Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks (EAI/Springer Innovations in Communication and Computing)

✍ Scribed by Satish R. Jondhale, R. Maheswar, Jaime Lloret

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 218
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book briefly summarizes the current state of the art technologies and solutions for location and tracking (L&T) in wireless sensor networks (WSN), focusing on RSS-based schemes. The authors offer broad and in-depth coverage of essential topics including range-based and range-free localization strategies, and signal path loss models. In addition, the book includes motion models and how state estimation techniques and advanced machine learning techniques can be utilized to design L&T systems for a given problem using low cost measurement metric (that is RSS). This book also provides MATLAB examples to demonstrate fundamental algorithms for L&T and provides online access to all MATLAB codes. The book allows practicing engineers and graduate students to keep pace with contemporary research and new technologies in the L&T domain.

✦ Table of Contents

Preface
Acknowledgment
Contents
About the Editors
Chapter 1: Fundamentals of Wireless Sensor Networks
1.1 Introduction to Wireless Sensor Network
1.2 WSN Versus Other Wireless Networks
1.3 Sensor Node Architecture
1.3.1 The Power Supply
1.3.2 The Sensing Unit
1.3.3 The Processor Unit
1.3.4 The Communication Unit
1.3.5 Location Finding Unit
1.4 Sensor Network Communication Architecture
1.5 Design Constraints for WSN
1.5.1 Power Consumption
1.5.2 Memory
1.5.3 Deployment, Topology, and Coverage
1.5.4 Communication and Routing
1.5.5 Security
1.5.6 Production Costs
1.5.7 Fidelity and Scalability
1.6 Existing WSN Platforms
1.6.1 Wins
1.6.2 Eyes
1.6.3 Pico-Radio
1.6.4 Mica Mote Family
1.7 Applications of WSN
1.7.1 Military Applications
1.7.2 Environment Monitoring Applications
1.7.3 Health Applications
1.7.4 Home Applications
1.7.5 Other Commercial Applications
References
Chapter 2: Target Localization and Tracking Using WSN
2.1 Introduction to WSN-Based L&T
2.1.1 Typical L&T Scenario in Wireless Sensor Networks
2.1.2 Classification of Target L&T Techniques
2.2 RSSI-Based Target L&T Approach
2.3 Environmental Characterization Through Path Loss Models
2.3.1 Free Space Path Loss Model
2.3.2 Two-Ray Ground Model
2.3.3 Log Normal Shadow Fading Model (LNSM)
2.3.4 OFPEDM
2.4 Technologies for RSSI-Based L&T
2.4.1 RFID
2.4.2 Wi-Fi
2.4.3 Bluetooth
2.4.4 Zigbee
2.5 Traditional Techniques for Target Localization
2.5.1 Trilateration
2.5.2 Triangulation
2.5.3 Fingerprinting
2.6 Mobility Models for Target Tracking
2.6.1 Constant Velocity (CV) Model
2.6.2 Constant Acceleration (CA) Model
2.7 State Estimation Techniques for Target Tracking
2.7.1 Standard Kalman Filter (KF)
2.7.2 UKF
2.8 Challenges Associated with RSSI-Based Indoor L&T
References
Chapter 3: Survey of Existing RSSI-Based L&T Systems
3.1 Survey of Application of Various Wireless Technologies for Indoor Tracking
3.2 Survey of Application of Bayesian Filtering in RSSI-Based Target Tracking
3.3 Survey of Application of ANN in RSSI-Based Target Tracking
3.4 Survey of Application of BLE Technology in RSSI-Based Target Tracking
3.5 Limitations in the Existing RSSI-Based L&T Systems
References
Chapter 4: Trilateration-Based Target L&T Using RSSI
4.1 System Assumptions and Design for Trilateration-Based L&T
4.2 Flow of Trilateration-Based L&T Algorithm
4.3 Performance Metrics for Assessment of L&T Performance
4.4 Discussion on Results
4.4.1 Case I Results: Testing the Impact of Environmental Dynamicity on L&T (Variation in RSSI Measurement Noise)
4.4.2 Case II Results: Testing the Impact of Anchor Density on L&T
4.5 Conclusions
MATLAB Code for Trilateration-Based Target L&T
References
Chapter 5: KF-Based Target L&T Using RSSI
5.1 System Assumptions and Design of KF-Based L&T
5.2 Flow of Trilateration+KF and Trilateration+UKF-Based L&T Algorithms
5.3 Performance Metrics for Assessment of L&T Performance
5.4 Discussion on Results
5.4.1 Case I Results
5.4.2 Case II Results
5.4.3 Case III Results
5.5 Conclusions
MATLAB Code for KF-Based Target L&T
References
Chapter 6: GRNN-Based Target L&T Using RSSI
6.1 GRNN Architecture for Target L&T Applications
6.2 System Assumption and Design
6.3 Flow of Trilateration+KF- and Trilateration+UKF-Based L&T Algorithms
6.4 Performance Metrics
6.5 Discussion on Results
6.5.1 Case I Results
6.5.2 Case II Results
6.5.3 Case III Results
6.6 Conclusions
MATLAB Codes for GRNN and KF Framework-Based Target L&T
References
Chapter 7: Supervised Learning Architecture-Based L&T Using RSSI
7.1 Supervised Learning Architectures for L&T
7.1.1 FFNT
7.1.2 Radial Basis Function Neural Network (RBFN or RBFNN)
7.1.3 Multilayer Perceptron (MLP)
7.2 Training Functions in ANN
7.3 Application of Supervised Learning Architectures for L&T
7.3.1 System Assumptions and Design
7.3.2 Evaluation Parameters
7.3.3 Algorithmic Flow of Proposed ANN Architectures
7.3.4 Discussion on Results
7.4 Conclusion
MATLAB Code for Cases I and II
References
Index