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Comprehensive guide to heterogeneous networks
Comprehensive Guide to Heterogeneous Networks discusses the fundamental motivations behind this cutting-edge development, along with a brief discussion on the diverse definitions of HNs. The future of heterogeneous wireless networks (HWNs) is covered, including test cases, cost configuration, economic benefits and basic challenges. Other sections cover the topology management method in context of heterogeneous sensor nodes with diverse communication and sensing range. In addition, an outline of the pros and cons of the clustering criteria in HWSNs and taxonomy are summarized and provide futuristic research directions. Final sections discuss the future evolution of HNs and their implementations in diverse applications. This is an essential reference book for advanced students on courses in wireless communications, clinical engineering and networking. It will also be of interest to researchers, network planners, technical mangers and other professionals in these fields.
Intro -- 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.