Efficient Energy Coverage And Compression Sensing Algorithm In WSNs

The target detection is an important application of WSNs, target detection model includes sampling the data which is stored in the nodes, transforming the sampled data and reconstructing the detected information. There have two important indicators which are the accuracy of the reconstruction and the life circle of the WSNs. In the traditional ways of locating, we need high accuracy in the time synchronous. There are two ways to extend the life cycle. First of all, reduce the dissemination of information energy efficiency (energy consumption/amount of information). The second approach is to balance the energy consumption of the nodes in the WSNs.According the problems above, we did the following work based the application of detection the target in WSNs:(1) Bring up a hot-suppression sub-loop multi-hop clustering algorithm in target detection systems. First, divide the entire circular network area into equally annular region. On the basis of equal energy consumption in every annular region, determined the optimal number of clusters on each ring. According the optimal number of every ring, we have the select threshold in every ring. Secondly, the algorithm select the routing through the cluster heads by normalized cluster Euclidean distance and the normalized node energy consumption has been used.(2) Bring up a compressed sensing detection model. Unlike the traditional located ways, we use safe data transformation and math ways to locate the nodes. And compressed sensing algorithm need less of the sampling data because there have correlation between transmit data, and ultimately recover the original data in high accuracy with reconstruct algorithm. Raised by this model, use the transmission consumption mode in target detection network as well as the characteristics of the cluster in the clustering algorithm, applied to the compressed sensing theory, respectively, as a sparse matrix and transfer matrix.(3)Research a suppression hot plot algorithm and compressed sensing algorithm in detecting target system. First of all, sample data under the algorithm of compressed sensing. The information collected from network nodes (superposition information sent from the target sources) and the information of network carve cluster were collected. The division of the network node location is based on a circular area of the grid the way, the location of each node is equal to the corresponding position in the matrix. Then enter the stage of data transformation, the sub-loop and multi-hop algorithm based on hot spot suppression, will the information collected from outside to inside pass to the heart, until the sink node receives the packet. The last to enter the target source information reconstruction stage, will compress the information collected through the Bayesian reconstruction algorithm for mathematical treatment, and ultimately to accurately reconstruct the location information of the target source.