## 1 Introduction and Related Work  
In recent years, the unmanned aerial vehicles (UAV) applications have grown rapidly [1, 2] because the UAV owns several characteristics [3, 4] such as higher mobility, less interference by fewer obstacles in the air, more flexible capability by equipped with different sensors, etc. Research in [5, 6] deployed UAVs in 5G cellular networks as relay stations to improve signal coverage. In [7, 8, 9, 10], several routing protocols of the Vehicular Ad hoc Network (VANET) were proposed. Further, the UAV was integrated with VANET as a temporary gateway whenever the network infrastructure malfunctioned after natural disasters like the earthquake or mudslide destroyed backbone communication [11, 12, 13, 14]. Several UAV image applications for disaster management have been proposed in [15, 16, 17, 18]. However, most research focuses on how to deploy UAVs to fully cover the disaster area [19, 20, 21, 22, 23, 24, 25, 26, 27, 28]. Fewer of them work on the scheduling policies to dispatch new UAVs for replacing old ones and extending the communication lifetime before they have drained all their battery energy. By considering the limited battery energy, a scheduling framework proposed in [22] dispatches a group of UAVs to their pre-assigned destinations. It further formulated these missions as a mixed integer linear programming (MILP) problem, which focuses on minimizing the total energy consumed by all UAVs. This work assumes the starting and finishing time of all missions must be known in advance. However, this assumption may not be fulfilled in real cases. Moreover, it has not mentioned how to dispatch new UAVs to hand over the energy-drained ones for continuing the missions. In [23], two types of the Ground Control Stations (GCS), i.e., fixed and mobile ones, were proposed to control, receive and process all the data of the UAV system. The optimal UAV flying speed has been measured and analyzed in [24]. It formulates the total energy consumption \(E_{UAV}\) as the sum of the energy, i.e., \(E_{truvel}\), consumed for flying to the destination, that, i.e., \(E_{horver}\), for hovering over the destination and that, i.e., \(E_{radio}\), for communicating with the underwater sensor network. Hence, the energy model is formulated as \(E_{UAV}=E_{truvel}+E_{horver}+E_{radio}\). Three communication scenarios between vehicles, between vehicle and UAV, and between UAVs have been modeled as ground-to-ground (G2G), air-to-ground (A2G)/ground-to-air (G2A), and air-to-air (A2A) communication, respectively [25]. According to the height and density of buildings, the A2G and G2A channels can be modeled as the line-of-sight (LoS) or non-line-of-sight (NLoS) propagation with corresponding probabilities. Oppositely, the A2A channel between UAVs is modeled as the LoS propagation because far fewer obstacles existed in the air. Based on these models, this paper will compute the signal-to-noise ratio (SNR) and the consumed signal power. By using the MAVLink protocol, a UAV replacement algorithm proposed in [26] executes uninterrupted multi-UAV waypoint mission for an Unmanned Aerial System (UAS). It also presented the mission continuity coefficient of the battery replacement scheme as being higher than the battery recharging one. However, it lacks analysis about how the number of batteries for replacement influences the performance of mission continuity and how much additional costs are. Similarly, a set of MAVLink messages presented in [27] supports UAV replacement automatically as the UAV has depleted its battery energy. However, it does not describe how to choose a new UAV to replace the energy-depleted one. Two Linear Integer Problem (LIP) optimization solutions for an IoT platform proposed in [28] execute a specific IoT task with the minimum energy consumption. For selecting appropriate UAVs for a task, diverse criteria, e.g., what sensors the UAVs own, how much energy they have charged, and how close they are to the task position, are considered in this paper.