Abstract
The data transmission process in Wireless Sensor Networks (WSNs) often experiences errors and packet losses due to the environmental interference. In order to address this problem, we propose a Compressive Sensing data gathering algorithm based on Packet Loss Matching (CS-PLM). It is proven that, under tree routing, the packet loss on communication links would severely undermine the data reconstruction accuracy in Compressive Sensing (CS) based data gathering process. It is further pointed out that the packet loss in CS based data gathering exhibits the correlation effect. Meanwhile, we design a sparse observation matrix based on packet loss matching and verify that the designed matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1. Therefore, reliable transmission of the compressed data can be guaranteed by adopting the multipath backup routing among CS nodes. It is shown in the simulation results that, with a 60% packet loss ratio of the link, the CS-PLM algorithm can still ensure the effective reconstruction of the data gathered by the CS algorithm and the relative reconstruction error is lower than 5%. Therefore, it is verified that the proposed algorithm could effectively alleviate the sensitivity to packet losses for the CS based data gathering algorithm on unreliable links.
Highlights
The nodes in the wireless sensor networks (WSNs) are usually densely deployed and a lot of redundancy exists in the data gathered, which leads to the waste of the energy of the nodes
In order to evaluate the performance of the algorithm under different degree of correlation, we choose two data sets with different degree of sparsity; i.e., the results are illustrated in Figure 5 for the reconstruction accuracy of the compressive sensing (CS)-PLM algorithm and the Sparsest Random Scheduling (SRS) algorithm, where the link packet loss ratio is set to 20%
In order to address the CS based data gathering problem on unreliable links, we have proposed a CS-PLM algorithm
Summary
The nodes in the wireless sensor networks (WSNs) are usually densely deployed and a lot of redundancy exists in the data gathered, which leads to the waste of the energy of the nodes. In order to balance and reduce the energy consumption of the nodes as well as prolong the network lifetime, researchers have proposed data gathering algorithms based on compressed sensing. It was proposed to employ the autoregressive AR model to predict data changes and adaptively adjust the number of measurements to achieve the optimal reconstruction performance It was pointed out in [9] that the environmental noise of the wireless links imposes prominent influences on the transmission of Wireless Communications and Mobile Computing the undersampled CS data in the network. For the CS based data gathering problem on unreliable links, we propose a compressive sensing data gathering algorithm based on packet loss matching (CS-PLM). CWe propose a multipath backup routing transmission scheme based on Hybrid CS to guarantee the reliable handover of the CS projection data
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