Multi-attribute compressive data gathering

Abstract

The data gathering is a fundamental operation in wireless sensor networks. Among approaches of the data gathering, the compressive data gathering (CDG) is an effective solution, which exploits the spatiotemporal correlation of raw sensory data. However, in the multi-attribute scenario, the performance of CDG decreases in every attribute's capacity because more measurements are on demand. In this paper, under the general framework of CDG, we propose a multi-attribute compressive data gathering protocol, taking into account the observed interattribute correlation in the multi-attribute scenario. Firstly, we find that 1) the rapid growth of the demand on measurements may decline the network capacity, 2) according to the compressive sensing theory, correlations among attributes can be utilized to reduce the demand on measurements without the loss of accuracy, and 3) such correlations can be found on real data sets. Secondly, motivated by these observations, we propose our approach to decline measurements. Finally, the real-trace simulation shows that our approach outperforms the original CDG under multiattribute scenario. Compared to the CDG, our approach can save 16% demand on measurements.