An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning-based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system.
P. Kumari et al., "An Energy Efficient Smart Metering System using Edge Computing in LoRa Network," IEEE Transactions on Sustainable Computing, vol. 7, no. 4, pp. 786 - 798, Institute of Electrical and Electronics Engineers, Oct 2022.
The definitive version is available at https://doi.org/10.1109/TSUSC.2021.3049705
Keywords and Phrases
Deep learning; edge device; energy efficiency; Internet of Things; LoRa; smart meter
International Standard Serial Number (ISSN)
Article - Journal
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01 Oct 2022
National Science Foundation, Grant CCF-1725755