Yuan, Y, Liang, C, Chen, X, Baker, T and Fu, X (2021) Adaptive Fuzzy Game-based Energy Efficient Localization in 3D Underwater Sensor Networks. ACM Transactions on Internet Technology, 22 (2). ISSN 1533-5399

Preview

Text Adaptive_EELA_Second_Submission_Ver (1).pdf.pdf - Accepted Version Download (2MB) | Preview

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 di!culty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspeci"ed underwater environment. Therefore, we propose the Adaptive Energy-E!cient Localization Algorithm (Adaptive EELA) to enable energy-e!cient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby 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 signi"cant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

Item Type:	Article
Additional Information:	Copyright 2021 held by Owner/Author. Publication Rights Licensed to ACM
Uncontrolled Keywords:	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing, 0806 Information Systems
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Computer Science & Mathematics
Publisher:	Association for Computing Machinery
Date Deposited:	07 Aug 2020 09:27
Last Modified:	19 Aug 2022 10:46
DOI or ID number:	10.1145/3406533
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13178

View Item