Energy Consumption Minimization in Dynamic UAV-assisted Mobile Edge Computing Networks

Unmanned aerial vehicles (UAVs) combining with mobile edge computing (MEC) networks have promoted the application of Internet of Things (IoT) devices, providing enhanced coverage with flexible computing services. But the energy consumption of data processing is still a shortage in the UAV-assisted MEC architecture. Motivated by that, we propose a dynamic UAV-assisted MEC network and formulate a problem and jointly optimize association strategies, UAV trajectory, data offloading, and resource distribution for minimizing total energy consumption. To deal with this tricky problem, we devise a dichotomy-based joint iterative optimization algorithm. Specifically, we divide the problem into three sub-problems, solving by the integer programming, successive convex optimization, and dichotomy method. Finally, the simulation consequences prove that the devised network and algorithm significantly reduce total energy consuming.