Security issues in wireless medical sensor networks

2020 ◽  
pp. 145-163
Author(s):  
Pranshu Dhingra ◽  
Gayathri Nagasubramanian ◽  
Rakesh Kumar Sakthivel ◽  
Ramesh Chandran
Keyword(s):  
Sensor Networks ◽  
Security Issues ◽  
Medical Sensor Networks

ReTrust: Attack-Resistant and Lightweight Trust Management for Medical Sensor Networks

2012 ◽  
Vol 16 (4) ◽  
pp. 623-632 ◽  
Author(s):  
Daojing He ◽  
Chun Chen ◽  
Sammy Chan ◽  
Jiajun Bu ◽  
Athanasios V. Vasilakos
Keyword(s):  
Sensor Networks ◽  
Trust Management ◽  
Medical Sensor Networks

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

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Gao ◽  
Wenfen Liu
Keyword(s):  
Bayesian Inference ◽  
Sensor Networks ◽  
Rapid Development ◽  
Trust Model ◽  
Tsallis Entropy ◽  
Single Node ◽  
Trust Mechanism ◽  
Trust Value ◽  
Medical Sensor Networks ◽  
Definition Of

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.

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