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

Abstract

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.

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

“…Future work will focus on expanding the database by integrating water quality data from multiple seasons to achieve a more comprehensive understanding of the dynamic changes in water quality [74]. This provides significant inspiration for our research and we plan to apply this approach in our study area, aiming to establish a comprehensive monitoring system integrating aerial and ground observations [75].…”

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

“…Numerous methods and algorithms have been proposed to address the node localization problem, aiming to achieve accurate and efficient localization results. We categorize the related work into three main approaches: range-based, range-free, and hybrid localization techniques [19][20][21][22][23][24][25][26][27][28].…”

“…Range-based localization techniques rely on distance or range measurements between sensor nodes to estimate their positions [19][20][21][22]. The authors in [19] consider the RSS method for location identification. In [20], an unconventional localization method employing the least-squares method was presented.…”

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

“…Compared to other methods, RSS-based schemes can obtain location information without requiring additional facilities or time synchronization [17]. Therefore, there has been a surge of interest in RSS-based underwater target localization [18][19][20][21].…”

“…Some research in the literature has been investigated to address these adverse impacts. For instance, Mei et al developed a coarseto-fine localization method (CFLM) that formulates the problem within an alternating nonnegative constrained least squares (ANCLS) framework and offers an iterative closed-form solution [18] to address the challenges. Similarly, Chang et al presented another closedform expression that transforms the underwater localization problem into a generalized trust region sub-problem (GTRS) framework.…”