ENERGY-SAVING GOSSIP ALGORITHM FOR COMPRESSED SENSING IN MULTI-AGENT SYSTEMS

In this paper, we present a new recovery algorithm for in-network compressed sensing from measurements acquired in multi-agent systems. Each agent has to recover a common signal taking advantage of local communication and simple computations. Such distributed problem typically incurs a high energy cost due to inter-node communications. In this paper we propose an iterative distributed algorithm to address this problem, featuring pairwise gossip communications and updates. We propose some theoretical results on its dynamics and numerical comparisons with the most recent approaches proposed in literature. The performance turns out to be competitive in terms of reconstruction accuracy, complexity, and energy consumption required for convergence.