Swarm Intelligence Optimization

Abhishek Kumar gained his PhD in computer science from the University of Madras, India in 2019. He is assistant professor at Chitkara University and has more than 80 publications in peer-reviewed international and national journals, books & conferences His research interests include artificial intelligence, image processing, computer vision, data mining and machine learning. Pramod Singh Rathore has a MTech in Computer Science & Engineering from the Government Engineering College Ajmer, Rajasthan Technical University, Kota India, where he is now an assistant professor. He has more than 60 papers, chapters, and a book to his credit and his research interests are in networking cloud and IoT. Vicente Garcia Diaz obtained his PhD in Computer Science in 2011 at the University of Oviedo, Spain where he is now an associate professor in the School of Computer Science. He has published more than 100 publications and his research interests include domain-specific languages, e-learning, decision support systems. Rashmi Agrawal obtained her PhD in Computer Applications in 2016 from Manav Rachna International University Faridabad, India, where she is now a professor in the Department of Computer Applications. Her research area includes data mining and artificial intelligence and she has published more than 65 publications to her credit.

Preface xv 1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence 1 Manju Payal, Abhishek Kumar and Vicente Garcia Diaz 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 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 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.