Communication-Efficient Decentralized Event Monitoring in Wireless Sensor Networks

In this paper, we consider monitoring multiple events in a sensing field using a large-scale wireless sensor network (WSN). The goal is to develop communication-efficient algorithms that are scalable to the network size. Exploiting the sparse nature of the events, we formulate the event monitoring task as an l(1) regularized nonnegative least squares problem where the optimization variable is a sparse vector representing the locations and magnitudes of events. Traditionally the problem can be reformulated by letting each sensor hold a local copy of the event vector and imposing consensus constraints on the local copies, and solved by decentralized algorithms such as the alternating direction method of multipliers (ADMM). This technique requires each sensor to exchange their estimates of the entire sparse vector and hence leads to high communication cost. Motivated by the observation that an event usually has limited influence range, we develop two communication-efficient decentralized algorithms, one is the partial consensus algorithm and the other is the Jacobi approach. In the partial consensus algorithm that is based on the ADMM, each sensor is responsible for recovering those events relevant to itself, and hence only consent with neighboring nodes on a part of the sparse vector. This strategy greatly reduces the amount of information exchanged among sensors. The Jacobi approach addresses the case that each sensor cares about the event occurring at its own position. Jacobi-like iterates are shown to be much faster than other algorithms, and incur minimal communication cost per iteration. Simulation results validate the effectiveness of the proposed algorithms and demonstrate the importance of proper modelling in designing communication-efficient decentralized algorithms.