4.7 Article

EODL: Energy Optimized Distributed Localization Method in three-dimensional underwater acoustic sensors networks

Journal

COMPUTER NETWORKS
Volume 141, Issue -, Pages 179-188

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.comnet.2018.05.025

Keywords

Underwater acoustic sensor networks; Distributed localization; Innermost intersection body

Funding

  1. National Natural Science Foundation of China [51779058, 51509057, 61572172]
  2. National Major Projects some intelligent control and support software
  3. Fundamental Research Funds for the Central Universities [DUTI7RC(3)094]
  4. National Science and Technology Major Project [20152X01041101-002]
  5. Program for Liaoning Excellent Talents in University [LR2017009]

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The localization represents one of the most important aspects of underwater acoustic sensor networks, offering three-dimensional monitoring of marine environment, military detection and attack missions. This paper proposes the Energy Optimized Distributed Localization Method (EODL) as an efficient technique for the distributed sensor network localization based on the mobile beacon water. This technique employs an Autonomous Underwater Vehicle (AUV) as the mobile beacon. The moving path of the AUV in the three-dimensional water environment and the time interval of the broadcast transmitting are found by applying a qualitative analysis of the superiority of the rectangular path. In such a technique, the unknown sensor node position is calculated by monitoring beacon signals without requiring time synchronization between nodes. We consider the innermost intersection body for the localization process, to calculate the geometric intersection relation of different information signal ranges. Then, we calculate the location information of the nodes. Moreover, the localization error analysis of the method will be presented. We compare the simulation results of the proposed method (EODL) with the LDSN. In addition, the experimental results verify that the proposed method introduces less error and higher coverage rate comparing with the conventional methods. (C) 2018 Elsevier B.V. All rights reserved.

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