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

• Published in: Frontiers in Marine Science 2023-09, Vol.10
• Main Authors: Mei, Xiaojun, Han, Dezhi, Saeed, Nasir, Wu, Huafeng, Miao, Fahui, Xian, Jiangfeng, Chen, Xinqiang, Han, Bing
• Format: Article
• Language: English
• Subjects: Absorption loss; Accuracy; Acoustics; Algorithms; Cramér-Rao low bound (CRLB); Global positioning systems; GPS; Localization; Methods; Optimization; Propagation; received signal strength (RSS); Simulation; Stratification; stratification effect; target localization; Transmission loss; Underwater; underwater wireless sensor networks (UWSNs)
• ISSN: 2296-7745; 2296-7745
• DOI: 10.3389/fmars.2023.1210519

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.