Information recovery via block compressed sensing in wireless sensor networks

Wireless sensor networks (WSNs) always collect an enormous amount of rich diverse environmental information. When WSNs grow large in scale, it is difficult for the sink to gather data due to the increasing transmission overhead. In order to improve the fidelity of data recovery and save energy, we propose a novel data aggregation and space-time global recovery scheme. The scheme exploits block Compressed Sensing (CS) to achieve both recovery fidelity and energy efficiency. We employ diffusion wavelets to partition a large WSN into sub-networks, which are regarded as blocks. For each sub-networks, diffusion wavelets are applied to get the sparse basis for the data to be compressed. In addition, we introduce temporal and spatial correlation into the optimum target function of global recovery algorithm. Simulation results show that space-time global recovery scheme holds higher fidelity of data recovery and greatly reduces the energy consumption. Typically, the normalized mean absolute error of our recovery scheme is less than 5%. Furthermore, the energy consumption is reduced more than 50% against plain CS.