Research On Compressing Dada Based On Compressed Sensing In Wireless Sensor Network

Wireless sensor network(WSN) is the fundamental of Internet of thing, Cyber Physical System, Internet of Vehicles and so on. So WSN is a hot research field since21 st century. But the power model of node cannot be replaced, so saving energy is becoming the key factors for extending the lifespan of WSN and the crucial issues in the WSN. Compressed Sensing(CS) is an effective method to solve the above problems.CS is a new signal sampling theory proposed by Tao in 2004. Comparing with traditional Nyquist sampling law, the biggest difference of CS is that the sampling rate depends on the structure of signal rather than the bandwidth of signals. So the CS can recovery the signal, even in the condition that the sampling rate is lower than that of Nyquist. And applying CS to WSN has become a hot area of research in recent years.Since CS can significantly reduce the dimensions of the transmitting information via linear operation, so the energy consumption of nodes in sending and receiving information is reduced greatly. But CS is just one kind of method to save energy. The purpose of applying WSN is to obtain information about monitoring area. So if the CS was applied into WSN, the critical problems is that how to compress the information as possible as we can to reduce the transmission and how to recovery signal to get outside information.To solve above problems, we focus on the designing of sensing matrix and improving the distributed compresses sensing(DCS). The contribution of design sensing matrix is that adopt the spherical geometry theory to design sensing matrix and certify the sensing matrix satisfying Restricted Isometry Property(RIP). The contribution of improving distributed compressed sensing is as follows: Firstly, we put forward a new weighted method to obtain the common component of all signals,and then a method of the lossy coding for shortening the length of common component is proposed. After that, we improve the calculation formula of the distributed compressed sensing to ensure that the common component can be received losslessly. The numerical results show that,both method can improve effect of signalrecovery.