Latency-minimum offloading decision and resource allocation for fog-enabled Internet of Things networks

The rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation-intensive and delay-sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency-minimum offloading decision and resource allocation scheme for fog-enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing-based latency-minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.