State of your neighboring UAVs and routes the considers the congestion state of the neighboring UAVs and routes the packet to a UAV packet to a UAV that moves closer for the location and has sufficient space in its buffer. that moves closer towards the destination and has sufficient space in its buffer. In extensive simulation experiments, LECAR demonstrated a high packet delivery In extensive simulation experiments, LECAR demonstrated a higher packet delivery ratio (on average, 27 increase than Spray and Wait) and low energy consumption (on (on ratio (on average, 27 boost than Spray and Wait) and low energy consumption typical, 42 reduce than Spray and Wait) when compared with the regarded routing proto typical, 42 reduce than Spray and Wait) in comparison with the regarded routing protocols. cols. Furthermore, in maximum circumstances, LECAR could retain a single copy per packet at a In addition, in maximum circumstances, LECAR could preserve a single copy per packet at a time time in the network. Additionally, it ensured low hop counts for routing a packet (on typical, 34 in the network. Additionally, it ensured low hop counts for routing a packet (on average, 34 much less less than Spray and Wait). Even though it generated a reasonably massive overhead, the number than Spray and Wait). Even though it generated a reasonably massive overhead, the number of transmissions per information packet outweighed the further SCH 51344 GPCR/G Protein overhead and resulted in low energy consumption. These results reveal that LECAR much better balances packet delivery ratio and energy consumption thinking about a sparsely populated FANET scenario. Although LECARSensors 2021, 21,18 ofis designed thinking about a particular situation and mobility model, the key thought is usually effortlessly extended and adapted to any other scenario or mobility model. In future operate, we strategy to extend LECAR to drastically lower the overhead even within a high-density network situation. We additional strategy to enhance LECAR for minimizing the delay in packet delivery, even for low-density scenarios.Author Contributions: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, information curation, JMS-053 Purity & Documentation writing–original draft preparation, writing–review and editing, and visualization, I.M.; supervision, project administration, and funding acquisition, Y.-Z.C. All authors have study and agreed for the published version on the manuscript. Funding: This research was funded in element by the Ministry of Education, 2018R1A6A1A03025109, and was funded by the Korean government, 2019R1A2C1006249. Institutional Evaluation Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: This investigation was supported in portion by the fundamental Science Research Plan by means of the National Investigation Foundation of Korea (NRF), funded by the Ministry of Education (No. NRF-2018R1A6A1A03025109), and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2019R1A2C1006249). Conflicts of Interest: The authors declare no conflict of interest.AbbreviationsUAV DTN LECAR FANET MANET VANET LADTR AODV ACK Spray and wait LAROD-LoDiS GPSR GPSR-Q LER Math symbols Tpheromone_update T1_hop_update TTL Curr_Cell_ID Nxt_Cell_ID hello_interval Tloc_update n tpassed ts dij (xi , yi , zi ) (xj , yj , zj ) avg_dnij d F_avg_dni d Unmanned aerial car Delay tolerant network Place estimation-based congestion-aware routing protocol Flying ad doc network Mobile ad hoc network Vehicular ad hoc network Location-aided delay tolerant routing p.
