Applying trust enhancements to reactive routing protocols in mobile ad hoc networks
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Due to the characteristics of mobile ad hoc networks, such networks are more susceptible to the destruction of malicious attacks or denial of cooperation. It would be easy for an adversary or a malicious node to launch attacks on routing function, especially attacks on packet routing. In order to mitigate these hazards, we incorporate the concept of ‘trust’ into MANETs, and abstract a decentralized trust inference model. The core of this model is trust computation, which is divided into two parts: historical trust assessment and trust prediction. We can quantify a node’s historical trust based on its historical behaviors via introducing multiple trust attributes. The fuzzy AHP method based on entropy weights is used to calculate the weight of trust attributes. By making use of the obtained historical trust data sequence, we propose an improved dynamic grey-Markov chain prediction measure to effectively estimate node’s trust prediction. In order to verify the validity of our trust model, we propose a trust-enhanced unicast routing protocol and a trust-enhanced multicast routing protocol, respectively. Both of the two new protocols can provide a feasible approach to kick out the untrustworthy nodes and choose the optimal trusted routing path. Moreover, the new proposed data-driven route maintenance mechanisms can reduce the routing overhead. The persuasive experiments have been conducted to evaluate the effectiveness of the new proposed trust-enhanced routing protocols in the aspects of packets delivery ratio, end-to-end latency, malicious node detection and attack resistance.
KeywordsMobile ad hoc network Malicious node Decentralized trust inference model Trust-enhanced routing protocol Data-driven route maintenance mechanism
We would like to thank anonymous referees for their helpful suggestions to improve this paper. This research is sponsored by the Natural Science Foundation of China (NSFC) under Grant Nos. 61402245, 61572267 and 61272425, the Project funded by China Postdoctoral Science Foundation under Grand Nos. 2015T80696 and 2014M551870, the Shandong Provincial Natural Science Foundation No. ZR2014FQ010, the Project funded by Qingdao Postdoctoral Science Foundation, the Open Project Foundation of Shandong Provincial Key Laboratory of Software Engineering under Grant No. 2013SE01, PAPD, CICAEET and Foundation of Huawei under Grant No. YB2013120027. And it is partially supported by NSF CNS-1015802, HK GRF 123609, China Thousand-Talent Program, China National 863 Program 2013AA013202.
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