Table of 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

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 Clusterings 235

12.3.4.1 ACO-Based WSN Clustering 235

12.3.4.2 PSO-Based WSN Clustering 237

12.3.4.3 ABC-Based WSN Clustering 240

12.3.4.4 CS Cuckoo–Based WSN Clustering 241

12.3.4.5 Other SI Technique-Based WSN Clustering 242

12.4 Challenges and Future Direction 246

12.5 Conclusions 247

References 253

13 Swarm Intelligence for Clustering in Wireless Sensor Networks 263
Preeti Sethi

13.1 Introduction 263

13.2 Clustering in Wireless Sensor Networks 264

13.3 Use of Swarm Intelligence for Clustering in WSN 266

13.3.1 Mobile Agents: Properties and Behavior 266

13.3.2 Benefits of Using Mobile Agents 267

13.3.3 Swarm Intelligence–Based Clustering Approach 268

13.4 Conclusion 272

References 272

14 Swarm Intelligence for Clustering in Wi-Fi Networks 275
Astha Parihar and Ramkishore Kuchana

14.1 Introduction 275

14.1.1 Wi-Fi Networks 275

14.1.2 Wi-Fi Networks Clustering 277

14.2 Power Conscious Fuzzy Clustering Algorithm (PCFCA) 278

14.2.1 Adequate Cluster Head Selection in PCFCA 278

14.2.2 Creation of Clusters 279

14.2.3 Execution Assessment of PCFCA 282

14.3 Vitality Collecting in Remote Sensor Systems 282

14.3.1 Power Utilization 283

14.3.2 Production of Energy 283

14.3.3 Power Cost 284

14.3.4 Performance Representation of EEHC 284

14.4 Adequate Power Circular Clustering Algorithm (APRC) 284

14.4.1 Case-Based Clustering in Wi-Fi Networks 284

14.4.2 Circular Clustering Outlook 284

14.4.3 Performance Representation of APRC 285

14.5 Modifying Scattered Clustering Algorithm (MSCA) 286

14.5.1 Equivalence Estimation in Data Sensing 286

14.5.2 Steps in Modifying Scattered Clustering Algorithm (MSCA) 286

14.5.3 Performance Evaluation of MSCA 287

14.6 Conclusion 288

References 288

15 Support Vector in Healthcare Using SVM/PSO in Various Domains: A Review 291
Vishal Dutt, Pramod Singh Rathore and Kapil Chauhan

15.1 Introduction 291

15.2 The Fundamental PSO 292

15.2.1 Algorithm for PSO 293

15.3 The Support Vector 293

15.3.1 SVM in Regression 299

15.3.2 SVM in Clustering 300

15.3.3 Partition Clustering 301

15.3.4 Hierarchical Clustering 301

15.3.5 Density-Based Clustering 302

15.3.6 PSO in Clustering 303

15.4 Conclusion 304

References 304

16 IoT-Based Healthcare System to Monitor the Sensor’s Data of MWBAN 309
Rani Kumari and ParmaNand

16.1 Introduction 310

16.1.1 Combination of AI and IoT in Real Activities 310

16.2 Related Work 311

16.3 Proposed System 312

16.3.1 AI and IoT in Medical Field 312

16.3.2 IoT Features in Healthcare 313

16.3.2.1 Wearable Sensing Devices With Physical Interface for Real World 313

16.3.2.2 Input Through Organized Information to the Sensors 313

16.3.2.3 Small Sensor Devices for Input and Output 314

16.3.2.4 Interaction With Human Associated Devices 314

16.3.2.5 To Control Physical Activity and Decision 314

16.3.3 Approach for Sensor’s Status of Patient 315

16.4 System Model 315

16.4.1 Solution Based on Heuristic Iterative Method 317

16.5 Challenges of Cyber Security in Healthcare With IoT 320

16.6 Conclusion 321

References 321

17 Effectiveness of Swarm Intelligence for Handling Fault-Tolerant Routing Problem in IoT 325
Arpit Kumar Sharma, Kishan Kanhaiya and Jaisika Talwar

17.1 Introduction 325

17.1.1 Meaning of Swarm and Swarm Intelligence 326

17.1.2 Stability 327

17.1.3 Technologies of Swarm 328

17.2 Applications of Swarm Intelligence 328

17.2.1 Flight of Birds Elaborations 329

17.2.2 Honey Bees Elaborations 329

17.3 Swarm Intelligence in IoT 330

17.3.1 Applications 331

17.3.2 Human Beings vs. Swarm 332

17.3.3 Use of Swarms in Engineering 332

17.4 Innovations Based on Swarm Intelligence 333

17.4.1 Fault Tolerance in IoT 334

17.5 Energy-Based Model 335

17.5.1 Basic Approach of Fault Tolerance With Its Network Architecture 335

17.5.2 Problem of Fault Tolerance Using Different Algorithms 337

17.6 Conclusion 340

References 340

18 Smart Epilepsy Detection System Using Hybrid ANN-PSO Network 343
Jagriti Saini and Maitreyee Dutta

18.1 Introduction 343

18.2 Materials and Methods 345

18.2.1 Experimental Data 345

18.2.2 Data Pre-Processing 345

18.2.3 Feature Extraction 346

18.2.4 Relevance of Extracted Features 346

18.3 Proposed Epilepsy Detection System 349

18.4 Experimental Results of ANN-Based System 350

18.5 MSE Reduction Using Optimization Techniques 351

18.6 Hybrid ANN-PSO System for Epilepsy Detection 353

18.7 Conclusion 355

References 356

Index 359

About the Author

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 García Díaz 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.