# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 1 Introduction
The authors in [4] proposed a novel CFG model in data collection and transmission, where UAVs are sent to serve randomly located missions, collect data and transmit it to a central command through wireless communication. In [23], the authors took the aerial back haul scheme among UAVs as the mechanism of network formation game, and proposed a model that utilizes UAVs as an on-demand flying network, where UAVs seek to form a multi-hop back haul network in the air. However, the current research of CFG has focused on connectivity enhancement, data transmission improvement and routing, but without considering the trade-off between the coverage reward and energy consumed by transmission in UAV networks. In that situation, we jointly consider a communication coverage and data transmission scenario in the UAV-assisted sensor network, and propose an efficient cooperative UAV deployment model based on coalition formation. The key of coalition formation is to describe the relationships between coverage reward and energy consumption. The main contribution in this paper can be summarized as follows:  
* An efficient cooperative multi-UAV deployment model based on coalition formation is proposed, describing the trade-off between the coverage reward and energy consumption in UAV-assisted network. Coverage utility function is characterized as a reference for coalition formation to accurately depict the transmission model. * Through game analysis, the stable solution of the proposed system model is proved to exist. The proposed UAV coverage and data transmission approach can achieve at least one stable coverage deployment and coalition formation scheme to maximize the coverage utility of the whole UAV network. * Coalition selection and position deployment algorithm based on Pareto order (CSPDA-PO) is designed to perform coalition selection and position deployment. The CSPDA-PO algorithm is able to achieve the stable state of our system model. Meanwhile, convergence of the proposed algorithm is also shown in the simulation. Note that authors in [7; 24] did some works that are relevant to our work, especially in constructing the UAV coverage model, including evaluation of coverage ability and deployment mechanism. The main differences can be summed up to: (i) the UAV deployment is not only concerned with the coverage ability, but also the coalition formation under the comprehensive coverage reward and energy consumption; and (ii) the energy consumption model for transmitting and receiving through radio frequency (RF) signals is introduced into coalition formation mechanism, which is associated with coverage reward under current deployment. The rest of the paper is organized as follows: Section 2 shows the system model of multi-UAV cooperative deployment and problem formulation. In Section 3, a coalition formation game approach for the system model is analyzed, and the learning algorithm is designed to converge the proposed problem to the stable state. Simulation results and analysis are presented in Section 4. Finally, the concluding remarks are given in Section 5.