Task Offloading and Resource Pricing Based on Game Theory in UAV-Assisted Edge Computing

Due to the limited battery capacity and computational resources of mobile devices, computation-intensive tasks generated by mobile devices can be offloaded to edge servers for processing. This paper investigates the multi-user task offloading and resource pricing issues in Autonomous aerial vehicle (AAV)-assisted Multi-Access Edge Computing (MEC) systems. The optimization objectives is optimizing the utility of the server and the utility of the Edge Users (EUs), with decision variables encompassing the offloading strategies of EUs and the pricing strategies of the server. We divide the entire optimization problem into two parts. When optimizing the server's utility, server energy consumption is a crucial metric; hence, in the first part, we formulate the user allocation problem with the goal of minimizing the server's overall energy consumption. Utilizing game theory, we transform the user allocation problem into a multi-user non-cooperative game and prove the existence of a Nash Equilibrium (NE). The Game-based User Allocation (GBUA) algorithm is proposed to obtain the user allocation strategy. After addressing the user allocation problem, we consider the simultaneous optimization of both server and EUs utility. Therefore, in the second part, we model the server and EUs's engagement using the Stackelberg game model and employ backward induction to verify the presence of a Stackelberg Equilibrium (SE). Additionally, we propose the Resource Pricing and Task Offloading (RPATO) algorithm, based on game theory, to obtain the SE solution. Finally, extensive experiments are conducted to validate the effectiveness of the proposed algorithms, and numerous comparative algorithms are tested to prove the advancement and innovation of our proposed algorithms.