Patent
Sensor network trust evaluation method based on node behaviors and D-S evidence theory
Renjian Feng,Jiangwen Wan,Yinfeng Wu,Xiaofeng Xu,Ning Yu +4 more
- 15 Sep 2010
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TL;DR: In this paper, a sensor network trust evaluation method based on node behaviors and a D-S evidence theory is proposed, comprising five steps: 1) designing various trust factor strategies for nodes in a wireless sensor network; 2) setting trust factor weights according to network application scenes and simultaneously calculating the node behavior coefficient to obtain the direct trust value and multiple indirect trust value of an evaluated object; 3) calculating a fuzzy subset membership function for each trust value by utilizing the concepts of membership and linguistic variables of a fuzzy set theory, performing fuzzy classification on the various the trust values to form
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Abstract: The invention discloses a sensor network trust evaluation method based on node behaviors and a D-S evidence theory, comprising the following five steps: 1) designing various trust factor strategies for nodes in a wireless sensor network; 2) setting trust factor weights according to network application scenes and simultaneously calculating the node behavior coefficient mu to obtain the direct trust value and multiple indirect trust value of an evaluated object; 3) calculating a fuzzy subset membership function for each trust value by utilizing the concepts of membership and linguistic variables of a fuzzy set theory, performing fuzzy classification on the various the trust values to form the basic confidence function of the D-S evidence theory; 4) calculating the evidence difference of thedirect trust value and the indirect trust values of the evaluated node and altering the weights of the indirect trust values; and 5) adopting the Dempster synthesis rule to obtain the comprehensive trust value of the evaluated node and a final basic confidence designated value according to the altered trust weights. The invention solves the problem of difficult identification of malicious nodes in a network and ensures the safety of network data transmission.
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Citations
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TL;DR: In this article, a multi-source data fusion method in a clustering wireless sensor network is presented, which comprises the following specific contents: a distributive data fusion structure is adopted; at all cluster-head nodes, an evidence set is preprocessed according to reliability degree of the member nodes in the cluster; based on the consistent intensity and the value of primitive supporting degree of evidence, the evidence conflicts are distributed, evidence combination sequence is optimized, and the rules of conflicting evidence combination are established to synthesize all evidences; in connection with the evidence combination results, the
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