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 -- 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 -- 6.5.3 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 -- 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 -- 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 175 -- 10.6.3 Middleware Layer 175 -- 10.7 Data Mining 175 -- 10.7.1 Functionalities of Data Mining 179 -- 10.7.2 Classification 180 -- 10.8 Clustering 182 -- 10.9 Affiliation Analysis 182 -- 10.10 Time Series Analysis 183 -- References 185 -- 11 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 189 Kapil Chauhan and Vishal Dutt -- 11.1 Introduction 190 -- 11.2 Information Mining Functionalities 192 -- 11.2.1 Classification 192 -- 11.2.2 Clustering 192 -- 11.3 Data Mining Using Ant Colony Optimization 193 -- 11.3.1 Enormous Information Investigation 194 -- 11.3.2 Data Grouping 195 -- 11.4 Computing With Ant-Based 196 -- 11.4.1 Biological Background 196 -- 11.5 Related Work 197 -- 11.6 Contributions 198 -- 11.7 SI in Enormous Information Examination 198 -- 11.7.1 Handling Enormous Measure of Information 199 -- 11.7.2 Handling Multidimensional Information 199 -- 11.8 Requirements and Characteristics of IoT Data 200 -- 11.8.1 IoT Quick and Gushing Information 200 -- 11.8.2 IoT Big Information 200 -- 11.9 Conclusion 201 -- References 202 -- 12 Swarm Intelligence–Based Energy-Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications 207 Devika G., Ramesh D.

and Asha Gowda Karegowda -- 12.1 Introduction 208 -- 12.1.1 Scope of Work 209 -- 12.1.2 Related Works 209 -- 12.1.3 Challenges in WSNs 210 -- 12.1.4 Major Highlights of the Chapter 213 -- 12.2 SI-Based Clustering Techniques 213 -- 12.2.1 Growth of SI Algorithms and Characteristics 214 -- 12.2.2 Typical SI-Based Clustering Algorithms 219 -- 12.2.3 Comparison of SI Algorithms and Applications 219 -- 12.3 WSN SI Clustering Applications 219 -- 12.3.1 WSN Services 233 -- 12.3.2 Clustering Objectives for WSN Applications 233 -- 12.3.3 SI Algorithms for WSN: Overview 234 -- 12.3.4 The Commonly Applied SI-Based WSN Clu ...