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Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

机译:基于改进河道动力学的无线传感器网络最优数据聚合树

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The restricted energy of nodes is one of the most important challenges in wireless sensor networks. Since data transmissions among nodes consume most of the nodes' energy, thus, minimizing the unnecessary transmissions reduces the consumed energy. One of the sources of this problem is the redundancy of raw data that can be eliminated at the aggregation points. As a result, data aggregation can be considered as an effective strategy to tackle the mentioned issue and to optimize the communication energy consumption. In this paper, the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree. For this, this paper proposes to apply a swarm intelligent algorithm named river formation dynamics. The simulation results show that the proposed algorithm outperforms in comparison to the famous ant colony optimization algorithm in terms of network lifetime. Simulations show that the proposed algorithm makes nearly 4% and 50% improvement in lifetime of wireless sensor networks than ant colony optimization and shortest path routing, respectively.
机译:节点的受限能量是无线传感器网络中最重要的挑战之一。由于节点之间的数据传输会消耗大部分节点的能量,因此,将不必要的传输减至最少会减少能耗。此问题的根源之一是原始数据的冗余,可以在聚合点处将其消除。结果,数据聚合可以被认为是解决上述问题并优化通信能耗的有效策略。在本文中,传感器节点以树状结构组织,并且数据聚合在树枝结的中间节点中完成。树协议的主要特征之一是通过优化数据聚合树的结构来减少能耗。为此,本文提出了一种名为河流形成动力学的群体智能算法。仿真结果表明,相对于著名的蚁群优化算法,该算法在网络寿命方面表现优于传统的蚁群算法。仿真表明,与蚁群优化和最短路径路由相比,该算法在无线传感器网络的寿命方面分别提高了近4%和50%。

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