SCIENCE CHINA Information Sciences, Volume 59, Issue 12: 122310(2016) https://doi.org/10.1007/s11432-015-5467-7

A secure routing scheme based on social network analysis in wireless mesh networks

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  • ReceivedAug 14, 2015
  • AcceptedSep 30, 2015
  • PublishedApr 22, 2016


As an extension of wireless ad hoc and sensor networks, wireless mesh networks (WMNs) are employed as an emerging key solution for wireless broadband connectivity improvement. Due to the lack of physical security guarantees, WMNs are susceptible to various kinds of attack. In this paper, we focus on node social selfish attack, which decreases network performance significantly. Since this type of attack is not obvious to detect, we propose a security routing scheme based on social network and reputation evaluation to solve this attack issue. First, we present a dynamic reputation model to evaluate a node's routing behavior, from which we can identify selfish attacks and selfish nodes. Furthermore, a social characteristic evaluation model is studied to evaluate the social relationship among nodes. Groups are built based on the similarity of node social status and we can get a secure routing based on these social groups of nodes. In addition, in our scheme, nodes are encouraged to enter into multiple groups and friend nodes are recommended to join into groups to reduce the possibility of isolated nodes. Simulation results demonstrate that our scheme is able to reflect node security status, and routings are chosen and adjusted according to security status timely and accurately so that the safety and reliability of routing are improved.



This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61302071, 61471109, 61502075), Fundamental Research Funds for the Central Universities (Grant Nos. N150404015, DUT15QY06, DUT15RC(3)009), China Postdoctoral Science Foundation Funded Project (Grant No. 2015M580224), Liaoning Province Doctor Startup Fund (Grant No. 201501166), and State Key Laboratory for Novel Software Technology, Nanjing University (Grant No. KFKT2015B12).


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