多传感器数据融合在水质无线监测中的应用 传感器网络

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摘要翻译：
本文研究了分层无线传感器网络在水质监测中的应用。采用分层结构，将传感器集合划分为多个簇，每个簇中的传感器参数值几乎不变。每个簇的成员将各自的感知信息发送到相应簇的本地融合中心(LFC)，在LFC中使用一定的融合规则对接收到的信息进行合并，然后可能发送到更高一级的中央融合中心(CFC)。还设想了一种两阶段处理方案，其中第一阶段专用于LFC中的检测，第二阶段专用于LFC和CFC中的估计。本文的重点是LFC中的判决融合问题：我们提出了硬判决和软判决最大后验概率(MAP)算法，它们在最小化第一阶段错误检测所带来的总代价方面表现出灵活性。对所提出的算法进行了仿真，并与传统的融合技术进行了比较。结果表明，所提出的技术具有较低的成本。此外，当传感器数目或污染量增加时，所提出的算法与现有方法的性能差距也会拉大。
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英文标题：
《Multisensor Data Fusion for Water Quality Monitoring using Wireless
  Sensor Networks》
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作者：
Ebrahim Karami, Francis M. Bui, and Ha H. Nguyen
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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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.
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PDF链接：
https://arxiv.org/pdf/1803.04066

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关键词：传感器 Applications Hierarchical Optimization Conventional sensors 应用 阶段 代价 进行