Abstract
Packet loss is one of the most critical factors affecting the accuracy of compressed sensing (CS)-based data gathering algorithms. In this paper, a data gathering algorithm is proposed to decrease energy consumption and resist packet loss. Each cluster head formulates a sparsest random measurement matrix (SRMM) via the received data to avoid the measurement of the lost node and decrease the number of measurements. To employ spatial correlation between clusters, the sink constructs a block diagonal matrix (BDM) as a measurement matrix via SRMMs and reconstructs the entire network data. Additionally, the optimal number of clusters is discussed under this framework to reach the minimum power consumption. The SR-BDM is evaluated on the emulated data and the real sensor data from GreenOrbs, respectively. The simulation results indicate the proposed algorithm reaches high precision, both with reliable links and with a 60% packet loss rate link, without causing increased energy consumption.
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