Robust Node Localization for Rough and Extreme Deployment Environments

TL;DR

This paper presents a robust localization method for sensor nodes in harsh environments, using compressive sensing to identify susceptible nodes and estimate their positions accurately with minimal anchors.

Contribution

It introduces a novel compressive sensing-based approach for identifying vulnerable nodes and robustly estimating their positions in extreme deployment conditions.

Findings

Achieves accurate localization with fewer anchors.

Demonstrates resilience under harsh environmental conditions.

Outperforms existing methods in robustness and efficiency.

Abstract

Many applications have been identified which require the deployment of large-scale low-power wireless sensor networks. Some of the deployment environments, however, impose harsh operation conditions due to intense cross-technology interference, extreme weather conditions (heavy rainfall, excessive heat, etc.), or rough motion, thereby affecting the quality and predictability of the wireless links the nodes establish. In localization tasks, these conditions often lead to significant errors in estimating the position of target nodes. Motivated by the practical deployments of sensors on the surface of different water bodies, we address the problem of identifying susceptible nodes and robustly estimating their positions. We formulate these tasks as a compressive sensing problem and propose algorithms for both node identification and robust estimation. Additionally, we design an optimal anchor configuration to maximize the robustness of the position estimation task. Our numerical results and comparisons with competitive methods demonstrate that the proposed algorithms achieve both objectives with a modest number of anchors. Since our method relies only on target-to-anchor distances, it is broadly applicable and yields resilient, robust localization.