Lightweight Digit Recognition in Smart Metering System Using Narrowband Internet of Things and Federated Learning

Abstract

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)GPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 μWh per day on average).

Keywords

• NB-IoT
• machine learning
• smart metering
• lightweight digit recognition
• federated learning