Fusion-Based Cooperative Support Identification for Compressive Networked Sensing

TL;DR

This paper introduces a fusion-based cooperative support identification method for distributed compressive sensing in wireless sensor networks, enhancing signal recovery efficiency through local decisions and minimal data exchange.

Contribution

It presents a novel support identification protocol combining local sensing, binary decision making, and data fusion, improving resource efficiency in distributed compressive sensing.

Findings

Effective support identification with reduced communication cost

Improved global signal reconstruction accuracy

Validated through computer simulations

Abstract

This paper proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted $\ell_1$-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.