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On Average Consensus Algorithm over Mobile Wireless Sensor Networks Modelled as Stationary Markovian Evolving Graphs

机译:固定马尔可夫演化图建模的移动无线传感器网络平均共识算法研究

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Mobile wireless sensor networks find application in various areas due to their specific character. However, their operation is affected by many negatives factors and therefore modern applications are equipped with complementary data aggregation mechanisms to supress negatives effects. In this paper, our attention is focused on five frequently applied weight models of the average consensus algorithm for distributed averaging over mobile wireless sensor networks modelled as stationary Markovian evolving graphs with a different size and a varying probabiltiy of edge formation. We use the mean square error over the iterations as a metric to evaluate the performance of the analyzed weight models and identify the weight model with the highest performance.
机译:移动无线传感器网络因其独特的特性而在各个领域找到了应用。但是,它们的操作受许多负面因素的影响,因此现代应用程序配备了互补的数据聚合机制来抑制负面影响。在本文中,我们的注意力集中在平均共识算法的五个经常应用的权重模型上,这些算法用于在移动无线传感器网络上进行分布式平均,该模型被建模为具有不同大小和边缘形成概率的固定马尔可夫演化图。我们使用迭代中的均方误差作为衡量评估权重模型性能的指标,并确定性能最高的权重模型。

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