Edge-Node-Aware Adaptive Data Processing Framework for Smart Grid

Abstract

Smart grid is an autonomous power generation and production system, that includes various energy management sub-systems such as energy efficient resources, smart appliances, renewable energy resources and smart meters. It is a sensory-based power network that adopts digital technology to supply electricity to consumers and industries. Recently, it is observed that the grid infrastructure is rapidly transforming network topology towards green and eco-friendly computing. For this, edge-computing environment is being adopted to inter-connect distributed IoT nodes in peer-to-peer sequential order. With this approach, the grid ideally cope to green computing issues than traditional infrastructure, however, it faces certain data processing problems such as differentiating IoT and non-IoT data segments, storing data chunk with end of file (EOF) assurance, management of replica data segments and systematic analytics of edge node datasets. This produces operational latency at edges as well as onto segment reservoir and results delay in exchanging data segments at smart grid. This paper presents an Edge-node-aware framework that empowers nodes to intelligently process IoT and counterpart data segments, a portable plugin to identify EOF data chunk, reliable replica management system and hassle-free dataset analytics. The simulation results show that the proposed framework effectively manage data segments and store into smart grid reservoir.