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Distributed faulty node detection and recovery scheme for wireless sensor networks using cellular learning automata

机译:基于蜂窝学习自动机的无线传感器网络分布式故障节点检测与恢复方案

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In a wireless sensor network (WSN), there is always the possibility of failure in sensor nodes. Quality of Service (QoS) of WSNs is highly degraded due to the faulty sensor nodes. One solution to this problem is to detect and reuse faulty sensor nodes as much as possible. Accordingly, QoS of WSNs can be improved. This paper proposes a distributed cellular learning automata faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. The proposed method uses cellular learning automata to assign a status to each node based on hardware conditions, which makes the nodes do one of the network's operations. The proposed algorithm is experimented extensively and the results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm.
机译:在无线传感器网络(WSN)中,传感器节点始终存在故障的可能性。由于传感器节点故障,WSN的服务质量(QoS)大大降低。解决该问题的一种方法是尽可能多地检测和重用有故障的传感器节点。因此,可以改善WSN的QoS。提出了一种用于无线传感器网络的分布式蜂窝学习自动机故障节点分类和管理方案,该方案可以根据故障传感器节点的故障状态进行检测和重用。所提出的方法使用蜂窝学习自动机根据硬件条件为每个节点分配状态,这使节点可以执行网络的操作之一。对所提算法进行了广泛的实验,并将结果与​​现有算法进行了比较,证明了所提算法的有效性。

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