Smart Sensor Networks Using AI for Industry 4.0: Applications and New Opportunities (Advances in Intelligent Decision-Making, Systems Engineering, and Project Management)

✍ Scribed by Soumya Ranjan Nayak (editor), Biswa Mohan Sahoo (editor), Muthukumaran Malarvel (editor), Jibitesh Mishra (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 263
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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