1. Introduction
1.1 Network Architecture of Underwater Sensor Networks
1.2 Prior Arts in Localization
1.3 Underwater Weak Communication Characteristics

2. Asynchronous Localization of Underwater Sensor Networks with Mobility Prediction
2.1 Introduction
2.2 System Modeling and Problem Formulation
2.3 Design of Asynchronous Localization Approach
2.4 Performance Analysis
2.4.1 Convergence
2.4.2 Cramer-Rao Lower Bound
2.5 Simulation
2.5.1 Simulation of Active Sensor Node
2.5.2 Simulation of Passive Sensor Node
2.6 Summary
References

3. Asynchronous Localization of Underwater Sensor Networks with Consensus-Based Unscented Kalman Filtering
3.1 Introduction
3.2 System Modeling and Problem Formulation
3.3 Design of Consensus-Based UKF Localization Approach
3.4 Performance Analysis
3.4.1 Observability Analysis
3.4.2 Convergence Conditions
3.4.3 Cramer-Rao Lower Bound
3.4.4 Computational Complexity Analysis
3.5 Simulation
3.6 Summary
Reference

4. Reinforcement Learning Based Asynchronous Localization of Underwater Sensor Networks
4.1 Introduction
4.2 System Modeling and Problem Formulation
4.3 Design of Reinforcement Learning Based Asynchronous Localization Approach
4.4 Performance Analysis
4.4.1 Convergence Conditions
4.4.2 Cramer-Rao Lower Bound
4.4.3 Computational Complexity Analysis
4.5 Simulation
4.5.1 Advantage of the RL-Based Localization Strategy
4.5.3 Simulation of Active Sensor Node
4.5.4 Simulation of Passive Sensor Node
4.6 Summary
Reference

5. Privacy Preserving Asynchronous Localization of Underwater Sensor Networks
5.1 Introduction
5.2 System Modeling and Problem Formulation
5.3 Design of Privacy Preserving Based Localization Approach
5.3.1 Design of Active Sensor Node Localization Strategy
5.3.2 Design of Ordinary Sensor Node Localization Strategy
5.4 Performance Analysis
5.4.1 Equivalence Analysis
5.4.1.1 Equivalence Analysis of Active Sensor Node
5.4.1.2 Equivalence Analysis of Ordinary Sensor Node
5.4.2 Level of Privacy Preservation
5.4.3 Communication Complexity Analysis
5.5 Simulation
5.5.1 Simulation of Active Sensor Node
5.5.2 Simulation of Ordinary Sensor Node
5.6 Summary
Reference

6. Privacy-Preserving Asynchronous Localization of Underwater Sensor Network with Attack Detection and Ray Compensation
6.1 Introduction
6.2 System Modeling and Problem Formulation
6.3 Design of Privacy-Preserving Localization Approach
6.4 Performance Analysis
6.4.1 Equivalence Analysis
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
6.6 Summary
Reference

7. Deep Reinforcement Learning Based Privacy-Preserving Localization of Underwater Sensor Networks
7.1 Introduction
7.2 System Modeling and Problem Formulation
7.3 Design of
About the Author:

Jing Yan received the B.Eng. degree in Automation from Henan University, Kaifeng, China, in 2008, and the Ph.D. degree in Control Theory and Control Engineering from Yanshan University, Qinhuangdao, China, in 2014. In 2014, he was a Research Assistant with the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China. From January 2016 to September 2016, he was a Postdoc with University of North Texas, Denton, US. From October 2016 to January 2017, he was a Research Associate with University of Texas at Arlington, Arlington, US. Currently, he is an Associate Professor with Yanshan University, Qinhuangdao, China. Meanwhile, he is also an Associate Editor for IEEE Access. His research interests cover in underwater acoustic sensor networks, networked teleoperation systems, and cyber-physical systems. He has published more than 80 peer-reviewed papers in leading academic journals and conferences. He has also received numerous awards, including the Excellence Paper Award from the National Doctoral Academic Forum of System Control and Information Processing in 2012, the Outstanding Doctorate Dissertation of Hebei Province in 2015, the Excellence Paper Award from the National Doctoral Academic Forum of System Control and Information Processing in 2012, the Youth Talent Support Program of Hebei Province in 2019, the Outstanding Young Foundation of Hebei Province in 2020, and the Excellence Adviser from Oceanology International Underwater Robot Competition in 2017.

Haiyan Zhao received the B.S. degree in Automation from Yanshan University, in 2017. Currently, she is pursuing the Ph.D. degree in Control Theory and Control Engineering at Yanshan University, Qinhuangdao, China. Her research interests cover in underwater acoustic sensor networks and autonomous underwater vehicle. She won the national scholarship in 2019 and presided over Postgraduate Innovation Fund Project of Hebei in 2019.

Yuan Meng received the B.S. degree in Measurement and Control Technology and Instruments from Liaoning Technical University, Huludao, China, in 2019. Currently, she is pursuing the Ph.D. degree in Control Theory and Control Engineering at Yanshan University, Qinhuangdao, China. Her research interests include localization of underwater sensor networks and networked underwater robot control. Besides that, she won the national scholarship in 2021 and presided over Postgraduate Innovation Fund Project of Hebei in 2021.

Xinping Guan received the B.S. degree in applied mathematics from Harbin Normal University, Harbin, China, in 1986, and the M.S. degree in applied mathematics and the Ph.D. degree in electrical engineering from the Harbin Institute of Technology, Harbin, in 1991 and 1999, respectively. He is currently a Chair Professor with Shanghai Jiao Tong University, Shanghai, China. He has authored and/or co-authored four research monographs, more than 270 papers in IEEE and other peer-reviewed journals, and numerous conference papers. His current research interests include industrial cyber-physical systems, wireless networking and applications in smart city and smart factory, and underwater sensor networks. Dr. Guan was a recipient of the National Outstanding Youth Honored by the NSF of China, the Changjiang Scholar by the Ministry of Education of China, and the State-Level Scholar of New Century Bai Qianwan Talent Program of China. He is an Executive Committee member of the Chinese Automation Association Council and the Chinese Artificial Intelligence Association Council.