Smart Sensor Networks Using AI for Industry 4.0: Applications and New Opportunities
- Length: 262 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-10-11
- ISBN-10: 0367702126
- ISBN-13: 9780367702120
- Sales Rank: #0 (See Top 100 Books)
Smart Sensor Networks (WSNs) using AI have left a mark on the lives of all by aiding in various sectors, such as manufacturing, education, healthcare, and monitoring of the environment and industries. This book covers recent AI applications and explores aspects of modern sensor technologies and the systems needed to operate them.
The book reviews the fundamental concepts of gathering, processing, and analyzing different AI-based models and methods. It covers recent WSN techniques for the purpose of effective network management on par with the standards laid out by international organizations in related fields and focuses on both core concepts along with major applicational areas.
The book will be used by technical developers, academicians, data sciences, industrial professionals, researchers, and students interested in the latest innovations on problem-oriented processing techniques in sensor networks using IoT and evolutionary computer applications for Industry 4.0.
Half Title Series Page Title Page Copyright Page Table of Contents Preface About the Editors Contributors Chapter 1: Optimization of Wireless Sensor Networks using Bio-Inspired Algorithm 1.1 Introduction 1.2 Literature Review 1.3 Genetic Algorithm (GA) 1.3.1 Roulette Wheel 1.3.2 Probability of Crossover (P c) 1.3.3 Probability of Mutation (P m) 1.3.4 Elitism 1.4 Ant Colony Optimization (ACO) 1.4.1 Mathematical Model of ACO 1.5 Particle Swarm Optimization Algorithm (PSO) 1.5.1 Mathematical Model of PSO 1.6 Implementation of Genetic Algorithm in a WSN 1.6.1 Simulation Results 1.7 Implementation of Ant Colony Optimization Algorithm in a WSN 1.7.1 Simulation Result 1.8 Implementation of Particle Swarm Optimization Algorithm in a WSN 1.8.1 Simulation Result 1.9 Comparative Analysis 1.10 Conclusion and Future Work References Chapter 2: An Improved Genetic Algorithm with Haar Lifting for Optimal Sensor Deployment in Target Covers Based Wireless Sensor Networks 2.1 Introduction 2.2 Related Works 2.3 Problem Formulation 2.4 Haar Lifting Scheme 2.5 GA-2D Haar Lifting Optimal Sensor Placement 2.6 Simulation Results 2.7 Conclusion References Chapter 3: Lifetime Enhancement of Wireless Sensor Network Using Artificial Intelligence Techniques 3.1 Introduction 3.2 Issues in Wireless Sensor Network 3.3 Factors Deciding WSN Lifetime 3.4 Artificial Intelligence Technique 3.4.1 Why AI Is Required in WSN 3.5 WSN Lifetime Enhancement Using AI 3.5.1 Data Aggregation Using AI 3.5.2 Coverage and Connectivity Determination Using AI 3.5.3 Node Localization Using AI 3.5.4 Routing Using AI 3.5.5 Scheduling Using AI 3.5.6 Node Deployment Using AI 3.6 Conclusion References Chapter 4: Research Issues of Information Security Using Blockchain Technique in Multiple Media WSNs: A Communication Technique Perceptive 4.1 Introduction: Background and Driving Forces 4.2 Background and Motivation 4.3 Application of Blockchain-Based WSN Communication 4.3.1 RFID-Based Food Supply Chain 4.3.2 Underwater Sensor Network Security 4.3.3 Telecom Roaming, Fraud, and Overage Management 4.4 Blockchain Key Characteristics 4.5 Need of Blockchain for Developing Countries 4.6 Real Life Uses of Blockchain 4.6.1 Blockchain for Humanities Aid 4.6.2 Bitcoin Cryptocurrency 4.6.3 Incent Customer Retention 4.8 Conclusion 4.7 Disadvantages of Blockchain References Chapter 5: Modified Artificial Fish Swarm Optimization Based Clustering in Wireless Sensor Network 5.1 Introduction 5.2 Related Work 5.3 Proposed Methodology 5.3.1 Clustering the Sensor Node 5.3.1.1 Weighted k-Means Clustering Algorithm 5.3.2 Cluster Head Selection 5.3.2.1 Modified Artificial Fish Swarm Algorithm (MAFS) 5.3.2.1.1 Initialization 5.3.2.1.2 Oppositional Behavior 5.3.2.1.2.1 Prey Behavior 5.3.2.1.2.2 Swarm Behavior 5.3.2.1.2.3 Follow Behavior 5.3.2.1.2.4 Termination Criteria 5.4 Performance Metrics 5.4.1 End-to-End Delay 5.4.2 Throughput 5.4.3 Network Lifetime 5.5 Comparative Analysis 5.5.1 Performance Evaluation 5.6 Conclusion References Chapter 6: Survey: Data Prediction Model in Wireless Sensor Networks Using Machine Learning and Optimization Methods 6.1 Introduction 6.2 Machine Learning (ML) Algorithms 6.3 Data Prediction Models in WSN 6.3.1 PCA 6.3.2 ARIMA Prediction Model 6.3.3 Multiple Linear Regression Models 6.3.4 Support Vector Machine 6.3.5 Ensemble Methods 6.3.6 Artificial Neural Network (ANN) 6.3.7 Multilayer Perceptron (MLP) 6.3.8 Long Short Term Memory (LSTM) 6.4 Hybrid Models 6.4.1 PSO-SVM 6.4.2 FFA-RF Model 6.4.3 HHO-ANN 6.5 Conclusion Acknowledgments References Chapter 7: Strategic Sink Mobility Based on Particle Swarm Optimization in Wireless Sensor Network 7.1 Introduction 7.1.1 Contributions 7.2 Related Work 7.3 The Operation of Proposed Work 7.3.1 System Consideration of Proposed Work 7.3.1.1 Network Model Assumptions Considered for Proposed Work 7.4 Simulation Setting Scenario 7.4.1 Simulation Parameters Values 7.4.2 Result and Analysis 7.5 Conclusion and Future Scope References Chapter 8: A Study on Outlier Detection Techniques for Wireless Sensor Network with CNN Approach 8.1 Introduction: Wireless Sensor Networks (WSN) 8.1.1 Application of WSN 8.1.2 WSN Design Challenges 8.2 Outliers 8.2.1 Definitions of Outliers 8.2.2 Types of Outliers 8.2.3 Sources of Outliers 8.2.4 Degree of Being an Outlier 8.2.5 Dimension of Outliers 8.2.6 Data Correlation 8.2.7 Architectural Structure 8.2.8 Issues of Outlier Detection 8.2.9 Use of Outlier Detection in WSN 8.3 Outlier Detection Methods 8.3.1 Statistical-Based Approach 8.3.1.1 Kernel-Based Approach 8.3.1.2 Nearest Neighbor-Based Approach 8.3.2 Clustering-Based Approach 8.3.3 Classification-Based Approach 8.3.4 Spectral Decomposition-Based Approach 8.3.5 Artificial Intelligence-Based Approach 8.4 Outlier Detection Using CNN 8.4.1 Proposed Approach 8.4.2 Experimental Setup 8.4.3 Evaluation Metric 8.5 Conclusion References Chapter 9: NEECH: A Novel Energy-Efficient Cluster Head Selecting Protocol in a Wireless Sensor Network 9.1 Introduction 9.1.1 Major Contributions 9.2 Related Work 9.3 Working of NEECH 9.3.1 Network Assumptions for NEECH 9.3.2 Radio Energy Consumption Model 9.3.3 Operation Steps of NEECH 9.4 Results and Discussion 9.4.1 Performance Metrics 9.5 Conclusion References Chapter 10: An Efficient Model for Toxic Gas Detection and Monitoring Using Cloud and Sensor Network 10.1 Introduction 10.2 Literature Review 10.3 Proposed Gas Detection and Monitoring Model 10.4 Proposed Gas Detection Algorithm 10.5 Implementation 10.5.1 Booting the Raspberry Pi 10.5.2 Securing All Hardware Connections 10.5.3 Importing Sensor Data onto the Cloud Platform 10.5.4 Enabling Twillio 10.5.5 Creating an Application 10.6 Results 10.7 Conclusion References Chapter 11: Particle Swarm Intelligence-Based Localization Algorithms in Wireless Sensor Networks 11.1 Introduction 11.1.1 Objectives of the Chapter 11.1.2 Scope of the Chapter 11.2 Existing Localization Algorithms 11.3 Cooperative Distributive Particle Swarm Optimization (CDPSO) 11.3.1 Simulation and Results Analysis 11.3.1.1 Simulation Setup 11.3.1.2 Results Analysis 11.3.2 PSO Assisted AKF Algorithm 11.3.3 Simulation and Results 11.3.4 CDPSO Localized Routing with Optimum References 11.3.5 Simulation Results and Analysis 11.3.6 Location Tracking of Patients Using PSO-AKF 11.3.7 Simulation Results and Analysis 11.4 Conclusion References Chapter 12: A Review on Defense Strategy Security Mechanism for Sensor Network 12.1 Introduction 12.2 Game Theory in Wireless Sensor Networks 12.2.1 Classification of Games 12.2.1.1 Noncooperative Games 12.2.1.2 Cooperative Games 12.2.1.3 Cooperation Enforcement Games 12.2.1.4 Other Classification 12.3 Security Defense Strategy Attack Graph 12.3.1 Game Theory for Sybil Attack 12.3.2 Defense Strategy for Denial of Service (DDoS) 12.3.3 Defense Mechanisms of Transport/Network Layer 12.3.3.1 Source-Based Mechanism 12.3.3.2 Routing Defense Mechanism Game Theory for Sensor 12.4 Open Issues and Challenges 12.5 Conclusion References Chapter 13: Securing Wireless Multimedia Objects Through Machine Learning Techniques in Wireless Sensor Networks 13.1 Introduction: Wireless Network 13.1.1 Wireless Sensor Network 13.1.2 Objectives of Wireless Sensor Network (WSN) 13.1.2.1 Coverage 13.1.2.2 Differentiated Detection Levels 13.1.2.3 Network Connectivity 13.1.2.4 Network Life Span 13.1.2.5 Data Fidelity 13.1.2.6 Energy Efficiency 13.1.2.7 Imperfection Tolerance and Load Balancing 13.1.3 WSN Relevance 13.1.3.1 Armed Forces Applications 13.1.4 WSN Features 13.1.4.1 Power Efficiency in Wireless Sensor Networks 13.1.4.2 WSN Scalability 13.1.4.3 WSNs Responsiveness 13.1.4.4 Steadfastness in Wireless Sensor Networks 13.1.4.5 WSN Mobility 13.1.5 SN Categories 13.1.5.1 Ground-Based WSNs 13.1.5.2 Underground-Based WSNs 13.1.5.3 Underwater Based WSNs 13.1.5.4 Multimedia WSNs (M-WSNs) 13.1.6 Unauthorized Access Point Detection in WSNs 13.1.6.1 Fake Access Point 13.1.6.2 Rogue Access Point 13.1.7 Wireless Multimedia Sensor Networks 13.1.8 Literature Survey 13.1.9 Paradigms of Intelligent Authentication for Efficient Multimedia Security 13.1.9.1 Parametric Learning Methods 13.1.9.2 Nonparametric Learning Methods 13.1.9.3 Supervised Learning Algorithms 13.1.10 Unsupervised Learning Algorithms 13.1.10.1 Reinforcement Learning Algorithms 13.2 Implementation of Machine Learning Algorithms in Multimedia Security 13.2.1 Supervised Learning Algorithm 13.2.2 Reinforcement Learning Algorithm 13.2.3 Unsupervised Learning Algorithm 13.3 Issues Related to the Present Approaches 13.3.1 Inconsistency 13.3.2 Obscurity in Pre-Designing 13.3.3 Uninterrupted Security to Genuine Components 13.3.4 Uninterrupted Security to Genuine Components: Time-Divergent Features 13.3.5 Dealing with Varied Network 13.3.6 Incorporating Authentication Protocols 13.4 Conclusion References Chapter 14: Low Power Communication in Wireless Sensor Networks and IoT 14.1 Introduction 14.2 Long Range Communication (LoRa) 14.3 Zigbee 14.4 IPv6 Low Power Personal Area Network (6LoWPAN) 14.5 Narrow Band Internet of Things (NBIoT) 14.6 SIGFOX 14.7 Conclusion Acknowledgment References Chapter 15: Localization Using Bat Algorithm in Wireless Sensor Network 15.1 Introduction 15.1.1 Problem Statement 15.1.2 Major Contributions 15.2 Literature Work 15.2.1 Bat Algorithm 15.3 Operational Functioning of LBA 15.3.1 Network Model 15.3.2 Simulation Parameters 15.4 Results and Discussion: Performance Evaluating Metrics 15.5 Summary 15.6 Conclusion References Index
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