Topology Partitioning-based Self-Organized Localization in Indoor WSNs with Unknown Obstacles

TL;DR

This paper introduces a topology partitioning-based localization method for indoor wireless sensor networks that effectively mitigates obstacle-induced errors, achieving over 99.99% accuracy by identifying and severing obstructed communication paths.

Contribution

It presents a novel approach that leverages network topology to improve localization accuracy in indoor environments with unknown obstacles, outperforming existing methods.

Findings

Successfully severed 87% of obstacle-affected paths on average.

Achieved localization accuracy exceeding 99.99%.

Effective across diverse obstacle configurations and node densities.

Abstract

Accurate indoor node localization is critical for practical Wireless Sensor Network (WSN) applications, as Global Positioning System (GPS) fails to provide reliable Line-of-Sight (LoS) conditions in most indoor environments. Real-world localization scenarios often involve unknown obstacles with unpredictable shapes, sizes, quantities, and layouts. These obstacles introduce significant deviations in measured distances between sensor nodes when communication links traverse them, severely compromising localization accuracy. To address this challenge, this paper proposes a robust range-based localization method that strategically identifies and severs obstructed communication paths, leveraging network topology to mitigate obstacle-induced errors. Across diverse obstacle configurations and node densities, the algorithm successfully severed 87% of obstacle-affected paths on average. Under the assumption that Received Signal Strength Indicator (RSSI) provides accurate distance measurements under LoS conditions, the achieved localization accuracy exceeds 99.99%.