Cost-efficient computation offloading in UAV-enabled edge computing

With the popularity of computationally intensive applications, more and more computing resources are required. Mobile edge computing (MEC) is widely applied as an effective method to meet the increasing computing demands. In a relatively stable state, MEC can provide computing services with low latency and energy consumption. However, in special cases such as communication traffic, the unmanned aerial vehicle (UAV), by taking advantage of its mobility and flexibility, can assist the edge server to cope with the challenge of instantaneous computing surge. In this study, the authors consider a UAV-enabled edge computing system. In addition to delay and energy consumption, the authors also consider computing resources costs in the offloading model. Besides, in order to minimise the computing cost of each mobile user (MU), they apply the non-cooperative game method to model the channel and computing resources competition among MUs. Then, the authors prove that the proposed game is an ordinal potential game and the existence of Nash equilibrium in the game. The authors propose the UAV-enabled computation offloading (UECO) algorithm to obtain the equilibrium strategy. Finally, the authors show that the UECO algorithm can quickly converge through iterative experiments, and it can achieve lower computing cost through comparative experiments.