Title

An Energy Efficient Smart Metering System using Edge Computing in LoRa Network

Abstract

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.

Department(s)

Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Publication Status

Early Access

Comments

Published online: 06 Jan 2021

The work of S. K. Das is partially supported by NSF grants under award numbers CNS-1818942, CCF-1725755, CNS-1545037, and SaTC-2030624.

Keywords and Phrases

Deep learning; Edge device; energy efficiency; Internet of Things; LoRa; smart meter

International Standard Serial Number (ISSN)

2377-3782

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

06 Jan 2021

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