Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

TL;DR

This paper introduces a distributed online algorithm for self-localization in wireless sensor networks, enabling nodes to estimate their positions with limited communication, even with sporadic data and link failures.

Contribution

It presents a novel distributed on-line localization method that is robust to measurement noise, sporadic data, and link failures, with proven consistency for fixed sensors.

Findings

Algorithm is consistent for fixed sensors.

Effective in scenarios with sporadic measurements and link failures.

Validated through simulations and real-world experiments.

Abstract

The present work considers the localization problem in wireless sensor networks formed by fixed nodes. Each node seeks to estimate its own position based on noisy measurements of the relative distance to other nodes. In a centralized batch mode, positions can be retrieved (up to a rigid transformation) by applying Principal Component Analysis (PCA) on a so-called similarity matrix built from the relative distances. In this paper, we propose a distributed on-line algorithm allowing each node to estimate its own position based on limited exchange of information in the network. Our framework encompasses the case of sporadic measurements and random link failures. We prove the consistency of our algorithm in the case of fixed sensors. Finally, we provide numerical and experimental results from both simulated and real data. Simulations issued to real data are conducted on a wireless sensor network testbed.