Recent advances in artificial intelligence (AI) have impacts on the development of communication networks. Diversified applications and scenarios put forward AI-native requirement for communication network. The network is envisioned to offer high data rate and low-latency communications, pervasive artificial intelligence, full coverage and programmable service. With the aid of AI, autonomous unmanned aerial vehicles (UAVs) play an indispensable role in the emerging scenarios. Owing to their flexibility and scalability, UAVs can be utilized as the mobile platform to deliver various kinds of services in dynamic and adversarial environments, especially the emergency scenarios. In this article, we deal with the post-disaster scenarios that UAVs, equipped with computing servers, work with the remaining infrastructures to provide communication and computing services for the ground mobile users (GMUs). With the aim of minimizing the energy cost and computing delay of task offloading, a two-layer optimization mechanism that can dynamically orchestrate the task offloading decision and the UAVs deployment is proposed. In lower layer, the problem of making offloading decisions is formulated into an evolutionary game. Replicator dynamics is exploited to help users to make decisions. In upper layer, a clustering approach for users is introduced to avoid unbalanced load distribution. By suggesting the locations and number of UAVs based the clusters, system cost is further reduced. The fairness of service load among UAVs is also achieved. Numerical results demonstrate the effectiveness of the proposed two-layer optimization mechanism.

With the rapid development of information technology, low-cost unmanned aerial vehicles (UAVs) appear. With advanced sensing and actuating technologies, they are being increasingly applied to a variety of scenarios. However, considering their limited computing resource and restricted battery capability, the computation-intensive tasks or data-intensive tasks will face tough challenges. With the aid of Mobile Edge Computing (MEC), moving computation-intensive tasks from resource-constrained UAVs to edge cloud servers can significantly save energy and finally achieve impressive performance.This paper proposes an evolutionary game based algorithm to solve the computation offloading problem for UAVs. By replicator dynamics, UAVs select the suitable service provider to offload the computation tasks via achieving a tradeoff between time delay, energy consumption and monetary cost when network externality exists. Simulation results show that the proposed algorithm can rapidly converge to evolutionary equilibrium and achieve desirable performance.