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Using trust in distributed consensus with adversaries in sensor and other networks

机译:在传感器和其他网络中对具有对手的分布式共识的信任

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Extensive research efforts have been devoted to distributed consensus with adversaries. Many diverse applications drive this increased interest in this area including distributed collaborative sensor networks, sensor fusion and distributed collaborative control. We consider the problem of detecting Byzantine adversaries in a network of agents with the goal of reaching consensus. We propose a novel trust model that establishes both local trust based on local evidences and global trust based on local exchange of local trust values. We describe a trust-aware consensus algorithm that integrates the trust evaluation mechanism into the traditional consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also provide a trust propagation scheme in order to take into account evidences of other nodes in the network. The algorithm is flexible and extensible to incorporate more complicated designs of decision rules and trust models. Then we show by simulation that the trust-aware consensus algorithm can effectively detect Byzantine adversaries and excluding them from consensus iterations even in sparse networks with connectivity less than 2f + 1, where f is the number of adversaries. These results can be applied for fusion of trust evidences as well as for sensor fusion when malicious sensors are present like for example in power grid sensing and monitoring.
机译:广泛的研究工作已经致力于与对手分发达成共识。许多不同的应用程序驱动对该区域的这种兴趣增加,包括分布式协作传感器网络,传感器融合和分布式协作控制。我们考虑了在达成共识的目标网络中检测拜占庭对手的问题。我们提出了一种新颖的信任模式,基于当地证据和基于本地信托价值的本地交换的地方证据和全球信任,建立了本地信任。我们描述了一种信任意识的共识算法,将信任评估机制集成到传统的共识算法中,并根据本地证据提出各种局部决策规则。为了进一步增强信任评估本身的稳健性,我们还提供了信任传播方案,以便考虑网络中其他节点的证据。该算法灵活且可扩展,以包含更复杂的决策规则和信任模型的设计。然后我们通过模拟显示信任感知的共识算法可以有效地检测拜占庭对手,并使它们不包括共识迭代,即使在稀疏网络中,连通性小于2F + 1,其中F是对手的数量。这些结果可以应用于信任证据的融合以及当例如在电网传感和监控中的恶意传感器时,传感器融合。

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