In order to improve the data reconstruction accuracy and alleviate the influence of packet loss over unreliable links on the Compressive Sensing (CS) data gathering in sensor networks

we propose a Packet Loss Matching Data Gathering Algorithm Based on Compressive Sensing (CS-MDGA) in this paper. This proposed algorithm establishes the correlation effect of the network data with the CS technique. We further design the Sparse Observation Matrix based on Packet Loss Matching (SPLM) in this paper. In addition

we prove that the designed observation matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1

which can guarantee the reliable delivery of the multi-path routing data among different nodes. The simulation results show that the relative reconstruction error of this proposed algorithm is still lower than 5% even when the packet loss rate of the link is as high as 60%. Therefore

it is verified that this proposed algorithm not only exhibits high reconstruction accuracy

but also effectively alleviates the influence of packet losses over unreliable links on the CS-based data collection.