Randomly Deployed Wireless Sensor Networks

✍ Scribed by Xi Chen

• Publisher: Elsevier
• Year: 2020
• Tongue: English
• Leaves: 133
• Edition: 1
• Category: Library

✦ Synopsis

Wireless sensor networks have a range of applications, including military uses and in environmental monitoring. When an area of interest is inaccessible by conventional means, such a network can be deployed in ways resulting in a random distribution of the sensors. Randomly Deployed Wireless Sensor Networks offers a probabilistic method to model and analyze these networks. The book considers the network design, coverage, target detection, localization and tracking of sensors in randomly deployed wireless networks, and proposes a stochastic model. It quantifies the relationship between parameters of the network and its performance, and puts forward a communication protocol. The title provides analyses and formulas, giving engineering insight into randomly deployed wireless sensor networks. Five chapters consider the analysis of coverage performance; working modes and scheduling mechanisms; the relationship between sensor behavior and network performance properties; probabilistic forwarding routing protocols; localization methods for multiple targets and target number estimation; and experiments on target localization and tracking with a Mica sensor system.

✦ Table of Contents

Contents
Acknowledgments
1 Introduction
1.1 Overview of WSNs
1.2 Research topics in WSNs
Sensing technology
Low-power-consumption sensor nodes
Low-cost sensor nodes
Wireless communication technology
Communication protocols
Node localization
Time synchronization
Network security
Application layer protocols
1.3 Applications of WSNs
Military applications
Precision agriculture
Industrial applications
Environmental monitoring
Health monitoring
Smart city
Civil engineering
Other applications
1.4 Outline of the book
References
2 Point coverage analysis
2.1 Coverage in WSNs: elements
2.1.1 Sensor sensing models
2.1.2 Coverage formulations
2.1.3 Deployment approaches
2.1.4 Scheduling mechanisms
2.2 Point coverage in homogeneous WSNs
2.2.1 Point coverage probability
2.2.2 Complete coverage vs. point coverage
2.2.3 Analysis of boundary effect
2.3 Point coverage in heterogeneous WSNs
2.3.1 Point coverage probability
2.3.2 Optimization
2.4 Simulation experiments
2.4.1 Homogeneous WSNs
2.4.2 Heterogeneous WSNs
2.5 Summary
References
3 Percentage coverage schemes
3.1 Location-based percentage coverage
3.1.1 Occupation area
3.1.2 Percentage coverage configuration protocol
3.1.3 Simulation and analysis
Performance test
Performance comparison: PCCP and CCP
3.2 Location-free percentage coverage
3.2.1 Occupation area
3.2.2 Standing guard protocol
Description of SGP
Analysis of SGP performance
3.2.3 Simulation and analysis
Performance test
Performance comparison: SGP and the three schemes in [21]
3.3 Summary
References
4 Dynamic target detection
4.1 State switching scheme
4.2 Analysis of detection probability
4.2.1 Detection probability of an individual sensor
4.2.2 Detection probability with given target's path
4.2.3 Number of nodes which success in detecting target
4.3 Performance optimization
4.3.1 Maximizing detection probability with given network lifetime
4.3.2 Maximizing network lifetime with budget limit
4.4 Summary
References
5 Probabilistic forwarding protocols
5.1 Probabilistic forwarding (ProFor)
5.1.1 Model description
Generating the gradient for each node
5.1.2 Analysis of relay probability
5.1.3 Simulation and analysis
5.2 Enhanced probabilistic forwarding (EnProFor)
5.2.1 Analysis of relay probability
5.2.2 Simulation and analysis
5.3 Analysis of energy consumption
Relaying by sibling nodes
Simulation and analysis of the number of relays
Multiple base stations
Message priority
5.4 Summary
References
6 Stochastic scheduling algorithms
6.1 Model description
Upper bound of the lifetime
6.2 Stochastic scheduling
6.2.1 Analysis of energy consumption
6.2.2 Dynamic tuning of working probability
6.2.3 Performance test
6.3 Stochastic scheduling considering node failure
6.3.1 Model of node failure
6.3.2 Dynamic turning of working probability
6.4 Performance improvement
6.4.1 Revised dynamic working probability
6.4.2 Optimization of the period length
6.5 Summary
References
7 Energy-based multisource localization
7.1 Multisource localization with given source number
Signal energy observation model
7.1.1 Localization using maximum likelihood estimation
7.1.2 Location initialization for localization
Single source localization
Multisource location initialization
Multiresolution search based on source location initialization
7.1.3 Simulation and analysis
7.2 Source number estimation
7.2.1 Estimation based on node selection and clustering
Node selection
Node clustering
7.2.2 Estimation based on minimum description length criterion
Minimum Description Length criterion (MDL)
Source parameters estimation with given source number
Estimation procedure based on minimum description length criterion
7.2.3 Simulation and analysis
7.3 Summary
References
Index