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Optimal Localization Prediction Using Red Vulture Arrival Approach in Underwater Sensor Networks

Published: 22 January 2024 Publication History

Abstract

This paper proposes an Angle Time of the Red Vulture Arrival Approach (ATRVA) to address the propagation speed, latency, and error of mobility of node localization in UWSNs. Time Difference of Arrival (TDOA) is utilized to determine the range of known and unknown nodes. The novel Red Vulture Optimization Algorithm (RVOA) promotes the precise estimation of the node's localization. Additionally, the Euclidean distance strategy combines the window prediction method to decrease estimation error and delay. Consequently, the node mobility model is used to predict each time point of velocity along with position, which allows for the conclusion of underwater location. The proposed approach is analyzed and compared with existing techniques such as Movement Prediction Localization (MPL), Genetic Algorithm -Scalable Localization with Mobility Prediction (GA-SLMP), Scalable Localization with Mobility Prediction (SLMP) and Localization Scheme for Large Scale (LSLS). Thus, the proposed one is superior to others in terms of energy consumption, position error, and location coverage.

References

[1]
Jing Yan, Xiaoning Zhang, Xiaoyuan Luo, Yiyin Wang, Cailian Chen, and Xinping Guan. 2018. Asynchronous localization with mobility prediction for Underwater Acoustic Sensor Networks. IEEE Transactions on Vehicular Technology 67, 3 (2018), 2543–2556.
[2]
Inam Ullah, Jingyi Chen, Xin Su, Christian Esposito, and Chang Choi. 2019. Localization and detection of targets in underwater wireless sensor using distance and angle based algorithms. IEEE Access 7 (2019), 45693–45704.
[3]
Shams, Pablo Otero, Muhammad Aamir, and Fozia Hanif Khan. 2021. Joint algorithm for multi-hop localization and time synchronization in underwater sensors networks using single anchor. IEEE Access 9 (2021), 27945–27958.
[4]
Guangjie Han, Songjie Shen, Hao Wang, Jinfang Jiang, and Mohsen Guizani. 2019. Prediction-based delay optimization data collection algorithm for Underwater Acoustic Sensor Networks. IEEE Transactions on Vehicular Technology 68, 7 (2019), 6926–6936.
[5]
Wenbo Zhang, Guangjie Han, Xin Wang, Mohsen Guizani, Kaiguo Fan, and Lei Shu. 2020. A node location algorithm based on node movement prediction in Underwater Acoustic Sensor Networks. IEEE Transactions on Vehicular Technology 69, 3 (2020), 3166–3178.

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      cover image ACM Other conferences
      ICDCN '24: Proceedings of the 25th International Conference on Distributed Computing and Networking
      January 2024
      423 pages
      ISBN:9798400716737
      DOI:10.1145/3631461
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 22 January 2024

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      Author Tags

      1. Localization Algorithm
      2. Mobility
      3. Optimization
      4. Underwater Node Location

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