Description
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.
Table of Contents
Chapter 1 Introduction.- Chapter 2 Asynchronous Localization of Underwater Sensor Networks with Mobility Prediction.- Chapter 3 Asynchronous Localization of Underwater Sensor Networks with Consensus-Based Unscented Kalman Filtering.- Chapter 4 Reinforcement Learning Based Asynchronous Localization of Underwater Sensor Networks.- Chapter 5 Privacy Preserving Asynchronous Localization of Underwater Sensor Networks.- Chapter 6 Privacy-Preserving Asynchronous Localization of Underwater Sensor Network with Attack Detection and Ray Compensation .- Chapter 7 Deep Reinforcement Learning Based Privacy-Preserving Localization of Underwater Sensor Networks.- Chapter 8 Conclusion and future perspective.



