Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information

The intelligent transportation system that combines Unmanned Aerial Vehicle (UAV) based traffic monitoring and Mobile Edge Computing (MEC) technologies is considered. In order to ensure the timeliness of data and reduce energy consumption in the system, a UAV computation offloading optimization method considering Age of Information (AoI) is proposed. Firstly, the UAV-assisted MEC system model is established to allow the MEC server to cache commonly used applications and provide UAVs with computation offloading, which supports the UAVs to perform traffic monitoring tasks. By jointly optimizing UAV task offloading decisions, UAV uplink and downlink communication bandwidth allocation, and computing resource allocation of offloaded tasks, the total energy consumption of all UAVs and the MEC server is minimized while satisfying constraints of AoI and resource capacities. Secondly, the system energy consumption minimizing problem is a mixed-integer non-convex optimization problem. Discretization and linearization methods are adopted to quickly obtain an approximately optimal solution to the problem. A discrete point generation algorithm is designed to adjust the approximation error. Finally, simulation results show that even for large non-convex problems, the proposed method can quickly obtain approximately optimal solutions and can satisfy constraints of AoI in different task scenarios, minimizing the system energy consumption as much as possible. The simulation results verify the effectiveness of the proposed method. © 2024 Science Press. All rights reserved.