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Reaction-diffusion based topology self-organization for periodic data gathering in wireless sensor networks

机译:基于反应扩散的无线传感器网络定期数据的拓扑自组织

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Considering characteristics of wireless sensor networks, control mechanisms must be scalable, adaptive, robust, fully distributed, and self-organizing. In this paper, we focus on topology control for periodic data gathering, which certainly is one of typical applications of WSNs. We propose a novel mechanism based on a biological self-organization mechanism, that is, a reaction-diffusion model, to organize the best topology in a self-organizing and autonomous way. A reaction-diffusion model is a mathematical model for pattern generation on the surface of body of fishes and mammals. Nodes generate spatially distributed spot patterns through mutual interaction among neighboring nodes. The node which has a peak of activator concentration is elected as cluster head, and other nodes send their data following the gradient of activator concentration to the cluster head. Through simulation experiments, it is shown that organized topology accomplishes as small energy consumption and delay as the best topology optimally derived.
机译:考虑到无线传感器网络的特性,控制机制必须是可扩展的,自适应,鲁棒,完全分布的和自组织。在本文中,我们专注于定期数据收集的拓扑控制,这肯定是WSN的典型应用之一。我们提出了一种基于生物自我组织机制的新机制,即反应扩散模型,以自组织和自主方式组织最佳拓扑。反应扩散模型是鱼类和哺乳动物表面上的模式生成的数学模型。节点通过相邻节点之间的相互交互生成空间分布的光斑模式。具有激活率浓度峰值的节点被选为集群头,并且其他节点在激活器浓度的梯度到簇头之后将其数据发送。通过仿真实验,表明有组织的拓扑结构实现了小的能量消耗和延迟作为最佳衍生的最佳拓扑。

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