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 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.

• Preface xv1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence 1Manju Payal, Abhishek Kumar and Vicente García Díaz1.1 Introduction 11.2 Methodology of SI Framework 31.3 Composing With SI 71.4 Algorithms of the SI 71.5 Conclusion 18References 182 Introduction to IoT With Swarm Intelligence 21Anant Mishra and Jafar Tahir2.1 Introduction 212.1.1 Literature Overview 222.2 Programming 222.2.1 Basic Programming 222.2.2 Prototyping 222.3 Data Generation 232.3.1 From Where the Data Comes? 232.3.2 Challenges of Excess Data 242.3.3 Where We Store Generated Data? 242.3.4 Cloud Computing and Fog Computing 252.4 Automation 262.4.1 What is Automation? 262.4.2 How Automation is Being Used? 262.5 Security of the Generated Data 302.5.1 Why We Need Security in Our Data? 302.5.2 What Types of Data is Being Generated? 312.5.3 Protecting Different Sector Working on the Principle of IoT 322.6 Swarm Intelligence 332.6.1 What is Swarm Intelligence? 332.6.2 Classification of Swarm Intelligence 332.6.3 Properties of a Swarm Intelligence System 342.7 Scope in Educational and Professional Sector 362.8 Conclusion 37References 383 Perspectives and Foundations of Swarm Intelligence and its Application 41Rashmi Agrawal3.1 Introduction 413.2 Behavioral Phenomena of Living Beings and Inspired Algorithms 423.2.1 Bee Foraging 423.2.2 ABC Algorithm 433.2.3 Mating and Marriage 433.2.4 MBO Algorithm 443.2.5 Coakroach Behavior 443.3 Roach Infestation Optimization 453.3.1 Lampyridae Bioluminescence 453.3.2 GSO Algorithm 463.4 Conclusion 46References 474 Implication of IoT Components and Energy Management Monitoring 49Shweta Sharma, Praveen Kumar Kotturu and Prafful Chandra Narooka4.1 Introduction 494.2 IoT Components 534.3 IoT Energy Management 564.4 Implication of Energy Measurement for Monitoring 574.5 Execution of Industrial Energy Monitoring 584.6 Information Collection 594.7 Vitality Profiles Analysis 594.8 IoT-Based Smart Energy Management System 614.9 Smart Energy Management System 614.10 IoT-Based System for Intelligent Energy Management in Buildings 624.11 Smart Home for Energy Management Using IoT 62References 645 Distinct Algorithms for Swarm Intelligence in IoT 67Trapty Agarwal, Gurjot Singh, Subham Pradhan and Vikash Verma5.1 Introduction 675.2 Swarm Bird–Based Algorithms for IoT 685.2.1 Particle Swarm Optimization (PSO) 685.2.1.1 Statistical Analysis 685.2.1.2 Algorithm 685.2.1.3 Applications 695.2.2 Cuckoo Search Algorithm 695.2.2.1 Statistical Analysis 695.2.2.2 Algorithm 705.2.2.3 Applications 705.2.3 Bat Algorithm 715.2.3.1 Statistical Analysis 715.2.3.2 Algorithm 715.2.3.3 Applications 725.3 Swarm Insect–Based Algorithm for IoT 725.3.1 Ant Colony Optimization 725.3.1.1 Flowchart 735.3.1.2 Applications 735.3.2 Artificial Bee Colony 745.3.2.1 Flowchart 755.3.2.2 Applications 755.3.3 Honey-Bee Mating Optimization 755.3.3.1 Flowchart 765.3.3.2 Application 775.3.4 Firefly Algorithm 775.3.4.1 Flowchart 785.3.4.2 Application 785.3.5 Glowworm Swarm Optimization 785.3.5.1 Statistical Analysis 795.3.5.2 Flowchart 795.3.5.3 Application 80References 806 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 83Kashinath Chandelkar6.1 Introduction 836.2 Content Management System 846.3 Data Management and Mining 856.3.1 Data Life Cycle 866.3.2 Knowledge Discovery in Database 876.3.3 Data Mining vs. Data Warehousing 886.3.4 Data Mining Techniques 886.3.5 Data Mining Technologies 926.3.6 Issues in Data Mining 936.4 Introduction to Internet of Things 946.5 Swarm Intelligence Techniques 946.5.1 Ant Colony Optimization 956.5.2 Particle Swarm Optimization 956.5.3 Differential Evolution 966.5.4 Standard Firefly Algorithm 966.5.5 Artificial Bee Colony 976.6 Chapter Summary 98References 987 Healthcare Data Analytics Using Swarm Intelligence 101Palvadi Srinivas Kumar, Pooja Dixit and N. Gayathri7.1 Introduction 1017.1.1 Definition 1037.2 Intelligent Agent 1037.3 Background and Usage of AI Over Healthcare Domain 1047.4 Application of AI Techniques in Healthcare 1057.5 Benefits of Artificial Intelligence 1067.6 Swarm Intelligence Model 1077.7 Swarm Intelligence Capabilities 1087.8 How the Swarm AI Technology Works 1097.9 Swarm Algorithm 1107.10 Ant Colony Optimization Algorithm 1107.11 Particle Swarm Optimization 1127.12 Concepts for Swarm Intelligence Algorithms 1137.13 How Swarm AI is Useful in Healthcare 1147.14 Benefits of Swarm AI 1157.15 Impact of Swarm-Based Medicine 1167.16 SI Limitations 1177.17 Future of Swarm AI 1187.18 Issues and Challenges 1197.19 Conclusion 120References 1208 Swarm Intelligence for Group Objects in Wireless Sensor Networks 123Kapil Chauhan and Pramod Singh Rathore8.1 Introduction 1238.2 Algorithm 1278.3 Mechanism and Rationale of the Work 1308.3.1 Related Work 1318.4 Network Energy Model 1328.4.1 Network Model 1328.5 PSO Grouping Issue 1328.6 Proposed Method 1338.6.1 Grouping Phase 1338.6.2 Proposed Validation Record 1338.6.3 Data Transmission Stage 1338.7 Bunch Hub Refreshing Calculation Dependent on an Improved PSO 1338.8 Other SI Models 1348.9 An Automatic Clustering Algorithm Based on PSO 1358.10 Steering Rule Based on Informed Algorithm 1368.11 Routing Protocols Based on Meta-Heuristic Algorithm 1378.12 Routing Protocols for Avoiding Energy Holes 1388.13 System Model 1388.13.1 Network Model 1388.13.2 Power Model 139References 1399 Swam Intelligence–Based Resources Optimization and Analyses and Managing Data in IoT With Data Mining Technologies 143Pooja Dixit, Palvadi Srinivas Kumar and N. Gayathri9.1 Introduction 1439.1.1 Swarm Intelligence 1439.1.1.1 Swarm Biological Collective Behavior 1459.1.1.2 Swarm With Artificial Intelligence Model 1479.1.1.3 Birds in Nature 1509.1.1.4 Swarm with IoT 1539.2 IoT With Data Mining 1539.2.1 Data from IoT 1549.2.1.1 Data Mining for IoT 1549.2.2 Data Mining With KDD 1579.2.3 PSO With Data Mining 1599.3 ACO and Data Mining 1619.4 Challenges for ACO-Based Data Mining 162References 16210 Data Management and Mining Technologies to Manage and Analyze Data in IoT 165Shweta Sharma, Satya Murthy Sasubilli and Kunal Bhargava10.1 Introduction 16510.2 Data Management 16610.3 Data Lifecycle of IoT 16710.4 Procedures to Implement IoT Data Management 17110.5 Industrial Data Lifecycle 17310.6 Industrial Data Management Framework of IoT 17410.6.1 Physical Layer 17410.6.2 Correspondence Layer 17510.6.3 Middleware Layer 17510.7 Data Mining 17510.7.1 Functionalities of Data Mining 17910.7.2 Classification 18010.8 Clustering 18210.9 Affiliation Analysis 18210.10 Time Series Analysis 183References 18511 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 189Kapil Chauhan and Vishal Dutt11.1 Introduction 19011.2 Information Mining Functionalities 19211.2.1 Classification 19211.2.2 Clustering 19211.3 Data Mining Using Ant Colony Optimization 19311.3.1 Enormous Information Investigation 19411.3.2 Data Grouping 19511.4 Computing With Ant-Based 19611.4.1 Biological Background 19611.5 Related Work 19711.6 Contributions 19811.7 SI in Enormous Information Examination 19811.7.1 Handling Enormous Measure of Information 19911.7.2 Handling Multidimensional Information 19911.8 Requirements and Characteristics of IoT Data 20011.8.1 IoT Quick and Gushing Information 20011.8.2 IoT Big Information 20011.9 Conclusion 201References 20212 Swarm Intelligence–Based Energy-Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications 207Devika G., Ramesh D. and Asha Gowda Karegowda12.1 Introduction 20812.1.1 Scope of Work 20912.1.2 Related Works 20912.1.3 Challenges in WSNs 21012.1.4 Major Highlights of the Chapter 21312.2 SI-Based Clustering Techniques 21312.2.1 Growth of SI Algorithms and Characteristics 21412.2.2 Typical SI-Based Clustering Algorithms 21912.2.3 Comparison of SI Algorithms and Applications 21912.3 WSN SI Clustering Applications 21912.3.1 WSN Services 23312.3.2 Clustering Objectives for WSN Applications 23312.3.3 SI Algorithms for WSN: Overview 23412.3.4 The Commonly Applied SI-Based WSN Clusterings 23512.3.4.1 ACO-Based WSN Clustering 23512.3.4.2 PSO-Based WSN Clustering 23712.3.4.3 ABC-Based WSN Clustering 24012.3.4.4 CS Cuckoo–Based WSN Clustering 24112.3.4.5 Other SI Technique-Based WSN Clustering 24212.4 Challenges and Future Direction 24612.5 Conclusions 247References 25313 Swarm Intelligence for Clustering in Wireless Sensor Networks 263Preeti Sethi13.1 Introduction 26313.2 Clustering in Wireless Sensor Networks 26413.3 Use of Swarm Intelligence for Clustering in WSN 26613.3.1 Mobile Agents: Properties and Behavior 26613.3.2 Benefits of Using Mobile Agents 26713.3.3 Swarm Intelligence–Based Clustering Approach 26813.4 Conclusion 272References 27214 Swarm Intelligence for Clustering in Wi-Fi Networks 275Astha Parihar and Ramkishore Kuchana14.1 Introduction 27514.1.1 Wi-Fi Networks 27514.1.2 Wi-Fi Networks Clustering 27714.2 Power Conscious Fuzzy Clustering Algorithm (PCFCA) 27814.2.1 Adequate Cluster Head Selection in PCFCA 27814.2.2 Creation of Clusters 27914.2.3 Execution Assessment of PCFCA 28214.3 Vitality Collecting in Remote Sensor Systems 28214.3.1 Power Utilization 28314.3.2 Production of Energy 28314.3.3 Power Cost 28414.3.4 Performance Representation of EEHC 28414.4 Adequate Power Circular Clustering Algorithm (APRC) 28414.4.1 Case-Based Clustering in Wi-Fi Networks 28414.4.2 Circular Clustering Outlook 28414.4.3 Performance Representation of APRC 28514.5 Modifying Scattered Clustering Algorithm (MSCA) 28614.5.1 Equivalence Estimation in Data Sensing 28614.5.2 Steps in Modifying Scattered Clustering Algorithm (MSCA) 28614.5.3 Performance Evaluation of MSCA 28714.6 Conclusion 288References 28815 Support Vector in Healthcare Using SVM/PSO in Various Domains: A Review 291Vishal Dutt, Pramod Singh Rathore and Kapil Chauhan15.1 Introduction 29115.2 The Fundamental PSO 29215.2.1 Algorithm for PSO 29315.3 The Support Vector 29315.3.1 SVM in Regression 29915.3.2 SVM in Clustering 30015.3.3 Partition Clustering 30115.3.4 Hierarchical Clustering 30115.3.5 Density-Based Clustering 30215.3.6 PSO in Clustering 30315.4 Conclusion 304References 30416 IoT-Based Healthcare System to Monitor the Sensor’s Data of MWBAN 309Rani Kumari and ParmaNand16.1 Introduction 31016.1.1 Combination of AI and IoT in Real Activities 31016.2 Related Work 31116.3 Proposed System 31216.3.1 AI and IoT in Medical Field 31216.3.2 IoT Features in Healthcare 31316.3.2.1 Wearable Sensing Devices With Physical Interface for Real World 31316.3.2.2 Input Through Organized Information to the Sensors 31316.3.2.3 Small Sensor Devices for Input and Output 31416.3.2.4 Interaction With Human Associated Devices 31416.3.2.5 To Control Physical Activity and Decision 31416.3.3 Approach for Sensor’s Status of Patient 31516.4 System Model 31516.4.1 Solution Based on Heuristic Iterative Method 31716.5 Challenges of Cyber Security in Healthcare With IoT 32016.6 Conclusion 321References 32117 Effectiveness of Swarm Intelligence for Handling Fault-Tolerant Routing Problem in IoT 325Arpit Kumar Sharma, Kishan Kanhaiya and Jaisika Talwar17.1 Introduction 32517.1.1 Meaning of Swarm and Swarm Intelligence 32617.1.2 Stability 32717.1.3 Technologies of Swarm 32817.2 Applications of Swarm Intelligence 32817.2.1 Flight of Birds Elaborations 32917.2.2 Honey Bees Elaborations 32917.3 Swarm Intelligence in IoT 33017.3.1 Applications 33117.3.2 Human Beings vs. Swarm 33217.3.3 Use of Swarms in Engineering 33217.4 Innovations Based on Swarm Intelligence 33317.4.1 Fault Tolerance in IoT 33417.5 Energy-Based Model 33517.5.1 Basic Approach of Fault Tolerance With Its Network Architecture 33517.5.2 Problem of Fault Tolerance Using Different Algorithms 33717.6 Conclusion 340References 34018 Smart Epilepsy Detection System Using Hybrid ANN-PSO Network 343Jagriti Saini and Maitreyee Dutta18.1 Introduction 34318.2 Materials and Methods 34518.2.1 Experimental Data 34518.2.2 Data Pre-Processing 34518.2.3 Feature Extraction 34618.2.4 Relevance of Extracted Features 34618.3 Proposed Epilepsy Detection System 34918.4 Experimental Results of ANN-Based System 35018.5 MSE Reduction Using Optimization Techniques 35118.6 Hybrid ANN-PSO System for Epilepsy Detection 35318.7 Conclusion 355References 356Index 359