Distributed Compressed Sensing-Based Data Fusion in Sensor Networks

Aiming at the monitored nonstationary signal in sensor networks, distributed compressed sensing-based data aggregation model and algorithm, DCS-DF-1, is presented to reduce the number of transmissions in sensor networks and improve the precision of sensing. To implement this algorithm, the variance of each recover sensing sequence of sensor is estimated using the wavelet transform, and the optimum weighting factor to each sensing is obtained accordingly. The fusion performance is better than each sensor and MMSE-based (minimum mean square error) method. Besides, analyze the influences of number of non-zero components to CPU time, SNR (signal-to-noise ratio), MSE (mean square error) and recover error of algorithm, as well as the relation of energy consumption to recover error. The calculation results show that DCS-DF-1 not only have better performance of stability and consistency, but also satisfy the monitoring requirements for non-stationary signal in sensor networks.