Robust Node Localization for Rough and Extreme Deployment Environments

2025-07-04

AI Summary

To address large localization errors of wireless sensor network nodes under harsh environments—such as strong interference, extreme weather, and severe motion—this paper proposes a robust joint localization framework based on compressed sensing. The method unifies vulnerable-node identification and position estimation into a sparse signal recovery problem, designs an efficient sparse reconstruction algorithm, and jointly optimizes anchor-node spatial deployment to enhance system stability. Compared with conventional approaches, the proposed framework achieves significant suppression of measurement distortions induced by environmental disturbances using only a small number of anchor nodes. Experimental results demonstrate a 32%–47% reduction in localization error under heavy rainfall and high-temperature conditions. The framework exhibits high robustness, low hardware overhead, and strong environmental adaptability, establishing a novel paradigm for reliable deployment of edge-aware systems under extreme operational conditions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Robust node localization in harsh environments
• Identifying susceptible nodes under extreme conditions
• Optimal anchor configuration for accurate positioning

Innovation

• Compressive sensing for node identification
• Optimal anchor configuration design
• Distance-based robust localization algorithm