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

TL;DR

This work tackles indoor WSN localization where unknown obstacles distort RSSI-based distance estimates. It introduces a topology-partitioning approach that identifies segmentation nodes near obstacle convex corners, constructs bisectors to partition the network into obstacle-free sub-networks, and localizes unknown nodes within each sub-network using a localized relative coordinate system calibrated to a globe frame. The method delivers strong performance, severing an average of 87% of obstacle-affected paths and achieving localization accuracy exceeding 99.9% under accurate RSSI conditions, across diverse obstacle geometries and node densities. By leveraging network topology and distributed localization within sub-networks, the approach reduces reliance on anchors, enhances scalability, and provides practical applicability for robust indoor localization in complex environments.

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%.

Figures (25)

• Figure 1: Our design can be applied to autonomous driving in indoor garages, where the WSN is deployed to detect the shape and position of obstacles, localizes sensor nodes, and dynamically determines the shortest path to the nearest available parking space. This enables real-time map construction and precise navigation in indoor environments.
• Figure 2: In RSSI technique, obstacles attenuate the RSSI signals between two nodes, leading to inaccurate distance estimation where errors are highly increased.
• Figure 3: The convex obstacles have the potential to augment the number of hops on shortest path between two nodes. The inaccurate estimation of the distance increases the error in node localization.
• Figure 4: The convex and concave corners of obstacles and their impact on the received signal quality
• Figure 5: Multiple communication paths can be established between each pair of nodes. Taking nodes $j$ and $k$ as an example, their communication paths are depicted in yellow, green, red, and violet. Among all these paths, the ones with the fewest hops are selected, as represented in green and red.