Dynamic resource optimization and altitude selection in uav-based multi-access edge computing

The aim of this work is to develop a dynamic optimization strategy to allocate communication and computation resources in a Multi-access Edge Computing (MEC) scenario, where Unmanned Aerial Vehicles (UAVs) act as flying base station platforms endowed with computation capabilities to provide edge cloud services on demand. Hinging on stochastic optimization tools, we propose a dynamic algorithmic framework that minimizes the overall energy spent by the system, while imposing latency constraints, and optimizing the altitude of the UAV in an online fashion. The method does not require a priori knowledge of channels and/or task arrival statistics. Numerical results illustrate the advantages of the proposed approach.