WMNs中基于节点可信度的机会路由改进算法

发布时间：2018-05-23 20:47

本文选题：WMNs + 机会路由　；参考：《计算机科学》2017年08期

【摘要】：机会路由提高了WMNs的可靠性和吞吐量,但同时由于节点候选集中存在恶意节点,导致网络性能下降。对于如何及时识别、隔离网络中的恶意节点的问题,建立了一种节点可信度评估模型。基于贝叶斯网络算法,考虑到非恶意因素带来的网络异常行为,引入不确定交互因子,改进了直接信任的评估方法,利用熵为信任值的计算和更新分配权重。引入反映节点真实参与度的行为积极因子并结合信任值得出节点的可信度,对可信度处于待定状态的节点进行未来可信度的预测,以甄别潜在的恶意节点。最后将该模型应用于机会路由ExOR中,提出了一种基于节点可信度的机会路由算法BTOR。实验结果表明,该算法可以有效检测恶意节点,在各项性能指标上比原路由算法更具优势。
[Abstract]:Opportunistic routing improves the reliability and throughput of WMNs, but at the same time due to the presence of malicious nodes in the candidate set of nodes, the network performance is degraded. For the problem of how to identify and isolate the malicious nodes in the network in time, a model for evaluating the credibility of the nodes is established. Based on Bayesian network algorithm, considering the network abnormal behavior caused by non-malicious factors, the uncertain interaction factor is introduced, and the evaluation method of direct trust is improved. Entropy is used to calculate and update the trust value to assign weight. In order to identify the potential malicious nodes, a positive behavioral factor reflecting the degree of real participation of nodes is introduced, and the credibility of the nodes is worth the trust to predict the future credibility of the nodes in the state to be determined in order to identify the potential malicious nodes. Finally, the model is applied to opportunistic routing ExOR, and an opportunistic routing algorithm based on node credibility is proposed. Experimental results show that the algorithm can detect malicious nodes effectively and has more advantages than the original routing algorithm in terms of performance.
【作者单位】：南京工业大学计算机科学与技术学院;
【基金】：国家自然科学基金项目(60673185,61073197)资助
【分类号】：TN929.5

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