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arXiv 1803.04066eess.SP
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基于无线传感器网络的水质监测多传感器数据融合

Multisensor Data Fusion for Water Quality Monitoring using Wireless Sensor Networks

Ebrahim Karami, Francis M. Bui, Ha H. Nguyen

摘要:

本文研究了层次式无线传感器网络在水质监测中的应用。采用层次结构,传感器的集合被划分为多个簇的传感参数的值几乎是恒定的,在每个簇。每个簇的成员将其感知信息发送到其对应簇的本地融合中心(LFC),在那里使用某种融合规则,将接收到的信息组合,然后可能发送到更高级别的中央融合中心(CFC)。一个两阶段的处理方案也被设想,其中第一阶段是专门用于检测在LFC,和第二阶段是专门用于估计在LFC和CFC。本文的重点是在LFC的决策融合的问题:我们提出了硬和软决策最大后验概率(MAP)算法,表现出灵活性,在最大限度地减少不正确的检测在第一阶段所施加的总成本。提出的算法进行了仿真,并与传统的融合技术。结果表明,所提出的技术的结果在较低的成本。此外,当传感器数量或污染量增加时,所提出的算法与现有方法之间的性能差距也会扩大。

英文摘要:

In this paper, the application of hierarchical wireless sensor networks in water quality monitoring is investigated. Adopting a hierarchical structure, the set of sensors is divided into multiple clusters where the value of the sensing parameter is almost constant in each cluster. The members of each cluster transmit their sensing information to the local fusion center (LFC) of their corresponding cluster, where using some fusion rule, the received information is combined, and then possibly sent to a higher-level central fusion center (CFC). A two-phase processing scheme is also envisioned, in which the first phase is dedicated to detection in the LFC, and the second phase is dedicated to estimation in both the LFC and the CFC. The focus of the present paper is on the problem of decision fusion at the LFC: we propose hard- and soft-decision maximum a posteriori (MAP) algorithms, which exhibit flexibility in minimizing the total cost imposed by incorrect detections in the first phase. The proposed algorithms are simulated and compared with conventional fusion techniques. It is shown that the proposed techniques result in lower cost. Furthermore, when the number of sensors or the amount of contamination increases, the performance gap between the proposed algorithms and the existing methods also widens.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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