Distributed Event Localization via Alternating Direction Method of Multipliers

TL;DR

This paper introduces two scalable distributed algorithms based on ADMM for event localization in sensor networks, capable of localizing targets outside the convex hull of sensors, with applications in homeland security and law enforcement.

Contribution

The paper presents novel ADMM-based algorithms that enable distributed event localization without the convex hull constraint, improving scalability and applicability.

Findings

Algorithms effectively localize events outside sensor convex hulls

Simulation results confirm algorithm effectiveness

Applicable to large-scale sensor networks

Abstract

This paper addresses the problem of distributed event localization using noisy range measurements with respect to sensors with known positions. Event localization is fundamental in many wireless sensor network applications such as homeland security, law enforcement, and environmental studies. However, most existing distributed algorithms require the target event to be within the convex hull of the deployed sensors. Based on the alternating direction method of multipliers (ADMM), we propose two scalable distributed algorithms named GS-ADMM and J-ADMM which do not require the target event to be within the convex hull of the deployed sensors. More specifically, the two algorithms can be implemented in a scenario in which the entire sensor network is divided into several clusters with cluster heads collecting measurements within each cluster and exchanging intermediate computation information to achieve localization consistency (consensus) across all clusters. This scenario is important in many applications such as homeland security and law enforcement. Simulation results confirm effectiveness of the proposed algorithms.