## 2 Related Work  
In this section, we briefly describe the recent DTN-based routing protocols for FANET, which focus on sparsely populated network scenarios. We also discuss the existing key energy-efficient routing protocols for FANET scenarios. [PERSON] et al. [14] presented a DTN-based routing protocol for the FANET, combining geographic beacon-less routing with the DTN-based store-carry-and-forward principle. They forwarded packets based on a greedy algorithm governed by the pheromone repel model and location service. A UAV regularly broadcasts the data until it locates a suitable UAV that holds that packet for forwarding, consuming a significant amount of energy. Another MANET-oriented DTN-based routing protocol, Spray and Wait [12], although achieving the highest packet delivery ratio, primarily disseminates data by broadcasting them to all neighbors in the vicinity. The neighbors broadcast the data to their neighbors, and so on; thus, the data reach the intended receiver. However, as its mechanism implies, it consumes considerable resources in terms of energy, memory, and number of transmissions. These issues make it highly inappropriate for FANETs. [PERSON] et al. [15] proposed a routing protocol incorporating location information. They considered a scenario where the path is preplanned and known to every UAV. The UAVs consider this information in making routing decisions and achieve a moderately better packet delivery ratio. However, this type of scenario is not common. Instead, missions, such as reconnaissance or target tracking, often involve cases where the UAVs choose their path in real time based on the path-planning mechanism and mission objective. [PERSON] et al. [19] combined the store-carry-and-forward-based routing approach with location-aided forwarding for the post-disaster operations of UAVs in their proposed LADTR routing protocol. They introduced the communication ferry UAVs, which physically carry the data to the destination or the next-hop relay. Additionally, they introduced a location prediction method based on the Guess-Markov model [20] and location data. However, this proposal mainly focuses on successful and timely delivery rather than energy-efficiency. [PERSON] et al. [21] proposed an energy-efficient routing protocol for FANETs. They considered the movement information and residual energy level of the UAVs and predicted sudden link breakage. Inspired by the AODV [22] link discovery process, the UAVs decide the routing paths based on the link breakage prediction, energy consumption, and degree of connectivity of the discovered paths. Although the focus was to find an energy-efficient routing solution for UAVs, they did not consider a sparsely populated scenario where the UAVs seldomly come across each other. [PERSON] et al. [23] proposed another routing protocol focusing on the energy-efficiency of the UAVs. The network is divided into several clusters. Among the member of a cluster, a cluster head is selected based on the energy level, degree of connectivity, and relative velocity. Intra-cluster communication is conducted through direct contact, whereas inter-cluster communication occurs only via the cluster head, considering that the cluster head has the highest energy. However, a major drawback is, because all the inter-cluster communication is tunneled through the cluster head, it soon runs out of energy and fails the strategy. Moreover, this solution does not consider sparsely populated scenarios of UAVs. [PERSON] et al. [24] proposed another cluster-based approach considering the energy-efficiency of UAVs. They used the received signal strength indication to calculate the positions of the undetermined UAVs. The cluster heads are elected based on a fuzzy-based localization algorithm. Nevertheless, the strategy generates a significant overhead during the formation of clusters and the election of cluster heads. Again, it only considers scenarios where a considerable number of UAVs are present.