His sort of situation will not be widespread. As an alternative, missions, like reconnaissance or target tracking, often involve circumstances exactly where the UAVs select their path in actual time based on the path-planning mechanism and mission objective. Arafat 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 for the location or the next-hop relay. Moreover, they introduced a location prediction system primarily based on the Guess-Markov model [20] and place data. However, this proposal mainly focuses on effective and timely delivery instead of energy-efficiency. Oubbati et al. [21] proposed an energy-efficient routing protocol for FANETs. They regarded the movement information and residual energy level of the UAVs and predicted sudden link breakage. Inspired by the AODV [22] link discovery course of action, the UAVs decide the routing paths primarily based around the link breakage prediction, energy consumption, and degree of connectivity of the found paths. While the concentrate was to seek out an energy-efficient routing option for UAVs, they didn’t contemplate a sparsely populated situation where the UAVs seldomly come across each other. Shi et al. [23] proposed a different routing protocol focusing around the SBFI-AM Technical Information energy-efficiency of your UAVs. The (-)-Rasfonin Technical Information network is divided into various clusters. Among the member of a cluster, a cluster head is selected based on the power level, degree of connectivity, and relative velocity. Intra-cluster communication is carried out through direct make contact with, whereas inter-cluster communication happens only via the cluster head, contemplating that the cluster head has the highest power. Having said that, a significant drawback is, since each of the inter-cluster communication is tunneled via the cluster head, it soon runs out of power and fails the method. Additionally, this remedy doesn’t consider sparsely populated scenarios of UAVs. Khelifi et al. [24] proposed one more cluster-based strategy taking into consideration the energyefficiency 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. Nonetheless, the technique generates a significant overhead during the formation of clusters and also the election of cluster heads. Once again, it only considers scenarios exactly where a considerable quantity of UAVs are present. Another cluster-based routing focusing energy-efficiency of UAVs has been proposed by Aadil et al. [25]. They focused on minimizing the overhead to decrease power consumption. They regarded dynamically adjustable communication range based around the separation distance among the communicating UAVs. The clusters are formed, and cluster heads are selected primarily based on the degree of your neighborhoods. Nonetheless, this strategy considers a pre-planned mobility model that is highly uncommon in most FANET scenarios. Besides, it doesn’t look at scenarios of a compact quantity of UAVs covering a big location. Table 1 compares the features among the existing major techniques together with the proposed LECAR. General, the current DTN-based routing protocols fail to serve the goal of energy-Sensors 2021, 21,4 ofefficient routing in most instances. On top of that, the existing energy-efficient routing protocols do not think about sparsely populated network scenarios. Hence, we are encouraged.