## 3 Problem Description  
In this work, considering a complicated and real-world scenario where UAVs must move apart and occasionally obtain the communication scope, we consider a reconnaissance mission. Following our previous work presented in [26], we consider a mission where a small number of UAVs have the task to simultaneously search for targets in a large area while intermittently tracking the detected targets and avoiding detection by the targets. We also consider that the UAVs follow the mobility model proposed in [26]. Although we designed LECAR especially for the mobility model proposed in [26], the concept of LECAR can be easily adapted to any other mobility model. In many mobility models, all UAVs use a shared map of the operational area for navigation, such as a probabilistic map, pheromone map, and others. The UAVs follow this map to determine their path. Following [26], we consider that the UAVs follow a pheromone map to select their real-time routes. The UAVs must continuously survey a 10 km \(\times\) 10 km area, and whenever they detect any target, they must follow it. The entire area is divided into small cells of 400m \(\times\) 400 m, and we consider the center of each cell as a waypoint. Figure 1 illustrates the considerations. The UAVs are equipped with high-resolution cameras. Whenever a UAV passes over a waypoint, it implies that the UAV has successfully observed that cell. Based on the observation of a cell, the UAV leaves a pheromone value for that cell. Thus, each cell contains a pheromone value, and all cells together build a pheromone map. This pheromone map is periodically exchanged between UAVs so that they can obtain an update for the entire area and complete the mission cooperatively. We encourage the interested readers to read our previously proposed work in [26] for further details. In addition, we consider that we have a limited number of UAVs to survey a large area. Therefore, the UAVs rarely encounter each other following the considered mobility model. Thus, UAVs have a concise time window to forward the packet to the destination. Whenever a UAV needs to send data to the command-and-control station or any other UAV, it might need to store that information in its buffer and forward that message whenever it encounters a suitable custodian. This data storage might lead to another problem of buffer overflow. For example, when a UAV sends a large amount of information, such as sensing data or high-resolution images, it requires ample space in the buffer to store the packets, which might lead to a buffer overflow. Therefore, to avoid packet drops, UAVs must be aware of the custodian's buffer information. By custodian, we imply a neighboring UAV that can meet or travel near the destination and has enough memory in its buffer to store the message. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Features** & **[PERSON]** & **[PERSON]** & **[PERSON] et al.** & **[PERSON]** & **[PERSON] et al.** & **[PERSON]** & **[PERSON]** & **Proposed** \\  & **et al.**[14] & **et al.**[12] & **[15] & **et al.**[19] & **[23] & **et al.**[24] & **et al.**[25] & **LECAR** \\ \hline Energy-efficient & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline Path prediction & \(\times\) & \(\times\) & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\checkmark\) & \(\checkmark\) \\ \hline Support for sparsely & & & & & & & & & \\ populated scenarios & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline Unicast & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline Single copy & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline Considers location & & & & & & & & \\ information & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}