An efficient task offloading strategy based on Aquila Student Psychology Optimization Algorithm in internet of vehicles‐fog computing systems

Abstract

SummaryInternet of vehicles (IoV) comprises connected vehicles and connected autonomous vehicles and offers numerous benefits for ensuring traffic and safety competence. Several IoV applications are delay‐sensitive and need resources for computation and data storage that are not provided by vehicles. Therefore, these tasks are always offloaded to highly powerful nodes, namely, fog, which can bring resources nearer to the networking edges, reducing both traffic congestion and load. Besides, the mechanism of offloading the tasks to the fog nodes in terms of delay, computing power, and completion time remains still as an open concern. Hence, an efficient task offloading strategy, named Aquila Student Psychology Optimization Algorithm (ASPOA), is developed for offloading the IoV tasks in a fog setting in terms of the objectives, such as delay, computing power, and completion time. The devised optimization algorithm, known as ASPOA, is the incorporation of Aquila Optimizer (AO) and Student Psychology Based Optimization (SPBO). Task offloading in the IoV‐fog system selects suitable resources for executing the tasks of the vehicles by considering several constraints and parameters to satisfy the user requirements. The simulation outcomes have shown that the devised ASPOA‐based task offloading method has achieved better performance by achieving a minimum delay of 0.0009 s, minimum computing power of 8.884 W, and minimum completion time of 0.441 s.