A Dynamic Trust Framework for Opportunistic Mobile Social Networks
A Dynamic Trust Framework for Opportunistic Mobile Social Networks
复制标题
机会性移动社交网络的动态信任框架
DOI:
10.1109/tnsm.2017.2776350
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发表时间:
2018-03
期刊:
影响因子:
--
通讯作者:
L.C.K.Hui
中科院分区:
文献类型:
--
作者:
Eric Ke Wang;Li Yueping;Yunming Ye;S.M.Yiu;L.C.K.Hui
Opportunistic mobile social network (OMSN) enables users to form an instant social network for information sharing (e.g., people watching the same soccer game can share their instant comments). OMSN is ad hoc in nature, thus relies on the cooperation of members regarding message transmission. However, some uncooperative or malicious behavior from abnormal members may reduce network performance, even damage the entire network. Currently, there does not exist effective mechanisms to detect selfish and malicious nodes. To tackle this problem, we propose a dynamic trust framework to facilitate a node to derive a trust value of another node based on the behavior of the latter. The novelty of our framework includes the following: 1) we design a new metric for a trust value of a node and 2) we propose a “two–hop feedback method” that requires intermediate nodes in a forwarding path to generate ACK messages to verify a node’s honesty if they are two hops away. In most existing trust models, final ACK messages are considered as critical factors. In OMSN, nodes are not fully connected and final ACK messages cannot be reliably received. In order to avoid the problem that few final ACK messages can be received, we propose a “two–hop feedback method.” Simulation results show that our approach is able to detect a majority of abnormal nodes including malicious nodes, selfish nodes, and those nodes launching conspiracy attacks. Thus, the entire network efficiency can be improved without negative impact of abnormal nodes. Besides, our trust framework can be easily applied to the current popular routing protocols of opportunistic networks.
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DOI:
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发表时间:
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期刊:
2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
影响因子:
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