Lecture Notes on Data Engineeringand Communications Technologies 53

V. SumaNoureddine BouhmalaHaoxiang Wang   Editors

Evolutionary Computing and Mobile Sustainable NetworksProceedings of ICECMSN 2020

Lecture Notes on Data Engineeringand Communications Technologies

Volume 53

Series Editor

Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain

The aim of the book series is to present cutting edge engineering approaches to datatechnologies and communications. It will publish latest advances on the engineeringtask of building and deploying distributed, scalable and reliable data infrastructuresand communication systems.

The series will have a prominent applied focus on data technologies andcommunications with aim to promote the bridging from fundamental research ondata science and networking to data engineering and communications that lead toindustry products, business knowledge and standardisation.

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V. Suma • Noureddine Bouhmala •

Haoxiang WangEditors

Evolutionary Computingand Mobile SustainableNetworksProceedings of ICECMSN 2020

123

EditorsV. SumaResearch and Industry Incubation Center,Department of Information Scienceand EngineeringDayananda Sagar College of EngineeringBangalore, India

Haoxiang WangGo Perception LaboratoryCornell UniversityIthaca, NY, USA

Noureddine BouhmalaDepartment of Technologyand Maritime InnovationUniversity of SoutheastHorten, Norway

ISSN 2367-4512 ISSN 2367-4520 (electronic)Lecture Notes on Data Engineering and Communications TechnologiesISBN 978-981-15-5257-1 ISBN 978-981-15-5258-8 (eBook)https://doi.org/10.1007/978-981-15-5258-8

© Springer Nature Singapore Pte Ltd. 2021This 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.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,Singapore

The 2020 ICECMSN conference is solelydedicated to all the editors, reviewers,and authors of the conference event.

Foreword

I extend my warm welcome in inviting you all to the proceedings of theInternational Conference on Evolutionary Computing and Mobile SustainableNetworks [ICECMSN 2020] organized at Sir M. Visvesvaraya Institute ofTechnology, on 20–21 February 2020.

The theme of the conference event is “Emerging advances in Sustainable MobileNetworks and Computational Intelligence”, topics that are quickly gaining researchattention from both academia and industries due to the relevance of maintainingsustainability and enhancing intelligence in smart mobile networks. The alreadyestablished track record of computational intelligence models and sustainablemobile networks seems to be very functional and reliable, where it mandates theneed for further exploration in this research area. This makes the ICECMSN 2020an excellent forum for exploring innovative research ideas in the smart and intel-ligent networks domain.

Wewould like to extend our sincere gratitude toOrganizingChairDr.V.R.Manjunath,Principal, SIR MVIT, Bangalore, India for his motivation and support to organizethe conference in a successful manner. We extend our hearty thanks to Keynote SpeakersDr.ManuMalek, Editor in Chief, Elsevier CEE and Former Professor, Stevens Institute ofTechnology, USA and Sri B. S. Bindumadhava Scientist G & Senior Director, Centrefor Development of Advanced Computing, Bengaluru, India for their valuable thoughtsand discussion.

The entire success of the ICECMSN 2020 conference event depends on theresearch talents and efforts of the authors in the intelligent mobile networks andcomputer science domains, who have contributed their submissions on almost allthe facets of the conference theme. An extensive appreciation is also deserved forall the conference program and review committee members who have invested their

vii

valuable time and professional expertise in assessing research papers from multipledomains by maintaining the quality standards for this conference. We extensivelythank Springer for their guidance before and after the conference event.

Conference ChairDr. Manjula Sanjay Koti

Professor and HODDepartment of MCA

SIR MVIT, Bangalore, India

viii Foreword

Preface

It is our pleasure to welcome you to the International Conference on EvolutionaryComputing and Mobile Sustainable Networks [ICECMSN 2020] in Bangalore,India. The major goal of this conference is to bring together the academicians,researchers and industrialists under a single roof to share and exchange theirresearch experience and results on various aspects of mobile sustainable networksand computational intelligence research and discuss about the real-time challengesand solutions adopted for it.

ICECMSN 2020 has received ample submissions of about 398 papers from bothacademia and industrial tracks and based on the selection of conference reviewcommittee and advisory committee members, a total of 90 papers appeared in theconference proceedings of ICECMSN 2020. It is to be noted that, all the papersregardless of their allotted tracks has extensively received at least 3 reviews fromthe research experts.

We hope the readers will have a productive, satisfying and informative expe-rience from the research works gathered from all over the world. Nevertheless, thisproceedings will provide a written record of a synergy of research works that existsin communication networks communities and provides significant framework for anew and futuristic research interactions. Moreover, this proceedings will pave wayfor the applications of computational intelligence in Mobile Sustainable Networks[MSN].

Bangalore, India Prof. Dr. V. SumaHorten, Norway Dr. Noureddine BouhmalaIthaca, USA Dr. Haoxiang Wang

ix

Acknowledgments

We ICECMSN 2020 would like to extend our sincere thanks to all who have helpedin making this conference event a great success. We are much pleased in thankingour educational institution Sir M. Visvesvaraya Institute of Technology, Bangalore,India for their pervasive support and effective help during the conference.

The extended support of the conference committee members before and duringthe conference event has helped to tackle many challenging tasks in a smooth andefficient way, where it has significantly contributed to excel the quality of theconference. Our special thanks belongs to all the conference reviewers, who playedan indispensable role in providing technical and semantic reviewing assistance toall the research manuscripts received for the conference. We are thankful for theirhelp in guiding us to select the state-of-the-art high-quality papers that deserves thepublication under this conference. We also wish to thank all our faculty membersand staffs for their technical and non-technical contribution for maintaining theconference participants’ contentment.

The conference organizers are particularly grateful for all the authors, who havecontributed their research ideas spanning over many active and emerging researchdomains. Our very special thanks will go exceptionally to all the conference del-egates for their active participation in the conference event.

At last, the organizers would like to gladly acknowledge the local organizingcommittee, who ensured that all the formal steps of the conference event has beencompleted in an effortless way.

xi

Contents

Optimal Resource Sharing Amongst Device-to-DeviceCommunication Using Particle Swarm Algorithm . . . . . . . . . . . . . . . . 1H. M. Nethravathi and S. Akhila

A Systemic Method of Nesting Multiple Classifiers Using EnsembleTechniques for Telecom Churn Prediction . . . . . . . . . . . . . . . . . . . . . . 13J. Beschi Raja, G. Mervin George, V. Roopa, and S. Sam Peter

Hybrid Method in Identifying the Fraud Detectionin the Credit Card . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Pooja Tiwari, Simran Mehta, Nishtha Sakhuja, Ishu Gupta,and Ashutosh Kumar Singh

Real-Time Human Locator and Advance Home SecurityAppliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Anand Kesharwani, Animesh Nag, Abhishek Tiwari, Ishu Gupta,Bharti Sharma, and Ashutosh Kumar Singh

A Novel Implementation of Haptic Robotic Arm . . . . . . . . . . . . . . . . . 51A. Kavitha, P. Sangeetha, Aijaz Ali Khan, and K. N. Chandana

A Survey on Partially Occluded Faces . . . . . . . . . . . . . . . . . . . . . . . . . 61Shashank M. Athreya, S. P. Shreevari, B. S. Aradhya Siddesh,Sandeep Kiran, and H. T. Chetana

Some Effective Techniques for Recognizing a PersonAcross Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Mrudula Nimbarte, Madhuri Pal, Shrikant Sonekar, and Pranjali Ulhe

A Comprehensive Survey on Federated Cloud Computingand its Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . 79S. R. Shishira and A. Kandasamy

xiii

Decoy Technique for Preserving the Privacy in FogComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89K. P. Bindu Madavi and DR. P. Vijayakarthick

Design of Book Recommendation System Using SentimentAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Addanki Mounika and Dr. S. Saraswathi

Review of Python for Solar Photovoltaic Systems . . . . . . . . . . . . . . . . 103R. Sivapriyan, D. Elangovan, and Kavyashri S. N. Lekhana

Data Exploratory Analysis for Classification in MachineLearning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Jesintha Bala Chandrasekar, Shivakumar Murugesh,and Vasudeva Rao Prasadula

Keystroke Dynamics for User Verification . . . . . . . . . . . . . . . . . . . . . . 127Ashwini Sridhar and H. R. Mamatha

Activity Prediction for Elderly Using Radio-Frequency IdentificationSensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Prashant Giridhar Shambharkar, Sparsh Kansotia, Suraj Sharma,and Mohammad Nazmud Doja

The Role of Predictive Data Analytics in Retailing . . . . . . . . . . . . . . . 153Mohammed Juned Shaikh Shabbir and C. M. Mankar

High-Performance Digital Logic Circuit Realization UsingDifferential Cascode Voltage Switch Logic (DCVSL) . . . . . . . . . . . . . . 161S. S. Kavitha and Narasimha Kaulgud

Analysis and Classification of Ripped Tobacco Leaves UsingMachine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171M. T. Thirthe Gowda and J. Chandrika

Next-Generation WSN for Environmental Monitoring EmployingBig Data Analytics, Machine Learning and ArtificialIntelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Rumana Abdul Jalil Shaikh, Harikumar Naidu, and Piyush A. Kokate

Generating Automobile Images Dynamically from TextDescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197N. Sindhu and H. R. Mamatha

Body Mass Index Implications Using Data Analysis in the SoccerSports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Akash Dasmondal and P. K. Nizar Banu

Biogeography-Based Optimization Technique for Optimal Designof IIR Low-Pass Filter and Its FPGA Implementation . . . . . . . . . . . . . 229K. Susmitha, V. Karthik, S. K. Saha, and R. Kar

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Invasive Weed Optimization-Based Optimally Designed High-PassIIR Filter and Its FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . 239V. Karthik, K. Susmitha, S. K. Saha, and R. Kar

Identification of Online Auction Bidding Robots Using MachineLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249Pooja Maan and R. Eswari

Machine Learning-Based Green and Energy Efficient TrafficGrooming Architecture for Next Generation Cellular Networks . . . . . 261Deepa Naik, Pothumudi Sireesha, and Tanmay De

Robust Image Encryption in Transform Domain Using Duo ChaoticMaps—A Secure Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271S. Aashiq Banu, M. S. Sucharita, Y. Leela Soundarya, Lankipalli Nithya,R. Dhivya, and Amirtharajan Rengarajan

Analysis of Attention Deficit Hyperactivity Disorder Using VariousClassifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283Hensy K. George and P. K. Nizar Banu

A Technique to Detect Wormhole Attack in Wireless Sensor NetworkUsing Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Moirangthem Marjit Singh, Nishigandha Dutta,Thounaojam Rupachandra Singh, and Utpal Nandi

A Survey on Methodologies and Algorithms for MutualAuthentication in IoT Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309Rashmi R. Sonth, Y. R. Pranamya, N. Harish Kumar, and G. Deepak

Emotion Scanning of the World’s Best Colleges Using Real-TimeTweets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Sanjay Kumar, Yash Saini, Vishal Bachchas, and Yogesh Kumar

Generating Feasible Path Between Path Testing and Data FlowTesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325C. P. Indumathi and A. Ajina

Soft Constraints Handling for Multi-objective Optimization . . . . . . . . 337Md. Shahriar Mahbub, Fariha Tahsin Chowdhury, and Anika Salsabil

Parking Management System Using Internet of Things . . . . . . . . . . . . 349Aditya Sarin and Deveshi Thanawala

Machine Learning based Restaurant Revenue Prediction . . . . . . . . . . 363G. P. Sanjana Rao, K. Aditya Shastry, S. R. Sathyashree, and Shivani Sahu

Solving Multi-objective Fixed Charged Transportation ProblemUsing a Modified Particle Swarm Optimization Algorithm . . . . . . . . . 373Gurwinder Singh and Amarinder Singh

Contents xv

Binomial Logistic Regression Resource Optimized Routing inMANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387M. Ilango, A. V. Senthil Kumar, and Amit Dutta

A Lightweight Approach for Policy-Based Messaging . . . . . . . . . . . . . 399P. P. Abdul Haleem

A Lightweight Effective Randomized Caesar Cipher Algorithm forSecurity of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411Vardaan Sharma, Sahil Jalwa, Abdur Rehman Siddiqi, Ishu Gupta,and Ashutosh Kumar Singh

RPL-Based Hybrid Hierarchical Topologies for Scalable IoTApplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421Animesh Giri and D. Annapurna

A Quick Survey of Security and Privacy Issues in Cloudand a Proposed Data-Centric Security Model for Data Security . . . . . 431Abraham Ekow Dadzie and Shri Kant

Homo Sapiens Diabetes Mellitus Detection and Classification . . . . . . . 445Anu Agarwal, Anjay Sahoo, Indrashis Das, Siddharth S. Rautaray,and Manjusha Pandey

Learning Platform and Smart Assistant for Students . . . . . . . . . . . . . . 455R. Rashmi, Sharan Rudresh, V. A. Sheetal,and Dexler information Solutions Pvt Limited

Eye Disease Detection Using YOLO and Ensembled GoogleNet . . . . . . 465Saikiran Gogineni, Anjusha Pimpalshende,and Suryanarayana Goddumarri

Comparative Analysis of MCT Load Balancing Approach in CloudComputing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483Shabina Ghafir, M. Afshar Alam, and Ranjit Biswas

A Comparative Study of Text Classification and Missing WordPrediction Using BERT and ULMFiT . . . . . . . . . . . . . . . . . . . . . . . . . 493Praveenkumar Katwe, Aditya Khamparia, Kali Prasad Vittala,and Ojas Srivastava

Data Formats and Its Research Challenges in IoT: A Survey . . . . . . . 503Sandeep Mahanthappa and B. R. Chandavarkar

Software Fault Prediction Using Cross-Validation . . . . . . . . . . . . . . . . 517Yeresime Suresh

Implementation of Recommendation System and Technologyfor Villages Using Machine Learning and IoT . . . . . . . . . . . . . . . . . . . 527B. Achyuth and S. Manasa

xvi Contents

IoT Based Inventory Management System with RecipeRecommendation Using Collaborative Filtering . . . . . . . . . . . . . . . . . . 543Atharva S. Devasthali, Adinath J. Chaudhari, Someshkumar S. Bhutada,Snehal R. Doshi, and Vaishali P. Suryawanshi

Survey Paper on Smart Veggie Billing System . . . . . . . . . . . . . . . . . . . 551T. V. Niteesh, B. Y. Lohith, Y. M. Gopalakrishna, R. Ashok Kumar,and J. Nagaraj

An Optimized Approach for Virtual Machine Live Migrationin Cloud Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559Ambika Gupta, Priti Dimri, and R. M. Bhatt

Digital Image Retrieval Based on Selective Conceptual BasedFeatures for Important Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Premanand Ghadekar, Sushmita Kaneri, Adarsh Undre, and Atul Jagtap

A Novel Repair and Maintenance Mechanism for ‘IntegratedCircuits’ of Ubiquitous IoT Devices by Performing Virtual ICInspection Based on ‘Light Field Technology’ . . . . . . . . . . . . . . . . . . . 581Vijay A. Kanade

Evolutionary Optimization of Spatial Light Modulator for AdvancedWavefront Control in an Optically Addressable ‘Electric See-Through Skin’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591Vijay A. Kanade

Retrieval of Videos of Flowers Using Deep Features . . . . . . . . . . . . . . 605V. K. Jyothi, D. S. Guru, N. Vinay Kumar, and V. N. Manjunath Aradhya

Analysis of Students Performance Using Learning Analytics—ACase Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615Manjula Sanjay Koti and Samyukta D. Kumta

A Case Study on Distributed Consensus Problem on Cloud-BasedSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627Ganeshayya Shidaganti, Ritu Pravakar, M. Shirisha, and H. R. Samyuktha

An IoT Framework for Healthcare Monitoring and MachineLearning for Life Expectancy Prediction . . . . . . . . . . . . . . . . . . . . . . . 637Anna Merine George, Anudeep Nagaraja, L. Ananth Naik, and J. Naresh

A Study on Discernment of Fake News Using Machine LearningAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645Utkarsh, Sujit, Syed Nabeel Azeez, B. C. Darshan,and H. A. Chaya Kumari

Detection of Diseased Plants by Using Convolutional NeuralNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659M. Maheswari, P. Daniel, R. Srinivash, and N. Radha

Contents xvii

Emoticon: Toward the Simulation of Emotion Using Android MusicApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673Aditya Sahu, Anuj Kumar, and Akash Parekh

Multi-document Text Summarization Tool . . . . . . . . . . . . . . . . . . . . . . 683Richeeka Bathija, Pranav Agarwal, Rakshith Somanna, and G. B. Pallavi

Cognitive Computing Technologies, Products, and Applications . . . . . 693N. Divyashree and Prasad K. S. Nandini

Breast Cancer Prognosis Using Machine Learning Techniquesand Genetic Algorithm: Experiment on Six Different Datasets . . . . . . . 703S. Jijitha and Thangavel Amudha

Detection Classification and Cutting of Fruits and VegetablesUsing Tensorflow Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713Rupal Mayo Diline D’Souza, S. R. Deepthi, and K. Aarya Shri

Detection of Counterfeit Cosmetic Products Using Image Processingand Text Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721Siddharth Mehta, Prajakta Divekar, Aditi Kolambekar,and Amol Deshpande

Multiclass Weighted Associative Classifier with Application-BasedRule Selection for Data Gathered Using Wireless SensorNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737Disha J. Shah and Neetu Agarwal

Void-Aware Routing Protocols for Underwater CommunicationNetworks: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747Pradeep Nazareth and B. R. Chandavarkar

A Comprehensive Survey on Content Analysis and ItsChallenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761Ankitha A. Nayak and L. Dharmanna

Applications of Blockchain and Smart Contract for SustainableTourism Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773Jaehun Joo, Joungkoo Park, and Yuming Han

Friendship and Location-Based Routing in Delay TolerantNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781Nidhi Sonkar, Sudhakar Pandey, and Sanjay Kumar

Fake Account Detection Using Machine Learning . . . . . . . . . . . . . . . . 791Priyanka Kondeti, Lakshmi Pranathi Yerramreddy, Anita Pradhan,and Gandharba Swain

xviii Contents

Multi-objective Task Scheduling Using Chaotic Quantum-BehavedChicken Swarm Optimization (CQCSO) in Cloud ComputingEnvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803G. Kiruthiga and S. Mary Vennila

ARIMA for Traffic Load Prediction in Software Defined Networks . . . 815Sarika Nyaramneni, Md Abdul Saifulla, and Shaik Mahboob Shareef

Improved Logistic Map Based Algorithm for Biometric ImageEncryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825Mahendra Patil, Avinash Gawande, and D. Shelke Ramesh

Data Security System for A Bank Based on Two DifferentAsymmetric Algorithms Cryptography . . . . . . . . . . . . . . . . . . . . . . . . 837Md. Ashiqul Islam, Aysha Akter Kobita, Md. Sagar Hossen,Laila Sultana Rumi, Rafat Karim, and Tasfia Tabassum

Digital Signature Authentication Using Asymmetric KeyCryptography with Different Byte Number . . . . . . . . . . . . . . . . . . . . . 845Md. Sagar Hossen, Tasfia Tabassum, Md. Ashiqul Islam, Rafat Karim,Laila Sultana Rumi, and Aysha Akter Kobita

Digital Signature Authentication for a Bank Using Asymmetric KeyCryptography Algorithm and Token Based Encryption . . . . . . . . . . . . 853Rafat Karim, Laila Sultana Rumi, Md. Ashiqul Islam, Aysha Akter Kobita,Tasfia Tabassum, and Md. Sagar Hossen

Accuracy Analysis of Similarity Measures in SurpriseFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 861Sanket Kamta and Vijay Verma

Computational Method for Cotton Plant Disease Detection of CropManagement Using Deep Learning and Internet of ThingsPlatforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875Bhushan V. Patil and Pravin S. Patil

Foot Ulcer and Acute Respiratory Distress Detection System forDiabetic Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887M. S. Divya Rani, T. K. Padma Gayathri, Sree Lakshmi, and E. Kavitha

Cluster-Based Prediction of Air Quality Index . . . . . . . . . . . . . . . . . . . 899H. L. Shilpa, P. G. Lavanya, and Suresha Mallappa

A Memetic Evolutionary Algorithm-Based Optimization forCompetitive Bid Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917Pritam Roy

Tunable Access Control for Data Sharing in Cloud . . . . . . . . . . . . . . . 927S. Sabitha and M. S. Rajasree

Contents xix

Methodology for Implementation of Building Management SystemUsing IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939Ankita Harkare, Vasudha Potdar, Abhishek Mishra, Akshay Kekre,and Hitesh Harkare

A Brief Understanding of Blockchain-Based Healthcare ServiceModel Over a Remotely Cloud-Connected Environment . . . . . . . . . . . 949Subhasis Mohapatra and Smita Parija

Ensemble Learning-Based EEG Feature Vector Analysis for BrainComputer Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 957Md. Sadiq Iqbal, Md. Nasim Akhtar, A. H. M. Shahariar Parvez,Subrato Bharati, and Prajoy Podder

Cluster-Based Data Aggregation in Wireless Sensor Networks:A Bayesian Classifier Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971Lokesh B. Bhajantri and Basavaraj G. Kumbar

An Optimized Hardware Neural Network Design Using DynamicAnalytic Regulated Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981V. Parthasarathy, B. Muralidhara, Bhagwan ShreeRam, and M. J. Nagaraj

Conceptual Online Education Using E-Learning Platform of CloudComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 991Vivek Sharma, Akhilesh Kumar Singh, and Manish Raj

Efficient Deployment of a Web Application in ServerlessEnvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 999Vivek Sharma, Akhilesh Kumar Singh, and Manish Raj

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005

xx Contents

About the Editors

Dr. V. Suma holds a B.E. in Information Science and Technology, M.S. inSoftware Systems and Ph.D. in Computer Science and Engineering. Currently, sheis working as Dean of the Research and Industry Incubation Centre, and a Professorat the Department of Information Science and Engineering, Dayananda SagarCollege of Engineering, Bangalore, India. She has more than 17 years of teachingexperience and has published over 180 papers, including research articles publishedin leading international journals, such as ACM, ASQ, Crosstalk, IET Software, andjournals published by MIT and Dartmouth College in the USA. Her research hasalso been published on NASA, UNI Trier, Microsoft, CERN, IEEE, ACM andSpringer portals.

Dr. Noureddine Bouhmala completed his Candidatus Magisterii in ComputerScience at the University of Bergen, Norway in 1992, and completed his Ph.D. inComputer Science at the University of Neuchâtel, Switzerland in 1998 (funded bythe Swiss National Science Foundation). Currently, he is working as an AssociateProfessor at the Faculty of Technology and Maritime Innovation at the UniversitySouthEast, and the Faculty of Engineering and Sciences at Agder University, bothin Norway. He has more than 25 years of teaching and research experience, and haspublished numerous research papers in international journals and internationalconferences. He is also an editorial board member for various respected interna-tional journals. His areas of interest include artificial intelligence, machine learning,optimization algorithms, parallel computing, data mining, autonomic andsafety-critical systems.

Dr. Haoxiang Wang is currently the Director and lead executive faculty memberof GoPerception Laboratory, New York, USA. His research interests includemultimedia information processing, pattern recognition and machine learning,remote sensing image processing and data-driven business intelligence. He hasco-authored over 60 papers in these fields in journals such as MTAP, ClusterComputing, IEEE TII, Communications Magazine, Computers & ElectricalEngineering, Computers, Environment and Urban Systems, Optik, Sustainable

xxi

Computing: Informatics and Systems, Journal of Computational Science, PatternRecognition Letters, Information Sciences, Computers in Industry, FutureGeneration Computer Systems, International Journal of Computers andApplications, and at conference such as IEEE SMC, ICPR, ICTAI, ICICI, CCIS,ICACI. He is a guest editor for IEEE Transactions on Industrial Informatics, IEEEConsumer Electronics Magazine, Multimedia Tools and Applications, MDPISustainability, International Journal of Information and Computer Security, Journalof Medical Imaging and Health Informatics, Concurrency and Computation:Practice and Experience.

xxii About the Editors

Optimal Resource Sharing AmongstDevice-to-Device Communication UsingParticle Swarm Algorithm

H. M. Nethravathi and S. Akhila

Abstract Device-to-Device Communication (D2D) has been described as one ofthe important innovations in the development of 5G networks. This form of D2Dnetworking has its own benefits of improved network capacity and reduced powerusage, making it as a primary candidate for the upcoming 5G cellular networks. Inthis work, a single cell scenario with one base station andmultiple cell users andD2Dpairs are considered. As sharing causes performance degradation due to interferencebetween CUs (cellular users) and D2D pairs, a permutation optimization strategybased on Particle swarm optimization (PSO) has been proposed to optimize resourcesharing between cellular users and D2D pairs. This technique is found to maximizesystem performance through better resource sharing.

Keywords 5G · D2D · 3GPP · PSO

1 Introduction

Worldwide, the number of users and data traffic is increasing every day. It overloadsthe base stations resulting in transmission delay, latency and low data speed. Insuch a situation, device-to-device (D2D) connectivity is a promising candidate forfifth-generation cellular communication. In D2D communication devices transmit orreceive data without assistance from the base station. This reduces the base stationoverload thereby increasing the overall system throughput. Optimal allocation ofresources for D2D interaction has become a very important research field, withresearchers showing a great deal of interest in this area. Each user will be able tooperate as a conventional cellular user or a D2D user depending on their chosen

H. M. Nethravathi (B) · S. AkhilaBMSCE, Bengaluru, Indiae-mail: nethrahm@gmail.com

S. Akhilae-mail: akhilas.ece@bmsce.ac.in

© Springer Nature Singapore Pte Ltd. 2021V. Suma et al. (eds.), Evolutionary Computing and Mobile Sustainable Networks,Lecture Notes on Data Engineering and Communications Technologies 53,https://doi.org/10.1007/978-981-15-5258-8_1

1

2 H. M. Nethravathi and S. Akhila

strategy of need and mode. Based on their model, the resources will be allocated tothe associated user so as to optimize the device throughput.

D2D communication has been considered as a powerful technology to provideimproved quality of wireless services. Due to the limitations in various technologies,such as power restrictions, highdelays due to network congestion and adoption of newservices, new techniques have been developed to replace the prevailing technologyi.e., Long Term Evolution (LTE), D2D. Such approaches tackle the limitations ofexisting networks and meet the new needs of end-users and of the operators [1].

The advantages that these techniques would bring to an end-user would be energysavings, efficiency gains or new nearby services that can save the connection to thebase station. It also provides benefits from the perspective of network operators, suchas increasing the coverage area, increasing spectrum utilization or being able to meetthe demand of a larger number of linked terminals in the future at the same time.3GPP considers the use of Long Term Evolution (LTE-Direct) and IEEE 802.11(Wi-Fi Direct) for D2D communications [2].

To achieve the expected benefits ofD2Dcommunications, the technical challengesthat may arise from the difficult conditions of transmission between mobile devicesneeds to be addressed. In addition, D2D communications can be highly inefficientunder conditions of uncertainty and low quality of connections [3]. With the useof link adaptation and Power Control techniques, this form of inefficiency can betackled.

Aranit et al. [4] provides a review on LTE to assess its capacity to support ITSand vehicular applications. The analysis conducted qualitatively captures the mainfeatures, strengths and weaknesses of the under-development standard guidelinesand solutions. In [5], the energy minimization problem for D2D communicationunderlying a multi-cell system has been considered to maximize throughput.

Multiantennamethods [6, 7] have also been implemented into the underlyingD2Dcommunication to eliminate the interference between D2D and cellular users. D2Dcommunication as a cellular network underlay can share resources orthogonally ornon-orthogonally with cellular users. In the orthogonal case, D2D users are allocateddedicated resources. Though simple to implement, they are unable to exploit thefull potential of D2D communication to increase spectral efficiency. This has beenaddressed in nonorthogonal resource sharing methods. Feng et al. [8] address bothD2D and cellular users’ QoS specifications while optimizing the sum frequency.D2D applications should share CU’s uplink resources since it is simple for the basestation (BS) to manage interference problems caused by underused uplink channels.

Liu et al. [9] investigated the power control for full-duplex D2D cellular networkcommunications. In this power control problem was formulated by maximizingthe achievable sum-rate of the full-duplex D2D connection while meeting thecellular connection’s minimum rate required under the cellular users and D2D users’maximum transmission power constraint.

Dynamic resource allocation is studied in [10,11, 12], where all subchannels canbe used by the D2D pairs. Nevertheless, the adjacent D2D pairs will inevitably sufferextreme interference with each other.

Optimal Resource Sharing Amongst Device-to-Device … 3

Social and physical attributes based content sharing approach with D2MD clusterformation approach is described in [13] for 5G cellular networks, enabling trafficoffloading from base stations to direct transmitting devices and enhancing power.

Interference due to resource sharing reduces network performance. In [8], authorshave proposed interference management strategy by incorporating orthogonalitybetween cellular links and D2D links.

Distance constrained based outage probability calculation is performed in [14],to justify the objective of utilizing all the possible resources in the cellular system.Deng et al. [15] proposed social aware distributed resource algorithm, this algorithmachieves convergence and stability without loss of fairness.

Khuntia et al. [16] proposed an optimal spectral allocation strategy to enhancethe throughput of D2D while maintaining QoS for CUs and performance of D2D isanalyzed using outage probability analysis [17].

The paper is organized in the following way. Section 2 is about the formulationof the system. Section 3 describes the methodology. The results of the simulationobtained in Sects. 4 and 5 ends with a conclusion.

2 Formulation of the System

Figure 1 shows a cellular system for sharing the uplink resources in a device to devicecommunication framework with N number of orthogonal users and a base station(BS) [18].

The frequency band indexed by each user is expressed by i = 1, . . . , N .hci represents the channel between the base station and cellular user i ,hdi is the channel between D2D receiver and cellular user i ,gci is the channel between BS and D2D transmitter for frequency band i .gci symbolizes the channel betweenD2D transmitter and the receiver for frequency

band i .Let xci and x

di be the transmitting signals for the cellular andD2Dusers respectively

for frequency band i .

Fig. 1 Cellular system inD2D communication

Cellular user 1

Cellular user 2

Cellular user N

cNh

D2D Rx D2D Tx

BS

2 c h

d ig

1 c h

c i g

2dh

1d hd

Nh

4 H. M. Nethravathi and S. Akhila

Equation (1) represents the signal received at the base station by the cellular useri :

yci = hci xci + gci x

di + nci (1)

where nci is the Gaussian noise with variance σ ci by cellular user i.

And, Eq. (2) represents the signal reception for D2D user with frequency band i :

ydi = gdi xdi + hdi x

ci + ndi (2)

where ndi is the additive zero-mean Gaussian noise with variance σ di by D2D user .

Suppose that the Gaussian codes are used by both D2D and cellular users, on eachfrequency band i which transmits powers of

qi � E∣∣xdi

∣∣2

(3)

pi � E∣∣xci

∣∣2

(4)

The throughputs for cellular and D2D users are given by the Eqs. (5) and (6)respectively [18].

Rci (pi , qi ) � log

[

1 +∣∣hci

∣∣2pi

σ ci + ∣

∣gci∣∣2qi

]

= log

(

1 + αi pi1 + θi qi

)

(5)

Rdi (pi , qi ) � log

[

1 +∣∣gdi

∣∣2qi

σ di + ∣

∣hdi∣∣2pi

]

= log

(

1 + γi qi1 + βi pi

)

(6)

where,αi � |hci |2σ ci, βi � |hdi |2

σ di, γi � |gdi |2

σ di

and θi � |gci |2σ di

represents the normalizedchannel gains.

The resource sharing between cellular and D2D users must be designed so thatthe D2D can achieve maximum benefit by fulfilling the cellular user’s requirements.This is accomplished through resource sharing between the D2D and cellular users.

Theoretically, this is achieved by maximizing the throughput of D2D link [18]and is represented as:

Maximizep, q

subjected to :

N∑

i=1Rdi (pi , qi )

Rci (pi , qi ) ≥ ρi , i = 1, . . . , N

0 ≤ pi ≤ Pi , 0 ≤ qi ≤ Qi , i = 1, . . . , NN∑

i=1qi ≤ Q

(7)

Optimal Resource Sharing Amongst Device-to-Device … 5

where the QoS threshold is symbolized by ρi , Pi is the power budget of cellularuser i , Qi for frequency band, i is the D2D user’s power limit and the overall powerbudget for D2D user is symbolized by Q.

It is a very challenging task to achieve optimal resource sharing. The problem in(7) is a non-convex problem as both Rc

i (pi , qi ) and Rdi (pi , qi ) are not jointly concave

in (pi , qi ). This work aims at providing an optimized solution for resource sharingusing the particle swarm optimization technique.

3 Methodology

The problem in Eq. (7) is feasible if and only ifωi � 2ρi −1 ≤ αi Pi f ori = 1, . . . , N[19].

The realization of optimal resource sharing is accomplished by assuming ωi ≤αi Pi , for i = 1, . . . , N .

Let (p∗, q∗) denote the optimal solution to (7).Define Ai � ωiβiθi (αiγi + ωiβiθi ),Bi � (αi + ωiβi )(2ωiβiθi + αiγi ), Ci (λ) �

(αi + ωiβi )(

αi + ωiβi − 1λαiγi

)

and Di � min{

Qi ,1

ωi θi(αi Pi − ωi )

}

for i =1, . . . , N .

IfN∑

i=1Di ≤ Q, then p∗

i = ωiαi

(1 + θi Di ) and q∗i = Di ;

N

If∑

i=1

Di > Q, thenp∗i = ωi

αi

(

1 + θi q∗i

)

q∗i =

⎡

⎣

√

B2i − 4AiCi (λ) − Bi

2Ai

⎤

⎦

Di

0

(8)

Where [D]Di0 symbolizes the projection onto the interval [0, Di ], and λ > 0 is

selected such thatN∑

i=1q∗i = 0.

Substituting p∗i into the frequency band i of D2D user denoted as Rd

i (pi , qi ) leadsto:

Rdi (pi , qi ) = log

(

1 + αiγi qiαi + ωiβi + ωiβiθi qi

)

(9)

Let h(qi ) � αiγi qi(αi+ωiβi+ωiβi θi qi )

, we get:

h′′(qi ) = − 2αiγiωiβiθi (αi + ωiβi )

(αi + ωiβi + ωiβiθi qi )3 ≤ 0 (10)

The above equation represents that h(qi ) is a concave function. Therefore, Eq. (7)can be rewritten as:

6 H. M. Nethravathi and S. Akhila

Maximizeq

subjected to

N∑

i=1log

(

1 + αiγi qiαi+ωiβi+ωiβi θi qi

)

0 ≤ qi ≤ Di , i = 1, . . . , NN∑

i=1qi ≤ Q

(11)

Since the objective in Eq. (11) is increasing for each qi , then the optimal solutionwill be q∗

i = Di , and optimal solution to pi will be Rdi (pi , qi ).

3.1 Particle Swarm Optimization (PSO)

Let f : Rn → R be the function that has to be minimized and S, the numberof particles that make up the swarm. Four vectors of dimension n are defined foreach particle as attributes: ki , vi , pbesti and gbesti . The position xi represents apotential solution for the objective function, the velocity vi represents the directionand intensity of the movement of the particle, pbesti represents the best positionfound individually and gbesti the best position found by the particles in their vicinityuntil the present moment [20].

In the PSOalgorithm the steps of the canonical version of PSOare described.Afterthe initialization of its attributes, each particle proceeds to traverse the search spaceby updating its speed and position. This process occurs iteratively and culminatesafter a predetermined number of iterations T has elapsed.

3.2 PSO Algorithm Applied for Optimal ParameterCalculation in D2D Communication

Define parameters constants and variables(

T, N , c1, c2, k0i , v0i

)

Output:pi , qiandRdi according to Eq. (11)

for j = 1 to N dopbest0i ← k0iend forfor j = 1 to N doUpdate gbest0iend forfor j = 1 to T dofor i = 1 to N doUpdate vti and ktiEvaluate fitness function and update pbest tiend forfor i = 1 to N do

Optimal Resource Sharing Amongst Device-to-Device … 7

Update gbest tiend forend forreturn gbest ← mingbesti

{

f(

gbestTi)}

The updation of velocity and positions are represented by:

vt+1i, j = vti, j + c1r

t1i, j

[

pbest ti, j − kti, j] + c2r

t2i, j

[

gbest ti, j − kti, j]

(12)

kt+1i, j = kti, j + vt+1

i, j (13)

The position and frequency of the movements produced by each particle in thesearch space, as shown in Eqs. (12) and (13), is determined by the influence of threecomponents. Thefirst is the impulse or impetus that represents the force that is exertedon the particle to continue the direction it leads at the current time. The second is thecognitive component that represents the force that arises from the attraction of theparticle by its pbest , and the third is the social component that represents the forcethat arises from the attraction of the particle by the gbest of his neighbourhood [21].

4 Simulation Results

This section evaluates the performance of the proposed system under variousparameters. Tables 1 and 2 provides simulation parameters and PSO parameters.

Figure 2 shows the average throughput versus SNR plot of optimal resourcesharing with 8 cellular users in D2D communication. It can be observed that averagethroughput increases as the number of D2D SNR increase proportionally.

Figure 3 shows the average throughput versus SNR plot of PSO based resourcesharing with 8 cellular users in D2D communication. It can be observed that averagethroughput increases as the number of D2D SNR increase proportionally.

Table 1 Simulationparameters

Parameter name Value

Base station 1

No. of the frequency band 8

No. of cellular user 8

No. of D2D transmitter and receiver 1

Number of distance 50:50:450

SNR range 2:2:16

Dcell 300

dD2DRX 300

8 H. M. Nethravathi and S. Akhila

Table 2 PSO parameters Parameter name Value

Cognitive parameter 1.2

Social parameter 0.012

Swarm size 100

Inertial weight 0.0004

Number of iteration 500

Fig. 2 Avg throughputversus SNR plot of optimalresource sharing with 8cellular users in D2D

Fig. 3 Avg throughputversus SNR plot of PSObased resource sharing with8 cellular users in D2Dcommunication

Figure 4 shows the average throughput versus distance between D2D and BS plotof optimal resource sharing with 8 cellular users. As the distance between D2D andBS increases throughput decreases proportionally

Optimal Resource Sharing Amongst Device-to-Device … 9

Fig. 4 Avg throughputversus distance betweenD2D and BS plot of optimalresource sharing with 8cellular user

From Fig. 5 it can be observed that average throughput is high as approximately 7bits/s/Hz at 16 dBwhen PSO is applied. Similarly, the optimal strategy gives averagethroughput is high as approximately 3bits/s/Hz at 16 dB. It can be observed that asD2D SNR increases throughput increases and can be concluded that PSO providesbetter performance than the optimal solution.

From Fig. 6 it can be observed that average throughput is high as 14 bits/s/Hz ata minimum distance between the base station and D2D users when PSO is applied.Further, the optimal strategy gives average throughput is high as 8 bits/s/Hz atminimum distance 50 m. The observed gain in throughput is approximate 6 bit/s/Hzat 50m. It is observed that as distance increases betweenBS andD2Duser throughputdecreases. It can be concluded that PSO enabled resource utilization performs betterthan the optimal solution.

Fig. 5 Comparative result ofPSO optimized and optimalstrategy based average D2Dthroughput vs D2D SNRwith 8 cellular users

10 H. M. Nethravathi and S. Akhila

Fig. 6 Comparative resultsof PSO optimized andoptimal strategy basedaverage D2D throughputversus the distance betweenthe D2D link and BS

5 Conclusions

In this work, an optimized resource sharing algorithm for D2D communicationsusing PSO has been proposed. The resource sharing problem ismodelled by underlayuplink resources of multiple cellular users. It is found that the number of frequencyband has been optimized. Results of the simulation show good performance of theproposed algorithm with a 6 Bit/s/Hz gain achievement in throughput at a minimumdistance of D2D devices.

References

1. Karagiannis G, Altintas O, Ekici E, Heijenk G, Jarupan B, Lin K, Weil T (2011) Vehicularnetworking: a survey and tutorial on requirements, architectures, challenges, standards andsolutions. IEEE Commun Surv Tutorials 13(4):584–616, Fourth Quarter

2. Third Generation Partnership Project, Study on enhancement of 3GPP Support for 5G V2XServices, Third Generation Partnership Project, 3GPP TR 22.886 v15.0.0, Dec. 2016. http://www.3gpp.org

3. Jiang D, Delgrossi L (2008) IEEE 802.11p: Towards an International Standard for Wire-less Access in Vehicular Environments, in VTC Spring 2008—IEEE Vehicular TechnologyConference, Singapore, Singapore, pp 2036–2040

4. Araniti G, Campolo C, Condoluci M, Iera A, Molinaro (2013) LTE for Vehicular Networking:A Survey, IEEE Communications Magazine, vol. 51, no. 5, pp 148–157

5. Third Generation Partnership Project, Evolved Universal Terrestrial Radio Access (E-UTRA)and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description;