摘要翻译：
物联网（IoT）是一项革命性的技术，旨在创建一个连接对象和嵌入式设备的生态系统，并在数万亿智能设备之间提供无处不在的连接，不仅是简单的传感器和执行器。尽管最近在具有更高计算能力的设备小型化和超低功耗通信技术方面的进步使得传感器和执行器在任何地方都得到了广泛的部署，但这种发展要求在硬件设计、软件、网络架构、数据分析、数据存储和电源方面进行根本性的变革。很大一部分物联网设备不再只能由电池供电，因为它们将安装在难以到达的地区，定期更换和维护电池是不可行的。一个可行的解决方案是从环境中清除和收集能量，然后为设备提供足够的能量来执行它们的操作。这将大大增加设备的寿命，并消除对电池作为能源的需求。本调查旨在提供一个全面的研究能量收集技术作为替代和有希望的解决方案的功率物联网设备。本文介绍了物联网设备在能量和功率方面的主要设计挑战，并为成功实现自供电物联网设备提供了设计考虑。然后重点讨论了压电能量采集和射频能量采集这两种最有前途的能量采集技术，并提出了主要的挑战和研究方向。我们还揭示了能源收集、启用物联网系统和绿色大数据的安全挑战。
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英文标题：
《Towards a Green and Self-Powered Internet of Things Using Piezoelectric
  Energy Harvesting》
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作者：
Mahyar Shirvanimoghaddam, Kamyar Shirvanimoghaddam, Mohammad Mahdi
  Abolhasani, Majid Farhangi, Vaid Zahiri Barsari, Hangyue Liu, Mischa Dohler,
  and Minoo Naebe
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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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英文摘要：
  Internet of things (IoT) is a revolutionizing technology which aims to create an ecosystem of connected objects and embedded devices and provide ubiquitous connectivity between trillions of not only smart devices but also simple sensors and actuators. Although recent advancements in miniaturization of devices with higher computational capabilities and ultra-low power communication technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytic, data storage and power sources. A large portion of IoT devices cannot be powered by batteries only anymore, as they will be installed in hard to reach areas and regular battery replacement and maintenance are infeasible. A viable solution is to scavenge and harvest energy from environment and then provide enough energy to the devices to perform their operations. This will significantly increase the device life time and eliminate the need for the battery as an energy source. This survey aims at providing a comprehensive study on energy harvesting techniques as alternative and promising solutions to power IoT devices. We present the main design challenges of IoT devices in terms of energy and power and provide design considerations for a successful implementations of self-powered IoT devices. We then specifically focus on piezoelectric energy harvesting and RF energy harvesting as most promising solutions to power IoT devices and present the main challenges and research directions. We also shed light on the security challenges of energy harvesting enabled IoT systems and green big data.
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PDF链接：
https://arxiv.org/pdf/1712.02277