Comprehensive Guide to Heterogeneous Networks

Front Cover
Comprehensive Guide to Heterogeneous Networks
Copyright
Contents
Contributors
Preface
Chapter 1: Heterogeneous wireless sensor networks: Deployment strategies and coverage models
	1. Introduction
	2. HWSN applications
		2.1. Health-care applications
		2.2. Safety-critical applications
		2.3. Security applications
		2.4. Public and commercial applications
		2.5. Scientific exploration
	3. HWSN deployment strategies
		3.1. Coverage maximization
		3.2. Maximizing connectivity
			3.2.1. Random deployment strategies
			3.2.2. Lloyd algorithm-based approach
			3.2.3. Deterministic annealing algorithm-based approach
		3.3. Energy efficiency and network lifetime
			3.3.1. VFA-based approach
			3.3.2. Game theory-based approach
			3.3.3. Particle swarm optimization algorithm-based approach
	4. Coverage concept, coverage schemes, and solution approaches
		4.1. Preliminaries
		4.2. Coverage schemes
			4.2.1. Area coverage
			4.2.2. Point coverage
			4.2.3. Barrier coverage
		4.3. Solution approaches
			4.3.1. GA-based approach
			4.3.2. Greedy heuristic-based approach
			4.3.3. Social spider optimization-based approach
	5. Conclusion and future outlook
	Acknowledgments
	Conflict of interest
	Disclaimer
	References
Chapter 2: Efficient multitasking in heterogeneous wireless sensor networks
	1. Introduction
		1.1. Background and overview of sensor network technology
		1.2. Evolution of sensor networks
		1.3. Convergence of heterogeneous and multitask wireless sensor networks
		1.4. Architectural elements of a sensor network
		1.5. The need for efficient multisensor task allocation
	2. Cooperative task allocation in heterogeneous wireless sensor networks
		2.1. Multitasking in WSNs
		2.2. Multiple sensors for data collection
	3. Protocol and algorithms of heterogeneous wireless sensor networks
		3.1. Clustering algorithm for heterogeneous WSNs
		3.2. Comparative analysis of clustering algorithms
			3.2.1. LEACH algorithm
			3.2.2. DEEC algorithm
			3.2.3. SEP algorithm
			3.2.4. PHC algorithm
			3.2.5. EBCS algorithm
			3.2.6. HNS algorithm
	4. Wireless sensor networks formation
		4.1. Centralized wireless sensor networks
			4.1.1. Hierarchical networks
			4.1.2. Static networks
	5. Open research challenges and future directions
	6. Comparison of various algorithms
	7. Conclusions
	References
Chapter 3: Network selection in a heterogeneous wireless environment based on path prediction and user mobility
	1. Introduction
	2. Network selection formulation
		2.1. MADM: Multiple attribute decision-making methods
			2.1.1. Simple additive weighting (SAW)
			2.1.2. Technique to order preference by similarity to ideal solution (TOPSIS)
			2.1.3. Weighted product model WPM (multiplicative exponential weighting (MEW))
			2.1.4. Analytic hierarchy process (AHP)
		2.2. Game theory
		2.3. Fuzzy logic
		2.4. Utility functions
	3. Mobility-based network selection
		3.1. First step: The prediction of paths
		3.2. Second step: Network selection
		3.3. Step three: Selecting the RAT's configuration
		3.4. Step four: Choosing an appropriate configuration
	4. Performance evaluation
		4.1. Testing the proposed framework
		4.2. Comparison with other works
	5. Conclusions
	References
Chapter 4: Reducing control packets using covering rough set for route selection in mobile ad hoc networks
	1. Introduction
		1.1. Overview of mobile ad hoc networks
		1.2. An overview of mobility nodes
			1.2.1. Random waypoint (RWP) mobility model
			1.2.2. Random direction (RD) mobility model
			1.2.3. Random walk (RW) mobility model
		1.3. Types of wireless routing protocols
			1.3.1. DSR (dynamic source routing)
			1.3.2. AODV (ad hoc on-demand distance vector routing)
		1.4. Covering rough set (CRS)
			1.4.1. Motivation and contributions
	2. A review of rough set-based techniques for reducing redundant packets
	3. Rough Set Theory (RST) mechanism in manets
	4. Reducing redundant control packets using covering rough set in manet for route selection
		4.1. Covering rough set in manets
		4.2. CRS-based route selection
		4.3. Applying CRS-based route selection to AODV
	5. Experimental results and simulation environment set-up
		5.1. Simulation tool
		5.2. Evaluation metrics
		5.3. Simulation analysis
	6. Conclusion and future direction
	References
Chapter 5: Optimization of hybrid broadcast/broadband networks for the delivery of linear services using stochastic geometry
	1. Introduction
	2. Hybrid models
		2.1. The modeling of the broadband network
			2.1.1. Modeling the common features for both modes
			2.1.2. Unicast: The default mode
			2.1.3. Multicast mode and the SFN deployment
		2.2. Broadcast network deployments
			2.2.1. Single BC transmitter
			2.2.2. Multiple BC transmitters
		2.3. Hybrid broadcast/broadband model
			2.3.1. Location-based user association
			2.3.2. Signal-quality-based user association
		2.4. Summary of the hybrid combinations
	3. Analytical analysis of the hybrid model
		3.1. Model settings and definitions
		3.2. The coverage probability
			3.2.1. Coverage probability for a BC user
			3.2.2. Coverage probability for a UC user
			3.2.3. Coverage probability for any user
			3.2.4. Verifying the formulas
		3.3. The power efficiency optimization
			3.3.1. Power utilization factor
			3.3.2. Optimization of power utilization
				Common observations
				BB network deployment
				Service requirements
				Transmission environment
				Users density
	4. Discussion and recommendations
	5. Conclusions and future scope
	References
Chapter 6: A comprehensive survey on heterogeneous cognitive radio networks
	1. Introduction
	2. Coexistence challenges for HetCWNs in TVWS
		2.1. Detection of available TV channels
			2.1.1. Detection of licensed networks in a TV channel
			2.1.2. Detection of unlicensed networks in a TV channel
		2.2. Spectrum sharing
		2.3. Interference issues for HetCWN for coexistence in TVWS
			2.3.1. Interference to/from licensed users
			2.3.2. Interference among HetCWNs
	3. RA in HetCWNs
		3.1. RA schemes
	4. MAC protocols for HetCWN
		4.1. Classification of MAC protocols for HetCWNs
	5. Security issues/challenges for heterogeneous cognitive radio networks
		5.1. Security intrusions
			5.1.1. Service denial intrusions at physical layer
			5.1.2. Service denial intrusions at link layer
			5.1.3. Service denial intrusions at network layer
			5.1.4. Service denial intrusions at cross layer
			5.1.5. Detection methods
			5.1.6. Strategies to prevent intrusions
	6. Global standardization activities on HetCWNs
		6.1. IEEE standards coordinating committee 41 (SCC-41)
		6.2. IEEE 802.22 standard
		6.3. ITU standards
	7. Conclusion and future scope
	References
Chapter 7: Evaluation and analysis of clustering algorithms for heterogeneous wireless sensor networks
	1. Introduction
		1.1. Types of heterogeneous resources
		1.2. Impact of heterogeneity on WSN
		1.3. Performance measures
		1.4. Types of node heterogeneity of HWSN
	2. Hierarchical routing protocols evaluation process
		2.1. Routing technique
			2.1.1. Proactive routing
			2.1.2. Reactive routing
			2.1.3. Hybrid routing
		2.2. Routing approach
		2.3. Control manner
		2.4. Mobility pattern
		2.5. Network architecture
	3. Clustering
		3.1. Why WSN clustering is required
		3.2. Classifying clustering schemes
		3.3. Residual energy consideration model
	4. Clustering scheme
		4.1. Hybrid energy-efficient distributed clustering (HEED)
		4.2. Distributed weight-based energy-efficient hierarchical clustering (DWEHC)
		4.3. Hybrid clustering approach (HCA)
		4.4. Energy-efficient heterogeneous clustered scheme (EEHCS)
		4.5. Distributed election clustering protocol (DECP)
		4.6. Dissipation forecast and clustering management (EDFCM)
		4.7. Energy-efficient unequal clustering (EEUC)
		4.8. Distributed energy-efficient clustering algorithm for HWSN (DEEC)
		4.9. Energy-efficient clustering scheme (EECS)
		4.10. Multihop routing protocol with unequal clustering (MRPUC)
	5. Simulation and results
		5.1. Evaluation of the death round of the first node
		5.2. Evaluation of number of nodes alive
		5.3. Evaluation based on residual energy
	6. Performance analysis
	7. Conclusions and future work
	References
Chapter 8: Analysis of energy-efficient cluster-based routing protocols for heterogeneous WSNs
	1. Introduction
		1.1. Problem definition
		1.2. Scope and motivation
		1.3. Key contributions
		1.4. Chapter organization
	2. Overview of heterogeneous WSN (HWSN)
	3. Overview of cluster-based routing protocols
		3.1. Classification of cluster-based routing protocols
			3.1.1. Low-energy adaptive clustering hierarchy (LEACH)
			3.1.2. Hybrid energy-efficient distributed clustering (HEED)
			3.1.3. Unequal clustering (UCS)
			3.1.4. Threshold sensitive energy-efficient sensor network protocol (TEEN)
			3.1.5. Two-tier data dissemination (TTDD)
			3.1.6. Power-efficient gathering in sensor information systems (PEGASIS)
	4. Literature review
		4.1. Homogeneous cluster-based routing protocols
		4.2. Heterogeneous cluster-based routing protocols
	5. Application of optimization for energy efficiency in WSN
	6. Evolutional optimization cluster-based routing protocol (EOCRP)
		6.1. System model
		6.2. Architecture
	7. Theoretical benefits of proposed EOCRP
	8. Challenges
		8.1. Challenges faced in HWSN
	9. Conclusion
	10. Future scope
	References
Chapter 9: Imperative load-balancing techniques in heterogeneous wireless networks
	1. Introduction
	2. Different types of load-balancing techniques
		2.1. Classification of static load balancing
			2.1.1. Round Robin algorithm
			2.1.2. Randomized algorithm
			2.1.3. Central manager algorithm
			2.1.4. Threshold algorithm
		2.2. Classification of dynamic load balancing
			2.2.1. Central queue algorithm
			2.2.2. Local queue algorithm
			2.2.3. Least connection algorithm
	3. Comparative analysis of load-balancing algorithms
	4. Impact of AI in wireless heterogeneous networks
		4.1. AI importance in load balancing
			4.1.1. Neural network in a wireless heterogeneous network
			4.1.2. Fuzzy logic for a wireless network
			4.1.3. Genetic algorithm
			4.1.4. Particle swarm optimization (PSO)
			4.1.5. Artificial bee colony (ABC)
			4.1.6. Markov models and Bayesian-based games
	5. Need of machine learning in a wireless heterogeneous network
		5.1. Supervised learning
			5.1.1. Support vector machine
			5.1.2. K nearest neighbors
		5.2. Unsupervised learning
			5.2.1. K-means clustering algorithm
			5.2.2. Principal component analysis
		5.3. Reinforcement learning
			5.3.1. Q-learning
			5.3.2. Multiarmed bandit learning
			5.3.3. Actor-critic learning
			5.3.4. Joint utility and strategy estimation learning
			5.3.5. Deep reinforcement learning
	6. Conclusion and future scope
	References
Chapter 10: Intelligent intersystem handover delay reduction algorithm for heterogeneous wireless networks
	1. Introduction
	2. Related work
		2.1. Fuzzy logic-based algorithms
		2.2. Dwell timer-based schemes
		2.3. Fuzzy logic-based algorithms
		2.4. MULTIMOORA-based algorithms
		2.5. Context-aware-based algorithms
		2.6. TOPSIS and AHP-based algorithms
		2.7. Media independent handover-based algorithms
		2.8. Comparison of related handover schemes
	3. Design of Intelligent Intersystem Handover Algorithm
		3.1. Gray prediction theory
		3.2. Multiattribute decision-making
		3.3. Fuzzy analytic hierarchy process and multiobjective optimization ratio
			3.3.1. Analysis
		3.4. Intelligent intersystem handover algorithm
	4. Simulation results
		4.1. Findings and discussions
			4.1.1. Scenario 1: Performance evaluation for real-time traffic (1-120km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.2. Scenario 2: Performance evaluation for real-time traffic (1-30km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.3. Scenario 3: Performance evaluation for real-time traffic (100-120km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.4. Scenario 4: Performance evaluation for real-time traffic (constant speed-60km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.5. Scenario 1: Performance evaluation for nonreal-time traffic (1-120km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.6. Scenario 2: Performance evaluation for nonreal-time traffic (1-30km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.7. Scenario 3: Performance evaluation for nonreal-time traffic (100-120km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
			4.1.8. Scenario 4: Performance evaluation for nonreal-time traffic (constant speed-60km/h)
				Average handover delay
				Average probability of ping-pong effect
				Average packet loss
				Average network throughput
	5. Conclusion and future work
	Acknowledgments
	Appendix: Table of notations
	References
Index
Back Cover