Cluster subdivision towards power savings for randomly deployed WSNs — An analysis using 2-D spatial poisson process

Dajin Wang
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引用次数: 2

Abstract

We propose to use a 2-dimensional (2-D for short) spatial Poisson process to model a WSN with randomly deployed sensors, and use the model to analyze a scheme that subdivides the clusters of a WSN to achieve an overall power savings. We assume no knowledge of how the sensors are spread in the sensing area, and hence need a statistical process to describe the distribution of all sensors. Assuming the 2-D spatial Poisson distribution, a comprehensive analysis is performed to estimate the power savings brought about by the proposed subdivision. Using hexagon as the shape of the cluster, the analysis shows that the subdivision scheme can yield significant savings in overall power consumption of sensors in the cluster.
随机部署wsn的聚类细分——基于二维空间泊松过程的分析
我们建议使用二维(简称二维)空间泊松过程对随机部署传感器的WSN进行建模,并使用该模型分析一种细分WSN簇的方案,以实现整体节能。我们假设不知道传感器如何在传感区域内分布,因此需要一个统计过程来描述所有传感器的分布。假设二维空间泊松分布,进行了全面的分析,以估计所提出的细分带来的电力节约。采用六边形作为集群形状,分析表明,细分方案可以显著节省集群中传感器的总体功耗。
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