On the energy self-sustainability of IoT via distributed compressed sensing
On the energy self-sustainability of IoT via distributed compressed sensing
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DOI：
10.23919/jcc.2020.12.003
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发表时间：
2013-12
影响因子：
4.1
通讯作者：
Wei Chen;N. Deligiannis;Y. Andreopoulos;I. Wassell
中科院分区：
文献类型：
--
作者：
Wei Chen;N. Deligiannis;Y. Andreopoulos;I. Wassell
本文提倡使用分布式压缩感知（DCS）范式来部署能量收集（EH）物联网（IoT）设备，以实现能源的自我可持续发展。我们考虑具有信号/能量模型的网络，这些模型捕获了不同设备收集的信号和收集的能量都可以表现出相关性的事实。我们对经典压缩感知（CS）方法和基于分布式CS （DCS）的EH物联网数据采集方法的性能进行了理论分析。此外，我们还对所提出的基于DCS的方法与分布式源编码（DSC）系统进行了深入的比较。这些性能表征和比较体现了各种系统现象和参数的影响，包括信号相关性、EH相关性、网络规模和能量可用性水平。我们的研究结果表明，与基于cs的方法相比，所提出的方法显著提高了数据收集能力，并且大大减少了相对于DSC系统的均方误差失真。
This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS- based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.