Metaheuristics and Reinforcement Techniques for Smart Sensor Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Author Biography
Chapter 1
	Chapter 1 Sensor Networks Overview
		1.1 Low-power Sensors
			1.1.1 Temperature Sensor
			1.1.2 
Proximity Sensor
			1.1.3 
Infrared Sensor
			1.1.4 
Ultrasonic Sensor
			1.1.5 Smoke and Gas Sensors
			1.1.6
 Alcohol Sensor
			1.1.7 
Humidity Sensor
		1.2 Wireless Sensor Networks
			1.2.1 Sensor Network Architecture
			1.2.2
 Communication Structure in Wsns
			1.2.3 
Deployment Strategies in Wirelessss Sensor Networks
		1.3 Wireless Network Standards
		1.4 Design Challenges
			1.4.1 
Energy Conservation
			1.4.2 
Self-organization and Cluststering
			1.4.3 
Wirelessss Domain and Connectivity Issssues
			1.4.4 
Energy Holes and Coverage Issssues
			1.4.5 
Routing Challenges in Sensor Networks
		1.5 Energy Management
		1.6 Data Aggregation
			1.6.1 
Design Issssues and Challenges
			1.6.2 Significance and Scope
			1.6.3 
Data-aggggregation Classssification
		References
Chapter 2
	Chapter 2 Sensor Network Applications
		2.1 Intelligent Sensor Networks
			2.1.1 Smart Homes
			2.1.2 Smart Healthcare
			2.1.3 
Intelligent Farm Sensing
			2.1.4 Terreststrial Sensor Network
			2.1.5 
Underwater Sensor Network
			2.1.6 
Underground Sensor Network
			2.1.7 
Intelligent Vehicle Sensors for Its
			2.1.8 Sensors for Automation and Control
			2.1.9 Sensors for Military Surveillance
			2.1.10 
Hyperspectral Sensors for Remote Observation
			2.1.11 Satellite Sensors for Space Exploration
		2.2 Integration of Social Network with Sensors
			2.2.1 
Random Graphs Formation
			2.2.2 
Degree Diststribution in Random Graphs
			2.2.3 
Isolation Probability in Random Graphs
			2.2.4 Sensors and Node Centrality
			2.2.5 Social Sensing Applications
		2.3 Integration of Neural Computing with Sensors
		References
Chapter 3
	Chapter 3 Coverage in Wireless Sensor Networks
		3.1 Introduction: Wireless Sensor Networks
		3.2 Coverage in Wireless Sensor Networks
		3.3 Energy Optimization in Coverage
			3.3.1 Target Coverage Problem
			3.3.2 
Definitions and Notations
		3.4 Variants of Coverage
			3.4.1 
Energy-efficient Full Coverage
			3.4.2
Energy-efficient K-coverage
			3.4.3 
Energy-efficient Partial Coverage
			3.4.4  Energy-efficient Connected Coverage
		3.5 Genetic Evolutionary Metaheuristic
			3.5.1 
Introduction to Genetic Algorithm-inspired Evolutionary Techniques
			3.5.2 
Energy-efficient Coverage with Genetic Metaheuriststics
		3.6 Conclusion
		References
Chapter 4
	Chapter 4 Connectivity and Communication in Wireless Sensor Networks
		4.1 Introduction: Wireless Sensor Networks
		4.2 Connectivity and Communication in Wsns
		4.3 Connectivity Issues in Wireless Sensor Networks
			4.3.1 Type of Architecture
			4.3.2 Type of Topology
			4.3.3 Type of Network
		4.4 Communication Paradigms in Wireless Sensor Networks
			4.4.1 
Communication Issssues in Wsns
			4.4.2 
Communication Protocols
		4.5 Connected Coverage and Reinforcement Methodology
			4.5.1 
Introduction to Connected Coverage
			4.5.2 
Overview of Reinforcement Methodologies
			4.5.3 
Connected Coverage Using Reinforcement Learning Automata
			4.5.4 Simulation and Experimentation
		4.6 Conclusion
		References
Chapter 5
	Chapter 5 Energy-efficient Clustering in Sensor Networks
		5.1 Introduction to Energy-efficient Clustering
			5.1.1 
Major Challenges in Wsns
		5.2 Significance of Clustering in Wsns
			5.2.1 
Cluststering Processss
			5.2.2 Types of Cluststering
		5.3 Metaheuristic-based Ch Selection Algorithms
		5.4 Non-metaheuristic-based Cluster Head Selection
		5.5 Role of Clustering in Energy Hole Problems
			5.5.1 
Key Characteriststics of the Energy Hole Problem in Wsns
			5.5.2 
Energy Hole Mitigation Techniques in Wsns
		5.6 Data Dissemination in Clustered Wsns
			5.6.1 
Classssification of Data Dissssemination Techniques
		5.7 Comparative Performance Analysis of the Clustered Wsns
			5.7.1 
Network Lifetime Enhancement and Load Balancing
			5.7.2 R
esidual Energy Consumption
			5.7.3 
Optimal Clustster Head Selection Techniques
			5.7.4 Throughput Optimization in Cluststered Wsns
		5.8 Conclusion
		References
Chapter 6
	Chapter 6 Routing Methods with Adjustable Sensing Range
		6.1 Introduction to Routing Methods in Wsns
		6.2 Classification of Routing Protocols
			6.2.1 
 Network Structure-based Routing
			6.2.2 
Path Eststablishment-based Routing
			6.2.3 
Protocol-oriented Routing
			6.2.4
 Communication Initiator-based Routing Protocols
		6.3 Advantages and Limitations of Routing Protocols
			6.3.1
 Advantages of Routing Protocols in Wsns
			6.3.2
 Limitations of Routing Protocols in Wsns
		6.4 Variable Sensing Range (vasera) and Its Impact in Wsns
		6.5 Energy-efficient Metaheuristics-based Routing Protocols
			6.5.1 
Optimized Genetic Algorithms for Clustster Head Selection
			6.5.2 
Data Collection by Sinks from the Nearby Sensor Nodes and Clustster Heads Directly
		6.6 Performance Analysis
			6.6.1 
Analysis of Ga with Static Single
			6.6.2 
Analysis of Ga with Multiple Sinks
			6.6.3 
Effect of Moveable Sink with Adjuststable Sensing Range and Direct Data Collection
		6.7 Conclusion
		References
Chapter 7
	Chapter 7 Performance Evaluation Metrics for Energy-constrained Sensor Networks
		7.1 Performance Metrics
			7.1.1 Jitter
			7.1.2 Latency
			7.1.3 Packet Lossss Rate
			7.1.4 Overall Throughput
			7.1.5 Frequency of Retransmissssions
			7.1.6 Data-aggggregation Error
			7.1.7 
Energy Consumption
			7.1.8 
Network Lifetime
		7.2 Need for Performance Evaluation
		7.3 Analytical Approaches for Evaluation
			7.3.1 
Error Analysis
			7.3.2 
Markov Chain Analysis
			7.3.3 
Queuing Analysis
			7.3.4 Static Priority and Aloha Networks
			7.3.5 
Network Simulation
		7.4 Self-similarity Phenomena in Sensor Networks
		References
Index