Sensor placement and resource allocation for energy harvesting IoT networks
KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-06-02
Online Publication Date2020-01-21
Print Publication Date2020-10
Embargo End Date2022-01-21
Permanent link to this recordhttp://hdl.handle.net/10754/660825
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AbstractOptimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. To efficiently round the obtained relaxed solution, we propose a randomized rounding algorithm which generalizes the existing algorithm.
CitationBushnaq, O. M., Chaaban, A., Chepuri, S. P., Leus, G., & Al-Naffouri, T. Y. (2020). Sensor placement and resource allocation for energy harvesting IoT networks. Digital Signal Processing, 102659. doi:10.1016/j.dsp.2020.102659
SponsorsTwo conferences precursors of this manuscript have been published in the Proceedings of the Twenty-Fifth European Signal Processing Conference, September 2017  and the Eighteenth International Workshop on Signal Processing Advances in Wireless Communications, July 2017 . This work was supported by the KAUST-MIT-TUD consortium grant OSR2015-Sensors-2700.
JournalDigital Signal Processing