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Proposition of a Bayesian model for the propagation of the information in a wireless sensor network

机译：贝叶斯模型的命题在无线传感器网络中传播信息的传播

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Wireless sensor networks (WSN) are specially distributed sensors deployed for the purpose of sensing and monitoring physical or environmental conditions. Sensor measurements in WSN usually suffer from both random eirors (noise) and systematic errors (bias). This poses a major problem in the end application, as the data from the network become progressively uninformative. This work proposes a model of spatial and temporal variations of the state of any scalar phenomenon under study. We present a novel estimation approach of the bias based on a message passing algorithm, in order to calibrate the network. The algorithm is designed to solve inference problems in a Bayesian network, which is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. Therefore it is a technique for representing the dependencies between the hidden variables and noisy observations as a graphical model. In our case, the hidden variables that we want to estimate are formed by the state of the scalar phenomenon to be studied, and the measurements taken by the sensors act as noisy observations. So we seek to develop message passing algorithms representing the exchange of information between sensors in a distributed manner.
机译：无线传感器网络（WSN）是专门分布的传感器，用于感应和监测物理或环境条件。 WSN中的传感器测量通常遭受随机兴利器（噪声）和系统错误（偏置）。这在最终应用中提出了一个主要问题，因为来自网络的数据变得逐渐无规则。这项工作提出了在研究下的任何标量现象的状态和时间变化的模型。我们提出了一种基于消息传递算法的偏置的新颖估计方法，以校准网络。该算法旨在解决贝叶斯网络中的推理问题，这是一个概率图形模型，表示一组随机变量及其条件依赖关系。因此，它是表示隐藏变量与噪声观测之间的依赖性作为图形模型的技术。在我们的情况下，我们想要估计的隐藏变量由要研究的标量现象的状态形成，并且传感器采用的测量作为嘈杂的观察。因此，我们寻求开发消息通过算法，以分布式方式表示传感器之间的信息交换。

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