Implementing an Intelligent Energy Management Framework for IoT Edge Devices

Cloud computing is a well-established model for creating service-centric systems in a traditional scenario. However, when it comes to analyzing and delivering results for a vast quantity of data, cloud computing is constrained by ultra-low latency, high bandwidth, security, and real-time analytics. These constraints can be overcome via with the hybrid design offered by fog and edge computing. The fog and edge based systems reduce the processing time, save operating expenses, and boost security. In edge computing, computational resources are distributed at the network's edge In order to build and run edge computing platforms, additional challenges have been created by the operational size of the system. Computing at the edge decreases latency, increases security, and enhances privacy by offloading tasks from cloud data centers to edge routers and edge nodes. There has been far less attention paid to energy efficiency in edge computing than in cloud data centers due to the complex relationships between edge devices, edge servers, and cloud data centers. Testing the energy efficiency of edge devices, edge servers, and cloud data centers is essential to achieving energy efficiency with edge computing. The purpose of this paper is to provide a general overview of the background and development of edge computing. Our discussion then moved to the key areas involved in edge computing. The final section of the paper discusses the challenges and opportunities associated with edge computing.