###### Abstract  
In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, the proposed algorithm pursues energy-efficient operations by deactivating selected surveillance cameras on the UAVs located in overlapping areas. For this objective, a message-passing based algorithm is used because the overlapping situations can be formulated using a max-weight independent set. Next, the unscheduled UAVs based on the message-passing fly to the charging towers to be charged. This algorithm computes the optimal matching between the UAVs and charging towers and the amount of energy allocation for the scheduled UAV-tower pairs. This joint optimization is initially formulated as non-convex, and it is then reformulated to be convex, which can guarantee optimal solutions. The proposed framework achieves the desired performance, as presented in the performance evaluation. U 1 2020 9  
formula, a set of UAVs can be selected and scheduled, and their CCTV cameras can be activated for surveillance. Here, we have to note that the MWIS problem is a well-known NP-hard problem [3]. Many approaches exist in order to solve the problem and, among them, a solution approach that is based on message-passing is investigated in this paper, which was inspired by the literature on wireless interference modeling [3]. For macro systemwide energy-efficient operation, we design a new charging mechanism for energy-limited UAV systems. Based on wireless energy transfer technologies [4; 5; 6; 7; 8], the UAVs that need to be charged can fly to charging towers for obtaining energy resources over a wireless medium. Based on this setting, we propose a new optimal control mechanism for charging matching between UAVs and charging towers. In addition, this mechanism determines the amount of energy allocation for the matched UAV-tower pairs. This joint optimization of matching and resource/energy allocation is a non-convex optimization framework, which cannot guarantee optimal solutions in polynomial time. To resolve this problem, we reformulate the mathematical optimization program into a convex programming framework that is equivalent to the original non-convex formulation. Finally, the reformulated convex programming framework can compute the optimal solutions in polynomial time. Based on these two sequential methods, we confirm that our proposed UAV-based surveillance system is able to conduct energy-efficient operations through (i) the minimization of overlapping surveillance areas with message-passing to solve the MWIS formulation and then (ii) the convex optimization of the matching decision between UAVs and charging towers and the associated energy allocation decision for the matched UAV-tower pairs. Throughout the data-intensive performance evaluation with various settings, we confirm that our proposed algorithm with these two sequential methods outperform the others. The main contributions of this paper can be summarized, as follows. * This research is the first attempt to characterize the overlapping surveillance areas with MWIS formulation that is feasible in practice. In the MWIS-based model, we design a novel solution approach that is based on the concept of message-passing. * We determine that the optimization problem for joint matching and energy allocation between UAVs and charging towers is non-convex. We then present a novel method for transforming the non-convex formulation into the convex programming, thus achieving optimal solutions. * In addition to the theoretical novelties, we conduct data-intensive simulations with various simulation settings. It is demonstrated that our joint matching and energy allocation method remarkably outperforms several baseline schemes.