Joint event detection and environment perception in decentralised wireless sensor networks

Abstract

In an event detection application, sensor nodes measure signals that are emitted from multiple events and attenuated by the environment, and then collaborate to estimate the locations and magnitudes of the events. Existing event detection algorithms often assume that the number of the events is known in advance and/or the attenuation coefficient of the environment is given. This paper considers the case that both the number of the events and the attenuation coefficient of the environment are unknown. Through exploiting the sparse nature of the events, we propose an ℓ-norm regularised least squares formulation that automatically estimates the number of the events as well as their locations and magnitudes; the attenuation coefficient of the environment also appears as an optimisation variable. We develop a decentralised algorithm and its accelerated variant to solve the joint event detection and environment perception problem using the alternating direction method of multipliers (ADMM).