AoI-Aware Joint Resource Allocation in Multi-UAV Aided Multi-Access Edge Computing Systems

Abstract

Compared with traditional latency, age of information (AoI) is regarded as a more sufficient metric to measure the freshness of information. In this paper, we investigate the AoI-aware unmanned aerial vehicle (UAV) aided multi-access edge computing (MEC) system, where the UAVs, equipped with MEC servers, provide computing service to the ground IoT devices, which have heterogeneous average peak AoI (APAoI) requirements. According to the Poisson process model, the probabilistic LoS channel model and the M/D/1 queue model, the APAoI of each IoT device is derived, which involves the hovering locations of the UAVs and the communication and computing resources. Then, considering the APAoI requirements of the IoT devices, we formulate the energy consumption minimization problem, in which the offloading strategy and the transmit power of the devices, and the communication and computing resources allocation as well as the hovering locations of the UAVs are jointly optimized. The formulated optimization problem is non-convex. To efficiently solve it, we decompose it into five subproblems and propose an alternative algorithm based on the traditional mathematical method, KKT conditions, and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.