Energy Efficient Offloading Strategy Over Multi-access Dual-Level Edge Computing Environment for IoT

Multi-access edge computing (MEC) technology sinks computing and storage resources to the edge of the network, which can significantly improve the computing power and real-time performance of the Internet of Things (IoT) system. However, MEC often faces the constraints of increasing computing requirements and limited energy. Therefore, efficient computing offloading and energy consumption optimization mechanisms are important research areas in MEC technology. To ensure computing efficiency and maximize the energy efficiency in the computing process, a dual-level edge node (ENs) relay network model is proposed, and an optimal energy consumption offloading with joint optimization of computing resources and channel resources name (OECA) is designed. The energy efficiency in MEC is modeled as a 0–1 knapsack problem where the system adaptively selects the computing mode and allocates wireless channel resources to minimize the system's overall energy consumption. The algorithm's performance is verified by simulations in the Python environment. The simulation results reveal that the OECA can increase the network capacity by 18.3%, and the energy consumption is reduced by 13.1% when compared with the prominent directed acyclic graph-based algorithm (DAGA).