Data Gathering and Processing for Large-Scale Wireless Sensor Networks

Mass data are usually collected and processed in large and ultra large-scale wireless sensor networks, and this will greatly affect the life of intelligent sensors and the performance of network. In this paper, we propose an approach to reduce the collected data from wireless sensor networks by using compressed sensing method. Compressed sensing is a new sampling method that the data sampling and compressing can be done simultaneously. Compressed sensing can significantly reduce the collected data size by lowering the sampling rates of sensors, but it is non-adaptive and its algorithm has high computational complexity as well. We put forward and achieved the parallel processing of compressed sensing algorithm for improving algorithms execution speed. Experiment results shows that the proposed scheme significantly outperforms existing solutions in terms of reconstruction accuracy.