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Cat Swarm Optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network

机译:基于CAT群优化基于网络分区的自主恢复,在异构水下无线传感器网络中的网络划分

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Underwater wireless sensor network is characterized with dynamic network topology owing to node mobility. Frequent changes in position of nodes due to water current cause network partitioning. This often results in frequent network failures and causes void spaces. Frequent network failures lead to unreliable data transmissions where nodes injudiciously drain their power resource. Such a network needs to adapt its network routing in order to diminish the challenges caused by node mobility. A dynamic approach addressing the issues of node mobility will also enhance the network lifetime. Node mobility creates articulation points (AP) in the network topology. AP is similar to bridges in a graph. AP leads to partitioning of the network that only yields failed and unreliable transmission. Cat Swarm Optimization is explored here for detecting a possible partition in the network prior to its occurrence in the seeking mode of the algorithm. In the tracking mode of the algorithm the cat with the closest distance to the predicted AP is selected to move towards the predicted AP in order to avoid the network from partitioning. The proposed approach enhances the network lifetime as it avoids disconnections and failed transmissions. It would conserve energy consumption of nodes by reducing the possibilities of failed and retried transmissions.
机译:由于节点移动性,水下无线传感器网络具有动态网络拓扑。由于水流导致网络划分的频繁变化导致网络分区。这通常会导致频繁的网络故障并导致空隙空间。频繁的网络故障导致不可靠的数据传输,其中节点造成损害其电源资源。这种网络需要调整其网络路由,以便减少节点移动性引起的挑战。解决节点移动性问题的动态方法也将增强网络生命周期。节点移动性在网络拓扑中创建铰接点(AP)。 AP类似于图中的桥梁。 AP导致网络仅划分仅产生失败和不可靠的传输。这里探讨了CAT群优化,用于在算法的寻求模式下检测网络中的可能分区。在算法的跟踪模式中,选择具有最接近预测AP的距离的CAT以朝向预测的AP移动,以避免网络分区。所提出的方法可以增强网络寿命,因为它避免了断开连接和变速箱。通过降低失败和翻回传输的可能性,它将节省节点的能量消耗。

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