Wireless Sensor Networks From Theory to Applications 1st Edition by Ibrahiem El Emary, Ramakrishnan 1466518103 9781466518100

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ISBN 10: 1466518103

ISBN 13: 9781466518100

Author: Ibrahiem M. M. El Emary, S. Ramakrishnan

Wireless Sensor Networks From Theory to Applications 1st Table of contents:

Part I Data Collection
1 Data Collection in Wireless Sensor Networks: A Theoretical Perspective
1.1 Introduction
1.1.1 Network Model
1.1.2 Communication Model
1.1.3 Capacity and Delay in Data Collection
1.1.4 Related Works
1.2 Data Collection in Random Sensor Networks
1.2.1 Preliminaries
1.2.2 Data Collection with a Single Sink
1.2.3 Data Collection with Multiple Sinks
1.2.3.1 Regularly Deployed Multiple Sinks
1.2.3.2 Randomly Deployed Multiple Sinks
1.2.4 Data Collection under the Physical and Generalized Physical Models
1.2.4.1 Data Collection under the Physical Model
1.2.4.2 Data Collection under the Generalized Physical Model
1.3 Data Collection in Arbitrary Sensor Networks
1.3.1 Data Collection under the Protocol Model
1.3.1.1 Data Collection Tree: BFS Tree
1.3.1.2 Branch Scheduling Algorithm
1.3.1.3 Capacity Analysis
1.3.2 Data Collection under the General Graph Model
1.3.2.1 Upper Bound of Collection Capacity
1.3.2.2 Lower Bound of Collection Capacity
1.3.3 Data Collection under the Physical and Generalized Physical Models
1.3.3.1 Data Collection under the Physical Model
1.3.3.2 Data Collection under the Generalized Physical Model
1.4 Conclusion
Acknowledgments
References
2 Data Aggregation and Data gathering
2.1 Introduction
2.2 Data Aggregation
2.3 Data Aggregation and Data Gathering in WSNs
2.4 Protocols for Data Aggregation and Data Gathering
2.5 Energy-Efficient Clustering and Data Aggregation Protocol for Heterogeneous WSNs
2.6 A Noble Data Aggregation Algorithm for Low Latency in Wireless SNs
2.7 A Scalable and Dynamic Data Aggregation Aware Routing Protocol for WSNs
2.8 A Hierarchical Multiparent Cluster-Based Data Aggregation Framework for WSNs
2.9 Cluster Tree-Based Data Gathering in WSN
2.10 Compressive Data Gathering for Large-Scale WSNs
2.11 An In-Network Approximate Data-Gathering Algorithm Exploiting Spatial Correlation in WSNs
2.11.1 Cluster Initialization
2.11.2 Cluster Reading Collection
2.11.3 Cluster Merging Problem
2.11.4 In-Network Cluster Merging
2.11.5 Reading Streaming Phase
References
3 Spatial Coverage Estimation and Optimization in Geosensor Networks Deployment
3.1 Introduction
3.2 Wireless Geosensor Networks: An Overview
3.3 Geometrical Issues in Deployment of Geosensor Wireless Networks
3.3.1 Sensing Models
3.3.1.1 Variable Sensibility
3.3.1.2 Noncircular Sensibility
3.3.2 C ommunication Models
3.3.3 Preliminary Geometric Structures
3.4 Spatial Coverage in Geosensor Networks
3.4.1 Coverage Estimation Based on the Concept of Exposure
3.5 Optimization Algorithms in Geosensor Networks Deployment
3.5.1 Global Approaches
3.5.2 Local Approaches
3.5.2.1 Voronoi-Based Solutions for Static Sensor Networks
3.5.2.2 Voronoi-Based Solutions for Mobile Sensor Networks
3.5.2.3 Voronoi-Based Solutions for Hybrid Sensor Networks
3.5.3 Node Scheduling
3.6 Open Problems and Research Challenges
3.6.1 K-Coverage Sensor Networks
3.6.2 Sensor Networks with Various Sensing Ranges
3.6.3 Directional Sensor Networks
3.6.4 Sensor Networks in a Three-Dimensional Environment
3.6.5 Integration of Spatial Information in Sensor Deployment Procedure
3.7 Conclusions and Perspectives
Acknowledgments
References
Part II Physical Layer and Interfacing
4 Overview of IEEE 802.15.4 Wireless Sensor Networks in Three-Dimensional Terrains
4.1 Introduction
4.1.1 ZigBee Topology
4.1.2 IEEE 802.15.4 MAC Superframe Structure
4.1.2.1 Beacon-Enabled Mode
4.1.2.2 Non-Beacon-Enabled Mode
4.1.3 IEEE 802.15.4 Association Procedure
4.1.4 Effect of 3-D Terrains in WSNs
4.1.5 ZigBee Nodes for Internet of Things
4.2 Related Work
4.3 Background Knowledge
4.3.1 Durkin’s Propagation Model
4.3.2 The Digital Elevation Model
4.3.3 3DEM Software
4.4 Simulation Model for 3-D ZigBee Sensor Networks
4.4.1 Simulation Model in a 3-D Terrain Model
4.4.2 BO and SO Adapted Scheme
4.5 Performance Evaluation
4.5.1 Network Configuration and Assumptions
4.5.2 Simulation Results
4.5.2.1 Simulation 1
4.5.2.2 Simulation 2
4.5.2.3 Simulation 3
4.6 Summary
References
5 Multi-Interface Wireless Networks: Complexity and Algorithms
5.1 Introduction
5.1.1 Coverage
5.1.2 Connectivity
5.1.3 Cheapest Path
5.1.4 Maximum Matching
5.1.5 Maximum Flow
5.1.6 Minimum Cost Flow
5.2 Preliminaries
5.3 Coverage and Connectivity
5.3.1 Coverage
5.3.1.1 Computational Complexity for k-CMI
5.3.1.2 Approximation Results for k-CMI
5.3.1.3 Solvable Cases for k-CMI
5.3.1.4 Unbounded CMI
5.3.2 Connectivity
5.3.2.1 Computational Complexity for Connectivity
5.3.2.2 Approximation Results for Connectivity
5.3.2.3 Solvable Cases for Connectivity
5.3.3 Min-Max Version
5.4 Cheapest Paths
5.5 Maximum Matching
5.5.1 Computational Complexity for 3MI
5.6 Maximum Flow and Minimum Cost Flow
5.6.1 Graph Transformation
5.6.2 Maximum Flow
5.6.3 Minimum Cost Flow
5.7 Conclusion
References
6 Sensor Bus Architecture for Real-Time Wireless Sensor Networks
6.1 Introduction
6.1.1 Background of the Problem
6.2 Existing Technologies
6.3 Theoretical Concepts of Sensor Bus Architecture
6.3.1 Sensor Network
6.3.1.1 Introduction
6.3.1.2 Sensor Network Standard
6.3.1.3 Sensor Web
6.3.2 Sensor Web Enablement
6.4 System Design, Implementation, and Performance Evaluation
6.4.1 System Design
6.4.1.1 Sensor Bus
6.4.1.2 Sensor Observation Service
6.4.1.3 SOS Adapter
6.4.1.4 Sensor Network
6.4.1.5 Sensor Adapter
6.4.2 Implementation
6.4.2.1 eZ430-RF2500 Sensor Network
6.4.2.2 Heterogeneous Sensor Network
6.4.3 Sensor Bus Architecture: Evaluation and Discussion
6.4.3.1 Introduction
6.4.3.2 Performance Evaluation of the Sensor Bus
6.4.3.3 Scalability
6.4.3.4 Reliability and Fault Tolerance
6.4.3.5 Adaptability and Heterogeneity
6.5 Conclusion and Recommendation
References
Part III Routing and Transport Protocols
7 Quality of Service and Routing Performance Evaluation for IEEE 802.15.6 Body Sensor Networks using an Accurate Physical Layer
7.1 Introduction
7.1.1 Motivation
7.1.2 Sensor Networks and Their Application in Modern Medicine
7.1.3 Medical Scenario of Interest
7.2 hysical Channel Modeling for On-Body Communications
7.2.1 On-Body Propagation (Guided Diffraction)
7.2.2 Reflections Off the Environment
7.3 Link-Layer Performance
7.3.1 Link Probability of Transmission in Multisensor Communications
7.3.1.1 Link Probability of Success with Short-Range Transmission in Indoor Scenarios
7.3.1.2 Link Probability of Success with Long-Range Transmission in Indoor Scenarios
7.3.1.3 Link Probability of Success in Outdoor Scenarios
7.3.2 Energy Consumption and Minimum Transmit Power
7.4 Link-Level Throughput and Delay
7.4.1 Random Access BANs
7.4.2 TDMA BANs
7.5 Reliable Routing and Optimal Topology in Multihop BAN
7.5.1 Routing in BAN Networks
7.5.2 Link-State Routing: Dijkstra’s Algorithm
7.5.3 Medical Nodes’ Maximum Probability of Transmission
7.5.4 Optimum Routing Tree Computation and Corresponding Route Performance
7.5.5 Route Delay and Optimization of the Link–Layer Access Scheme
7.6 Conclusions
Appendix A
References
8 Routing Protocols
8.1 Routing
8.1.1 Flat Routing Protocols
8.1.2 Location-Based Routing
8.1.3 Hierarchical Protocols
8.2 Design Constraints for Routing in WSNs
8.3 Clustered Architecture
8.4 Clustering Objective
8.4.1 Maximizing Network Lifetime
8.4.2 Fault-Tolerance
8.4.3 Load Balancing
8.5 LEACH
8.5.1 Design
8.5.2 CH Selection Algorithm
8.5.3 Cluster Formation Process of LEACH
8.6 LEACH Protocol Phases
8.6.1 Setup Phase
8.6.2 Steady Phase
8.7 Power-Efficient Gathering in Sensor Information Systems
8.8 Hybrid, Energy-Efficient Distributed Clustering
8.9 LEACH-C
8.9.1 Setup Phase
8.9.2 Steady Phase
8.10 V-LEACH
8.11 N-LEACH
8.12 Energy Consumption Model for LEACH
8.12.1 Average Current Drawn by Regular Nodes
8.12.2 Average Current Drawn by CHs
References
9 A Survey of Connected Dominating Set Construction Techniques for Ad Hoc Sensor Networks
9.1 Introduction
9.2 Network Models
9.2.1 Background
9.2.2 CDS as Virtual Backbones in Ad Hoc Sensor Networks
9.2.2.1 Shortest Path Routing
9.2.2.2 Dynamic Source Routing
9.2.3 Centralized Algorithms
9.2.4 Distributed Algorithms
9.2.5 Unbounded Distributed Construction
9.2.6 Bounded Distributed Construction
9.3 Approximation Factor Computation for Bounded Distributed CDS Construction Algorithms
9.4 Performance Comparison for Bounded Distributed CDS Construction Algorithms
9.4.1 Aggregation-Based Energy Model
Epilogue
References
10 Transport Protocols in Wireless Sensor Networks
10.1 Introduction
10.2 Guidelines for Designing Transport Protocols in WSNs
10.2.1 Performance Metrics
10.2.2 Congestion Control
10.2.3 Loss Recovery
10.2.4 Design Guidelines
10.3 Issues and Challenges of Data Transport over WSNs
10.3.1 Issues
10.3.2 Challenges
10.4 Transport Control Protocols for WSNs
10.4.1 Protocols for Reliability
10.4.1.1 Pump Slowly/Fetch Quickly
10.4.1.2 Improved PSFQ
10.4.1.3 Reliable Multiple-Segment Transport
10.4.1.4 GARUDA
10.4.1.5 Distributed TCP Caching
10.4.1.6 Distributed Transport for Sensor Network
10.4.1.7 Reliable Bursty Convergecast
10.4.1.8 Energy-Efficient and Reliable Transport Protocol
10.4.2 Protocols for Congestion Control
10.4.2.1 Congestion Detection and Avoidance in Sensor Networks
10.4.2.2 Enhanced Congestion Detection and Avoidance
10.4.2.3 FUSION
10.4.2.4 Trickle
10.4.2.5 Congestion Control and Fairness (CCF)
10.4.2.6 SenTCP
10.4.2.7 Siphon
10.4.2.8 Congestion Control for Multiclass Traffic
10.4.2.9 Priority-Based Congestion Control Protocol
10.4.2.10 Prioritized Heterogeneous Traffic-Oriented Congestion Control Protocol
10.4.2.11 Fairness-Aware Congestion Control
10.4.3 Protocols for Both Congestion Control and Reliability
10.4.3.1 Event-to-Sink Reliable Transport
10.4.3.2 Price-Oriented Reliable Transport Protocol
10.4.3.3 Sensor Transmission Control Protocol
10.4.3.4 Flush
10.4.3.5 Asymmetric and Reliable Transport
10.4.3.6 Loss-Tolerant Reliable Event-Sensing Protocol
10.4.3.7 Rate-Controlled Reliable Transport
10.4.3.8 Tunable Reliability with Congestion Control for Information Transport
10.4.3.9 Delay-Sensitive Transport Protocols
10.5 Conclusion
References
11 Energy-Efficient Medium Access Control Protocols for Wireless Sensor Networks
11.1 Introduction
11.2 Analysis of Energy Consumption at the Link Layer
11.2.1 Radio Energy Consumption Models
11.2.2 Contemporary MAC Schemes for Wireless Networks
11.2.2.1 TDMA
11.2.2.2 FDMA
11.2.2.3 CDMA
11.2.2.4 CSMA
11.3 MAC Layer Issues for WSNs
11.3.1 Design Goals for MAC Protocols in WSNs
11.3.2 Energy Trade-Offs and Metrics
11.3.2.1 Energy Trade-Offs
11.3.2.2 MACs’ Performance Metrics
11.4 State of the Research
11.4.1 Contention-Based Protocols
11.4.1.1 SmartNode
11.4.1.2 Power-Aware Medium Access and Signaling Protocol
11.4.1.3 Sensor-MAC
11.4.1.4 Berkeley-MAC
11.4.1.5 Convergent MAC
11.4.1.6 X-MAC
11.4.1.7 Informative Preamble Sampling MAC
11.4.1.8 Demand-Wakeup MAC
11.4.1.9 Receiver-Initiated MAC
11.4.1.10 Adaptive Transmission Rate
11.4.2 Reservation Based Protocols
11.4.2.1 Collision-Free MAC
11.4.2.2 Single-Sink Setup
11.4.2.3 Multicluster Scheduling
11.4.2.4 BitMAC
11.4.2.5 Dynamic, Energy-Efficient MAC
11.4.2.6 Lightweight Medium Access Protocol
11.4.2.7 Traffic Adaptive MAC
11.4.3 Hybrid Approaches
11.4.3.1 Power-Aware Reservation-Based MAC
11.4.3.2 Distributed Energy-Aware Node Activation
11.4.3.3 Self-Organizing Sensor Networks
11.4.3.4 Z-MAC
11.4.3.5 Emergency Response MAC
11.4.3.6 Funneling-MAC
11.4.3.7 IEEE 802.15.4 Standard
11.4.4 Emerging MAC Protocols
11.4.4.1 Multichannel MAC Protocols
11.4.4.2 Cross-Layer Approaches
11.4.4.3 QoS Based Approaches
11.5 Conclusion and Open Research Issues
References
Part IV Energy Saving Approaches
12 Pulse Switching: A Packetless Networking Paradigm for Energy-Constrained Monitoring Applications
12.1 Introduction
12.1.1 Event Monitoring in Sensor Networks
12.1.2 Energy as the Primary Design Constraint
12.1.3 Pulse Switching as an Energy-Efficient Alternative to Packets
12.1.3.1 Objective
12.1.3.2 Challenges
12.1.3.3 Contributions
12.2 Pulse Switching Protocol
12.2.1 Pulse Switching Using UWB-IR
12.2.1.1 UWB Slotting for Pulse Switching
12.2.1.2 Modulation and Synchronization
12.2.1.3 Energy Budget
12.2.1.4 Sources of Error
12.2.2 Event Localization Methods
12.2.2.1 Hop-Angular Event Localization
12.2.2.2 Cellular Event Localization
12.2.3 Pulse as Protocol Data Unit
12.3 Hop-Angular Pulse Switching Protocol
12.3.1 Joint MAC-Routing Frame Structure
12.3.2 Hop-Distance Self-Discovery
12.3.3 Pulse Forwarding
12.3.4 Synchronized State Transitions
12.4 Cellular Pulse Switching Protocol
12.4.1 Joint MAC-Routing Frame Structures
12.4.2 Route Discovery
12.4.3 Pulse Forwarding
12.4.4 Protocol State Machine
12.5 Energy-Saving Measures
12.5.1 Intraframe Interface Shutdown
12.5.1.1 Intraframe Interface Shutdown in HPS
12.5.1.2 Intraframe Interface Shutdown in CPS
12.5.2 Constrained Routing
12.5.2.1 Constrained Routing in HPS
12.5.2.2 Constrained Routing in CPS
12.5.3 Pulse Aggregation and Compression
12.5.3.1 Aggregation via Pulse Merging
12.5.3.2 Pulse Compression
12.5.3.3 Spatial Compression
12.5.3.4 Temporal Compression
12.6 Physical Layer Cooperation Mitigation Measures
12.6.1 Mitigating Cooperation in HPS
12.6.1.1 Mitigating Cooperation during Hop-Distance Discovery
12.6.1.2 Immunity to Node Cooperation during Pulse Forwarding
12.6.2 Immunity to Node Cooperation in CPS
12.7 Performance Evaluation
12.7.1 HPS
12.7.1.1 Static Event Scenario
12.7.1.2 Moving Event Scenario
12.7.2 CPS
12.7.2.1 Pulse Transmission Count
12.7.2.2 Different Source Cells
12.7.2.3 Effects of Route Diversity
12.7.2.4 Error Analysis
12.8 Conclusion
References
13 Handling the Energy in Wireless Sensor Networks
13.1 Introduction
13.1.1 Topology
13.1.2 Routing
13.1.3 Mobility
13.1.4 Duty Cycling
13.1.5 Adaptive Sampling
13.1.6 Transmission Policy
13.1.7 Deployment Strategy
13.1.8 Clustering
13.1.9 Multiple Base Stations
13.2 Experimental Results
13.2.1 Node Density
13.2.2 Reporting Rate
13.2.3 Energy Consumption as a Function of Reporting Rate
13.2.4 Dropped Packets as a Function of Time
13.2.5 Energy Consumption as a Function of Dropped Packets
13.2.6 Packet Size
13.2.7 Energy Consumption as a Function of Packet Size
13.2.8 Network Topology
13.3 Conclusion
References
14 Cooperative Systems in Wireless Sensor Networks
14.1 Introduction
14.2 Motivation of Cooperative MIMO in WSNs
14.3 The Concepts of Cooperative MIMO
14.3.1 Forms of MIMO
14.3.1.1 Multiantenna Types
14.3.1.2 Multiuser Types
14.3.2 Basic Building Blocks
14.3.3 Advantages of Multiple Antennas
14.3.3.1 Array Gain
14.3.3.2 Diversity Gain
14.3.3.3 Multiplexing Gain
14.3.3.4 Interference Reduction
14.4 Energy Consumption Enhancement Techniques
14.5 Energy Efficiency in Cooperative Systems
14.5.1 System Model
14.5.2 Energy Model
14.5.2.1 Data Aggregation
14.5.2.2 Node Selection
14.6 Effects of WSN Parameters
14.6.1 Effect of Long-Haul Distance
14.6.2 Effect of Local Transmission Distance
14.6.3 Effect of Residual Energy
14.6.4 Effect of Channel Estimate Energy
14.6.5 Effect of Mobility
14.6.6 Effect of Power Circuit
14.6.7 Effect of Channel Loss
References
15 Evolution of Virtual Clustering in Wireless Sensor Networks
15.1 Introduction
15.2 Clustering in WSN
15.2.1 Clustering Requirement
15.2.2 Cluster Formation
15.2.3 Cluster Maintenance
15.2.4 Factors Affecting Clustering
15.2.4.1 Network Architecture
15.2.4.2 Deployment of Nodes
15.2.4.3 Data Processing
15.2.4.4 Load Balancing
15.2.4.5 Fault Tolerance
15.2.4.6 Increased Connectivity and Reduced Delay
15.2.4.7 Minimal Cluster Count
15.2.4.8 Maximizing Network Longevity
15.2.4.9 Scalability
15.2.4.10 Hardware Constraints
15.3 Protocol Overview
15.3.1 LEACH
15.3.1.1 Advertising Phase
15.3.1.2 Cluster Setup Phase
15.3.1.3 Schedule Creation
15.3.1.4 Data Transmission
15.3.2 Hybrid, Energy-Efficient, Distributed Clustering
15.3.2.1 Initialization Phase
15.3.2.2 Repetition Phase
15.3.2.3 Finalization Phase
15.3.3 Distributed Weight-Based Energy-Efficient Hierarchical Clustering
15.3.4 Energy Dissipation Forecast and Clustering Management
15.3.5 Energy-Efficient Unequal Clustering
15.4 Analysis of the Existing Algorithms
15.4.1 Scope and Functionality
15.4.2 Cluster Properties
15.4.3 CH Capabilities
15.4.4 Clustering Process
15.5 Proposed Protocol
15.5.1 Staggered Clustering Protocol: An Efficient Clustering Approach for WSN
15.5.2 Motivations
15.5.3 Network and System Models
15.5.4 Clustering Parameters
15.5.5 Staggered Clustering Protocol
15.5.6 Simulation and Result Discussion
15.6 Application of WSN in Watering
15.6.1 System Description
15.6.2 Hardware Description
15.6.2.1 Sensor Motes
15.6.2.2 Soil Sensors
15.6.2.3 Electrovalves
15.7 Conclusion and Future Scope
References
Part V Mobile and Multimedia Wsn
16 GPS-Free Indoor Localization for Mobile Sensor Networks
16.1 Introduction
16.2 Related Work
16.3 Our Models
16.3.1 Network Model
16.3.2 Observation Model
16.3.3 Motion Model
16.4 Our Scheme
16.4.1 Our Localization Problem Description
16.4.2 Our GFIL Scheme
16.4.3 Localization Accuracy
16.4.4 Move Trajectory Discussion of Our Scheme
16.5 Simulation and Results
16.5.1 Simulation Setup
16.5.2 Simulation Results and Analyses for the GFIL Scheme
16.5.2.1 Effect of the Transmission Radius
16.5.2.2 Effect of the Move Speed
16.5.3 Comparative Simulation Results and Analyses
16.5.3.1 Square Random Topology
16.5.3.2 Square Regular Topology
16.5.3.3 C Random Topology
16.5.3.4 C Regular Topology
16.6 Conclusion and Future Work
References
17 Mobile Wireless Sensor Networks: A Cognitive Approach
17.1 Introduction
17.2 Types and Applications of WSNs
17.2.1 Environmental and Health Monitoring
17.2.2 Military Applications
17.2.3 Habitat Monitoring
17.2.4 Other Applications
17.3 MWSNs Challenges and Design Issues
17.3.1 Resource Constraints
17.3.2 Dynamic Topologies
17.3.3 Quality of Service
17.3.4 Variable-Link Capacity
17.3.5 Ad Hoc Nature
17.3.6 Design Goals
17.3.6.1 Resource-Efficient Design
17.3.6.2 Adaptive Network Operation
17.3.6.3 Secure Design
17.3.6.4 Self-Configuring and Self-Organizing Networks
17.3.7 Design Issues
17.3.7.1 Scalable Architectures
17.3.7.2 Hardware and Software Design
17.3.7.3 Itinerary Planning
17.4 QoS in MWSN
17.5 Cognitive Radio
17.6 CR-Based WSN
17.7 CWSN Architecture
17.8 Spectrum-Sensing Schemes for CWSN
17.9 Learning in CWSN
17.10 Challenges in Designing a CWSN
17.10.1 Lifetime Maximization or Energy Efficiency
17.10.2 PU Detection and Localization
17.10.3 Fusion
17.10.4 Routing
17.10.5 Resource Allocation Problems
17.10.6 Power Allocation
17.10.7 Optimization of the Radio Module
17.10.8 Spectrum Sensing
17.10.9 Representation of Network Architecture of CWSN
17.10.10 Futuristic Concepts
17.11 Conclusion and Summary
References
18 Wireless Multimedia Sensor Networks: Correlation-Based Communication
18.1 Spatial Correlation of Visual Information
18.1.1 Spatial Correlation Model for Visual Information
18.1.1.1 Sensing Model
18.1.1.2 System Model
18.1.1.3 Projection Geometry
18.1.1.4 Spatial Correlation Coefficient
18.1.2 Performance Evaluation
18.2 Joint Effect of Multiple Correlated Cameras
18.2.1 Entropy-Based Approach
18.2.2 Joint Entropy Estimation
18.2.2.1 Area Division for Overlapped FoVs
18.2.2.2 Estimating the Joint Entropy of a Region
18.2.3 Coding Efficiency Prediction
18.2.4 Performance Evaluation
18.3 Collaborative Image Compression Using Clustered Source Coding
18.3.1 Data Compression Using Clustered Source Coding
18.3.1.1 OCC Problem
18.3.1.2 Integer Program Formulation of OCC Problem
18.3.2 Distributed Multicluster Coding Protocol
18.3.2.1 Approximation Ratio
18.3.3 Performance Evaluation
18.4 Correlation-Aware QoS Routing
18.4.1 Preliminaries
18.4.1.1 Metrics for Correlation of Visual Information
18.4.1.2 Video In-Network Compression
18.4.1.3 Energy Consumption Models
18.4.2 CAQR Routing
18.4.2.1 Correlation Groups Construction
18.4.2.2 Intermediate Node Selection for Correlation-Aware Differential Coding
18.4.2.3 Differential Coding-Based Intermediate Node Selection
18.4.2.4 QoS-Guaranteed Next-Hop Selection
18.4.2.5 Distributed Correlation-Aware QoS Routing
18.4.2.6 Protocol Operation
18.4.3 Performance Evaluation
18.5 Summary
Exercises
References
Part VI Data Storage and Monitoring
19 Distributed Data Storage and Retrieval Schemes in RPL/IPv6-Based Networks
19.1 Introduction and Motivations
19.2 State of the Art
19.2.1 Related Work on the IoT
19.2.2 Standard Communication Protocols
19.2.3 Distributed Storage
19.3 Redundant Distributed Storage
19.3.1 Introduction
19.3.2 LG Mechanism
19.3.3 Performance Evaluation
19.4 Redundant Distributed Storage with RPL
19.4.1 Overview of RPL
19.4.2 RG Mechanism
19.4.3 Performance Evaluation
19.4.3.1 Effect of the Number of Replicas
19.4.4 How to Retrieve the Stored Data?
19.5 Data Retrieval with RPL
19.5.1 Related Work
19.5.2 Data Retrieval Mechanism with RG
19.5.3 Performance Evaluation
19.6 Conclusion
Acknowledgment
References
20 Monitoring Mechanism for Wireless Sensor Networks: Challenges and Solutions
20.1 Introduction
20.2 Related Work
20.2.1 The Monitoring Mechanisms
20.2.2 Energy-Aware Mechanisms
20.3 MAC IEEE 802.15.4
20.4 Challenges in the Monitoring Process for WSNs
20.4.1 Preliminary and Definitions
20.4.2 Different Hidden Regions
20.4.3 Problem with Hidden Regions
20.4.3.1 Monitored Vulnerable Hidden Region
20.4.3.2 Monitor Vulnerable Hidden Region
20.4.4 Impact of the Distance on the Hidden Areas
20.4.5 Energy Consumption by the Monitoring Mechanism
20.5 Solutions and Analysis
20.5.1 Network Model
20.5.2 In the Case of Non-Beacon-Enabled
20.5.2.1 Probability of Condition 1
20.5.2.2 Probability of Condition 2
20.5.3 In the Case of Beacon-Enabled
20.5.3.1 Evaluation of the Monitor’s Overhearing Time
20.5.3.2 Simulation Results of Overhearing Time
20.5.3.3 Evaluation of the Probability of Having a Monitor’s Accurate Observation (Pw)
20.5.3.4 Simulation Results of the Probability of Having an Accurate Observation Pw
20.5.4 Case of Reputation Metric Assessment with the Monitoring Mechanism
20.6 Conclusion
References
21 Building and Orchestrating Centralized Remote Management Procedures for Wireless Sensor Networks
21.1 Managing Wireless Sensor Networks
21.1.1 Introduction
21.1.2 The TinyOS Platform
21.1.3 Application Development in TinyOS
21.1.4 The WSNs IM Scenery
21.2 General Purpose, Open, IM
21.2.1 OpenRSM Overview
21.2.2 Fundamental IM Tasks
21.3 Integrating WSN Management in OpenRSM
21.3.1 Introduction
21.3.2 Installing TinyOS Remotely
21.3.3 Running TinyOS Applications and Sensing
21.3.4 Concentrating Measurements
21.4 Conclusions and Future Perspective
References
22 Quality of Services in Wireless Sensor Networks
22.1 Introduction
22.2 Challenges for QoS Support in WSNs
22.2.1 Autonomous
22.2.2 Scalability
22.2.3 Random Deployment
22.2.4 Network Dynamics
22.2.5 Resource Constraints
22.2.6 Unbalanced Traffic
22.2.7 Data Redundancy
22.2.8 Energy Balance
22.2.9 Data-Centric
22.2.10 Multiple Sinks
22.2.11 Application Specific
22.2.12 Packet Criticality
22.2.13 Noisy Medium
22.2.14 Standardization
22.2.15 Variable Data Rate
22.2.16 Heterogeneous Networking
22.3 QoS Performance Metrics in WSNs
22.3.1 Network-Level QoS Metrics
22.3.1.1 Throughput
22.3.1.2 Delay
22.3.1.3 Jitter
22.3.1.4 Packet Loss
22.3.2 Application-Level QoS Metrics
22.3.2.1 Node Deployment
22.3.2.2 Network Connectivity
22.3.2.3 Optimum Number of Sensors
22.3.2.4 Reliability
22.3.2.5 Energy Efficiency
22.3.2.6 Network Lifetime
22.3.2.7 Coverage
22.3.2.8 Spatial Accuracy
22.3.2.9 Temporal Accuracy
22.4 QoS Perspectives in WSNs
22.4.1 Application-Specific QoS
22.4.2 Network-Specific QoS
22.4.2.1 The Event-Driven Application
22.4.2.2 The Query-Driven Application
22.4.2.3 Continuous Application
22.4.2.4 Hybrid
22.5 QoS Protocols
22.5.1 Management Platforms
22.5.1.1 Energy Management Platform
22.5.1.2 Mobility Management Platform
22.5.1.3 Task Management Platform
22.5.2 Layers
22.5.2.1 Physical Layer
22.5.2.2 Data-Link Layer
22.5.2.3 Network Layer
22.5.2.4 Transport Layer
22.5.2.5 Application Layer
22.6 Types of Services in WSNs
22.6.1 Real-Time Services
22.6.2 Non-Real-Time Services
22.7 Mechanisms to Achieve QoS in WSNs
22.7.1 Sleep-Wake Scheduling
22.7.2 Localization
22.7.3 Data Aggregation
22.7.4 Network Topology
22.7.5 Clustering
22.7.6 Cross-Layer Designs
22.7.7 Routing
22.7.8 Mobility
22.7.9 Security
22.7.10 Ultrawideband Transmission Technology
References
Part VII Applications
23 Artificial Eye vision Using Wireless Sensor Networks
23.1 Introduction
23.2 Wireless Body Area Networks
23.2.1 Types of WBAN Devices
23.2.1.1 Wireless Sensor Node
23.2.1.2 Wireless Actuator Node
23.2.1.3 Wireless Personal Device
23.2.2 WBANs Data Rates
23.2.3 Differences between WSN and WBAN
23.2.4 WBAN System Prototypes
23.2.4.1 Physiological Monitoring
23.2.4.2 Motion and Activity Monitoring
23.2.4.3 Large-Scale Physiological and Behavioral Studies
23.3 Available Sensors
23.4 Benefits of Using WBAN
23.5 Challenges in a WBAN
23.5.1 Power
23.5.2 Computation
23.5.3 Security and Interference
23.5.4 Material Constraints
23.5.5 Mobility
23.5.6 Robustness
23.5.7 Continuous Operation
23.5.8 Regulatory Requirements
23.5.9 Sensitivity of Sensors
23.6 Artificial Retina
23.6.1 Retinal Diseases: AMD and RP
23.6.2 Architecture of the Human Retina
23.6.3 How an Artificial Retina Works
23.6.4 Progression of Disease States
23.6.5 Advancements in Using WBAN in an Artificial Retina
23.7 Conclusion
23.8 Future Directions
References
Related Web Sites for More Information
24 Wireless Sensor Networks: A Medical Perspective
24.1 Introduction
24.2 WSNs and Medical Sensing
24.2.1 CodeBlue
24.2.2 Alarm-Net
24.2.3 UbiMon
24.2.4 MobiCare
24.2.5 MEDiSN
24.3 Tele-Health Care: Different Perspectives
24.3.1 From the Patient’s Perspective
24.3.2 From the Physician’s Perspective
24.3.3 From the Health Care Center’s Perspective
24.4 Health Care Information Technology: Challenges and Design Issues
24.4.1 Technical Issues
24.4.1.1 Energy
24.4.1.2 Operation
24.4.1.3 Synchronization
24.4.1.4 Bandwidth
24.4.1.5 User-Friendliness
24.4.2 Nontechnical Issues
24.4.2.1 Security Concerns
24.4.2.2 Authentication
24.4.2.3 Data Confidentiality
24.4.2.4 Data Integrity
24.4.2.5 Availability
24.4.2.6 Data Freshness
24.4.2.7 Biomedical Security Threats
24.4.2.8 Privacy Concerns
24.5 Wireless Body Area Network
24.5.1 Structure, Methodology, and Components
24.5.2 Applications
24.5.2.1 Medical Applications
24.5.2.2 Lifestyle, Sports, and Other Applications
24.6 Conclusion and Future Directions
References
25 Smart Environmental Monitoring Using Wireless Sensor Networks
25.1 Introduction
25.2 Environmental Monitoring: A Background
25.3 Smart Environmental Monitoring: An Overview
25.3.1 Architecture
25.3.2 Challenges and Design Issues
25.3.2.1 Deployment
25.3.2.2 Localization
25.3.2.3 Security
25.3.2.4 Privacy
25.4 Smart Environmental Monitoring Applications
25.4.1 Agriculture
25.4.2 Air and Water Quality
25.4.3 Noise Pollution
25.4.4 Climate Change Monitoring and Weather Forecasting
25.4.5 Structural Health Monitoring
25.4.6 Natural Disaster Detection
25.5 Conclusions and Future Directions

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Tags: Ibrahiem El Emary, Ramakrishnan, Wireless, Applications