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Application Of Spatial-temporal Correlation In Data Fusion For Wireless Sensor Network

Posted on:2012-07-10Degree:MasterType:Thesis
Country:ChinaCandidate:S Q TangFull Text:PDF
GTID:2218330362460258Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
Spatial-temporal correlation along with the collaborative nature of the Wireless sensor network brings significant potential advantages for the development of data fusion. This paper focuses on the application of spatial-temporal correlation in data fusion for wireless sensor network. We analyze the spatial-temporal correlation models and do research on virtual sampling scheme, adaptive data fusion scheme and a active buffer management algorithm based on the spatial-temporal correlation.Firstly,this paper improves the point source model and analyzes the temporal correlation, spatial correlation and spatial-temporal correlation along with the distortion function of them basing on data fusion model, Also, analysis of the spatial-temporal correlation is carried out basing on the distortion function of it.Secondly,this paper studies VSS based on spatial-temporal correlation. VSS primarily utilizes redundancy in the nodes to get some subsets to sample the environment at any one time. Nodes not sampling the environment are in low-power sleep mode. The virtual cluster technique based on the spatial-temporal correlation fits for target tracking application in researches. The virtual cluster network has better adaptive capabilities for the uncertainty of the object state. Furthermore, VSS can balance the energy consumption amongst nodes by using a round robin method and reduce redundant sensor data to conserve energy while retaining the meaningful information.Thirdly, the paper proposes a novel adaptive data fusion algorithm basing on the spatial-temporal correlation. Firstly, it proposes a adaptive sampling algorithm based on the temporal-correlation. The algorithm determines the state of node sampling according to the distortion function of temporal correlation, and then adjusts the sampling frequency adaptively according to the different states. The algorithm is so strong self-adaptive that it can effectively capture the change of target and adjust the sampling frequency adaptively. Secondly, basing on the spatial correlation, it proposes an algorithm that can adjust the degree of spatial data fusion adaptively. Basing on the distortion function of spatial correlation, the minimum number of reliable nodes and the maximum numbers of redundancy nodes are determined, according to which the spatial state is determined. And the degree of spatial fusion is adjusted. The algorithm makes fusion meet the reliability and redundancy requirement and reduce the energy consumption, and ensure the accuracy of data collection.In the end, this paper designs a buffer management algorithm for wireless sensor nodes basing on the spatial-temporal correlations. It introduces the buffer management algorithm from three parts: calculating queue's probability to drop a packet, selecting a packet and active buffer management algorithm based on spatial correlation. Differing from other buffer management algorithm based on packet priority, the algorithm uses real queue length instead of average queue length. thus it is easy to calculate queue length and can drop packets on the spatial correlation, both theory analysis and simulation results prove that the algorithm is designed to drop redundant data and drop packets in a early time in order to buffer burst data in conflict and keep fair among the sub-clusters.
Keywords/Search Tags:spatial-temporal correlation, data fusion, virtual cluster, adaptive fusion, active buffer management
PDF Full Text Request
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