Computational access point selection based on resource allocation optimization to reduce the edge computing latency
K Kumaran, E. Sasikala · Measurement Sensors · 2022
Mobile Devices (MD) and the Internet of Things (IoT) transfer compute tasks to the client through Computational Access Point (CAP) through the extensive implementation of Local Area Networks (LAN). If all IoT or mobile devices choose the same ports of entry to offload their work, then the calculation of offloading leads to an increase in operating costs. In a network of different CAP to Edge Computing (EC), this research proposes a resource allocation optimization technique and a computation offloading strategy that aims to reduce network costs by recommending the best transmit power distribution, bandwidth assignment, and computation task planning. The optimization problem of NP-hard solved by the proposed technique separates from sub-problems of resource allocation and offloading technique. An algorithm called Dynamic Weighed Quantum Arithmetic Optimization Algorithm (DWQAOA) was proposed for this offloading problem NP-hard. To achieve the ideal solution, a transition probability introduced the optimal Pareto connection during the optimization process. Numerous simulation results show that the edge task latency could be reduced from 3 to 25% relative to competing solutions.