Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks

TL;DR

This paper introduces two novel color filtering-based algorithms, PCFL and ACFL, for accurate and energy-efficient localization of mobile nodes in underwater acoustic sensor networks, demonstrating significant error reduction in simulations.

Contribution

The paper presents two innovative localization algorithms using color filtering technology tailored for underwater sensor networks, improving accuracy and energy efficiency over existing methods.

Findings

PCFL reduces localization error by 30.4% compared to AFLA.

Both algorithms achieve timely and accurate localization of mobile nodes.

Simulation results validate the effectiveness of the proposed methods.

Abstract

Accurate localization for mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. Also the proportion factor of distance is proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. And the normalized nearness degrees are considered as the weighted standards to calculate coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can timely localize the mobile node. The average localization error of PCFL can decline by about 30.4% than the AFLA method.