Delay Optimization with FCFS Queuing Model in Mobile Edge Computing-Assisted UAV Swarms: A Game-Theoretic Learning Approach

With the advantages of flexibility, diversity and intelligence, unmanned aerial vehicle (UAV) networks have been widely used in executing missions, which can take advantage of mobile edge computing (MEC) technology to shorten the completion time of tasks. This paper investigates the minimization of total delay of all members in MEC-assisted UAV swarms. Firstly, the offloading model is proposed, where members adjust the offloading ratio and transmission channels jointly. And the First Come First Served (FCFS) Queuing Model is introduced in remote computing, which is proved to have a better performance than averaging computing resource. Secondly, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (NE). To solve the NE, a distributed offloading algorithm based on concurrent best response (CBR) is designed. Finally, the simulations show that the convergence speed of the proposed algorithm is much faster than traditional BR and the proposed optimization method can reduce the delay of missions effectively.