Optimizing Energy consumption and Latency based on computation offloading and cell association in MEC enabled Industrial IoT environment

Mobile edge computing emerges as a promising technology for the industrial internet of things (IIoT). It provides more opportunities and efficient computing resources for end-users when edge nodes are deployed at the nearest IoT devices (IDs). However, IDs have limited computing capability and battery life in the IIoT environment due to their high computational tasks. The IDs can offload the computational tasks to edge nodes to achieve low latency and energy consumption. Our proposed work examines the cell association and computational offloading problem in the MEC-enabled IIoT environment. This problem is formulated as a cost execution problem (total sum of energy consumption and latency). Three different computing modes (full-local, full-MEC, and partial) are used for task execution, where the end-user can choose one of them. To achieve the optimal solution Khun-Munkres algorithm and extensive search method are deployed. Experimental results demonstrate the better performance of the proposed method in latency and energy consumption.