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Journal of Sensors
Volume 2014 (2014), Article ID 649392, 10 pages
Research Article

BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks

Yan Gao1,2 and Wenfen Liu1,2

1Zhengzhou Institute of Information Science and Technology, Zhengzhou 450002, China
2State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China

Received 24 July 2014; Revised 11 November 2014; Accepted 11 November 2014; Published 3 December 2014

Academic Editor: Romeo Bernini

Copyright © 2014 Yan Gao and Wenfen Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the rapid development and application of medical sensor networks, the security has become a big challenge to be resolved. Trust mechanism as a method of “soft security” has been proposed to guarantee the network security. Trust models to compute the trustworthiness of single node and each path are constructed, respectively, in this paper. For the trust relationship between nodes, trust value in every interval is quantified based on Bayesian inference. A node estimates the parameters of prior distribution by using the collected recommendation information and obtains the posterior distribution combined with direct interactions. Further, the weights of trust values are allocated through using the ordered weighted vector twice and overall trust degree is represented. With the associated properties of Tsallis entropy, the definition of path Tsallis entropy is put forward, which can comprehensively measure the uncertainty of each path. Then a method to calculate the credibility of each path is derived. The simulation results show that the proposed models can correctly reflect the dynamic of node behavior, quickly identify the malicious attacks, and effectively avoid such path containing low-trust nodes so as to enhance the robustness.