scholarly journals Trust evaluation model for wireless sensor networks

Author(s):  
Junbcom Hur ◽  
Younho Lee ◽  
Hyunsoo Yoon ◽  
Daeseon Choi ◽  
Seunghun Jin
2018 ◽  
Vol 131 ◽  
pp. 1156-1163 ◽  
Author(s):  
Tao Yang ◽  
Xu Xiangyang ◽  
Li Peng ◽  
Li Tonghui ◽  
Pan Leina

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhengwang Ye ◽  
Tao Wen ◽  
Zhenyu Liu ◽  
Xiaoying Song ◽  
Chongguo Fu

Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs). In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.


2010 ◽  
Vol 159 ◽  
pp. 29-34
Author(s):  
Shu Ming Xiong ◽  
Xiao Qian Qu ◽  
Yong Zhao Zhan ◽  
Xin Sheng Wang ◽  
Liang Min Wang

Due to the node failures incurred by intrusion threat, a wireless sensor networks will initiate topology re-generation, which is based on correct availability evaluation of current intrusion-tolerant topology. The paper proposes an availability evaluation model based on semi-Markov process (SMP) to estimate topology availability of the intrusion-tolerant topology concerning the effects from intrusion behaviors. In view of some limitations of node computation ability and storage ability, this model reduces the complexities resulting from modeling the different intrusion threats and is set up on the uniform intruding results to simplify the model design. Using the DTMC model embedded in SMP topology availability is computed and finally we analyze the sensitivity to parameters in the model.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiming Zhang ◽  
Yu Yang ◽  
Wei Yang ◽  
Fuying Wu ◽  
Ping Li ◽  
...  

The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.


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