Edge Intelligence for Real-Time Data Analytics in an IoT-Based Smart Metering System
Hailin Hu, Liangrui Tang
Abstract
The recent widespread deployment of smart meters on a global scale has created an immense amount of fine-grained smart meter data, which requires effective and real-time analysis. Although the cloud center has powerful data processing capabilities, it is insufficient for real-time analysis, especially in the case of huge and distributed data volumes. Correspondingly, intelligent edge computing is merged with smart meters in this work to create an Internet-of-Things-based architecture for an edge-intelligence-enabled smart meter (EI-smart meter) system. To achieve its potential, we also propose two (one offline and one online) ultra-low-latency cloud-edge collaboration schemes regarding real-time data analytics. Unlike the existing work, we integrate a deep neural network (DNN) into the cloud-edge collaboration scheme in a bid to reduce execution time and improve the adaptability. Finally, numerical results are presented to validate the performance of our proposed system.