The Heterogeneous Demands Satisfaction in IoT Network: Air-Ground Collaborative Deployment
Xingyue Yu, Ducheng Wu, Dianxiong Liu, Hai Wang, Zhiqiang Qin
Abstract
This paper investigates the joint optimization of the three-dimensional (3D) position deployment of multiple unmanned aerial vehicles (UAVs) and the access UAVs selection of ground machine type devices (MTDs). Cache-enabled UAVs are deployed over the Internet of Things (IoT) networks to provide flexible and reliable delivery services for ground devices. Since each MTD has different data demands and the ultimate goal of communication is to provide personalized services for MTDs, we propose an air-ground collaborative deployment strategy focusing on optimizing users’ satisfaction. In the proposed strategy, the hierarchical game model is proposed. Specifically, the 3D position deployment of multiple UAVs is at the upper layer and the access UAV selection of ground MTDs is at the lower. Since there is no central controller and the users are autonomous, we structure a local interaction game to achieve global optimization for the network satisfaction problem. We design a hierarchical deployment access based log-linear learning (HDALL) algorithm to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of air-ground collaborative deployment strategy as well as HDALL algorithm.