Wireless Sensor Networks based on Distributed Compressed-Sensing.docVIP

A Detection Probability Localization Algorithm of Wireless Sensor Networks based on Distributed Compressed-Sensing Lin Shuo, Liu Wei Abstract With fast development of science and technology, People gradually need more and more information, causing significant pressure on the sampling. The sampling rate must be two times higher than the highest frequency of the signal based on Nyquist sampling theorem. Compressed Sensing (CS) employs a special sampling method which can capture and represent compressible signals at a rate significantly below the Nyquist rate. It can relieve the pressure of sampling process in Wireless Sensor Networks. And a cooperative self-localization method based on probability for wireless sensor networks is proposed. The method firstly estimates the initial Position of the located node based on the joint Probability density function of the distance between the located node and the connected reference nodes. Further, based on the refinement principle, a cooperative localization method is studied by making the best of the neighbour nodes and giving the neighbour nodes some confidence. The method improves the estimation accuracy as well as makes more unknown nodes to be located. Key Words Wireless Sensor Networks, Localization, DV-Hop, Compressed Sensing Introduction Wireless Sensor Networks (WSNs) distinguish themselves from other traditional wireless or wired networks through sensor and actuator based interaction with the environment. Localization is a fundamental and essential issue for wireless sensor networks. In context-aware applications, localization enables the intelligent selection of appropriate devices, and may support useful coordination among devices. The desired granularity of localization is itself application-dependent. Many of the applications and communication protocols of WSNs are based on the location information of sensor nodes, such as calculating the coverage of WSNs, tracking the location of events and intruders, geo