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Quality assurance for data acquisition in error prone WSNs

机译:容易出错的WSN中数据采集的质量保证

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This paper proposes a data acquisition scheme which supports probabilistic data quality assurance in an error-prone wireless sensor network (WSN). Given a query and a statistical model of real-world data which is highly correlated, the aim of the scheme is to find a sensor selection scheme which is used to deal with inaccurate data and probabilistic guarantee on the query result. Since most sensor readings are real-valued, we formulate the data acquisition problem as a continuous-state partially observable Markov decision process (POMDP). To solve the continuous-state POMDP, the fitted value iteration (FVI) is applied to find a sensor selection scheme. Numerical results show that FVI can achieve high average long-term reward and provide probabilistic guarantees on the query result more often when compared to other algorithms.
机译:本文提出了一种在易错无线传感器网络(WSN)中支持概率数据质量保证的数据采集方案。给定查询和高度相关的现实世界数据的统计模型,该方案的目的是找到一种传感器选择方案,该方案用于处理不准确的数据和查询结果的概率保证。由于大多数传感器读数均为实值,因此我们将数据采集问题公式化为连续状态的部分可观察的马尔可夫决策过程(POMDP)。为了求解连续状态POMDP,应用拟合值迭代(FVI)来找到传感器选择方案。数值结果表明,与其他算法相比,FVI可以获得较高的平均长期报酬,并且更频繁地为查询结果提供概率保证。

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