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

机译:对传感器和其他网络中的对手使用分布式共识中的信任

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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:广泛的研究工作已致力于与对手达成分布式共识。许多不同的应用程序推动了人们对该领域日益增长的兴趣,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。我们考虑在以达成共识为目标的代理商网络中检测拜占庭式对手的问题。我们提出了一种新颖的信任模型,该模型既可以建立基于本地证据的本地信任,也可以基于基于本地信任值的本地交换来建立全局信任。我们描述了一种将信任评估机制集成到传统共识算法中的信任感知共识算法,并基于本地证据提出了各种本地决策规则。为了进一步增强信任评估本身的鲁棒性,我们还提供了一种信任传播方案,以考虑网络中其他节点的证据。该算法灵活且可扩展,以合并决策规则和信任模型的更复杂的设计。然后,通过仿真表明,即使在连接性小于2f +1的稀疏网络中,信任感知的共识算法也可以有效地检测拜占庭式对手并将其从共识迭代中排除,其中f是对手的数量。这些结果可用于信任证据的融合,以及当存在恶意传感器(例如在电网传感和监控中)时用于传感器融合。

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