Improved Sparsest Random Scheduling for Compressive Data Gathering under Unreliable Link in Wireless Sensor Networks

Abstract

Compressive sensing (CS) is a promising data acquisition and compression technology that can be used to reduce and balance the transmission cost in wireless sensor networks (WSNs). However, the transmission links are unreliable frequently and data loss is very common in wireless sensor networks. What's more, the reconstruction accuracy is reduced greatly due to unreliable links and data loss. In order to address this problem, a data gathering and reconstruction algorithm is proposed, which is based on improved sparsest random measurement matrix and collection tree protocol (CTP). It needs less necessary number of measurements than state-of-the-art methods to reach high reconstruction accuracy, and has the ability to resist the negative impact of data loss on the recovery accuracy. The real sensory data from Intel Indoor project are used in simulation and the results show that the proposed algorithm is effective.