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Localization in Underwater Sensor Networks

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Ocean covers 70.8% of the Earth’s surface, and it plays an important role in supporting all life on Earth. Nonetheless, more than 80% of the ocean’s volume remains unmapped, unobserved and unexplored. In this regard, Underwater Sensor Networks (USNs), which offer ubiquitous computation, efficient communication and reliable control, are emerging as a promising solution to understand and explore the ocean. In order to support the application of USNs, accurate position information from sensor nodes is required to correctly analyze and interpret the data sampled. However, the openness and weak communication characteristics of USNs make underwater localization much more challenging in comparison to terrestrial sensor networks.

In this book, we focus on the localization problem in USNs, taking into account the unique characteristics of the underwater environment. This problem is of considerable importance, since fundamental guidance on the design and analysis of USN localization is very limited at present. To this end, we first introduce the network architecture of USNs and briefly review previous approaches to the localization of USNs. Then, the asynchronous clock, node mobility, stratification effect, privacy preserving and attack detection are considered respectively and corresponding localization schemes are developed. Lastly, the book’s rich implications provide guidance on the design of future USN localization schemes.

The results in this book reveal from a system perspective that underwater localization accuracy is closely related to the communication protocol and optimization estimator. Researchers, scientists and engineers in the field of USNs can benefit greatly from this book, which provides a wealth of information, useful methods and practical algorithms to help understand and explore the ocean.

Author(s): Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Series: Wireless Networks
Publisher: Springer
Year: 2021

Language: English
Pages: 237
City: Singapore

Preface
Contents
About the Authors
Acronyms
Symbols
1 Introduction
1.1 Underwater On-Line Monitoring System
1.2 Localization Schemes for Wireless Sensor Networks
1.2.1 Localization with AOA Measurements
1.2.2 Localization with Distance-Related Measurements
1.2.2.1 Localization with TOA Measurement
1.2.2.2 Localization with TDOA Measurement
1.2.2.3 Localization with RSS Measurement
1.2.2.4 Localization with Lighthouse Approach
1.2.3 Localization with RSS Profiling Measurements
1.3 Unique Characteristics of USNs
1.4 Problems Studied in This Book
References
2 Asynchronous Localization of Underwater Sensor Networks with Mobility Prediction
2.1 Introduction
2.2 Network Architecture and Overview of the Localization
2.2.1 Network Architecture
2.2.2 Overview of the Localization
2.3 Asynchronous Localization Approach Design
2.3.1 Relationship Between Delay and Position
2.3.2 Mobility Prediction for AUVs and Sensor Nodes
2.3.3 Asynchronous Localization Optimization Problem
2.4 Position Solving and Performance Analysis
2.4.1 Position Solving for Sensor Nodes
2.4.2 Convergence of the Iterative Squares Estimators
2.4.3 Cramér-Rao Lower Bound
2.5 Simulation Results
2.5.1 Simulation Settings
2.5.2 Results and Analysis
2.6 Conclusion
References
3 Async-Localization of USNs with Consensus-Based Unscented Kalman Filtering
3.1 Introduction
3.2 Network Architecture and Overview of the Localization Procedure
3.2.1 Network Architecture
3.2.2 Overview of the Localization Procedure
3.3 Consensus-Based UKF Localization Approach
3.3.1 Relationship Between Delay and Position
3.3.2 Asynchronous Localization Optimization Problem
3.3.3 Consensus-Based UKF Localization Algorithm
3.4 Performance Analysis
3.4.1 Convergence Conditions
3.4.2 Cramér-Rao Lower Bound
3.4.3 Error of Acoustic Wave Speed
3.4.4 Computational Complexity Analysis
3.5 Simulation Results
3.5.1 Simulation Settings
3.5.2 Results and Analysis
3.6 Conclusion
References
4 Reinforcement Learning-Based Asynchronous Localization of USNs
4.1 Introduction
4.2 System Description and Problem Formulation
4.3 RL-Based Localization for USNs
4.3.1 AUV-Aided Asynchronous Localization Protocol
4.3.2 RL-Based Localization Algorithm
4.3.3 Performance Analysis
4.4 Simulation and Experimental Results
4.4.1 Simulation Results
4.4.2 Experimental Results
4.5 Conclusion
References
5 Privacy Preserving Asynchronous Localization of USNs
5.1 Introduction
5.2 Network Architecture and the Asynchronous Localization Protocol
5.2.1 Network Architecture
5.2.2 Asynchronous Localization Protocol
5.3 Asynchronous Localization Algorithms
5.3.1 PPS-Based Localization for Active Sensor
5.3.2 PPS and PPDP Based Localization for Ordinary Sensor
5.3.3 Consequence when There Exist Dishonest Nodes
5.4 Performance Analyses
5.4.1 Equivalence Analyses
5.4.2 Level of Privacy Preservation
5.4.3 Collision Avoidance of Packet
5.4.4 Communication Overhead
5.5 Simulation and Experiment Results
5.5.1 Simulation Studies
5.5.2 Experiment Studies
5.6 Conclusion
References
6 Privacy Preserving Asynchronous Localization with Attack Detection and Ray Compensation
6.1 Introduction
6.2 Network Model and Problem Formulation
6.2.1 Network Architecture
6.2.2 Clock and Stratification Models
6.2.3 Attack and Privacy Models
6.2.4 Problem Formulation
6.3 Privacy-Preserving Localization for USNs
6.3.1 Privacy-Preserving Asynchronous Transmission Protocol
6.3.2 Privacy-Preserving Estimator with Ray Compensation
6.4 Performance Analyses
6.4.1 Equivalence with the Privacy-Lacking Estimation
6.4.2 Influencing Factors of Localization Errors
6.4.3 Privacy-Preserving Property
6.4.4 Tradeoff Between Privacy and Transmission Cost
6.5 Simulation and Experiment Results
6.5.1 Simulation Studies
6.5.2 Experimental Studies
6.6 Conclusion
References
7 Deep Reinforcement Learning Based Privacy Preserving Localization of USNs
7.1 Introduction
7.2 Network Architecture and Problem Formulation
7.2.1 Network Architecture
7.2.2 Adversary and Privacy Models
7.2.3 Scenario Description
7.2.4 Problem of Interest
7.3 Privacy-Preserving Localization Protocol
7.4 DRL-Based Localization Estimator
7.4.1 Localization when All Data Is Unlabelled
7.4.2 Localization when Labelled Data Occupies the Majority
7.4.3 Localization when Unlabelled Data Occupies the Majority
7.4.4 Performance Analysis
7.5 Simulation Results
7.6 Conclusion
References
8 Future Research Directions
8.1 Space-Air-Ground-Sea Network Architecture
8.2 Intergradation Design of Localization Protocol
8.3 Learning-Based Optimization Estimator