DILAND: An Algorithm for Distributed Sensor Localization with Noisy Distance Measurements

TL;DR

This paper introduces DILAND, a robust distributed algorithm for sensor localization in noisy and unreliable communication environments, ensuring almost sure convergence to true sensor positions.

Contribution

DILAND extends previous algorithms by handling noisy measurements and unreliable links, guaranteeing convergence under minimal network assumptions.

Findings

DILAND converges almost surely to true sensor locations.

The algorithm is robust to communication noise and link failures.

It requires minimal assumptions on network connectivity.

Abstract

In this correspondence, we present an algorithm for distributed sensor localization with noisy distance measurements (DILAND) that extends and makes the DLRE more robust. DLRE is a distributed sensor localization algorithm in $\mathbb{R}^m$ $(m\geq1)$ introduced in \cite{usman_loctsp:08}. DILAND operates when (i) the communication among the sensors is noisy; (ii) the communication links in the network may fail with a non-zero probability; and (iii) the measurements performed to compute distances among the sensors are corrupted with noise. The sensors (which do not know their locations) lie in the convex hull of at least $m+1$ anchors (nodes that know their own locations.) Under minimal assumptions on the connectivity and triangulation of each sensor in the network, this correspondence shows that, under the broad random phenomena described above, DILAND converges almost surely (a.s.) to the exact sensor locations.