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.
Chen, W, Deligiannis, N, Andreopoulos, Y & Wassell, IJ 2020, 'On the energy self-sustainability of IoT via distributed compressed sensing', China Communications, vol. 17, no. 12, 9312790, pp. 37-51. https://doi.org/10.23919/JCC.2020.12.003
Chen, W., Deligiannis, N., Andreopoulos, Y., & Wassell, I. J. (2020). On the energy self-sustainability of IoT via distributed compressed sensing. China Communications, 17(12), 37-51. Article 9312790. https://doi.org/10.23919/JCC.2020.12.003
@article{e42163d621c742bead2de5fe169c31ed,
title = "On the energy self-sustainability of IoT via distributed compressed sensing",
abstract = "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.",
keywords = "distributed compressed sensing, energy harvesting, energy self-sustainability, internet of things",
author = "Wei Chen and Nikolaos Deligiannis and Yiannis Andreopoulos and Wassell, {Ian J.}",
year = "2020",
month = dec,
doi = "10.23919/JCC.2020.12.003",
language = "English",
volume = "17",
pages = "37--51",
journal = "China Communications",
issn = "1673-5447",
publisher = "Institute of Electrical and Electronics Engineers",
number = "12",
}