Task offloading in Internet of Things based on the improved multi-objective aquila optimizer

The Internet of Things (IoT) is a network of tens of billions of physical devices that are all connected to each other. These devices often have sensors or actuators, small microprocessors and ways to communicate. With the expansion of the IoT, the number of portable and mobile devices has increased significantly. Due to resource constraints, IoT devices are unable to complete tasks in full. To overcome this challenge, IoT devices must transfer tasks created in the IoT environment to cloud or fog servers. Fog computing (FC) is a computing paradigm that bridges the gap between the cloud and IoT devices and has lower latency compared to cloud computing. An algorithm for task offloading should have smart ways to make the best use of FC resources and cut down on latency. In this paper, an improved multi-objective Aquila optimizer (IMOAO) equipped with a Pareto front is proposed to task offloading from IoT devices to fog nodes with the aim of reducing the response time. To improve the MOAO algorithm, opposition-based learning (OBL) is used to diversify the population and discover optimal solutions. The IMOAO algorithm has been evaluated by the number of tasks and the number of fog nodes in order to reduce the response time. The results show that the average response time and failure rate obtained by IMOAO algorithm are lower compared to particle swarm optimization (PSO) and firefly algorithm (FA). Also, the comparisons show that the IMOAO model has a lower response time compared to multi-objective bacterial foraging optimization (MO-BFO), ant colony optimization (ACO), particle swarm optimization (PSO) and FA.