摘要翻译：
本文研究了异构传感器网络中的空间场重构和传感器选择两个基本问题。我们考虑部署两种类型的传感器的情况：第一种由昂贵的、高质量的传感器组成；第二种是廉价的低质量传感器，只有当空间场的强度超过预定义的激活阈值时才被激活（如风传感器）。此外，这些传感器是通过能量收集来供电的，它们随时间变化的能量状态影响到可能获得的测量精度。我们通过将能量收集过程编码为加性噪声的二阶矩性质来解释这一现象，从而产生空间异方差过程。然后，我们解决了以下两个重要问题：（一）如何有效地基于同时从两个网络获得的测量值进行空间场重建；以及(ii)如何在保证预测MSE性能的前提下进行基于查询的传感器集选择。我们首先表明，所产生的预测后验分布，这是融合这些不同的观测的关键，涉及解决棘手的积分。为了克服这一问题，我们开发了一种基于空间最佳线性无偏估计的低复杂度算法（S-BLUE）来解决第一个问题。其次，在S-BLUE的基础上，我们解决了第二个问题，提出了一种性能保证的基于查询的传感器集选择算法。我们的算法是基于交叉熵方法的，它以一种高效的方式解决了组合优化问题。我们提出了一个全面的研究，通过使用合成和真实的保险风暴潮数据库，即极端风暴目录，用低质量传感器增加高质量传感器，可以获得的性能增益。
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英文标题：
《Spatial Field Reconstruction and Sensor Selection in Heterogeneous
  Sensor Networks with Stochastic Energy Harvesting》
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作者：
Pengfei Zhang, Ido Nevat, Gareth W. Peters, Francois Septier and
  Michael A. Osborne
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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：
  We address the two fundamental problems of spatial field reconstruction and sensor selection in het- erogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (eg. wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We account for this phenomenon by encoding the energy harvesting process into the second moment properties of the additive noise, resulting in a spatial heteroscedastic process. We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. We first show that the resulting predictive posterior distribution, which is key in fusing such disparate observations, involves solving intractable integrals. To overcome this problem, we solve the first problem by developing a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue.
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PDF链接：
https://arxiv.org/pdf/1801.05356