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Titlebook: Localization in Underwater Sensor Networks; Jing Yan,Haiyan Zhao,Xinping Guan Book 2021 The Editor(s) (if applicable) and The Author(s), u

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31#
發(fā)表于 2025-3-26 21:35:42 | 只看該作者
Introduction,This chapter presents the network architecture of underwater sensor networks (USNs). According to the different measurement ways, the localization schemes for wireless sensor networks are briefly reviewed. Based on this, the weak communication characteristics of USNs are summarized, through which the problems studied in this book are provided.
32#
發(fā)表于 2025-3-27 01:37:16 | 只看該作者
33#
發(fā)表于 2025-3-27 07:19:40 | 只看該作者
Localization in Underwater Sensor Networks978-981-16-4831-1Series ISSN 2366-1186 Series E-ISSN 2366-1445
34#
發(fā)表于 2025-3-27 11:00:23 | 只看該作者
35#
發(fā)表于 2025-3-27 15:31:23 | 只看該作者
Asynchronous Localization of Underwater Sensor Networks with Mobility Prediction,in this chapter can reduce the localization time as compared with the exhaustive search-based localization method. Meanwhile, it can effectively eliminate the influences of clock asynchronization and node mobility.
36#
發(fā)表于 2025-3-27 18:52:04 | 只看該作者
Async-Localization of USNs with Consensus-Based Unscented Kalman Filtering,ower bounds and convergence conditions are also analyzed. Finally, simulation results show that the proposed localization algorithm can reduce the localization time as compared with the exhaustive search method. Meanwhile, the proposed localization algorithm can improve localization accuracy by comparing with other works.
37#
發(fā)表于 2025-3-28 00:07:41 | 只看該作者
Privacy Preserving Asynchronous Localization of USNs,nd experiment results show that the proposed localization algorithms can avoid the leakage of location information, while the localization accuracy can be significantly enhanced by comparing with the other works.
38#
發(fā)表于 2025-3-28 02:08:52 | 只看該作者
39#
發(fā)表于 2025-3-28 08:45:48 | 只看該作者
40#
發(fā)表于 2025-3-28 12:29:35 | 只看該作者
Reinforcement Learning-Based Asynchronous Localization of USNs,lem due to its insensitivity to the local optimal. Besides that, the performance analyses of proposed algorithm are given. Finally, simulation and experimental results show that the localization performance in this chapter can be significantly improved as compared with the other works.
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