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May 19, 2021IEEE Internet of Things Journal167 citations

Localization Algorithm for Underwater Sensor Network: A Review

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JLJunhai LuoYYYang YangZWZhiyan Wang

Key Points

  • To review cutting-edge localization algorithms, network architectures, and acoustic communication challenges within underwater wireless sensor networks (UWSNs).
  • Surveyed underwater localization techniques and compared operational differences between terrestrial and underwater sensor networks.
  • Classified localization algorithms using a taxonomy based on distance measurement, network scale, and anchor utilization.
  • Identified fundamental constraints of underwater acoustic communication, structural network requirements, routing methods, and localization evaluation metrics.
  • Demonstrated algorithmic trade-offs across distance-measurement techniques, anchor-node dependencies, and scalability across varying network dimensions.

Abstract

As a significant component of ocean exploration, underwater localization has attracted extensive attention in both military and civil fields. Due to its low cost and convenience, underwater wireless sensor networks (UWSNs) is favored by related fields. As an important part of the Internet of Things (IoT), it can strengthen the trinity of land, sea, and air. The location of the underwater sensor node is the foundation of the UWSN application and is one of the research hotspots today. Many relevant research scholars have optimized the localization algorithm or introduced new methods to better locate the target nodes, thus promoting the development of related fields. In this article, the challenges of underwater acoustic communication and underwater positioning, the comparison between UWSNs and terrestrial wireless sensor networks (WSNs), the network structure, routing technology, and localization evaluation criteria are all introduced in detail. Moreover, we survey many cutting-edge underwater localization algorithms based on a new taxonomy (i.e., distance measurement, network scale, and anchor utilization). Moreover, these localization algorithms are compared and analyzed from various aspects. Meanwhile, localization discussion and research prospects are illustrated in this article.

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Cite This Study

Luo et al. (2021) studied this question.

synapsesocial.com/papers/69d8f7b233ca018b39ae45d4https://doi.org/10.1109/jiot.2021.3081918
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