Game-Based Task Offloading and Resource Allocation in Multi-UAV-Assisted MEC Networks

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is envisioned as a promising paradigm to accomplish latency-sensitive and computation-intensive tasks in infrastructure-limit areas. However, as the number of UAVs and smart devices increase, the design of an efficient task offloading and resource allocation scheme for the multi-UAV-assisted MEC network becomes a challenge, especially in a resource-limit environment. To address this challenge, we first model the interaction between the UAV and mobile users as a matching game based on dynamic preference. Then, the system utility which is defined as the ratio of system payoff to the system cost as the objective function and the decision basis is formulated to jointly optimize user association, transmit power of users, and UAV bandwidth allocation under strict latency constraints. To solve this problem, a game-based task offloading and resource allocation (GTORA) algorithm is proposed, wherein the association between mobile users and UAVs is tackled by the dynamic preference better matching (DPBM) algorithm, based on which the transmit power of users and UAV bandwidth allocation are alternately explored. The simulation results demonstrate that the proposed GTORA algorithm can significantly improve the system utility when compared to the other state-of-art baselines.