Survey of node localization scheme in underwater wireless sensor network
Highly precise node localization in underwater wireless sensor networks has been regarded as a fundamental guarantee for ensuring the reliability of data in critical tasks such as ocean monitoring and disaster early warning. However, localization errors were found to potentially induce data deviatio...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025141/ |
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| _version_ | 1849227168080461824 |
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| author | YANG Qiuling ZHU Rongxin TANG Zhichao LI Wenjie HUANG Xiangdang |
| author_facet | YANG Qiuling ZHU Rongxin TANG Zhichao LI Wenjie HUANG Xiangdang |
| author_sort | YANG Qiuling |
| collection | DOAJ |
| description | Highly precise node localization in underwater wireless sensor networks has been regarded as a fundamental guarantee for ensuring the reliability of data in critical tasks such as ocean monitoring and disaster early warning. However, localization errors were found to potentially induce data deviations and even cascading failures of system functions. To address this issue, the research progress of current underwater localization technologies was systematically reviewed. Representative methods were divided into three categories, namely traditional algorithms, deep learning models, and reinforcement learning strategies, and they were summarized and compared in terms of their fundamental principles, performance characteristics, and applicable scenarios. Furthermore, key technical aspects such as time synchronization mechanisms and communication strategies in localization systems were analyzed in depth, and their impact on overall system performance as well as associated challenges was investigated. Based on these findings, potential future directions are outlined, including collaborative optimization across layers with high energy efficiency, the integration of artificial intelligence and edge intelligence, resilient cooperative localization for dynamic topology networks, and the exploration of localization approaches inspired by biology. |
| format | Article |
| id | doaj-art-8b1a30aecde949a7b28312b03506b640 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8b1a30aecde949a7b28312b03506b6402025-08-23T19:00:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-01-01116123123686Survey of node localization scheme in underwater wireless sensor networkYANG QiulingZHU RongxinTANG ZhichaoLI WenjieHUANG XiangdangHighly precise node localization in underwater wireless sensor networks has been regarded as a fundamental guarantee for ensuring the reliability of data in critical tasks such as ocean monitoring and disaster early warning. However, localization errors were found to potentially induce data deviations and even cascading failures of system functions. To address this issue, the research progress of current underwater localization technologies was systematically reviewed. Representative methods were divided into three categories, namely traditional algorithms, deep learning models, and reinforcement learning strategies, and they were summarized and compared in terms of their fundamental principles, performance characteristics, and applicable scenarios. Furthermore, key technical aspects such as time synchronization mechanisms and communication strategies in localization systems were analyzed in depth, and their impact on overall system performance as well as associated challenges was investigated. Based on these findings, potential future directions are outlined, including collaborative optimization across layers with high energy efficiency, the integration of artificial intelligence and edge intelligence, resilient cooperative localization for dynamic topology networks, and the exploration of localization approaches inspired by biology.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025141/underwater wireless sensor networklocalization schemedeep learningreinforcement learning |
| spellingShingle | YANG Qiuling ZHU Rongxin TANG Zhichao LI Wenjie HUANG Xiangdang Survey of node localization scheme in underwater wireless sensor network Tongxin xuebao underwater wireless sensor network localization scheme deep learning reinforcement learning |
| title | Survey of node localization scheme in underwater wireless sensor network |
| title_full | Survey of node localization scheme in underwater wireless sensor network |
| title_fullStr | Survey of node localization scheme in underwater wireless sensor network |
| title_full_unstemmed | Survey of node localization scheme in underwater wireless sensor network |
| title_short | Survey of node localization scheme in underwater wireless sensor network |
| title_sort | survey of node localization scheme in underwater wireless sensor network |
| topic | underwater wireless sensor network localization scheme deep learning reinforcement learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025141/ |
| work_keys_str_mv | AT yangqiuling surveyofnodelocalizationschemeinunderwaterwirelesssensornetwork AT zhurongxin surveyofnodelocalizationschemeinunderwaterwirelesssensornetwork AT tangzhichao surveyofnodelocalizationschemeinunderwaterwirelesssensornetwork AT liwenjie surveyofnodelocalizationschemeinunderwaterwirelesssensornetwork AT huangxiangdang surveyofnodelocalizationschemeinunderwaterwirelesssensornetwork |