New Delhi, India Fukuoka Institute of Technology, Japan Okayama University of Science, Japan Seikei University, Japan

12. Embedded Systems and Wearable Computers Track Co-chairs Jiankang Ren Keita Matsuo Kangbin Yim

Dalian University of Technology, China Fukuoka Institute of Technology, Japan SCH University, Korea

IMIS-2020 Organizing Committee

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PC Members Yong Xie Xiulong Liu Shaobo Zhang Kun Wang Fangmin Sun Kyungroul Lee Kaoru Sugita Tomoyuki Ishida Noriyasu Nan Guo

Xiamen University of Technology, Xiamen, China The Hong Kong Polytechnic University, Hong Kong Hunan University of Science and Technology, China Liaoning Police Academy, China Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China SCH University, Korea Fukuoka Institute of Technology, Japan Fukuoka Institute of Technology, Japan Yamamoto, Fukuoka Institute of Technology, Japan Northeastern University, China

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IMIS-2020 Organizing Committee

IMIS-2020 Reviewers Leonard Barolli Makoto Takizawa Fatos Xhafa Isaac Woungang Hyunhee Park Hae-Duck Joshua Jeong Fang-Yie Leu Kangbin Yim Marek Ogiela Makoto Ikeda Keita Matsuo Francesco Palmieri Massimo Ficco Salvatore Venticinque Noriki Uchida Kaoru Sugita Admir Barolli Elis Kulla Arjan Durresi Bhed Bista Hsing-Chung Chen Kin Fun Li Hiroaki Kikuchi Lidia Ogiela Nan Guo Hwamin Lee Tetsuya Shigeyasu Fumiaki Sato Kosuke Takano Flora Amato Tomoya Enokido Minoru Uehara Santi Caballé

Tomoyuki Ishida Hwa Min Lee Jiyoung Lim Tianhan Gao Danda Rawat Farookh Hussain Jong Suk Lee Omar Hussain Donald Elmazi Nadeem Javaid Zahoor Khan Chi-Yi Lin Luigi Catuogno Akimitsu Kanzaki Wen-Yang Lin Tomoki Yoshihisa Masaki Kohana Hiroki Sakaji Baojiang Cui Takamichi Saito Arcangelo Castiglione Shinji Sakamoto Miralda Cuka Kevin Bylykbashi Massimo Cafaro Mauro Iacono Barbara Masucci Ray-I Chang Gianni D’Angelo Remy Dupas Aneta Poniszewska-Maranda Wang Xu An

IMIS-2020 Keynote Talks

Semantics, Patterns and Compiler Techniques for Portable App Development in Multiple Cloud and Big Data Platforms Beniamino Di Martino University of Campania “Luigi Vanvitelli”, Aversa, Italy

Abstract. Cloud vendor lock-in and interoperability gaps arise (among many reasons) when semantics of resources and services and of application programming interfaces is not shared. The same issue arises with big data platforms: different programming, deployment and execution models, many different Machine Learning Libraries and related APIs. Standards and techniques borrowed from SOA and semantic Web services areas might help in gaining shared, machine readable description of cloud and big data offerings (resources, services at platform and application level, libraries and their API groundings), thus allowing automatic discovery, matchmaking, and thus selection, brokering, interoperability end composition of cloud services among multiple clouds, and seamless programming of analytics on multiple big data platforms. This talk will illustrate in particular the outcomes of the EU-funded projects MOSAIC (http://www.mosaic-cloud.eu) and TOREADOR (http://www.toreador-project.eu).

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Distributed Systems of the Day for Efficient Digital Data Exchange and Sharing: A System Transition from Peer-to-Peer to Cloud-Fog-Edge Computing Shinji Sugawara Chiba Institute of Technology, Chiba, Japan

Abstract. With the recent increase of bandwidth for communication networks, the major improvement of computing processors and the spread of cloud computing, the exchanges or sharing of various types and huge amount of data or digital contents has become very active among a great many users on a large-scale network represented by the Internet. For this, various distributed systems have been used so far and major system architecture has been continuously changing according to the functions and purposes required at each time period. In this talk, we describe the historical changes and classifications of distributed systems used for searching, exchanging, storing and sharing data deployed on networks and their respective advantages. We introduce examples of actually implemented systems. Furthermore, we discuss the possibility of future development of distributed systems for data sharing.

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Contents

Performance Evaluation of WMN-PSOSA-DGA Simulation System Considering Linearly Decreasing Vmax Method and Rational Decreament of Vmax Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Admir Barolli, Shinji Sakamoto, Seiji Ohara, Leonard Barolli, and Makoto Takizawa IoT Node Elimination and Selection for Completing Tasks in Opportunistic Networks: A Fuzzy Logic Approach . . . . . . . . . . . . . . Miralda Cuka, Donald Elmazi, Keita Matsuo, Makoto Ikeda, Leonard Barolli, and Makoto Takizawa A Fuzzy-Based Simulation System for Driving Risk Management in VANETs Considering Weather Condition as a New Parameter . . . . . Kevin Bylykbashi, Ermioni Qafzezi, Makoto Ikeda, Keita Matsuo, Leonard Barolli, and Makoto Takizawa Research on Association Analysis Technology of Network Attack Trace Based on Web Log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiyu Li and Baojiang Cui Ethical Use of Mobile Technology in the Academic Environment . . . . . Liliana Mâță A Hybrid Information-Based Smartphone Indoor-Position Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Szu-Yin Lin, Fang-Yie Leu, Chia-Yin Ko, Ming-Chien Shih, and Wei-Jia Tang An Accounting Ledger System Using the Hyperledger Fabric-Based Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiha Kim, HyeWon Kim, WooSeok Hyun, and Hae-Duck J. Jeong

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Protocol Design for Boarding Confirmation Through Arduino Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiyoon Kim, Soobin Ahn, and Jiyoung Lim

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A Security Analysis Approach for Secure Color-Code Key Exchange Protocol by Using BAN Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hsing-Chung Chen, Mosiur Rahaman, and Cahya Damarjati

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An UWB Cyclostationary Detection Algorithm Based on Nonparametric Cusum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao-ou Song and Xiao-rong Wang

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Proposal of a Disaster Evacuation Support System Using Beacon . . . . . 104 Tomoyuki Ishida and Kanae Sakamoto A Convolutional Neural Network Model for Object Detection Based on Receptive Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Yifeng Dong and Tianhan Gao The Study and Realization of a Binary-Based Address Sanitizer Based on Code Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Yuchao Wang and Baojiang Cui Modeling the Risk of Data Breach Incidents at the Firm Level . . . . . . . 135 Kazuki Ikegami and Hiroaki Kikuchi IoT Cryptosecurity: Overview and Potential Solutions . . . . . . . . . . . . . . 149 Jacek Wytrębowicz and Michał Goworko “Guess Who?” Large-Scale Data-Centric Study of the Adequacy of Browser Fingerprints for Web Authentication . . . . . . . . . . . . . . . . . . 161 Nampoina Andriamilanto, Tristan Allard, and Gaëtan Le Guelvouit Adaptive Architecture and Principles for Securing the IoT Systems . . . 173 Asif Qumer Gill, Ghassan Beydoun, Mahmood Niazi, and Habib Ullah Khan A Visual Particle System Based on Mechanism Model Data in Digital Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Zuan Gu and Tianhan Gao An EEG Emotion Classification System Based on One-Dimension Convolutional Neural Networks and Virtual Reality . . . . . . . . . . . . . . . 194 Xinbei Jiang and Tianhan Gao Implementation of a Moving Omnidirectional Access Point Robot and a Position Detecting System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Atushi Toyama, Kenshiro Mitsugi, Keita Matsuo, and Leonard Barolli

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Uplink Resource Allocation Based on Short Block-Length Regime in Heterogeneous Cellular Networks for Smart Grid . . . . . . . . . . . . . . . 213 Sai Wu, Zhihui Wang, Zhe Li, Zheng WeiJun, Weiping Shao, Baojuan Ma, Shunyu Yao, and Ying Wang Multiuser Channel Access with RTS-CTS Algorithm for Li-Fi Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Hyunhee Park Design and Implementation of a Novel Testbed for Automotive Security Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Youngho An, Junyoung Park, Insu Oh, Myoungsu Kim, and Kangbin Yim Reinforcement Learning-Based Fuzzing Technology . . . . . . . . . . . . . . . 244 Zheng Zhang, Baojiang Cui, and Chen Chen Research on IoT Device Vulnerability Mining Technology Based on Static Preprocessing and Coloring Analysis . . . . . . . . . . . . . . . . . . . . 254 Min Yao, Baojiang Cui, and Chen Chen Computer-Aided English Education in China: An Online Automatic Essay Scoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Dapeng Li, Shaodong Zhong, Zhizhang Song, and Yijia Guo Survey of Cyberspace Resources Scanning and Analyzing . . . . . . . . . . . 279 Yuanwei Hou, Xiaoxiao Chen, Yongle Hao, Zhiwei Shi, and Shiyu Yang Modeling and Analysis of Cyberspace Threat Sources Based on Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Yijia Guo, Yuanwei Hou, Yongle Hao, and Wenjue Xu A Dynamic Instrumentation Technology for IoT Devices . . . . . . . . . . . . 304 Chen Chen, Weikong Qi, Wenting Jiang, and Peng Sun Theoretical Research and Practical Exploration on the Teaching Reform of Higher Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Yiyue Sun Research on Experimental Teaching Reform of Electronic Technology Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 Chunming Zhang, Yiyue Sun, and Xiaofeng Zhang The Exploration and Research of Blended Teaching Mode Based on “Internet+” Big Data Cloud Platform . . . . . . . . . . . . . . . . . . . . . . . . 334 Xiang Ma, Xuhui Fan, Wei Li, Jiangtao Li, and Qiong Li The Analysis of Higher Mathematics Teaching Strategy Based on the Innovative Teaching Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Xuhui Fan, Wei Li, Zhimin Wang, Yiyue Sun, and Lili Su

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Design of Gradient Push Algorithm in Time-Varying Directed Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 Qingchao Zhu, Yue Wang, Xiao-ou Song, and Hongmei Huang Generalized Snell’s Law and Its Verification by Metasurface . . . . . . . . 364 Liming Zheng and Yi Zhao A Targeted Fuzzing Technique Based on Neural Networks and Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Yuyue Wang, Chen Chen, and Baojiang Cui Anomaly Network Traffic Detection Based on Deep Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Pengfei Xiong, Baojiang Cui, and Zishuai Cheng Distributed Facial Feature Clustering Algorithm Based on Spatiotemporal Locality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Qiutong Lin, Bihua Zhuo, Lili Jiao, Li Liao, and Jiangtao Guo Developing an AR-Based Ubiquitous Learning System for a Smart Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Shian-Shyong Tseng, Shih-Nung Chen, and Tsung-Yu Yang The Development and Evaluation of a Smart E-Learning Platform for Programming Instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Anthony Y. H. Liao and Shun-Pin Huang Research on Logistics Distribution Model of E-commerce Based on Improved Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . 426 Yue Wang, Qingchao Zhu, Xiao-ou Song, Hongmei Huang, and Qiu Yang Logical Network Separation and Update Inducing Techniques of Non-updated Vaccine Host by Creating Flow Rule in SDN . . . . . . . . 436 Dohyeon Bak, Mintae Kim, Jonghoon Lim, Raeseung Jang, Wonyoung Jang, and Sun-Young Lee Group Delegated ID-Based Proxy Re-encryption for PHR . . . . . . . . . . . 447 Won-Bin Kim, Im-Yeong Lee, and Kang-Bin Yim Analysis on Account Hijacking and Remote Dos Vulnerability in the CODESYS-Based PLC Runtime . . . . . . . . . . . . . . . . . . . . . . . . . 457 Eunseon Jeong, Junyoung Park, Insu Oh, Myoungsu Kim, and Kangbin Yim Vestiges of Past Generation: Threats to 5G Core Network . . . . . . . . . . 468 Seongmin Park, Bomin Choi, Youngkwon Park, Dowon Kim, Eunseon Jeong, and Kangbin Yim Feasibility Study of Introducing Daily Rental Suites Business Model into Long-Term Care Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Kuei-Yuan Wang, Ying-Li Lin, Chien-Kuo Han, and Tsung-Chih Hung

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Black-Litterman Model and Momentum Strategy: Evidence of Taiwan Top 50 ETF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 Shyh-Weir Tzang, Chun-Ping Chang, Chih-Hsing Hung, and Yung-Shun Tsai Investment Timing and External Effects . . . . . . . . . . . . . . . . . . . . . . . . 498 Chun-Ping Chang, Munkhnaran Duurenjargal, Zolzaya Batjargal, and Shyh-Weir Tzang The Choice Between Foreign Direct Investment and Export with Industry Competition and Marginal Cost . . . . . . . . . . . . . . . . . . . . 504 Chun-Ping Chang, Yung-Shun Tsai, Shyh-Weir Tzang, and Zolzaya Batjargal Information Asymmetry, Market Liquidity and Abnormal Returns . . . 510 Yung-Shun Tsai, Shyh-Weir Tzang, and Chun-Ping Chang Examining the Impacts of Strategic Alliance – Examples of the Taiwan Semiconductor Industry . . . . . . . . . . . . . . . . . . . . . . . . . 519 Mei-Hua Huang, Chia-Hui Hsieh, Perng-Fei Huang, and Chiung-Yen Chen The Relationship Between Long-Term Care Issues and Disability Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Ying-Li Lin, Kuei-Yuan Wang, and Li-Lu Hsu Is Enforcing the Production and Filing of Corporate Social Responsibility Reports Conducive to Improving Corporate Performance? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Chiung-Yen Chen, Mei-Hua Huang, Kuan-Ling Li, Ho-Yun Chao, Ke-Jie Shen, and Szu-Han Yu An Inventory Model for Perishable Items Under Upstream and Downstream Trade Credit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 Ya-Lan Chan, Sue-Ming Hsu, and Mei-Hua Liao The Social Performance of University Social Responsibility Elderly Project: The Perspective of Social Return on Investment . . . . . . . . . . . . 561 Ya-Lan Chan, Sue-Ming Hsu, Neo Koe Hsin, and Mei-Hua Liao Investment Concentration and Home Bias . . . . . . . . . . . . . . . . . . . . . . . 573 Mei-Hua Liao, Wei-Li Kuo, and Ya-Lan Chan Investor Sentiment and Governance Mechanisms . . . . . . . . . . . . . . . . . 579 Mei-Hua Liao, Chieh-Lin He, Ruirui Cui, and Ya-Lan Chan A Static Instrumentation Method for IoT Firmware ELF Binary Emulation Patching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586 Yiqi Sun, Baojiang Cui, Chen Chen, and Yifei Wang

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Pseudonym Schemes Based on Location Privacy Protection in VANETs: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Tianhan Gao and Lei Zhao Application of Blockchain Technology in Data Management of University Scientific Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606 Xiao-Feng Zhang Research on Vulnerability Site Location and Vulnerability Similarity Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 Xiaochen Wang, Baojiang Cui, Xinda Xu, and Qian Ma Implementation of Digital Signature on QR Symbol by Area Division Using Rhombic Sub-cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Nobuyuki Teraura, Isao Echizen, and Keiichi Iwamura Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639

Performance Evaluation of WMN-PSOSA-DGA Simulation System Considering Linearly Decreasing Vmax Method and Rational Decreament of Vmax Method Admir Barolli1 , Shinji Sakamoto2 , Seiji Ohara3 , Leonard Barolli4(B) , and Makoto Takizawa5 1

4

Department of Information Technology, Aleksander Moisiu University of Durres, L.1, Rruga e Currilave, Durres, Albania [email protected] 2 Department of Computer and Information Science, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino-shi, Tokyo 180-8633, Japan [email protected] 3 Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected] Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected] 5 Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Kajino-Machi, Koganei-Shi, Tokyo 184-8584, Japan [email protected]

Abstract. The Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system considering Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Distributed Genetic Algorithm (DGA), called WMN-PSOSA-DGA. In this paper, we present the performance of WMNs using WMN-PSOSADGA simulation system considering Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). Simulation results show that a good performance is achieved for RDVM compared with the case of LDVM.

1

Introduction

The wireless networks and devices are becoming increasingly popular and they provide users access to information and communication anytime and anywhere [1,3,11,12,14,18,21,22]. Wireless Mesh Networks (WMNs) are gaining a lot of attention because of their low cost nature that makes them attractive for c Springer Nature Switzerland AG 2021  L. Barolli et al. (Eds.): IMIS 2020, AISC 1195, pp. 1–10, 2021. https://doi.org/10.1007/978-3-030-50399-4_1

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providing wireless Internet connectivity. A WMN is dynamically self-organized and self-configured, with the nodes in the network automatically establishing and maintaining mesh connectivity among them-selves (creating, in effect, an ad hoc network). This feature brings many advantages to WMNs such as low up-front cost, easy network maintenance, robustness and reliable service coverage [2]. Mesh node placement in WMN can be seen as a family of problems, which are shown (through graph theoretic approaches or placement problems, e.g. [9,16]) to be computationally hard to solve for most of the formulations [34]. We consider the version of the mesh router nodes placement problem in which we are given a grid area where to deploy a number of mesh router nodes and a number of mesh client nodes of fixed positions (of an arbitrary distribution) in the grid area. The objective is to find a location assignment for the mesh routers to the cells of the grid area that maximizes the network connectivity and client coverage. Network connectivity is measured by Size of Giant Component (SGC) of the resulting WMN graph, while the user coverage is simply the number of mesh client nodes that fall within the radio coverage of at least one mesh router node and is measured by Number of Covered Mesh Clients (NCMC). Node placement problems are known to be computationally hard to solve [13,35]. In previous works, some intelligent algorithms have been investigated for node placement problem [4,10,17,19,20,26,27,36]. In [25], we implemented a Particle Swarm Optimization (PSO) and Simulated Annealing (SA) based simulation system, called WMN-PSOSA. Also, we implemented another simulation system based on Genetic Algorithm (GA), called WMN-GA [4,15], for solving node placement problem in WMNs. Then, we designed a hybrid intelligent system based on PSO, SA and DGA, called WMN-PSOSA-DGA [24]. In this paper, we evaluate the performance of WMNs using WMN-PSOSADGA simulation system considering Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The rest of the paper is organized as follows. We present our designed and implemented hybrid simulation system in Sect. 2. The simulation results are given in Sect. 3. Finally, we give conclusions and future work in Sect. 4.

2

Proposed and Implemented Simulation System

Distributed Genetic Algorithm (DGA) has been focused from various fields of science. DGA has shown their usefulness for the resolution of many computationally hard combinatorial optimization problems. Also, Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are good approachs for solving NP-hard problems. 2.1

Velocities and Positions of Particles

WMN-PSOSA-DGA decide the velocity of particles by a random process considering the area size. For instance, when the area size is W × H, the velocity

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√ √ is decided randomly from − W 2 + H 2 to W 2 + H 2 . Each particle’s velocities are updated by simple rule [23]. For SA mechanism, next positions of each particle are used for neighbor solution s . The fitness function f gives points to the current solution s. If f (s ) is larger than f (s), the s is better than s so the s is updated to s . However, if f (s) is not larger  than f (s), the s may be updated by using the probability of 

(s) . Where T is called the “Temperature value” which is decreased exp f (s )−f T with the computation so that the probability to update will be decreased. This mechanism of SA is called a cooling schedule so that the next Temperature value of computation is calculated as Tn+1 = α × Tn . In this paper, we set the starting temperature, ending temperature and number of iterations, we calculate α as

 α=

SA ending temperature SA starting temperature

1.0/number of iterations .

It should be noted that the positions are not updated but the velocities are updated in the case of the solution s is not updated. 2.2

Routers Replacement Methods

A mesh router has x, y positions and velocity. Mesh routers are moved based on velocities. There are many router replacement methods. In this paper, we use LDVM and RDVM. Constriction Method (CM) CM is a method which PSO parameters are set to a week stable region (ω = 0.729, C1 = C2 = 1.4955) based on analysis of PSO by M. Clerc et al. [5,8,30]. Random Inertia Weight Method (RIWM) In RIWM, the ω parameter is changing randomly from 0.5 to 1.0. The C1 and C2 are kept 2.0. The ω can be estimated by the week stable region. The average of ω is 0.75 [7,28,32]. Linearly Decreasing Inertia Weight Method (LDIWM) In LDIWM, C1 and C2 are set to 2.0, constantly. On the other hand, the ω parameter is changed linearly from unstable region (ω = 0.9) to stable region (ω = 0.4) with increasing of iterations of computations [6,33]. Linearly Decreasing Vmax Method (LDVM) In LDVM, PSO parameters are set to unstable region (ω = 0.9, C1 = C2 = 2.0). A value of Vmax which is maximum velocity of particles is considered. With increasing of iteration of computations, the Vmax is kept decreasing linearly [7,29,31]. Rational Decrement of Vmax Method (RDVM) In RDVM, PSO parameters are set to unstable region (ω = 0.9, C1 = C2 = 2.0). The Vmax is kept decreasing with the increasing of iterations as Vmax (x) =



W 2 + H2 ×

T −x . x

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Where, W and H are the width and the height of the considered area, respectively. Also, T and x are the total number of iterations and a current number of iteration, respectively [23].

Fig. 1. Model of WMN-PSOSA-DGA migration.

Fig. 2. Relationship among global solution, particle-patterns and mesh routers in PSOSA part.

2.3

DGA Operations

Population of individuals: Unlike local search techniques that construct a path in the solution space jumping from one solution to another one through local perturbations, DGA use a population of individuals giving thus the search a larger scope and chances to find better solutions. This feature is also known as “exploration” process in difference to “exploitation” process of local search methods. Selection: The selection of individuals to be crossed is another important aspect in DGA as it impacts on the convergence of the algorithm. Several selection schemes have been proposed in the literature for selection operators trying to cope with premature convergence of DGA. There are many selection methods in GA. In our system, we implement 2 selection methods: Random method and Roulette wheel method.

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Crossover operators: Use of crossover operators is one of the most important characteristics. Crossover operator is the means of DGA to transmit best genetic features of parents to offsprings during generations of the evolution process. Many methods for crossover operators have been proposed such as Blend Crossover (BLX-α), Unimodal Normal Distribution Crossover (UNDX), Simplex Crossover (SPX). Mutation operators: These operators intend to improve the individuals of a population by small local perturbations. They aim to provide a component of randomness in the neighborhood of the individuals of the population. In our system, we implemented two mutation methods: uniformly random mutation and boundary mutation. Escaping from local optimal: GA itself has the ability to avoid falling prematurely into local optimal and can eventually escape from them during the search process. DGA has one more mechanism to escape from local optimal by considering some islands. Each island computes GA for optimizing and they migrate its gene to provide the ability to avoid from local optimal. Convergence: The convergence of the algorithm is the mechanism of DGA to reach to good solutions. A premature convergence of the algorithm would cause that all individuals of the population be similar in their genetic features and thus the search would result ineffective and the algorithm getting stuck into local optimal. Maintaining the diversity of the population is therefore very important to this family of evolutionary algorithms. In following, we present fitness function, migration function, particle pattern and gene coding. 2.4

Fitness and Migration Functions

The determination of an appropriate fitness function, together with the chromosome encoding are crucial to the performance. Therefore, one of most important thing is to decide the determination of an appropriate objective function and its encoding. In our case, each particle-pattern and gene has an own fitness value which is comparable and compares it with other fitness value in order to share information of global solution. The fitness function follows a hierarchical approach in which the main objective is to maximize the SGC in WMN. Thus, the fitness function of this scenario is defined as Fitness = 0.7 × SGC(xij , y ij ) + 0.3 × NCMC(xij , y ij ). Our implemented simulation system uses Migration function as shown in Fig. 1. The Migration function swaps solutions between PSOSA part and DGA part. 2.5

Particle-Pattern and Gene Coding

In order to swap solutions, we design particle-patterns and gene coding carefully. A particle is a mesh router. Each particle has position in the considered area

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and velocities. A fitness value of a particle-pattern is computed by combination of mesh routers and mesh clients positions. In other words, each particle-pattern is a solution as shown is Fig. 2. A gene describes a WMN. Each individual has its own combination of mesh nodes. In other words, each individual has a fitness value. Therefore, the combination of mesh nodes is a solution. Table 1. WMN-PSOSA-DGA parameters. Parameters

Values

Clients distribution

Normal distribution

Area size

32.0 × 32.0

Number of mesh routers

16

Number of mesh clients

48

Number of GA islands

16

Number of particle-patterns 32 Number of migrations

3

200

Evolution steps

32

Radius of a mesh router

2.0–3.5

Selection method

Roulette wheel method

Crossover method

SPX

Mutation method

Boundary mutation

Crossover rate

0.8

Mutation rate

0.2

SA starting value

10.0

SA ending value

0.01

Total number of iterations

6400

Replacement method

LDVM, RDVM

Simulation Results

In this section, we show simulation results using WMN-PSOSA-DGA system. In this work, we analyze the performance evaluation of WMNs considering LDVM and RDVM router replacement methods. The number of mesh routers is considered 16 and the number of mesh clients 48. We conducted simulations 10 times, in order to avoid the effect of randomness and create a general view of results. We show the parameter setting for WMN-PSOSA-DGA in Table 1. We show simulation results in Fig. 3 and Fig. 4. We see that for SGC, the performance of LDVM and RDVM are almost the same, but for RDVM the convergence is faster. While for NCMC, the RDVM performs better than LDVM.

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Fig. 3. Simulation results of WMN-PSOSA-DGA for SGC.

Fig. 4. Simulation results of WMN-PSOSA-DGA for NCMC.

Fig. 5. Visualized simulation results of WMN-PSOSA-DGA for different replacement methods.

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The visualized simulation results are shown in Fig. 5. For RDVM, all the routers are connected and all clients are covered by routers.

4

Conclusions

In this work, we evaluated the performance of WMNs using a hybrid simulation system based on PSO, SA and DGA (called WMN-PSOSA-DGA) considering LDVM and RDVM router replacement methods. Simulation results show that the performance is better for RDVM compared with the case of LDVM. In our future work, we would like to evaluate the performance of the proposed system for different parameters and patterns.

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IoT Node Elimination and Selection for Completing Tasks in Opportunistic Networks: A Fuzzy Logic Approach Miralda Cuka1(B) , Donald Elmazi2 , Keita Matsuo2 , Makoto Ikeda2 , Leonard Barolli2 , and Makoto Takizawa3 1

Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected] 2 Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected], {kt-matsuo,barolli}@fit.ac.jp, [email protected] 3 Department of Advanced Sciences, Hosei University, 2-17-1 Fujimi, Chiyoda, Tokyo 102-8160, Japan [email protected]

Abstract. In this work, we implement two Fuzzy-Based Systems: Node Elimination System (NES) and Node Selection System (NSS) for IoT node elimination and selection in OppNets. We use three input parameters for NES: Node’s Distance to Event (NDE), Node’s Battery Level (NBL), Node’s Free Buffer Space (NFBS) and four input parameters for NSS: Node’s Number of Past Encounters (NNPE), Node’s Unique Encounters (NUE), Node Inter Contact Time (NICT), Node Contact Duration (NCD). The output parameter is IoT Node Selection Possibility (NSP). The results show that the proposed systems make a proper elimination and selection decision for IoT nodes in OppNets.

1

Introduction

Communication systems are becoming increasingly complex, involving thousands of heterogeneous nodes with diverse capabilities and various networking technologies interconnected with the aim to provide users with ubiquitous access to information and advanced services at a high quality level, in a cost efficient manner, any time, any place, and in line with the always best connectivity principle. The Opportunistic Networks (OppNets) can provide an alternative way to support the diffusion of information in special locations within a city, particularly in crowded spaces where current wireless technologies can exhibit congestion issues. Sparse connectivity, no infrastructure and limited resources further complicate the situation [1,2]. Routing methods for such sparse mobile networks use a different paradigm for message delivery. These schemes utilize node mobility by c Springer Nature Switzerland AG 2021  L. Barolli et al. (Eds.): IMIS 2020, AISC 1195, pp. 11–22, 2021. https://doi.org/10.1007/978-3-030-50399-4_2

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having nodes carry messages, waiting for an opportunity to transfer messages to the destination or the next relay rather than transmitting them over a path [3]. Hence, the challenges for routing in OppNets are very different from the traditional wireless networks and their utility and potential for scalability makes them a huge success. Internet of Things (IoT) seamlessly connects the real world and cyberspace via physical objects embedded with various types of intelligent sensors. A large number of Internet-connected machines will generate and exchange an enormous amount of data that make daily life more convenient, help to make a tough decision and provide beneficial services. The IoT probably becomes one of the most popular networking concepts that has the potential to bring out many benefits [4,5]. The Fuzzy Logic (FL) is unique approach that is able to simultaneously handle numerical data and linguistic knowledge. The fuzzy logic works on the levels of possibilities of input to achieve the definite output. In this paper, we propose and implement two Fuzy-based systems: Node Elimination System (NES) and Node Selection System (NSS). We use three input parameters for NES: Node’s Distance to Event (NDE), Node’s Battery Level (NBL), Node’s Free Buffer Space (NFBS) and four input parameters for NSS: Node’s Number of Past Encounters (NNPE), Node’s Unique Encounters (NUE), Node Inter Contact Time (NICT) and Node Contact Duration (NCD). The output parameter Node Selection Possibility (NSP), for NES shows which IoT nodes have a very low possibility to be selected to take part in the selection process. While in NSS, NSP represents the possibility that IoT nodes will be selected to complete the task. We evaluate both systems and present the simulation results for different values of input parameters. The remainder of the paper is organized as follows. In the Sect. 2, we present the IoT node elimination and selection process. In Sect. 3, we introduce the design of fuzzy-based simulation systems. The simulation results are shown in Sect. 4. Finally, conclusions and future work are given in Sect. 5.

2

IoT Node Elimination and Selection Process

In order to make a more robust decision when selecting IoT nodes, a big pool of parameters must be selected and combined for multiple nodes. This redundancy of parameters, although covers a large space of possible combinations, decreases the performance by increasing overhead and computational time. The process of deciding how many parameters to use in a system for the selection process is limited by each node’s computational capabilities. To identify the proper parameters for each system, OppNets challenges were considered. In this work we have used two systems implemented in FL, NES and NSS. By using two systems, one for node elimination and one for selection, we eliminate redundant nodes from participating in the selection process, maximizing resource usage in the heterogeneous OppNet. Since most of the nodes are mobile devices with limited resources, battery level and storage will determine how long will the

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IoT node operate. Furthermore, a long distance puts a strain to other resources such as battery, so we have selected it as one of the parameters for the first system. After each IoT node takes part in the elimination process, a second system is used to select the nodes which will benefit the task more. In an OppNet, nodes are continuously detected and evaluated for their usability and resource availability. However, to preserve these resources as much as possible we used NES as a system for eliminating nodes that do not fulfill the basic criteria for participating in task completion. Finding which nodes to remove from the selection process is an NP-Hard problem. That is why our proposed systems are based on FL. We will first evaluate the nodes based on the three most fundamental parameters battery, storage and distance to task. After we have assessed which nodes are less likely to be selected due to the lack of minimum resources, we eliminate them from participating in the selection process. Once the elimination process is completed, the second system NSS will evaluate nodes for their usefulness as potential candidates to execute diverse tasks. Parameters chosen for the second system differ depending on the type of system and the task at hand. If the node satisfies the criteria for completing a task, it is integrated into the OppNet as a helper. This node selection process is continued until enough nodes are found to complete the task.

3

Design of Fuzzy-Based Simulation Systems

In this work, we use FL to implement the proposed systems. Fuzzy sets and FL have been developed to manage vagueness and uncertainty in a reasoning process of an intelligent system such as a knowledge based system, an expert system or a logic control system [6–19]. Our proposed systems consist of one Fuzzy Logic Controller (FLC), which is the main part of our system and its basic elements which are shown in Fig. 1. Parameters represented from numerical inputs to linguistic variables as shown in Table 1, build the FRB. The control rules which are shown in Table 2 for NES and Table 3 for NSS have the form: IF “conditions” THEN “control action”. In Table 2 some rules are highlighted in red for the IoT nodes that do not fulfill the minimum requirements and are excluded from participating in the selection process, thus eliminated. When constructing FRB, a compromise between overfitting and computational power must be made.

Fig. 1. FLC structure.

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Fig. 2. Proposed system NES.

Fig. 3. Proposed system NSS.

Fig. 4. Fuzzy membership functions for NES.

3.1

Parameters Selection and Description

The proposed systems for node elimination and selection are shown in Fig. 2 and Fig. 3. The FMFs for each system are shown in Fig. 4 and Fig. 5 for NES and NSS, respectively. We have decided to use triangular and trapezoidal FMFs due to their simplicity and computational efficiency [20]. However, the overlap triangle-to-triangle and trapezoid-to-triangle fuzzy regions can not be addressed by any rule. It depends on the parameters and the specifics of their applications.

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Fig. 5. Fuzzy membership functions for NSS.

We have consider the following parameters for implementation of our proposed systems. For NES we have used three input parameters: 1) Node’s Distance to Event (NDE); 2) Node’s Battery Level (NBL); 3) Node’s Free Buffer Space (NFBS). For the NSS, we have used four parameters: 1) 2) 3) 4)

Node’s Number of Past Encounters (NNPE); Node’s Unique Encounters (NUE); Node Inter Contact Time (NICT); Node Contact Duration (NCD).

Node’s Number of Past Encounters (NNPE) Node’s Unique Encounters (NUE) Node Inter Contact Time (NICT) Node Contact Duration (NCD)

NSS

Output Node Selection Possibility (NSP)

Node’s Distance to Event (NDE) Node’s Battery Level (NBL) Node’s Free Buffer Space (NFBS)

Parameter

NES

Systems

Extremely Low Selection Possibility (ELSP), Very Low Selection Possibility (VLSP), Low Selection Possibility (LSP), Medium Selection Possibility (MSP), High Selection Possibility (HSP), Very High Selection Possibility (VHSP), Extremely High Selection Possibility (EHSP)

Rarely (Ra), Sometime (Smt), Frequently (Frq) Few (Fe), Several (Sv), Many (Mn) Short (Sh), Medium (Mdm), Long (Ln) Short (Sho), Sufficient (Sf), Long (Lng)

Near (Ne), Middle (Mi), Far (Fa) Low (Lw), Medium (Med), High (Hi) Small (Sm), Medium (Md), Big (Bg)

Term sets

Table 1. Parameters and their term sets for FLC.

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Table 2. FRB for the 3 parameters system. No. NDE NBL NFBS NSP 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

4

Ne Ne Ne Ne Ne Ne Ne Ne Ne Mi Mi Mi Mi Mi Mi Mi Mi Mi Fa Fa Fa Fa Fa Fa Fa Fa Fa

Lw Lw Lw Med Med Med Hi Hi Hi Lw Lw Lw Med Med Med Hi Hi Hi Lw Lw Lw Med Med Med Hi Hi Hi

Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg Sm Md Bg

VLSP MSP VHSP LSP HSP EHSP VHSP EHSP EHSP ELSP VLSP MSP VLSP LSP HSP LSP HSP EHSP ELSP ELSP LSP ELSP VLSP MSP VLSP MSP VHSP

Simulation Results

In this work we proposed two systems, NES and NSS. We used NES to show which IoT nodes do not have the basic resources such as sufficient battery, sufficient storage and good distance from the task considering OppNets characteristics. The simulation results of NES are shown in Fig. 6. From these simulation results we eliminate IoT nodes that lack the necessary resources to participate in the selection system. In realistic OppNet environments, some IoT nodes might roam the network with random mobility patterns exploiting contacts to communicate. It is important to study the properties of contacts made between nodes, that is why for

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

Ra

1

2

3

4

5

6

7

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9

10

11

12

13

14

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16

17

18

19

20

21

22

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Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Ln

Ln

Ln

Lng

Sf

Sho

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

32

35

41 43 45 47 49 51

VHSP 54

EHSP 53

VHSP 52

MSP

VHSP 50

MSP

VLSP 48

MSP

VLSP 46

MSP

VHSP 44

MSP

VLSP 42

MSP

VLSP 40

ELSP 39

VLSP 38

ELSP 37

VLSP 36

MSP

VLSP 34

ELSP 33

LSP

ELSP 31

ELSP 30

ELSP 29

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Smt

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Ln

Ln

Ln

Lng

Sf

Sho

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

59 61 63 65

70

69

68

67

76

75

74

73

72

78

VHSP 81

EHSP 80

VHSP 79

HSP

EHSP 77

HSP

LSP

HSP

LSP

HSP

EHSP 71

HSP

LSP

HSP

LSP

ELSP 66

LSP

ELSP 64

LSP

VHSP 62

LSP

VLSP 60

MSP

VLSP 58

ELSP 57

VLSP 56

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Frq

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Mn

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Sv

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Fe

Sho Lng

Sf

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Lng

Sf

Sho

Ln

Ln

Ln

Lng

Sf

Sho

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

Ln

Ln

Ln

Mdm Lng

Mdm Sf

Mdm Sho

Sh

Sh

Sh

EHSP

EHSP

EHSP

EHSP

EHSP

VHSP

MSP

VHSP

MSP

VHSP

EHSP

VHSP

HSP

EHSP

HSP

LSP

HSP

LSP

HSP

EHSP

HSP

MSP

VHSP

LSP

VLSP

LSP

VLSP

No. NNPE NUE NICT NCD NSP

ELSP 55

No. NNPE NUE NICT NCD NSP

ELSP 28

No. NNPE NUE NICT NCD NSP

Table 3. FRB.

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Fig. 6. Simulation results for NES.

Fig. 7. Simulation results for NSS.

NSS we have used parameters related to IoT node’s contact characteristics. To evaluate the effect of the four input parameters as presented in Fig. 7, simulation results where carried out.

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Some encounters made between nodes are probabilistic which means that two nodes have met each other in the past or they follow a schedule which is known by both nodes. We have summarized this type of encounter as NNPE. If we compare Fig. 7(a) with Fig. 7(c) and Fig. 7(c) with Fig. 7(e), for NCD = 0.6 and NICT = 0.1, we see that NSP has increased 32% and 8%, respectively. NUE represents the number of new contacts that IoT nodes establish with other nodes. Comparing Fig. 7(e) with Fig. 7(f), for NICT = 0.9 and NCD = 0.5, NSD has increased 36%. Frequent new contacts mean more nodes are encountered and end-to-end delivery is maximized. IoT nodes that take a longer time to come in contact with other nodes will create less connections, thus the possibility that one IoT node gets selected, decreases with the increase of NICT. We can see the effect of NICT in NSP in Fig. 7(a), for NCD = 0.5. When NICT decreases from 0.9 to 0.5, NSP increases 20% and when NICT decreases from 0.9 to 0.1, NSP increases 40%. Contact durations between IoT nodes are very short due to high node mobility. However, contact duration must be long enough so that the message can be transferred during a single contact. For example, in Fig. 7(e), for NICT = 0.1 when NCD increases from 0.2 to 0.4, NSD increases 40%. For NCD from 0.4 to 0.6, it is evident that NSD remains unaltered because all these values of NCD are considered equally good amounts of contact duration.

5

Conclusions and Future Work

In this paper, we proposed two fuzzy-based systems for IoT node elimination and selection in OppNets. NES makes the decision which nodes should be excluded from the selection system while NSS makes the selection decision and chooses which IoT nodes from the remaining nodes are better suited for a certain task. In NES, we saw that IoT nodes that have sufficient basic resources are selected to go through the selection process for completing tasks. For NSS, NSP increases with the increase of NNPE and NUE and decreases with the increase of NICT. However, for NCD we need to find an optimal time for IoT nodes to stay in contact with each other. In the future work, we will also consider other parameters for IoT node selection and make extensive simulations and experiments to evaluate the proposed system.

References 1. Dhurandher, S.K., Sharma, D.K., Woungang, I., Bhati, S.: HBPR: history based prediction for routing in infrastructure-less opportunistic networks. In: 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 931–936. IEEE (2013) 2. Spaho, E., Mino, G., Barolli, L., Xhafa, F.: Goodput and pdr analysis of AODV, OLSR and DYMO protocols for vehicular networks using cavenet. Int. J. Grid Util. Comput. 2(2), 130–138 (2011)

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3. Abdulla, M., Simon, R.: The impact of intercontact time within opportunistic networks: protocol implications and mobility models. TechRepublic White Paper (2009) 4. Kraijak, S., Tuwanut, P.: A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends. In: 16th International Conference on Communication Technology (ICCT), pp. 26– 31. IEEE (2015) 5. Arridha, R., Sukaridhoto, S., Pramadihanto, D., Funabiki, N.: Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. Int. J. Space Based Situated Comput. 7(2), 82–93 (2017) 6. Inaba, T., Sakamoto, S., Kolici, V., Mino, G., Barolli, L.: A CAC scheme based on fuzzy logic for cellular networks considering security and priority parameters. In: The 9th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2014), pp. 340–346 (2014) 7. Spaho, E., Sakamoto, S., Barolli, L., Xhafa, F., Barolli, V., Iwashige, J.: A fuzzybased system for peer reliability in JXTA-overlay P2P considering number of interactions. In: The 16th International Conference on Network-Based Information Systems (NBiS-2013), pp. 156–161 (2013) 8. Matsuo, K., Elmazi, D., Liu, Y., Sakamoto, S., Mino, G., Barolli, L.: FACS-MP: a fuzzy admission control system with many priorities for wireless cellular networks and its performance evaluation. J. High Speed Netw. 21(1), 1–14 (2015) 9. Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. Eur. J. Oper. Res. 89(3), 445–456 (1996) 10. Inaba, T., Elmazi, D., Liu, Y., Sakamoto, S., Barolli, L., Uchida, K.: Integrating wireless cellular and ad-hoc networks using fuzzy logic considering node mobility and security. In: The 29th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA-2015), pp. 54–60 (2015) 11. Kulla, E., Mino, G., Sakamoto, S., Ikeda, M., Caball´e, S., Barolli, L.: FBMIS: a fuzzy-based multi-interface system for cellular and ad hoc networks. In: International Conference on Advanced Information Networking and Applications (AINA2014), pp. 180–185 (2014) 12. Elmazi, D., Kulla, E., Oda, T., Spaho, E., Sakamoto, S., Barolli, L.: A comparison study of two fuzzy-based systems for selection of actor node in wireless sensor actor networks. J. Ambient. Intell. Hum. Comput. 6(5), 635–645 (2015) 13. Zadeh, L.: Fuzzy logic, neural networks, and soft computing. ACM Commun. 37(3), 77–84 (1994) 14. Spaho, E., Sakamoto, S., Barolli, L., Xhafa, F., Ikeda, M.: Trustworthiness in P2P: performance behaviour of two fuzzy-based systems for JXTA-overlay platform. Soft Comput. 18(9), 1783–1793 (2014) 15. Inaba, T., Sakamoto, S., Kulla, E., Caballe, S., Ikeda, M., Barolli, L.: An integrated system for wireless cellular and ad-hoc networks using fuzzy logic. In: International Conference on Intelligent Networking and Collaborative Systems (INCoS-2014), pp. 157–162 (2014) 16. Matsuo, K., Elmazi, D., Liu, Y., Sakamoto, S., Barolli, L.: A multi-modal simulation system for wireless sensor networks: a comparison study considering stationary and mobile sink and event. J. Ambient. Intell. Hum. Comput. 6(4), 519–529 (2015) 17. Kolici, V., Inaba, T., Lala, A., Mino, G., Sakamoto, S., Barolli, L.: A fuzzy-based CAC scheme for cellular networks considering security. In: International Conference on Network-Based Information Systems (NBiS-2014), pp. 368–373 (2014)

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A Fuzzy-Based Simulation System for Driving Risk Management in VANETs Considering Weather Condition as a New Parameter Kevin Bylykbashi1(B) , Ermioni Qafzezi1 , Makoto Ikeda2 , Keita Matsuo2 , Leonard Barolli2 , and Makoto Takizawa3 1

Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected], [email protected] 2 Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka 811-0295, Japan [email protected], {kt-matsuo,barolli}@fit.ac.jp 3 Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, 3-7-2, Kajino-machi, Koganei-shi, Tokyo 184-8584, Japan [email protected]

Abstract. In this paper, we propose an intelligent Fuzzy-based Simulation System for Driver Risk Management in Vehicular Ad hoc Networks (VANETs). The proposed system considers Vehicle’s Environment Condition (VEC), Weather Condition (WC), Vehicle Speed (VS) and Driver’s Health Condition (DHC) to assess the risk level. The input parameters’ data can come from different sources, such as on board and on road sensors, sensors in the infrastructure and communications. Based on the system’s output i.e., risk level, a smart box informs the driver and provides assistance. We show through simulations the effect of the considered parameters on the determination of the risk level and demonstrate a few actions that can be performed accordingly.

1

Introduction

Traffic accidents, road congestion and environmental pollution are persistent problems faced by both developed and developing countries, which have made people live in difficult situations. Among these, the traffic incidents are the most serious ones because they result in huge loss of life and property. For decades, we have seen governments and car manufacturers struggle for safer roads and car accident prevention. The development in wireless communications has allowed companies, researchers and institutions to design communication systems that provide new solutions for these issues. Therefore, new types of networks, such as Vehicular Ad Hoc Networks (VANETs) have been created. VANET consists of a network of vehicles in which vehicles are capable of communicating among c Springer Nature Switzerland AG 2021  L. Barolli et al. (Eds.): IMIS 2020, AISC 1195, pp. 23–32, 2021. https://doi.org/10.1007/978-3-030-50399-4_3

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themselves in order to deliver valuable information such as safety warnings and traffic information. Nowadays, every car is likely to be equipped with various forms of smart sensors, wireless communication modules, storage and computational resources. The sensors will gather information about the road and environment conditions and share it with neighboring vehicles and adjacent roadside units (RSU) via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication. However, the difficulty lies on how to understand the sensed data and how to make intelligent decisions based on the provided information. As a result, various intelligent computational technologies and systems such as fuzzy logic, machine learning, neural networks, adaptive computing and others, are being or already deployed by many car manufacturers [6]. They are focusing on these auxiliary technologies to launch and fully support the driverless vehicles. Fully autonomous vehicles still have a long way to go but driving support technologies are becoming widespread, even in everyday cars. The goal is to improve both driving safety and performance relying on the measurement and recognition of the outside environment and their reflection on driving operation.

Fig. 1. Proposed system architecture.

On the other hand, we are focused not only on the outside information but also on the in-car information and driver’s health information to detect a potential accident or a risky situation, and alert the driver about the danger, or take over the steering if it is necessary. We aim to realize a new intelligent driver support system which can provide an output in real-time by combining information from many sources. In this work we implement a fuzzy-based simulation system for driving risk management considering vehicle’s environment condition, weather condition, vehicle speed and driver’s health condition as input parameters. The model of our proposed system is given in Fig. 1. Based on the output

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parameter value, it can be decided if an action is needed, and if so, which is the appropriate task to be performed in order to provide a better driving support. The structure of the paper is as follows. Section 2 presents a brief overview of VANETs. Section 3 describes the proposed fuzzy-based simulation system and its implementation. Section 4 discusses the simulation results. Finally, conclusions and future work are given in Sect. 5.

2

Vehicular Ad Hoc Networks (VANETs)

VANETs are a special case of Mobile Ad hoc Networks (MANETs) in which mobile nodes are vehicles. In VANETs nodes (vehicles) have high mobility and tend to follow organized routes instead of moving at random. Moreover, vehicles offer attractive features such as higher computational capability and localization through GPS. VANETs have huge potential to enable applications ranging from road safety, traffic optimization, infotainment, commercial to rural and disaster scenario connectivity. Among these, the road safety and traffic optimization are considered the most important ones as they have the goal to reduce the dramatically high number of accidents, guarantee road safety, make traffic management and create new forms of inter-vehicle communications in Intelligent Transportation Systems (ITS). The ITS manages the vehicle traffic, support drivers with safety and other information, and provide some services such as automated toll collection and driver assist systems [7]. Despite the attractive features, VANETs are characterized by very large and dynamic topologies, variable capacity wireless links, bandwidth and hard delay constrains, and by short contact durations which are caused by the high mobility, high speed and low density of vehicles. In addition, limited transmission ranges, physical obstacles and interferences, make these networks characterized by disruptive and intermittent connectivity. To make VANETs applications possible, it is necessary to design proper networking mechanisms that can overcome relevant problems that arise from vehicular environments.

3

Proposed Fuzzy-Based Simulation System

Although the developments in autonomous vehicle design indicate that this type of technology is not that far away from deployment, the current advances fall only into the Level 2 of the Society of Automotive Engineers (SAE) levels [16]. However, the automotive industry is very competitive and there might be many other new advances in the autonomous vehicle design that are not launched yet. Thus, it is only a matter of time before driverless cars are on the road. On the other side, there will be many people who will still be driving even on the era of autonomous cars. The high cost of driverless cars, lack of trust and not wanting to give up driving might be among the reasons why those people will continue to drive their cars. Hence, many researchers and automotive engineers

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Fig. 2. Proposed system structure.

keep working on Advanced Driver Assistance Systems (ADASs) as a primary safety feature, required in order to achieve full marks in safety. ADASs are intelligent systems that reside inside the vehicle and help the driver in a variety of ways. These systems rely on a comprehensive sensing network and artificial intelligence techniques, and have made it possible to commence the era of connected cars. They can invoke action to maintain driver attention in both manual and autonomous driving. While the sensors are used to gather data regarding the inside/outside environment, vehicle’s technical status, driving performance and driver’s condition, the intelligent systems task is to make decisions based on these data. If the vehicle measurements are combined with those of the surrounding vehicles and infrastructure, a better environment perception can be achieved. In addition, with different intelligent systems located at these vehicles as well as at geographically distributed servers more efficient decisions can be attained. Our research work focuses on developing an intelligent non-complex driving support system which determines the driving risk level in real-time by considering different types of parameters. In the previous works, we have considered different parameters including in-car environment parameters such as the ambient temperature and noise, and driver’s vital signs, i.e. heart and respiratory rate for which we implemented a testbed and conducted experiments in a real scenario [2,4]. The considered parameters include environmental factors and driver’s health condition which can affect the driver capability and vehicle performance. In [3], we included vehicle speed in our intelligent system for its crucial impact on the determination of risk level. In this work, we consider the weather condition as a new parameter. The weather condition affects not just the driver visibility and its comfort but also the road condition, which is a critical aspect of many accidents. We use fuzzy logic to implement the proposed system as it can make real-time decisions based on the uncertainty and vagueness of the provided information [1,5,8–15,17,18]. The proposed system called Fuzzy-based System for Driving Risk Management (FSDRM) is shown in Fig. 2. For the implementation of FSDRM, we consider four input parameters: Vehicle’s Environment Condition (VEC), Weather

A Fuzzy-Based Simulation System for Driving Risk Management in VANETs Table 1. Parameters and their term sets for FSDRM. Parameters

Term sets

Vehicle’s Environment Condition (VEC)

Very Uncomfortable (VUnC), Uncomfortable (UnC), Comfortable (C)

Weather Condition (WC)

Extremely Bad (EB), Very Bad (VB), Bad (B), Good (G)

Vehicle Speed (VS)

Slow (Sl), Moderate (Mo), Fast (Fa)

Driver’s Health Condition (DHC)

Very Bad (VBa), Bad (Ba), Good (Go)

Driving Risk Management Safe (Sf), Low (Lw), Moderate (Md), High (DRM) (Hg), Very High (VH), Severe (Sv), Danger (D)

Fig. 3. Membership functions.

27

28

K. Bylykbashi et al.

Condition (WC), Vehicle Speed (VS) and Driver’s Health Condition (DHC) to determine the Driving Risk Management (DRM). The term sets of linguistic parameters are defined respectively as: T (V EC) = {V ery U ncomf ortable (V U nC), U ncomf ortable (U nC), Comf ortable (C)}; T (W C) = {Extremely Bad (EB), V ery Bad (V B), Bad (B), Good (G)}; T (V S) = {Slow (Sl), M oderate (M o), F ast (F a)}; T (DHC) = {V ery Bad (V Ba), Bad (Ba), Good (Go)}. T (DRM ) = {Saf e (Sf ), Low (Lw), M oderate (M d), High (Hg), V ery High (V H),

(1)

Severe (Sv), Danger (D)}.

Table 2. FRB of FSDRM. No VEC

No VEC WC VS DHC DRM

No

VEC WC VS DHC DRM

1

VUnC EB

WC VS DHC DRM Sl

VBa

Sv

37

UnC EB

Sl

VBa

Sv

73

C

EB

Sl

VBa

VH

2

VUnC EB

Sl

Ba

VH

38

UnC EB

Sl

Ba

VH

74

C

EB

Sl

Ba

Hg

3

VUnC EB

Sl

Go

Hg

39

UnC EB

Sl

Go

Hg

75

C

EB

Sl

Go

4

VUnC EB

Mo VBa

D

40

UnC EB

Mo VBa

D

76

C

EB

Mo VBa

5

VUnC EB

Mo Ba

Sv

41

UnC EB

Mo Ba

Sv

77

C

EB

Mo Ba

VH

6

VUnC EB

Mo Go

VH

42

UnC EB

Mo Go

VH

78

C

EB

Mo Go

Hg

7

VUnC EB

Fa

VBa

D

43

UnC EB

Fa

VBa

D

79

C

EB

Fa

VBa

D

8

VUnC EB

Fa

Ba

D

44

UnC EB

Fa

Ba

D

80

C

EB

Fa

Ba

Sv

9

VUnC EB

Fa

Go

D

45

UnC EB

Fa

Go

Sv

81

C

EB

Fa

Go

VH

10

VUnC VB

Sl

VBa

VH

46

UnC VB

Sl

VBa

VH

82

C

VB

Sl

VBa

Hg

11

VUnC VB

Sl

Ba

Hg

47

UnC VB

Sl

Ba

Md

83

C

VB

Sl

Ba

Lw

12

VUnC VB

Sl

Go

Md

48

UnC VB

Sl

Go

Lw

84

C

VB

Sl

Go

13

VUnC VB

Mo VBa

Sv

49

UnC VB

Mo VBa

Sv

85

C

VB

Mo VBa

VH

14

VUnC VB

Mo Ba

VH

50

UnC VB

Mo Ba

Hg

86

C

VB

Mo Ba

Hg

15

VUnC VB

Mo Go

Hg

51

UnC VB

Mo Go

Md

87

C

VB

Mo Go

Lw

16

VUnC VB

Fa

VBa

D

52

UnC VB

Fa

VBa

D

88

C

VB

Fa

Sv

17

VUnC VB

Fa

Ba

D

53

UnC VB

Fa

Ba

Sv

89

C

VB

Fa

Ba

VH

18

VUnC VB

Fa

Go

Sv

54

UnC VB

Fa

Go

VH

90

C

VB

Fa

Go

Hg

19

VUnC B

Sl

VBa

VH

55

UnC B

Sl

VBa

Hg

91

C

B

Sl

VBa

Md

20

VUnC B

Sl

Ba

Hg

56

UnC B

Sl

Ba

Md

92

C

B

Sl

Ba

Lw

21

VUnC B

Sl

Go

Lw

57

UnC B

Sl

Go

Lw

93

C

B

Sl

Go

Sf

22

VUnC B

Mo VBa

Sv

58

UnC B

Mo VBa

VH

94

C

B

Mo VBa

23

VUnC B

Mo Ba

VH

59

UnC B

Mo Ba

Hg

95

C

B

Mo Ba

Md

24

VUnC B

Mo Go

Hg

60

UnC B

Mo Go

Md

96

C

B

Mo Go

Lw

25

VUnC B

Fa

VBa

D

61

UnC B

Fa

VBa

Sv

97

C

B

Fa

VBa

VH

26

VUnC B

Fa

Ba

Sv

62

UnC B

Fa

Ba

VH

98

C

B

Fa

Ba

Hg

27

VUnC B

Fa

Go

VH

63

UnC B

Fa

Go

Hg

99

C

B

Fa

Go

Md

28

VUnC G

Sl

VBa

Hg

64

UnC G

Sl

VBa

Md

100 C

G

Sl

VBa

Lw

29

VUnC G

Sl

Ba

Md

65

UnC G

Sl

Ba

Lw

101 C

G

Sl

Ba

Sf

30

VUnC G

Sl

Go

Lw

66

UnC G

Sl

Go

Sf

102 C

G

Sl

Go

31

VUnC G

Mo VBa

VH

67

UnC G

Mo VBa

Hg

103 C

G

Mo VBa

32

VUnC G

Mo Ba

Hg

68

UnC G

Mo Ba

Md

104 C

G

Mo Ba

Lw

33

VUnC G

Mo Go

Md

69

UnC G

Mo Go

Lw

105 C

G

Mo Go

Sf

34

VUnC G

Fa

VBa

Sv

70

UnC G

Fa

VBa

Sv

106 C

G

Fa

VBa

VH

35

VUnC G

Fa

Ba

VH

71

UnC G

Fa

Ba

VH

107 C

G

Fa

Ba

Hg

36

VUnC G

Fa

Go

Hg

72

UnC G

Fa

Go

Hg

108 C

G

Fa

Go

Md

VBa

Md Sv

Sf

Hg

Sf Md

A Fuzzy-Based Simulation System for Driving Risk Management in VANETs

29

All the term sets for the linguistic parameters used for FSDRM are shown in Table 1. Based on the linguistic description of input and output parameters we make the Fuzzy Rule Base (FRB). The FRB forms a fuzzy set of dimensions | T (x1 ) | × | T (x2 ) | × · · · × | T (xn ) |, where | T (xi ) | is the number of terms on T (xi ) and n is the number of input parameters. FSDRM has four input parameters, with three parameters having three linguistic terms each and one parameter having four, therefore, there are 108 rules in the FRB, which is shown in Table 2. The control rules of FRB have the form: IF “conditions” THEN “control action”. The membership functions used for fuzzification are given in Fig. 3(a), Fig. 3(b), Fig. 3(c) and Fig. 3(d). In Fig. 3(e) are shown the membership functions used for the output parameter. We use triangular and trapezoidal membership functions because they are suitable for real-time operation.

Fig. 4. Simulation results for VEC = 0.2.

Fig. 5. Simulation results for VEC = 0.5.

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4

Simulation Results

In this section, we present the simulation results for our proposed system. The simulation results are presented in Fig. 4, Fig. 5 and Fig. 6. We consider the VEC and WC as constant parameters. We show the relation between DRM and DHC for different VS values. The VS values considered for simulations are 0.1, 0.3 and 0.65, which simulate a slow, moderate and high speed, respectively. In Fig. 4, we consider the VEC value 0.2 and change the WC from 0.05 to 0.35. From Fig. 4(a), we can see that most of DRM values show extremely high driving risk. The only scenario when the DRM is decided as “High” is when the driver is driving very slowly and the health condition parameter indicates a very good status of his/her health. All other scenarios are decided as “Very High”, “Severe” or “Danger”, with the latter accounting for most scenarios. When the weather conditions are improved a little (see Fig. 4(b)), we see that the DRM values are decreased, with the lowest risk situation implying a driving scenario with a “Moderate” risk level. In Fig. 5, we present the simulation results for VEC 0.5. In Fig. 5(a) is considered the scenario with an uncomfortable vehicle and a very bad weather. By comparing with Fig. 4(b), we can see the improvements on the risk levels due to the increase of inside comfort for the same weather conditions. Since the inside

Fig. 6. Simulation results for VEC = 0.8.

A Fuzzy-Based Simulation System for Driving Risk Management in VANETs

31

conditions are slightly better, the risk level is decreased and there is not such a high risk for an accident, especially when the driver is in good health condition and drives slowly. With the weather conditions getting better, there is not any scenario with the highest degree of risk and only a few situations are decided with a “Severe” driving risk. In Fig. 6, we show the results for a comfortable vehicle interior environment and for WC 0.05, 0.35, 0.65 and 0.95, representing “Extremely Bad”, “Very Bad”, “Bad” and “Good” weather conditions, respectively. A big difference on the DRM values is seen when the WC increases from 0.05 to 0.95. Weather conditions affect directly and indirectly the vehicle performance and the driver’s ability to drive; therefore, driving safely with a moderate speed is decided only when the weather condition is good. Driving with a high speed has a risk itself, regarding the weather conditions, thus, these situations are not decided with a ”Safe” driving risk level on any occasion. In the cases when the risk level is above the moderate level for a relatively long time, the system can perform a certain action. For example, when the DRM value is slightly above the moderate level the system may take an action in order to lift the driver’s mood, and when the DRM value is very high, the system could even decide to limit the vehicle’s operating speed to a speed that the risk level is decreased significantly.

5

Conclusions

In this paper, we proposed a fuzzy-based system to decide the driving risk management. We considered four parameters: vehicle’s environment condition, weather condition, vehicle speed and driver’s health condition. We showed through simulations the effect of the considered parameters on the determination of the risk level. In addition, we demonstrated a few actions that can be performed based on the output of our system. However, it may occur that the system provides an output which determines a low risk, when actually the chances for an accident to happen are high, or the opposite scenario, which is the case when the system’s output implies a false alarm. Therefore, we intend to implement the system in a testbed and estimate the system performance by looking into correct detection and false positives to determine its accuracy.

References 1. Bylykbashi, K., Elmazi, D., Matsuo, K., Ikeda, M., Barolli, L.: Effect of security and trustworthiness for a fuzzy cluster management system in VANETs. Cogn. Syst. Res. 55, 153–163 (2019). https://doi.org/10.1016/j.cogsys.2019.01.008 2. Bylykbashi, K., Elmazi, D., Matsuo, K., Ikeda, M., Barolli, L.: Implementation of a fuzzy-based simulation system and a testbed for improving driving conditions in VANETs. In: International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 3–12. Springer (2019). https://doi.org/10.1007/978-3-03022354-01

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3. Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L.: A fuzzy-based system for driving risk measurement (FSDRM) in VANETs: a comparison study of simulation and experimental results. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 14–25. Springer (2019) 4. Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L.: Fuzzy-based Driver Monitoring System (FDMS): implementation of two intelligent FDMSs and a testbed for safe driving in VANETs. Futur. Gener. Comput. Syst. 105, 665–674 (2020). https://doi.org/10.1016/j.future.2019.12.030 5. Cuka, M., Elmazi, D., Ikeda, M., Matsuo, K., Barolli, L.: IoT node selection in opportunistic networks: implementation of fuzzy-based simulation systems and testbed. Internet Things 8, 100105 (2019) 6. Gusikhin, O., Filev, D., Rychtyckyj, N.: Intelligent vehicle systems: applications and new trends. In: Informatics in Control Automation and Robotics, pp. 3–14. Springer (2008). https://doi.org/10.1007/978-3-540-79142-31 7. Hartenstein, H., Laberteaux, L.: A tutorial survey on vehicular ad hoc networks. IEEE Commun. Mag. 46(6), 164–171 (2008) 8. Kandel, A.: Fuzzy Expert Systems. CRC Press, Boca Raton (1991) 9. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall Inc., Upper Saddle River (1987) 10. Matsuo, K., Cuka, M., Inaba, T., Oda, T., Barolli, L., Barolli, A.: Performance analysis of two WMN architectures by WMN-GA simulation system considering different distributions and transmission rates. Int. J. Grid Util. Comput. 9(1), 75–82 (2018) 11. McNeill, F.M., Thro, E.: Fuzzy Logic: A Practical Approach. Academic Press, Cambridge (1994) 12. Munakata, T., Jani, Y.: Fuzzy systems: an overview. Commun. ACM 37(3), 69–77 (1994). https://doi.org/10.1145/175247.175254 13. Ozera, K., Bylykbashi, K., Liu, Y., Barolli, L.: A fuzzy-based approach for cluster management in VANETs: performance evaluation for two fuzzy-based systems. Internet Things 3, 120–133 (2018) 14. Ozera, K., Inaba, T., Bylykbashi, K., Sakamoto, S., Ikeda, M., Barolli, L.: A wlan triage testbed based on fuzzy logic and its performance evaluation for different number of clients and throughput parameter. Int. J. Grid Util. Comput. 10(2), 168–178 (2019) 15. Qafzezi, E., Bylykbashi, K., Ikeda, M., Matsuo, K., Barolli, L.: Coordination and management of cloud, fog and edge resources in SDN-VANETs using fuzzy logic: a comparison study for two fuzzy-based systems. Internet Things 11, 100169 (2020) 16. SAE On-Road Automated Driving (ORAD) Committee: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Technical report, Society of Automotive Engineers (SAE) (2018). https://doi.org/10. 4271/J3016201806 17. Zadeh, L.A., Kacprzyk, J.: Fuzzy Logic for the Management of Uncertainty. Wiley, New York (1992) 18. Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Springer, New York (1996). https://doi.org/10.1007/978-94-015-8702-0

Research on Association Analysis Technology of Network Attack Trace Based on Web Log Shiyu Li and Baojiang Cui(&) School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected], [email protected]

Abstract. With the rapid development of the Internet, Web applications have been used more and more widely in various industries, and the accompanying security issues have gradually received attention. In the field of network security research, in addition to defense technologies such as intrusion detection and firewall technology, forensic analysis and traceability of network attacks are also the focus of research. Based on this, this paper is devoted to mining the associations of attack traces existing in web application logs, and to provide assistance for forensic analysis and attack source tracing. In this paper we propose a method for analyzing associations of network attack traces based on Web logs. We collect features of common Web attack methods and extracts attack traces. We also propose attack event description models based on key attributes, and improves the Apriori algorithm to adapt the model. The attack trace correlation analysis method proposed in this paper makes full use of and analyzes the infrequently discovered correlations in the log data, which has greatly helped the development of network attack traceability technology.

1 Introduction At the current stage of the rapid development of Internet technology, more and more enterprises and institutions cannot run without Internet. Web applications, as an important part of the Internet, have penetrated into all walks of life, and some companies have used Web applications as a carrier of core business. In this context, the security of Web applications should be given enough attention. Under the current background of network security technology, there are already many types of attack methods for Web applications. OWASP will summarize the 10 most common security problems in Web application scenarios every year. Common web applications record logs, but the detail of logging is different by default. At present, web log-based research in network security research mostly focuses on the field of intrusion detection. For example, monitoring logs in real time to detect ongoing network attacks. Another part of log-based research involves the correlation analysis of network attack traces. But few studies have been conducted, and almost all of these methods have certain limitations and cannot fully utilize the effective information in log data. Based on the above background, in this paper we proposes a log analysis method. On the one hand, it can be combined with firewalls and intrusion detection systems to sense the ongoing network attacks. On the other hand, you can © Springer Nature Switzerland AG 2021 L. Barolli et al. (Eds.): IMIS 2020, AISC 1195, pp. 33–43, 2021. https://doi.org/10.1007/978-3-030-50399-4_4

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analyze the attack behavior that has occurred, restore the attack path, find the characteristics of the attacker, and also restore the attack path in more detail in the case of certain missing log data. In this paper we proposes a method for analyzing multi-source web logs using association analysis technology. First, traces of attacks were extracted from a large number of multi-source logs according to the types of web attacks proposed by OWASP. The extracted traces of attacks include common types of web attacks such as sensitive file scanning, weak password brute force, and SQL injection. With reference to the characteristics of each vulnerability, the corresponding one or more log data was extracted as an attack trace. To facilitate the analysis of large amounts of data, it is necessary to model attack events. Combining traces from the same attack event, the content of the attack event includes key information such as the type of attack contained in the attack, the time of the attack, the source address, and the destination address. After the establishment of the attack event description model, we use the improved Apriori algorithm to perform correlation analysis on a large number of attack events. The original Apriori algorithm is also used for correlation analysis. However, we have improved it to adapt to the attack event description model. In order to verify the method proposed in this paper, a large amount of web log data was collected, and an attack trace extraction and correlation analysis experiment was designed. The experimental results show that the log correlation analysis method proposed in this paper is completely feasible and efficient, mining the correlation between attack traces that are easily ignored in Web logs. The structure of this paper is as follows. Section 2 introduces the related works of analysis algorithms and log data mining. Section 3 describes the construction of attack event model and the method of extracting attack traces. Section 4 introduces the attack trace correlation analysis method based on the improved Apriori algorithm. Section 5 designs experiments to verify the research method proposed in this paper. Section 6 describes the conclusions of the study and the advantages and disadvantages of the method.

2 Related Work Some related work has been done in the field of log correlation analysis. Hajamydeen et al. [1] proposed a new unsupervised framework UHAD, which uses a two-step strategy to cluster log events and then uses a filtering threshold to reduce the amount of events used for analysis. Dutt et al. [2] proposed an improved algorithm based on the Apriori algorithm, whose purpose is to reduce the number of database scans to one by generating a bit matrix of compressed data structure, thereby reducing time and space complexity. Xu et al. [5] proposed a security audit system based on association rule mining, and proposed an improved E-Apriori algorithm based on the traditional Apriori algorithm, which can reduce the scope of the transaction set to be scanned and reduce the algorithm’s time complexity. Zou et al. [3] introduced the process of web log mining, and proposed an improved WFPM algorithm based on FP-Growth, and used it for web log mining. Shafiq et al. [4] proposed a method that allows the semantic formal

Research on Association Analysis Technology of Network Attack Trace

35

representation of logs during Web service execution, and established a semantic FP-Tree-based Web service association rule learning technology. Other aspects of log analysis have also been studied in academia. Hernandez et al. [6] proposed a model-driven method based on a unified metamodel to obtain a conceptual model of web log data. Yang et al. [7] proposed an attack IP recognition model based on access behaviors and network relationships and an IP real person attribute determination model based on sliding time windows by analyzing network logs. Zhang et al. [8] proposed a method for analyzing security threats based on Web logs. It uses the characteristic model of threat behaviors to accurately find various threats to websites. Ma et al. [9] proposed a web intrusion detection method based on web server access log based on neural network method.

3 Model Construction and Attack Trace Extraction 3.1

Model Construction

In order to facilitate subsequent trace extraction and data correlation analysis, an attack event description model needs to be constructed. The attack event description model proposed in this paper is used to combine network attack traces extracted from a large number of Web logs to restore the attack events recorded in the logs. A schematic diagram of this model is shown in Fig. 1.

Fig. 1. Attack event description model

In the model, the subject is a series of interconnected traces of web attacks. Each attack trace consists of four key attributes: timestamp, source address, destination address, and attack type name. Referring to the general characteristics of Web logs, each log message itself contains information related to these four key attributes. The default log style of commonly used web server software such as Apache/Nginx will record the time when each web request is generated. Take the time of the first web request in each web log file as a reference. Subtract the time generated by the first web request in this log file from the time of the web request corresponding to the extracted attack trace to obtain a relative time as the timestamp attribute of the attack trace, in seconds. For those attack types that require more than one log to analyze together, the

36

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time corresponding to the first log message is used as the basis for generating timestamp attributes. The same Web log file is from the same server, so under normal circumstances all Web requests in the same file are sent to the same destination address, that is, the attribute IP.dst is the same. The IP.src attribute can also be obtained directly in each web log. The attribute of attack type name will be described in the introduction of trace extraction method. After extracting all attack traces in the web log, each attack trace is stored in json format data for subsequent analysis. The following definition is an example of an extracted attack trace: ð1Þ All attack traces are classified according to the two attributes IP.src and IP.dst. Traces where the both attributes are the same are considered the same attack event. The same attack event is sorted by time stamp, and the time stamp attribute is expressed by time in formula (1). The correlation between different attack traces in the same attack event is the content of this paper. After collecting a certain number of attack events, analyze the support between attack traces. At the same time, because the attack traces have a sequence, the forward correlation degree and the backward correlation degree between different attack traces are finally obtained. The expression of correlation degree has similarities with conditional probability, and the two major rules in association analysis——support and confidence are also related to probability. So in the attack event description model, the correlation between attack traces is expressed in the form of conditional probability. For example, P(B|A)represents the forward correlation degree from A to B, and P’(A|B) represents the backward correlation degree from B to A. The purpose of proposing two kinds of correlation degree is to consider the case where the log information is partially missing, and the traces of forward and backward loss can be recovered through known attack traces. In the analysis process, the analysis method with Apriori algorithm as the main body was used, and its algorithm content was improved to make it adapt to the model proposed in this paper. 3.2

Attack Trace Extraction

We uses feature-based data analysis methods to extract attack traces from log data. Because the technology studied in this paper does not have very high requirements for the extraction speed of attack traces, in the case of small data volumes, the specified feature extraction method is preferred, and the deep learning model is not considered. When the amount of subsequent data increases and the types of attacks that need to be extracted increase, then we will consider using a deep learning model. Figure 2 shows the framework of the attack trace extraction method.

Research on Association Analysis Technology of Network Attack Trace

37

Fig. 2. Attack trace extraction method framework

Before extracting attack traces based on features, the log data needs to be cleaned first. There are some errors in the original log data. Without data cleaning, the accuracy of attack trace extraction will be affected. In the data set selected in this paper, the log format is fixed, so we can directly write a script to specify the format for data cleaning. Delete unnecessary data at the beginning and end of each log file, and delete useless data at the same time. Useless data includes: each log records a total of 27 entries with more than 10 blank entries, and data records have errors. Each log records a lot of information, including timestamp, number of requests, source address and port, destination address and port, Web request method, URL, User Agent, POST body, etc. The useful information is only the timestamp, source address, destination address mentioned in the previous section, and the URL, User Agent, and POST body used to extract the name of the attack type. Other information can be deleted directly. After data cleaning, a log file with only valid information is obtained. After obtaining the cleaned valid data, the data can be extracted. Based on the experience accumulated in the past during the penetration testing process and previous research results in the field of intrusion detection, some key keywords of Web attack characteristics are summarized, as shown in Table 1. Table 1. Keywords of web attack characteristics Web attack type SQL Injection

XSS Arbitrary File Read RCE Upload Webshell OGNL Injection PHP Unserialization

Keywords select, union, order by, concat, information, or, and, update, insert, sleep, from, table, column, updatexml, extractvalue, floor, substr, mid, ascii, load_file, sqlmap script, alert, javascript, iframe, onerror, onclick, location.href, document.cookie, window.open, , php://, file://, include,/etc./,/usr/local/,/var/,../ phpinfo, net user, ifconfig, uname -a, ps aux, netstat, whoami (File in POST Body)phpinfo(), eval, assert, system, exec, Runtime, request,