Research On Low-power Wireless Sensor Networks Based On Compressed Sensing

With extensively applied of wireless sensor network (WSN), the advantage of WSN becomesincreasingly prominent, but the energy is limited and the work environment is particular. Thenenergy conservation becomes the primary consideration in WSN. In order to improve the survivallife of the network, the compressive sensing theory (CS), which is the latest information processingtechnology, is applied to WSN. So the original signal would be recovered efficiently with fewer bitsof information. Thereby, it is possible for WSN to reduce the traffic. Then energy consumption isdecreased and the survival time is extended of WSN benefiting by CS.The paper studies the signal sparse compressed sensing theory firstly. On this basis, CS isapplied to WSN, and the LEACH routing model based on CS is designed and analyed. Thesimulation results show that the energy consumption of WSN reduces obviously with the effect ofCS theory. The distributed compressed sensing theory (DCS) uses for making a better use of thecorrelation between the signals. At the same time, its three joint sparse models, OSGA andDCS-SOMP algorithms are the objects for simulation and analysis. Comparison shows that theWSN model has a better performance with DCS than that with CS in terms of energy consumption.In order to further reduce network energy consumption, the paper improves cluster head selectionmethod and MAC layer access mechanism in traditional model, according to the characteristics ofthe DCS. Moreover making use of joint reconstruction algorithm of DCS to solve the problem ofdata collision. Then presents a Cluster Head Rotated within cluster and Collision Accepted base onDCS network model HRCA-DCS. The simulation results show that the improved HRCA-DCSmodel has a better network viability than the model based on DCS.