Swarm Intelligence Optimization Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected]) Swarm Intelligence Optimization Algorithms and Applications Edited by Abhishek Kumar, Pramod Singh Rathore, Vicente Garcia Diaz and Rashmi Agrawal This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2021 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. 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Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-77874-5 Cover image: Pixabay.Com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1 Contents Preface xv 1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence 1 Manju Payal, Abhishek Kumar and Vicente García Díaz 1.1 Introduction 1 1.2 Methodology of SI Framework 3 1.3 Composing With SI 7 1.4 Algorithms of the SI 7 1.5 Conclusion 18 References 18 2 Introduction to IoT With Swarm Intelligence 21 Anant Mishra and Jafar Tahir 2.1 Introduction 21 2.1.1 Literature Overview 22 2.2 Programming 22 2.2.1 Basic Programming 22 2.2.2 Prototyping 22 2.3 Data Generation 23 2.3.1 From Where the Data Comes? 23 2.3.2 Challenges of Excess Data 24 2.3.3 Where We Store Generated Data? 24 2.3.4 Cloud Computing and Fog Computing 25 2.4 Automation 26 2.4.1 What is Automation? 26 2.4.2 How Automation is Being Used? 26 2.5 Security of the Generated Data 30 2.5.1 Why We Need Security in Our Data? 30 2.5.2 What Types of Data is Being Generated? 31 2.5.3 Protecting Different Sector Working on the Principle of IoT 32 2.6 Swarm Intelligence 33 2.6.1 What is Swarm Intelligence? 33 2.6.2 Classification of Swarm Intelligence 33 2.6.3 Properties of a Swarm Intelligence System 34 2.7 Scope in Educational and Professional Sector 36 2.8 Conclusion 37 References 38 v vi Contents 3 Perspectives and Foundations of Swarm Intelligence and its Application 41 Rashmi Agrawal 3.1 Introduction 41 3.2 Behavioral Phenomena of Living Beings and Inspired Algorithms 42 3.2.1 Bee Foraging 42 3.2.2 ABC Algorithm 43 3.2.3 Mating and Marriage 43 3.2.4 MBO Algorithm 44 3.2.5 Coakroach Behavior 44 3.3 Roach Infestation Optimization 45 3.3.1 Lampyridae Bioluminescence 45 3.3.2 GSO Algorithm 46 3.4 Conclusion 46 References 47 4 Implication of IoT Components and Energy Management Monitoring 49 Shweta Sharma, Praveen Kumar Kotturu and Prafful Chandra Narooka 4.1 Introduction 49 4.2 IoT Components 53 4.3 IoT Energy Management 56 4.4 Implication of Energy Measurement for Monitoring 57 4.5 Execution of Industrial Energy Monitoring 58 4.6 Information Collection 59 4.7 Vitality Profiles Analysis 59 4.8 IoT-Based Smart Energy Management System 61 4.9 Smart Energy Management System 61 4.10 IoT-Based System for Intelligent Energy Management in Buildings 62 4.11 Smart Home for Energy Management Using IoT 62 References 64 5 Distinct Algorithms for Swarm Intelligence in IoT 67 Trapty Agarwal, Gurjot Singh, Subham Pradhan and Vikash Verma 5.1 Introduction 67 5.2 Swarm Bird–Based Algorithms for IoT 68 5.2.1 Particle Swarm Optimization (PSO) 68 5.2.1.1 Statistical Analysis 68 5.2.1.2 Algorithm 68 5.2.1.3 Applications 69 5.2.2 Cuckoo Search Algorithm 69 5.2.2.1 Statistical Analysis 69 5.2.2.2 Algorithm 70 5.2.2.3 Applications 70 5.2.3 Bat Algorithm 71 5.2.3.1 Statistical Analysis 71 5.2.3.2 Algorithm 71 5.2.3.3 Applications 72 Contents vii 5.3 Swarm Insect–Based Algorithm for IoT 72 5.3.1 Ant Colony Optimization 72 5.3.1.1 Flowchart 73 5.3.1.2 Applications 73 5.3.2 Artificial Bee Colony 74 5.3.2.1 Flowchart 75 5.3.2.2 Applications 75 5.3.3 Honey-Bee Mating Optimization 75 5.3.3.1 Flowchart 76 5.3.3.2 Application 77 5.3.4 Firefly Algorithm 77 5.3.4.1 Flowchart 78 5.3.4.2 Application 78 5.3.5 Glowworm Swarm Optimization 78 5.3.5.1 Statistical Analysis 79 5.3.5.2 Flowchart 79 5.3.5.3 Application 80 References 80 6 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 83 Kashinath Chandelkar 6.1 Introduction 83 6.2 Content Management System 84 6.3 Data Management and Mining 85 6.3.1 Data Life Cycle 86 6.3.2 Knowledge Discovery in Database 87 6.3.3 Data Mining vs. Data Warehousing 88 6.3.4 Data Mining Techniques 88 6.3.5 Data Mining Technologies 92 6.3.6 Issues in Data Mining 93 6.4 Introduction to Internet of Things 94 6.5 Swarm Intelligence Techniques 94 6.5.1 Ant Colony Optimization 95 6.5.2 Particle Swarm Optimization 95 6.5.3 Differential Evolution 96 6.5.4 Standard Firefly Algorithm 96 6.5.5 Artificial Bee Colony 97 6.6 Chapter Summary 98 References 98 7 Healthcare Data Analytics Using Swarm Intelligence 101 Palvadi Srinivas Kumar, Pooja Dixit and N. Gayathri 7.1 Introduction 101 7.1.1 Definition 103 7.2 Intelligent Agent 103 viii Contents 7.3 Background and Usage of AI Over Healthcare Domain 104 7.4 Application of AI Techniques in Healthcare 105 7.5 Benefits of Artificial Intelligence 106 7.6 Swarm Intelligence Model 107 7.7 Swarm Intelligence Capabilities 108 7.8 How the Swarm AI Technology Works 109 7.9 Swarm Algorithm 110 7.10 Ant Colony Optimization Algorithm 110 7.11 Particle Swarm Optimization 112 7.12 Concepts for Swarm Intelligence Algorithms 113 7.13 How Swarm AI is Useful in Healthcare 114 7.14 Benefits of Swarm AI 115 7.15 Impact of Swarm-Based Medicine 116 7.16 SI Limitations 117 7.17 Future of Swarm AI 118 7.18 Issues and Challenges 119 7.19 Conclusion 120 References 120 8 Swarm Intelligence for Group Objects in Wireless Sensor Networks 123 Kapil Chauhan and Pramod Singh Rathore 8.1 Introduction 123 8.2 Algorithm 127 8.3 Mechanism and Rationale of the Work 130 8.3.1 Related Work 131 8.4 Network Energy Model 132 8.4.1 Network Model 132 8.5 PSO Grouping Issue 132 8.6 Proposed Method 133 8.6.1 Grouping Phase 133 8.6.2 Proposed Validation Record 133 8.6.3 Data Transmission Stage 133 8.7 Bunch Hub Refreshing Calculation Dependent on an Improved PSO 133 8.8 Other SI Models 134 8.9 An Automatic Clustering Algorithm Based on PSO 135 8.10 Steering Rule Based on Informed Algorithm 136 8.11 Routing Protocols Based on Meta-Heuristic Algorithm 137 8.12 Routing Protocols for Avoiding Energy Holes 138 8.13 System Model 138 8.13.1 Network Model 138 8.13.2 Power Model 139 References 139 Contents ix 9 Swam Intelligence–Based Resources Optimization and Analyses and Managing Data in IoT With Data Mining Technologies 143 Pooja Dixit, Palvadi Srinivas Kumar and N. Gayathri 9.1 Introduction 143 9.1.1 Swarm Intelligence 143 9.1.1.1 Swarm Biological Collective Behavior 145 9.1.1.2 Swarm With Artificial Intelligence Model 147 9.1.1.3 Birds in Nature 150 9.1.1.4 Swarm with IoT 153 9.2 IoT With Data Mining 153 9.2.1 Data from IoT 154 9.2.1.1 Data Mining for IoT 154 9.2.2 Data Mining With KDD 157 9.2.3 PSO With Data Mining 159 9.3 ACO and Data Mining 161 9.4 Challenges for ACO-Based Data Mining 162 References 162 10 Data Management and Mining Technologies to Manage and Analyze Data in IoT 165 Shweta Sharma, Satya Murthy Sasubilli and Kunal Bhargava 10.1 Introduction 165 10.2 Data Management 166 10.3 Data Lifecycle of IoT 167 10.4 Procedures to Implement IoT Data Management 171 10.5 Industrial Data Lifecycle 173 10.6 Industrial Data Management Framework of IoT 174 10.6.1 Physical Layer 174 10.6.2 Correspondence Layer