Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Frontiers in Marine Science

Marine Science

Fahui Miao¹  Dezhi Han¹  Xiaojun Mei²  Nasir Saeed³  Jiangfeng Xian⁴  Xinqiang Chen⁴  Huafeng Wu⁵  Bing Han⁶ 

[1] College of Information Engineering, Shanghai Maritime University, Shanghai, China;College of Information Engineering, Shanghai Maritime University, Shanghai, China;National Engineering Research Center of Ship & Shipping Control System, Shanghai, China;Department of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab Emirates;Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China;Merchant Marine College, Shanghai Maritime University, Shanghai, China;National Engineering Research Center of Ship & Shipping Control System, Shanghai, China;Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai, China;

关键词: target localization;    underwater wireless sensor networks (UWSNs);    received signal strength (RSS);    stratification effect;    Cramér-Rao low bound (CRLB);   

DOI  :  10.3389/fmars.2023.1210519

 received in 2023-04-22, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Underwater wireless sensor networks (UWSNs) are the primary enabling technology for the Internet of underwater things (IoUT), with which all underwater objects can interact and communicate. In UWSNs, localization is vital for military or civilized applications since data collected without location are meaningless. However, accurate localization using acoustic signals in UWSNs is challenging, especially for received signal strength (RSS)-based techniques. The adverse effect of hybrid loss (path and absorption loss) and stratified propagation may severely impact localization accuracy. Even though some schemes have been proposed in the literature, the accuracy is unsatisfactory. To this end, this study proposes a coarse-to-fine localization method (CFLM). The problem is reformed into an alternating nonnegative constrained least squares (ANCLS) framework, where a constrained ellipse adjustment (CEA) using block principal pivoting is proposed to obtain the coarse estimation. A refined step using a Taylor series expansion is then further presented, in which a corrected solution is acquired by iteration. Additionally, this study derives the Cramér-Rao lower bound (CRLB) to evaluate the proposed method. Simulation results show that the proposed CFLM improves the localization accuracy by up to 66 percent compared with weighted least squares (WLS), privacy-preserving localization (PPSL), two-step linearization localization approach (TLLA), particle swarm optimization-based (PSO) localization, and differential evolution-based (DE) localization under different scenarios.

【 授权许可】

Unknown   
Copyright © 2023 Mei, Han, Saeed, Wu, Miao, Xian, Chen and Han