摘要翻译：
在这篇综述论文中，我们的目标是讨论无线传感器网络中基于压缩感知(CS)的解决方案的最新进展，包括该领域正在进行/最近的主要研究工作、挑战和研究趋势。在无线传感器网络中，基于CS的技术不仅受到不同形式的稀疏性的影响，而且在非稀疏信号的情况下，在传输功率和通信带宽方面也需要有效的网络内处理。为了有效地将CS应用于各种无线传感器网络应用，除了标准CS框架之外，还需要考虑一些因素。我们首先简要介绍了CS的理论，然后描述了CS在无线传感器网络应用中潜在应用的动机因素。然后，我们确定了标准CS框架扩展的三个主要领域，以便CS能够有效地应用于解决WSNS特有的各种问题。我们特别强调了将CS框架扩展到(i)的意义。在设计投影矩阵和重建算法时考虑通信约束，用于集中和分散环境中的信号重建，(ii)用压缩数据解决各种推理问题，如检测、分类和参数估计，而无需信号重建，(iii)考虑测量量化、物理层保密约束和不完善的信道条件等实际通信方面。最后，讨论了开放的研究问题和挑战，为未来的研究方向提供了展望。
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英文标题：
《Application of Compressive Sensing Techniques in Distributed Sensor
  Networks: A Survey》
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作者：
Thakshila Wimalajeewa and Pramod K. Varshney
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最新提交年份：
2019
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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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  In this survey paper, our goal is to discuss recent advances of compressive sensing (CS) based solutions in wireless sensor networks (WSNs) including the main ongoing/recent research efforts, challenges and research trends in this area. In WSNs, CS based techniques are well motivated by not only the sparsity prior observed in different forms but also by the requirement of efficient in-network processing in terms of transmit power and communication bandwidth even with nonsparse signals. In order to apply CS in a variety of WSN applications efficiently, there are several factors to be considered beyond the standard CS framework. We start the discussion with a brief introduction to the theory of CS and then describe the motivational factors behind the potential use of CS in WSN applications. Then, we identify three main areas along which the standard CS framework is extended so that CS can be efficiently applied to solve a variety of problems specific to WSNs. In particular, we emphasize on the significance of extending the CS framework to (i). take communication constraints into account while designing projection matrices and reconstruction algorithms for signal reconstruction in centralized as well in decentralized settings, (ii) solve a variety of inference problems such as detection, classification and parameter estimation, with compressed data without signal reconstruction and (iii) take practical communication aspects such as measurement quantization, physical layer secrecy constraints, and imperfect channel conditions into account. Finally, open research issues and challenges are discussed in order to provide perspectives for future research directions.
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PDF链接：
https://arxiv.org/pdf/1709.10401