Lecture Notes in Networks and Systems 125

Sabu M. Thampi · Elizabeth Sherly · Soura Dasgupta · Jaime Lloret Mauri · Jemal H. Abawajy · Evgeny Khorov · Jimson Mathew   Editors

Applied Soft Computing and Communication NetworksProceedings of ACN 2019

Lecture Notes in Networks and Systems

Volume 125

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

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Fernando Gomide, Department of Computer Engineering and Automation—DCA,School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil

Okyay Kaynak, Department of Electrical and Electronic Engineering,Bogazici University, Istanbul, Turkey

Derong Liu, Department of Electrical and Computer Engineering, Universityof Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academyof Sciences, Beijing, China

Witold Pedrycz, Department of Electrical and Computer Engineering,University of Alberta, Alberta, Canada; Systems Research Institute,Polish Academy of Sciences, Warsaw, Poland

Marios M. Polycarpou, Department of Electrical and Computer Engineering,KIOS Research Center for Intelligent Systems and Networks, University of Cyprus,Nicosia, Cyprus

Imre J. Rudas, Óbuda University, Budapest, Hungary

Jun Wang, Department of Computer Science, City University of Hong Kong,Kowloon, Hong Kong

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Sabu M. Thampi • Elizabeth Sherly •

Soura Dasgupta • Jaime Lloret Mauri •

Jemal H. Abawajy • Evgeny Khorov •

Jimson MathewEditors

Applied Soft Computingand CommunicationNetworksProceedings of ACN 2019

123

EditorsSabu M. ThampiSchool of Computer Scienceand Information TechnologyIndian Institute of InformationTechnology and ManagementTrivandrum, Kerala, India

Elizabeth SherlySchool of Computer Scienceand Information TechnologyIndian Institute of InformationTechnology and ManagementTrivandrum, Kerala, India

Soura DasguptaDepartment of Electrical and ComputerEngineeringUniversity of IowaIowa, IA, USA

Jaime Lloret MauriDepartment of CommunicationsPolytechnic University of ValenciaValencia, Spain

Jemal H. AbawajySchool of Information TechnologyDeakin UniversityGeelong, Melbourne, VIC, Australia

Evgeny KhorovMoscow Institute of Physics and TechnologyMoscow, Russia

Institute for Information TransmissionProblems (IITP)Moscow, Russia

Jimson MathewDepartment of Computer Scienceand EngineeringIndian Institute of Technology PatnaPatna, Bihar, India

ISSN 2367-3370 ISSN 2367-3389 (electronic)Lecture Notes in Networks and SystemsISBN 978-981-15-3851-3 ISBN 978-981-15-3852-0 (eBook)https://doi.org/10.1007/978-981-15-3852-0

© Springer Nature Singapore Pte Ltd. 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof 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 transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe 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 thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

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Preface

The International Conference on Applied Soft Computing and CommunicationNetworks (ACN‘19) organized by Indian Institute of Information Technology andManagement-Kerala (IIITM-K), Trivandrum, was held in Trivandrum, India, dur-ing December 18–21, 2019. The conference served as a forum for exchange anddissemination of ideas and the latest findings in the aspects of computing andcommunication networks.

All submissions were evaluated on the basis of their significance, novelty, andtechnical quality. A double-blind review process was conducted to ensure that theauthor names and affiliations were unknown to the TPC. This volume contains 16papers selected for presentation at the symposium.

Special thanks are given to the Conference Committee for the commitment to theconference organization. We would also like to thank all the authors who con-tributed with their papers to the success of the conference. The conference could nothave happened without the commitment of the Local Organizing Committee, whohelped in many ways to assemble and run the conference. We express our mostsincere thanks to all keynote speakers who shared with us their expertise andknowledge. Finally, we would like to acknowledge Springer for active cooperationand timely production of the proceedings.

Trivandrum, India Sabu M. ThampiIowa, USA Soura DasguptaValencia, Spain Jaime Lloret MauriGeelong, Melbourne, Australia Jemal H. AbawajyTrivandrum, India Elizabeth SherlyMoscow, Russia Evgeny KhorovPatna, India Jimson Mathew

v

Contents

Fruit Fly Optimization-Based Reliable Routing Algorithmfor Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1T. Siron Anita Susan and B. Nithya

User Authentication of IoT Devices for Decentralized ArchitectureUsing Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Raja Lavanya, K. Sundarakantham, S. Mercy Shalinie, R. Divya,and K. Selvamani

Detection of Packet Dropping Nodes in Wireless Mesh Networks . . . . . 27R. Thillaikarasi and S. Mary Saira Bhanu

An Enhanced Bat Algorithm for Parallel Localization Basedon a Mobile Beacon Sensor in Wireless Sensor Networks . . . . . . . . . . . 43Miloud Mihoubi, Abdellatif Rahmoun, and Pascal Lorenz

Design of a Virtual Channel Router Architecture for Low Poweron Mesh-of-Grid Topology for Network on Chip . . . . . . . . . . . . . . . . . . 63K. Somasundaram

Agent-Based Traffic Obstacles Information System . . . . . . . . . . . . . . . . 81Kamil Ruta, Damian Rakus, Maria Ganzha, and Marcin Paprzycki

Hybridization of Constriction Coefficient-Based Particle SwarmOptimization and Chaotic Gravitational Search Algorithm for SolvingEngineering Design Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Sajad Ahmad Rather and P. Shanthi Bala

Parallel Association Rules Pruning Algorithm on HadoopMapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Mohamed A. Alasow, Salahadin A. Mohammed, and El-Sayed M. El-Alfy

A Statistical Analysis on KDD Cup’99 Dataset for the NetworkIntrusion Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Satish Kumar, Sunanda, and Sakshi Arora

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Efficient Certificateless Privacy-Preserving Public Auditingfor Cloud-Based IIoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Tapaswin Padhy and Syam Kumar Pasupuleti

Classification of Virtual Machine Consolidation Techniques:A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175Saloni Sureja and Tarannum Bloch

Enhanced Honey Bee Load Balancing in Large HeterogeneousCloud Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191B. Nithya, Harshit Mogalapalli, Kamalesh Khanna,and Sasmita Moharana

Real-Time Communication Alert System for Missing Vesselsin Deep Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207M. T. Chitra, B. Gayatri Menon, and Elizabeth Sherly

A Study of CNN Architectures over Two Hand Indian SignLanguage Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Vaidehi Sharma, Mohita Jaiswal, Abhishek Sharma, and Raghuvir Tomar

Periocular Recognition Under Unconstrained ConditionsUsing CNN-Based Super-Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235Vineetha Mary Ipe and Tony Thomas

Performance Evaluation of Recurrent Neural Networksfor Short-Term Investment Decision in Stock Market . . . . . . . . . . . . . . 247Alexandre P. da Silva, Silas S. L. Pereira, Mário W. L. Moreira,Joel J. P. C. Rodrigues, Ricardo A. L. Rabêlo, and Kashif Saleem

viii Contents

Editors and Contributors

About the Editors

Sabu M. Thampi is a Professor at Indian Institute of Information Technology andManagement-Kerala (IIITM-K), Technopark Campus, Trivandrum, Kerala, India.His current research interests include cognitive computing, Internet of Things (IoT),authorship analysis, trust management, biometrics, social networks, nature inspiredcomputing and video surveillance. He has published papers in book chapters,journals, and conference proceedings. He has authored and edited a few books.Sabu has served as Guest Editor for special issues in few journals and programcommittee member for many international conferences and workshops. He hasco-chaired several international workshops and conferences. He has initiated and isalso involved in the organization of several annual conferences/symposiums. Sabuis currently serving as Editor for Elsevier Journal of Network and ComputerApplications (JNCA), Connection Science - Taylor & Francis, and Associate Editorfor IEEE Access and International Journal of Embedded Systems,Inderscience, UK;and reviewer for several reputed international journals. Sabu is a Senior Member ofIEEE and ACM.

Elizabeth Sherly received her Ph.D in Computer Science from University ofKerala in 1995, currently working as Senior Professor (HAG) at Indian Institute ofInformation Technology and Management-Kerala (IIITM-K). She served as theDirector of the institute in 2009-2012. Having more than 30 years of experience inteaching and research in Computer Science, her major research work involvesCloud Computing and Machine Learning. She predominantly works in the areas ofMedical Image Processing, Natural Language Processing and Automatic SpeechRecognition System using Machine Learning and Soft computing techniques. HerPh.D thesis work was in Artificial Neural Networks in Biological Control Systemsnow produced Eleven PhDs and actively guiding number of Ph.D Students. She isactively working on promoting virtual learning system with a pedagogy model ofTechnology Enhanced learning to the Educational Community. Cloud Computing

ix

and Marine Wireless Networks and surveillance are her other passionate areas. InCloud Computing, she works for data replication, migration strategies in SaaSsystems. She has been served/serving the Executive Board Member andTechnical/Expert member of several Government and Central Government bodiesand Editorial Board member of several International and national Journals.Acclaimed with several awards and fellowships which include EURECA(UK) fellowship, Rational Certified (India) Trainer, one among 7 scientists in Indiafor e-Science Grid Group (London) etc. She is the Chief Investigator of twoprestigious MeitY projects, Government of India sponsored projects of TDIL(Technology Development in Indian Languages) and one another MICRONetproject of ITRA, Media Lab Asia, GOI. Established and heading two centres,Virtual Resource Centre for Language Computing (VRCLC) and Center ofExcellence in Pattern and Image Analysis (CEPIA), where Language computingresearch and Image Processing development using Machine Intelligence are car-rying out. More than 100 papers in her credit which includes international, nationaland conference papers. Her work on Circular Mesh based shape descriptors of MRIbreast images for lesion identification had been filed for patent. Editorial member ofseveral journals and conference proceedings, Life Member of CSI, member ofIEEE, ACM Chapters, co-authored one book and book chapters also.

Soura Dasgupta is a Professor of Electrical and Computer Engineering at theUniversity of Iowa, U.S.A. He is a past Associate Editor of the IEEE Transactions onAutomatic Control, IEEE Control Systems Society Conference Editorial Board, andthe IEEE Transactions on Circuits and Systems-II. He is a co-recipient of theGullimen-Cauer Award for the best paper published in the IEEE Transactions onCircuits and Systems for the calendar years of 1990 and 1991, and a member of theeditorial boards of the International Journal of Adaptive Control and SignalProcessing, and the EURASIP Journal of Wireless Communications. He received theUniversity Iowa Collegiate Teaching award in 2012. In the same year he was selectedby the graduating class for an award on excellence in teaching and commitment tostudent success. In 1993 he was a selected as a US National Science FoundationPresidential Faculty Fellow, an award given annually to 15 early career engineers and15 early career scientists for excellence in research and teaching. His present researchinterests are in Distributed communications, multi-agent control, compressed sensingand stability in aggregate computing. He was elected a Fellow of the IEEE in 1998.

Jaime Lloret Mauri received his B.Sc+M.Sc. in Physics in 1997, his B.Sc.+M.Sc.in electronic Engineering in 2003 and his Ph.D. in telecommunication engineering(Dr. Ing.) in 2006. He is a Cisco Certified Network Professional Instructor. Heworked as a network designer and administrator in several enterprises. He is cur-rently Associate Professor in the Polytechnic University of Valencia. He is theChair of the Integrated Management Coastal Research Institute (IGIC) and he is thehead of the “Active and collaborative techniques and use of technologic resourcesin the education (EITACURTE)” Innovation Group. He is the director of theUniversity Diploma “Redes y Comunicaciones de Ordenadores” and he has been

x Editors and Contributors

the director of the University Master “Digital Post Production” for the term2012-2016. He has been Internet Technical Committee chair (IEEECommunications Society and Internet society) for the term 2013-2015. He hasauthored 22 book chapters and has more than 380 research papers published innational and international conferences, international journals (more than 140 withISI Thomson JCR). He has been the co-editor of 40 conference proceedings andguest editor of several international books and journals. He iseditor-in-chief of the“Ad Hoc and Sensor Wireless Networks” (with ISI Thomson Impact Factor), theinternational journal “Networks Protocols and Algorithms”, and the InternationalJournal of Multimedia Communications, IARIA Journals Board Chair (8 Journals)and he is (or has been) associate editor of 46 international journals (16 of them withISI Thomson Impact Factor). He has been involved in more than 400 Programcommittees of international conferences, and more than 150 organization andsteering committees. He leads many national and international projects. He iscurrently the chair of the Working Group of the Standard IEEE 1907.1. He has beengeneral chair (or co-chair) of 38 International workshops and conferences. He isIEEE Senior and IARIA Fellow.

Jemal H. Abawajy is a full professor at school of Information Technology, Faculty ofScience, Engineering and Built Environment, Deakin University, Australia. He wasawarded the higher doctoral degree, Doctorate of Science (DSc.) in 2016, by DeakinUniversity for my outstanding research achievements. He is a Senior Member of IEEEComputer Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEETechnical Committee on Dependable Computing and Fault Tolerance and IEEECommunication Society. Jemal’s leadership is extensive spanning industrial, academicand professional areas. He has served on the Academic Board, Faculty Board, IEEETechnical Committee on Scalable Computing Performance track coordinator, ResearchIntegrity Advisory Group, Research Committee, Teaching and Learning Committee andExpert of International Standing Grant and external PhD thesis assessor. Prof. Abawajyhas delivered more than 50 keynote addresses, invited seminars, andmedia briefings andhas been actively involved in the organization ofmore than 200 national and internationalconferences in various capacity including chair, general co-chair, vice-chair, best paperaward chair, publication chair, session chair and program committee. He has also servedon the editorial-board of numerous international journals and currently serving as asso-ciate editor of the International Journal of Big Data Intelligence and International Journalof Parallel, Emergent and Distributed Systems. He has also guest edited many specialissues. Prof. Abawajy is actively involved in funded research supervising large number ofPhD students, postdoctoral, research assistants and visiting scholar in the area of CloudComputing, Big Data, Network and System Security,Decision Support System, andE-healthcare. He is the author/co–author of five books, more than 250 papers in confer-ences, book chapters and journals such as IEEE Transactions on Computers and IEEETransactions on Fuzzy Systems. He also edited 10 conference volumes.

Evgeny Khorov received his BS and MS degrees with honors from MIPT in 2008and 2010, respectively, and PhD degree in Telecommunications from IITP RAS in

Editors and Contributors xi

2012. His PhD thesis focused on Quality ofService (QoS) provisioning in multihopwireless networks. Evgeny Khorov is also a recipient of MoscowGovernment Prizefor Young Scientists (2013) for in-depth study of channel access in wirelessmulti-hop networks. Currently, Evgeny Khorov is the Head of Wireless NetworksLab, IITP RAS, established within Megagrant Project led by Prof. Ian FuatAkyildiz. Evgeny Khorov has developed numerous mathematical models of net-working protocols and designed several algorithms and protocols, which aredescribed in over 80 papers, two of which were evaluated with the Best PaperAwards from IEEE ISWCS (2012) and Elsevier Computer Communications (2018).Evgeny has also received Scopus Award Russia (2018). Evgeny has led manynational and international projects sponsored by academia foundations (RSF,RFBR, Ministry of Science and Education, Government) and industry. Forbreakthrough results of the joint projects, in 2015, 2017 and 2018 Huawei awardedhim as the Best Cooperation Project Leader. Evgeny Khorov is also a votingmember and contributor of IEEE 802.11 that develops and standardizes Wi-Fi. In2015-2016, he designed several improvements, which were included in the802.11ax standard aka high efficiency WLANs. In 2015 he studied technologies forthe Internet of Things as a Visiting Research Fellow in King's College London. In2016 Evgeny Khorov received Russian Government Prize in Science andTechnology for Young Scientists for remarkable results of research and develop-ment of wireless networks. As a Deputy Head of Department and AssociateProfessor at MIPT, Evgeny Khorov is responsible for the TelecommunicationProgram. He supervises BS, MS, and PhD students. Two of his students,Vyacheslav Loginov and Alexey Kureev, have received awards at telecommuni-cation Olympiads. Apart from that, Evgeny Khorov has developed a novel courseson Wireless Networking Protocols. Evgeny Khorov gives keynotes &tutorials andparticipates in panels at large conferences (incl. IEEE Globecom 2017,IEEE PIMRC 2017, IEEE ICC 2016, ISWCS 2014, NEW2AN 2018, etc.). He is aTPC Chair of IEEE Globecom 2018 Workshop on Cloudified Architectures for 5gabd beyond Systems, IEEE BlackSeaCom 2019, Executive Chair of WiFlex 2013.He serves as an expert of Russian Academy of Sciences and Russian ScienceFoundation, and also as an editor for Ad Hoc Networks.

Jimson Mathew is currently an Associate Professor and Head of the Departmentin the Computer Science and Engineering, Indian Institute of Technology Patna,India. He received the Masters in computer engineering from NanyangTechnological University, Singapore and the Ph.D. degree in computer engineeringfrom the University of Bristol, Bristol, U.K. He has held positions with the Centrefor Wireless Communications, National University of Singapore, Bell LaboratoriesResearch Lucent Technologies North Ryde, Australia, Royal Institute ofTechnology KTH, Stockholm, Sweden and Department of Computer Science,University of Bristol, UK. His research interests include fault-tolerant computing,computer arithmetic, Machine Learning, and IoT Systems and cognitive radiosystems.

xii Editors and Contributors

Contributors

Mohamed A. Alasow King Fahd University of Petroleum and Minerals, Dhahran,Saudi Arabia

Sakshi Arora Faculty of Engineering, School of CSE, SMVD University, Katra,Jammu, India

P. Shanthi Bala Pondicherry University, Puducherry, India

Tarannum Bloch Department of Information Technology, Marwadi University,Rajkot, Gujarat, India

M. T. Chitra Indian Institute of Information Technology and Management-Kerala, Trivandrum, India

Alexandre P. da Silva Federal Institute of Education Science, and Technology ofCeará, Aracati, CE, Brazil

R. Divya Department of Computer Science Engineering, Thiagarajar College ofEngineering, Madurai, India

El-Sayed M. El-Alfy King Fahd University of Petroleum and Minerals, Dhahran,Saudi Arabia

Maria Ganzha Warsaw University of Technology, Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

B. Gayatri Menon Indian Institute of Information Technology and Management-Kerala, Trivandrum, India

Vineetha Mary Ipe Indian Institute of Information Technology and Management-Kerala, Trivandrum, India

Mohita Jaiswal The LNM Institute of Information Technology, Jaipur, Rajasthan,India

Kamalesh Khanna Department of Computer Science and Engineering, NationalInstitute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

Satish Kumar School of CSE, SMVD University, Katra, Jammu, India

Raja Lavanya Department of Computer Science Engineering, ThiagarajarCollege of Engineering, Madurai, India

Pascal Lorenz IRIMAS Laboratory, University of Haute Alsace, Colmar, France

S. Mary Saira Bhanu National Institute of Technology Tiruchirappalli,Tiruchirappalli, Tamil Nadu, India

S. Mercy Shalinie Department of Computer Science Engineering, ThiagarajarCollege of Engineering, Madurai, India

Editors and Contributors xiii

Miloud Mihoubi EEDIS Laboratory, University of Djillali Liabes, Sidi Bel-Abbes, Algeria;University of Ibn Khaldoun, Tiaret, Algeria;LabRI-SBA Laboratory, High School of Computer Sciences, Sidi Bel Abbès,Algeria

Harshit Mogalapalli Department of Computer Science and Engineering, NationalInstitute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

Salahadin A. Mohammed King Fahd University of Petroleum and Minerals,Dhahran, Saudi Arabia

Sasmita Moharana Department of Computer Science and Engineering, NationalInstitute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

Mário W. L. Moreira Federal Institute of Education Science, and Technology ofCeará, Aracati, CE, Brazil

B. Nithya Department of Computer Science and Engineering, National Institute ofTechnology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

Tapaswin Padhy International Institute of Information Technology,Bhubaneswar, India

Marcin Paprzycki Warsaw University of Technology, Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Syam Kumar Pasupuleti Institute for Development and Research in BankingTechnology, Hyderabad, India

Silas S. L. Pereira Federal Institute of Education Science, and Technology ofCeará, Aracati, CE, Brazil

Ricardo A. L. Rabêlo Federal University of Piauí, Teresina, PI, Brazil

Abdellatif Rahmoun LabRI-SBA Laboratory, High School of ComputerSciences, Sidi Bel Abbès, Algeria

Damian Rakus Warsaw University of Technology, Warsaw, Poland

Sajad Ahmad Rather Pondicherry University, Puducherry, India

Joel J. P. C. Rodrigues Federal University of Piauí, Teresina, PI, Brazil;Instituto de Telecomunicações, Lisbon, Portugal;Center of Excellence in Information Assurance (CoEIA), King Saud University,Riyadh, Saudi Arabia

Kamil Ruta Warsaw University of Technology, Warsaw, Poland

Kashif Saleem Center of Excellence in Information Assurance (CoEIA), KingSaud University, Riyadh, Saudi Arabia

xiv Editors and Contributors

K. Selvamani Department of Computer Science Engineering, Thiagarajar Collegeof Engineering, Madurai, India

Abhishek Sharma The LNM Institute of Information Technology, Jaipur,Rajasthan, India

Vaidehi Sharma The LNM Institute of Information Technology, Jaipur,Rajasthan, India

Elizabeth Sherly Indian Institute of Information Technology and Management-Kerala, Trivandrum, India

T. Siron Anita Susan Department of Computer Science Engineering, NationalInstitute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

K. Somasundaram Department of Mathematics, Amrita School of Engineering,Amrita Vishwa Vidyapeetham, Coimbatore, India

Sunanda Faculty of Engineering, School of CSE, SMVD University, Katra,Jammu, India

K. Sundarakantham Department of Computer Science Engineering, ThiagarajarCollege of Engineering, Madurai, India

Saloni Sureja Department of Computer Engineering, Marwadi EducationFoundation, Rajkot, Gujarat, India

R. Thillaikarasi National Institute of Technology Tiruchirappalli, Tiruchirappalli,Tamil Nadu, India

Tony Thomas Indian Institute of Information Technology and Management-Kerala, Trivandrum, India

Raghuvir Tomar The LNM Institute of Information Technology, Jaipur,Rajasthan, India

Editors and Contributors xv

Fruit Fly Optimization-Based ReliableRouting Algorithm for Wireless SensorNetworks

T. Siron Anita Susan and B. Nithya

Abstract Routing in wireless sensor network is a predominant task to transmit thepackets efficiently from a source to destination. The sensor nodes are scattered in thenetwork and should be self-manageable to route their packets. Due to the stringentresource constraints, the routing needs to be more reliable with minimum energy toprolong the network life time. Considering these points, this paper proposes a novelalgorithm, namely reliable routing algorithm (RRA) to find a reliable optimal pathfrom source to destination. To achieve this goal, the proposed RRA algorithm adoptsfruit fly optimization approach along with the runtime dynamic decision metrics.The decision metrics such as expected transmission count (ETX), neighborhoodstatus and residual energy are utilized to make the judgment about the reliability ofthe path. Due to these dynamics, the proposed RRA algorithm effectively controlsnode failure and link failure, thereby ensuring stable and reliable routing in WSN.The performance of the proposed RRA algorithm is validated with the predictedremaining delivery (PRD)-based routing algorithm and multi-objective fractionalparticle lion algorithm (MOFPL) in terms of packet delivery ratio (PDR), delay andenergy balance factor (EBF).

Keywords Wireless sensor network · Routing · Fruit fly optimization ·Reliability · Node failure · Network life time

1 Introduction

Wireless sensor networks (WSNs) have various captivating applications of sensingand monitoring remote locations where human access is not possible. This senseddata should be routed from source to the desired destination by taking care of various

T. Siron Anita Susan (B) · B. NithyaDepartment of CSE, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Indiae-mail: anitasusan.t@gmail.com

B. Nithyae-mail: nithya@nitt.edu

© Springer Nature Singapore Pte Ltd. 2020S. M. Thampi et al. (eds.), Applied Soft Computing and Communication Networks,Lecture Notes in Networks and Systems 125,https://doi.org/10.1007/978-981-15-3852-0_1

1

2 T. Siron Anita Susan and B. Nithya

network and sensor constraints. This makes routing in WSN as a quiet challengingtask that leads to big evolving research area. The process of routing protocol is toselect the suitable routing path to take away the data from one node to another withthe aid of routing table. Due to the characteristics of WSN, the routing algorithmneeds to find the routing path as a reliable, stable and optimal path till the destinationnode. There are different routing schemes based on the number and type of destina-tion nodes. They are: (i) unicast: routing a data from a source node to one desireddestination node, (ii) broadcast: routing a data to a set of nodes of a network, (iii)multicast: routing to group of nodes which are interested to receive the data, (iv)anycast: routing from a source to any one of its nearest node among a group of nodesand (v) geocast: routing based on geographic nodes. These categories of routing aredepicted in Fig. 1.

It is inferred from the figure that the routing algorithm designed for a particularrouting category will not be suitable for remaining categories. This paper aims togive efficient routing algorithm for unicast transmission. Related to this, lot of routingprotocols for WSN are proposed in the literature which are broadly classified basedon their functioning mode, participation style and network structure [1, 2] as shownin Fig. 2.

With respect to functioningmode, the routing protocols are classified as proactive,reactive and hybrid routing protocols. Proactive routing protocol stores all the routesbetween a source to its destination in the routing table in prior and transmits the dataaccordingly. Whereas in reactive routing protocol, the node obtains its desired pathonlywhen it needs to transmit the data. Hybrid routing protocols acquire themarks ofboth proactive and reactive protocols. The node exchanges data among other devicesusing proactive protocol, if they are within the same region (zone). For outside zonecommunication, the reactive routing protocol is utilized.

Based on the participation style, the routing protocols are classified as direct com-munication, flat, clustering. In direct communication, the routing of data happenedbetween the node and base station (BS) without any intermediate nodes. In flat rout-ing, the same functionalities are imposed on each node. It means that all of the nodes

Fig. 1 Different routing schemes

Fruit Fly Optimization-Based Reliable Routing … 3

Fig. 2 Classification of routing protocols

in the network are expected to play the same role for the data transmission. In clus-tering type of routing protocol, the network is divided into clusters with cluster head(CH), and members within the cluster communicate with others through CH.

In terms of network structure, the routing protocols are classified as data centricand location-based routing protocols. In data centric, query-based routing model isadopted. The requesting node sends its query to all other nodes, and nodeswhich havethe requested data will reply back. In location-based routing, location informationwhich is obtained using GPS is utilized for routing among nodes.

There are lot of metaheuristic algorithm to solve a problem optimally, and thosealgorithms are also used to find an optimal route or path in WSN. The optimiza-tion algorithm finds the best solution based on the objective function of the desiredproblem, and it guarantees to achieve the better solution. The most widely usedapproaches are ant colony optimization algorithm (ACO) [3], particle swarm op-timization (PSO) [4], lion optimization algorithm (LOA) [5], firefly optimization,fruit fly optimization (FFO) [6] and so on. Among these approaches, FFO techniquehas certain advantages over other surveyed optimization techniques [6]. FFO is fastin solving the problems and is simple structured. It is a stable algorithm with fewparameters and its adaptability to the real-time applications. Due to these reasons,the proposed RRA algorithm adopts FFO approach to find the optimal path. Fruit flyis more admirable than other flies, and they can reach the food source by using theirsmell and vision organs. Initially, the fruit flies use their smell character to find allscents of its food and then fly toward the food. This FFO is developed based on theforaging behavior of fruit fly. The two main phases of this optimization algorithmare smell phase and vision phase. In smell phase, the flies fly closer to their foodby using their smell capability, and in vision phase, the flies will go nearer to theirfood. These two steps are repeated till the fly catches its food. In case of routing, thesmell phase identifies the fitness value for an optimal path which is constructed inthe vision phase.

4 T. Siron Anita Susan and B. Nithya

Based on the above discussion, it is inferred that routing of the data from a sourceto its destination needs to be handled in an efficient way by using suitable algorithmsto reduce the complexity and delay, thereby leading to more reliability. There arenumerous routing schemes proposed in the literature to attain these goals, and someof them are discussed in the next section. The rest of the paper is structured as follows.In Sect. 2, related work is briefly explained. Section3 gives the proposed work, andfinally Sect. 4, provides the conclusion of the paper.

2 Related Work

Due to various changes in the wireless environment, the quality of link between anytwo nodes plays an important role while routing the data. To enhance the routing andoverall network performance, link and node failures need to be minimized. Many ofthe existing routing protocol uses minimum hop count, distance, link weight, etc., tofind the least cost and efficient route. But, there might be some significant lossy link.To overcome this and to predict a high throughput link or path, expected transmissioncount (ETX) metric is used. Many researchers [7, 8] have used ETX for estimatingthe link quality between the nodes. ETX is the predicted number of transmission andretransmission of data packet over the link. This ETX is calculated with the help offorward and reverse delivery ratio of a node. The number of packets that the link isable to transmit is said to be forward ratio (FR) of a link, whereas the number ofpackets sent back as retransmission on the same link is termed as backward ratio(BR) of that link. The ETX of a link is calculated as

ETX = 1

BR ∗ FR(1)

Xiaohan et al. [7] have used a routing metric called predicted remaining deliveriesto attain energy efficient and link reliable routing. Theweights and delay are assignedto each link, and accordingly, the node status is estimated. Initially, a hellomessage isbroadcasted by each sensor node to its neighbor and is also used to calculate the ETXbetween sensor nodes. Upon receiving the hello message, each node sends its ID,energy level, ETX value and the delay to its neighbors and is stored in the neighbortable and is updated frequently. Based on the stored values, the score of individuallink and entire route is calculated to estimate the route score of each route. Finally,best route score is chosen by the parent node to forward its data.

Singh et al. [4] have proposed a method for routing in a cluster-basedWSNwherethe characteristics of PSO and VLEACH protocol are combined. The basic LEACHprotocol is a cluster-based protocol where the cluster head is selected periodicallybased on the energy level. VLEACH is an advanced LEACH protocol where eachcluster will have a vice cluster head. It means that it acts as an alternative cluster head.The data will be routed to the base station when a CH dies. Here, PSO technique isused to optimally choose the CH which has the maximum energy.

Fruit Fly Optimization-Based Reliable Routing … 5

ACO [3] is one of the probabilistic approaches in that the ant colony and thebehavioral actions of ants toward the search of the food are modeled. Based on thisapproach, Sun et al. [9] have proposed an efficient routing protocol. Here, transmis-sion distance, direction and residual have been used to choose an optimal routingpath. The ants of this ACO select the path according to the pheromone and heuristicvalue obtained from the gathered information.

LOA [5] is a new population-based algorithm which is derived from the lifestyleand cooperative activity of lions. This algorithm is designed by modeling the char-acteristics of lion. The initial population is divided as nomad lions and prides. Somepercentages of prides are considered as females and rest as males, and this consid-eration is vice verse in nomad lions. For each member, the best position is obtainedby comparing with previously obtained visited position. In this algorithm, an areacalled pride territory is taken with the best visited position of each member. Also,some female lions are selected randomly for hunting which move toward their foodand catch it. Bhardwaj et al. [10] have proposed an algorithm based on particle lionalgorithm called multi-objective fractional particle lion (MOFPL) algorithm. In this,an optimal cluster head is chosen and then an optimal routing path is calculatedbased on multi-objective function. This algorithm combines fractional theory andPSO along with LOA to find optimal path. Based on the calculated multi-objectivefitness function, the fitness of each node is calculated in MOFPL algorithm and anoptimal path is calculated.

FFO [6, 11] is a new optimization algorithm which gives optimal results based onthe food searching characteristics of the fruit flies. Hazim et al. [12] used a modifiedFFO to find an optimal solution for the well-known traveling salesman problem(TSP). In this problem, the smell values are calculated for the initial arrangementof city, and the best value is stored that will be used in vision phase. The authormodified the vision phase by constructing and updating a city arrangement matrixbased on the best solution in each generation.

2.1 Overall Inference and Motivation

Based on the above discussion on various optimization techniques used in routing,it is inferred that the FFO has certain advantages over other surveyed optimizationtechniques [7]. FFO algorithm is simple structure and stable algorithm and is bestsuited to find reliable routing path. It is fast in solving the problems with few param-eters, and it is easily adaptable for real-time problem-solving techniques. Moreover,the reliability of link and the path should also be considered for choosing a reliablelink for routing. With this motto, this paper proposes an adaptive algorithm, namelyreliable routing algorithm (RRA) based on fruit fly optimization algorithm. Themainobjective of the proposed RRA algorithm is to combine the reliability in optimal pathcalculation since the existing algorithm only concentrated on optimal path and not

6 T. Siron Anita Susan and B. Nithya

on reliability. To find a reliable and an optimal path for routing, RRA utilized ETXmetric for estimating the reliability of the link and uses fruit fly Optimization algo-rithm to maintain the optimality of the path. The transmission range and energy levelof the node influence the proposed RRA algorithm to elect the potential intermediarynodes to route the data.

3 Proposed Reliable Routing Algorithm (RRA)

The main objective of the proposed RRA algorithm is to find an optimal path froma source to the corresponding destination. An optimal path toward the destinationneeds to have lesser number of connected intermediate nodes with sufficient energyresources. The path is optimally chosen by inheriting fruit fly optimization algorithmwhere it uses ETX value for each link. The proposed RRA algorithm works in twophases, namely refining phase and construction phase. In the refining phase, thecapable and potential nodes are elected. This means that all the elected nodes arewithin the transmission range of source and have the energy level greater than thethreshold value. This threshold energy value is the minimum required amount ofenergy needed for data transmission. The construction phase is further subdivided assmell phase and vision phase. The smell phase is completed with the determinationof ETX for the links of the chosen nodes. With this vital information, the visionphase is started to find out an optimal path using fruit fly optimization approach. Thecomplete overview of the fruit fly optimization algorithm is depicted below:

1. In every optimization problem, the number of generations and the convergenceof result at every generation are required to be determined.

2. Every generation will have group of members called as population.3. Each member will be the possible solution for the fruit flies. In the proposed

algorithm, every member is considered as a path. It may not have all the nodesand may not be of same length.

4. For every member, the fitness value (smell function) is calculated and from thata path with the highest fitness value is selected.

5. In vision phase, the fitness value is sent to every member which is obtained fromsmell function.

6. Each member will be interpolated with itself based on the rules proposed by op-timization algorithm. Using that, a new member for next generation is generated.

7. After all members are sent to second step, the newly generated members (popu-lation) need to be sent to the next generation, and the entire process is repeated.

8. This process is terminated when a path is obtained with the highest fitness valuefor more number of consecutive generations and declare that path is an optimalpath toward the destination.

Fruit Fly Optimization-Based Reliable Routing … 7

3.1 Phase 1: Refining Phase

In this phase, the initial refinement of the network is performed. The network Ni isrefined based on the transmission range and remaining energy level, and the resultantnetwork Ns is then used to carry out for further processing.

Initially, hello or probe packets are sent continuously to all one-hop neighbors, andbased on the number of transmitted packets, the FR is determined. Once the packet isreceived by the destination, it should acknowledge the received packet along with itsremaining energy level.With the aid of the number of acknowledgment, the BR of thelink is calculated. The nodes that receive the acknowledgment check for their energylevel of the neighbor nodes which are within its transmission range with a minimumthreshold value. The node will choose all its neighboring nodes that have sufficientremaining energy for further transmissions. The energy-deficient nodes are neglectedas depicted in Fig. 3. Initially, among 7 nodes of a network Ni , nodes 2 and 6 areneglected due to lesser energy level which is less than the threshold value Th. Theremaining nodes of the network Ns are chosen, and the links of the neglected nodesare also disconnected. These nodes of the Ns are renamed sequentially. Based on theestimated forward and backward ratio, the ETX value for the links is determined.This ETX value will give the metric score value for a good reliable link with whicha reliable link is calculated as mentioned in Eq. (1).

3.2 Phase 2: Constructing Phase

The construction phase is further subdivided as smell and vision phase. The smellfunction checks the calculated ETX value and considers the highest reliable value inits state. This chosen value is the fitness value of this smell function. Each generationcalculates its fitness value. The vision function checks for the maximum optimalfitness value and gets updated toward the best reliable solution of that generation.

Fig. 3 Initial network Ni and final chosen nodes Ns

8 T. Siron Anita Susan and B. Nithya

The initial population(pt) is generated with all possible paths among n nodes. Ineach generation, the best ETX value is chosen, and based on that value, the path iscalculated in vision phase. To get an optimal path, this process is repeated till theETX value is converged to the same value. Once, it is congregated, the obtainedpath is considered as optimal path which has sufficient remaining energy to route thedata. Let the variables ETXT, ETXG and ETXMAX denote the ETX value of thepath (members), the maximum ETX value of a member in present generation andthe ETX value of member with respect to all generation, respectively.

Sub-phase 1: Smell phase- For the valid paths in the population(pt), the ETXT iscalculated as given in the proposed Eq. (2).

ETXT = ETXT + ETX[path[i] − 1][path[i + 1] − 1] (2)

where ETXT calculates the ETX value of the nodes which are getting added eachtime in the optimal path. path[i − 1][[i + 1] − 1] represents the link from one nodeto another for which the corresponding ETX value to be added to the ETXT. Themaximum value of the present generation is calculated, and the corresponding ETXTvalue is stored in ETXG. The path which hasmaximumETX is selected and assignedto ETXMAX. Also, the path corresponding to the ETXMAX is assigned to pmax.

Sub-phase 2: Vision phase- For all paths in the population, 30% [11, 12] of thenodes are selected in the path and checked if it is connected to the rest of the nodesin the path. If the selected nodes are found to be connected to the rest of the nodes,then arrange the selected node consecutively to the connected node. The path resultis recorded in temp. If there is any node to be disconnected, the corresponding nodeis deleted and the resultant value of temp is stored as the new value in populationat the same position. The first 30% (except the source node) of the best P path ischosen, and the nodes sequence of this solution is applied to the neighbor solutionsin connected manner. On considering the path for reinsertion, taking the ceil of the30% cut value and is given as

Cut = ceil(length of path ∗ 0.30) (3)

Consider a node Y from 30% and compare with existing nodes in path, one by one. Ifa node X is connected to the node Y (which we selected from 30%), then insert nodeY to next of the node X , else compare with next node in the path. Repeat this stepfor all nodes in 30%. If the node Y is not connected to any one of the nodes in thepath, then neglect that node. Make the resultant path as P1. Now consider path P1;check Xi node is connected to Xi + 1 node, if it is connected, continue else removethe node Xi + 1 from the path. Repeat this step to all nodes in the path. Finally, theoptimal path is constructed. This path results in the best ETX value comparativelyto all other paths. The complete code of the proposed RRA algorithm is given inAlgorithm 1.

Fruit Fly Optimization-Based Reliable Routing … 9

Algorithm 1: Reliable Routing Algorithm (RRA)Input: ETX matrix(Mi ), Initial population(pt), Transmission range(Ti ) and Threshold

energy level(Th)Output: Selected nodes(Ns), Path, Total ETX(ETXMAX), Total generation(gen).

1 Initialization of Network Ni, ETXMAX=0, ETXG, gen=cnt=0 and array C.2 Broadcast Hello/probe packets to one hop neighbors.3 Calculate ETX as of Eq. (1)4 for each Ni do5 if Transmission(Ni )<Ti and energy(Ni )>Th then6 Store in NS7 end8 end9 Generate pt and initialize Mi for Ns .

10 repeat11 Call procedure Smell ()12 Call procedure Vision ()13 until cnt<1514 Procedure Smell ()15 for each path pti in p do16 Calculate ETX value of connected path of Eq. (2).17 Find maximum ETX value for geni .18 Store the calculated ETX value of the path in ETXG.19 Store the corresponding path of the ETXG to optimal path.20 end21 if ETXG>ETXMAX then22 ETXMAX=ETXG and pmax=optimal path23 else24 cnt++25 end26 if ETXMAX is constant for consecutive 15 generation then27 Display it as the final optimal path.28 end29 End Procedure30 Procedure Vision ()31 for each path pti in p do32 Calculate cut as of Eq. (3).33 Store all nodes between 0 to cut of path in C.34 for each node Ni in C do35 if Ni is connected to node in path then36 Insert node Ni to new path at j+1th location.37 else38 neglect the node.39 end40 Store new path.41 end42 Neglect the nodes which are not connected in new path.43 end44 End Procedure

10 T. Siron Anita Susan and B. Nithya

4 Simulation and Performance Analysis

The simulation is conducted by MATLAB R2016b on an Intel(R) core(TM) i7-6700CPU with 3.40GHz and 16 GB RAM. Initial population is constructed based onall possible paths of N nodes. The number of nodes varies from 5, 10 …20. Sincethe energy levels of the nodes are calculated in advance, it is assumed that no nodewill get disconnected due to energy loss and time taken for the death of a node getsincreased.

4.1 ETX Convergence Versus ETX Value

Based on the continuous stabilized value of 15 generations, the best ETX and thecorresponding optimal path are chosen. If the node density is less than the estimatedETX is converged within the lesser number of generations. However, the lessernumber of generations is not feasible as it may lead to same path for several nodes.Due to this, the number of generations is assumed to be as 15 that may be changeddepending upon the population. Since the elected path is constant for more numberof generations, it will be more stable and optimal. Figure4 depicts the convergencegraph to show that the ETX value of certain path remains constant for 15 generations.The corresponding path is taken as on optimal path, and its corresponding ETX valueis an optimal ETX value.

Fig. 4 Optimal ETX value

Fruit Fly Optimization-Based Reliable Routing … 11

4.2 Packer Delivery Ratio (PDR)

Packet delivery ratio is the ratio of the backward ratio (BR) of a node to the forwardratio (FR) of that node. It is calculated as follows:

PDR = BR

FR(4)

Figure5depicts thePDRachievedby the proposedRRAalgorithmandPRD. Initially,the delivery ratio is 100% and starts to decrease due to the usage of some poor qualitylinks at some situation. If the lifetime of the node is expired due to energy depletion,other sensor nodes which have poor links may get a chance in routing thus leading todrastic decrease in packet delivery. Since the proposed RRA has chosen the averageenergy level nodes for sending and receiving packets, the lifetime of the node isincreased, thereby giving more reliable and stable path. Due to this capability, theproposed RRA attains better PDR than PRD algorithm.

4.3 Network Delay

It is one of the performance metrics which designates the time taken to transfer thedata from one node to its destination. It depends on EXT value, transmission andpropagation delay, and the delay is calculated as

Fig. 5 Comparison of PDR

12 T. Siron Anita Susan and B. Nithya

Fig. 6 Comparison of delay

Delay =N∑

i=0

Ei(Trans_Delay+ Propa_Delay(i)) (5)

where Ei represents the ETX value of i th node, Trans_Delay indicates the trans-mission delay of the network, and Propa_Delay(i) represents the propagationdelay of i th node. The transmission delay of the network depends on the packetsize and transmission speed. When comparing to Bhardwaj et al. [10] transmissionspeed of the network, the transmission speed of the proposed RRA algorithm getsincreased which results in lesser delay. The transmission speed directly depends onan efficient link performance. The proposed RRA calculates the reliable link for eachtransmission which increases the transmission speed and results in lesser delay asdepicted in Fig. 6.

4.4 Energy Balance Factor (EBF)

EBF is calculated based on the standard deviation of the residual energy of the nodesand is given as

EBF =√√√√ 1

N

N∑

i=1

(Energyres(i) − EnergyAvg)2 (6)