鲁棒与鲁棒的传感器选择与随机场重构 发展中国家的成本效益异构天气传感器网络

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摘要翻译：
本文研究了异构传感器网络中空间场重构和传感器选择的两个基本问题：（一）如何基于同时从具有高质量和低质量传感器的网络中获得的测量值有效地进行空间场重构；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。对于第一个问题，我们提出了一种基于空间最佳线性无偏估计的低复杂度算法（S-BLUE）。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。
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英文标题：
《Sensor Selection and Random Field Reconstruction for Robust and
  Cost-effective Heterogeneous Weather Sensor Networks for the Developing World》
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作者：
Pengfei Zhang and Ido Nevat and Gareth W. Peters and Wolfgang
  Fruehwirt and Yongchao Huang and Ivonne Anders and Michael Osborne
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最新提交年份：
2017
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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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 heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed 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.
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PDF链接：
https://arxiv.org/pdf/1711.04308

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关键词：发展中国家 成本效益 传感器 国家的 Applications selection 提出 网络 选择 algorithm