Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Yali Yuan, Chencheng Liang, Xu Chen, Thar Baker, Xiaoming Fu

Research output: Contribution to journalArticlepeer-review

Abstract

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg-Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

Original languageEnglish
Article number29
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Internet Technology
Volume22
Issue number2
DOIs
Publication statusPublished - 22 Oct 2021

Bibliographical note

Funding Information:
Y. Yuan and C. Liang contributed equally to this research. This project received funding from the European Union’s Horizon 2020 COSAFE project under grant agreement 824019. Authors’ addresses: Y. Yuan (corresponding author) and X. Fu, University of Goettingen, Goldschmidtstraße 7, Goettingen, Germany, 37077; emails: {yali.yuan, fu}@cs.uni-goettingen.de; C. Liang, Uppsala University, hus 1, Lägerhyddsvägen 2, Uppsala, Sweden 75237; email: chencheng.liang@it.uu.se; X. Chen, Sun Yat-sen University, Guangzhou, 510275, China; email: chenxu35@mail.sysu.edu.cn; T. Baker, Department of Computer Science, University of Sharjah, P.O.Box: 27272 Sharjah, UAE. email: tshamsa@sharjah.ac.ae. Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 1533-5399/2021/10-ART29 $15.00 https://doi.org/10.1145/3406533

Publisher Copyright:
© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • Adaptive EELA
  • dynamic environment
  • energy consumption
  • localization
  • localization coverage
  • Underwater sensor networks

Cite this