Joint Optimization of Serial Task Offloading Decision and UAV Position in Cache-Based Mobile Edge Computing Networks

In drone-assisted Mobile Edge Computing (MEC) networks, Unmanned Aerial Vehicle (UAV) can serve as servers or relays. Correct content caching and optimal drone placement are crucial for reducing service pressure and enhancing user experience. For serial tasks, appropriate task offloading decisions help reduce latency. In this paper, we work on the joint optimization of serial task offloading decision and UAV position in cache-based mobile edge computing networks. This is an optimization problem focused on minimizing total delay, which can be decomposed into content caching subproblem and task offloading with UAV position optimization subproblem. For the content caching subproblem, it is formulated as an Integer Knapsack Problem (IKP). A dynamic programming algorithm using a sliding window technique is applied to efficiently solve this. The task offloading with UAV position optimization subproblem is framed as a constrained single-objective optimization problem, focusing on task offloading under specific constraints. A combined Genetic Algorithm (GA) and Simulated Annealing (SA) approach is proposed to address it. Simulation results indicate that the proposed algorithm notably decreases task completion latency.