Noisy Data Gathering in Wireless Sensor Networks via Compressed Sensing and Cross Validation.

In wireless sensor networks (WSNs), sensor data are usually corrupted by the noise. Meanwhile, it is inevitable to face the problems of node energy in WSNs. For both of these questions, this paper proposes a data gathering method via compressed sensing combined with cross validation. In the proposed method, data gathering via CS can save and balance energy consumption of sensor nodes due to the features of CS, and CV technique is used to judge whether stable reconstruction have been obtained. This method is essentially an adaptive intelligent method. Unlike the existing methods, the proposed method does not need the knowledge of signal sparsity, noise information and/or regularization parameter while those knowledge is expensive to acquire, especially in adaptive systems. That is to say, the method proposed in this paper is not sensitive to signal sparsity, noise, regularization parameters and/or other information when it is used for WSNs data collection for noise case, but the existing methods rely heavily on the prior information. Experimental results show that the proposed data gathering method can obtain stable reconstruction results for noisy WSNs in the case of unknown signal sparsity, noise and/or regularization parameters.