Resource Scheduling and Delay Optimization of IoT Devices in Drone-Assisted Multiaccess Edge Computing

Multiaccess edge computing (MEC) plays a crucial role in providing low-latency and high-data transmission services to Internet of Things (IoT) devices. However, in remote areas where deploying edge devices is challenging, optimizing delay remains a significant research focus. To address this issue, our research investigates a multidrones-assisted IoT task offloading model. In this model, tasks generated by IoT devices equipped with energy harvesting (EH) capabilities are offloaded to MEC servers with the assistance of multiple drones. In order to monitor and manage the energy consumption of IoT devices and the task backlog of edge servers, the energy consumption and task update queues are established. We formulate a mixed integer nonlinear programming (MINLP) problem, which aims to optimize the allocation of communication and computation resources to minimize the execution latency of IoT devices. To ensure the stability of each queue, we employ the weighted perturbation method within the Lyapunov optimization framework to decompose the original problem. And a low complexity multidrones assisted offloading (MUAO) algorithm is designed. Simulation results show that MUAO consistently exhibits lower latency and energy consumption compared to the baseline scheme and other existing algorithms, while maintaining a low packet loss rate of only 5%.