点对点网络中节点撤销的自适应方法

G. Reshmi, S. D. Madhu Kumar
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引用次数: 0

摘要

针对点对点网络中行为不端的节点撤销问题,许多研究者提出了不同的解决方案。特别是,重点是开发具有低计算和通信成本的解决方案。现有的两个主要解决方案是基于法定人数的投票和自我撤销。在节点撤销的自适应方法中,网络中的每个节点单独选择最合适的方式来响应检测到的错误行为。在这项工作中,我们提出了一个决策过程,根据网络参数为网络中的每个节点选择最佳撤销策略。我们正在调整现有的自适应节点撤销方法在车辆网络中,用于点对点网络。我们还开发了一种机制来识别有资格参与决策过程的可信节点,并根据该算法的要求收集和共享某些信息。指出了建立节点可信性的重要性,并采用特征信任算法实现节点可信性。最后,通过仿真研究了该方法在各种网络条件下的效率,并与现有方法进行了比较。实验结果表明,我们的自适应方法比原有的投票方法更快。自撤销方法在速度上与自适应方法相似。在决策正确性方面,自适应方法表现出比投票和自我撤销策略更好的性能。本文还描述了所提出的方法在云计算场景中的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive method for node revocation in peer-to-peer networks
Different solutions have been proposed by many researchers for revocation of misbehaving nodes in peer to peer networks. In particular, the focus is on developing solutions with low computation and communication costs. Two main existing solutions are quorum-based voting and self-revocation. In an adaptive method for node revocation, each node in a network individually selects the most suitable way to respond to a detected mis-behaviour. In this work, we present a decision making procedure which chooses the best revocation strategy for each node in the network, depending on the network parameters. We are tuning an existing method for adaptive node revocation in vehicular networks, for peer to peer networks. We have also developed a mechanism to identify the trusted nodes eligible to participate in the decision making procedure as well as to collect and share certain information as required by this algorithm. The importance of establishing trustworthiness of the nodes has been identified and Eigen trust algorithm is employed for implementing the same. Finally, the efficiency of the proposed method under various network conditions is studied in comparison with the existing methods by simulation. The experimental results show that our adaptive method is faster than the original voting method. The self-revocation method is similar to adaptive method in speed. The adaptive method has showed better performance than voting and self-revocation strategies in terms of correctness of decisions. The relevance of the proposed method in a cloud computing scenario is also described.
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