Lyapunov optimization

Algorithm

Distributed computing

Physics

Mathematical optimization

Astronomy

Geodesy

Trajectory

Optimization problem

Artificial intelligence

Queue

Benchmark (surveying)

Ecology

Lyapunov exponent

Stochastic optimization

Geography

Mobile edge computing

Chaotic

Energy consumption

Resource allocation

Computer network

Mathematics

Lyapunov redesign

Computer science

Real-time computing

Biology

Server

IEEE Transactions on Wireless Communications

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.

Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems