A learning based joint compressive sensing for wireless sensing networks

Abstract

In wireless sensor networks (WSNs), due to the complex deployment environment, the sparse expression capability of the same sparse transformation basis may vary greatly in different time or different applications. These dynamic characteristics will further affect the recovery performance of compressive sensing in WSNs. Traditional predefined sparse transformation basis cannot satisfy the requirement of such dynamic change. Traditional dictionary learning technique which trains sparse transformation basis from historical data also has some problems. First, in WSN applications, the acquisition of a large number of historical data is costly, or even impossible. Secondly, sparse transformation basis learned from specific historical data is in fact a static transformation basis, which still faces the less dynamic problem. In this paper, we present a sparse expression model as well as a training method which can learn the sparse transformation basis from compressive sensing measurement results rather than original historical data. These training data can be easily obtained from compressive sensing based schemes in WSNs, and thus the sparse transformation basis can be updated in time, which enhances the dynamic adaptability. We also present a joint recovery scheme to explore the spatio-temporal relationship among multiple sources, and further improve the compressive sensing recovery performance. Evaluation results based on real data demonstrate that the proposed scheme can achieve the performance superior to the most closely related work.