Research On The Applications Of Compressed Sensing In Wireless Sensor Network

The data compression technology is one of the efficient measures for increasing the speed of wireless data communication. Traditional data compression technology is based on Nyquist sampling theorem, reaching the goal of compression by decreasing redundancy of information. In recent years, Compressed Sensing(CS) comes out as a new sampling theory, it doesn't have to obey Nyquist sampling theorem, and it can keep the original structure of signals by attaining the non-adaptive linear projections. So, CS can gather the compressed data directly and get more information from less data.In this paper, CS and Distributed Compressed sensing(DCS) are introduced firstly. Then, the measurement rate and time complexity of the two theories for reconstructing signals are analyzed and compared. Based on the above theoretical study, the two theories are taken into processing sensory signals by conjoining actual datasets and the energy model in wireless sensor netwok(WSN). The experimental results show that if there's no noise and interference, compared with CS, DCS can achieve the same effect with less number of measurement. So, in this situation, DCS can do better in saving enegy of the netwok. In addition to this, the different affection of quantization noise for the both theories are fully discussed. The simulation results indicate that DCS also need less measurements, but it need more quantification bit number because it is more sensitive to quantization noise.