Abstract
FANET (flying ad-hoc network) has provided broad area for research and deployment due to efficient use of the capabilities of drones and UAVs (unmanned ariel vehicles) in several military and rescue applications. Drones have high mobility in 3D (3 dimensional) environment and low battery power, which produce various problems such as small journey time and infertile routing. The optimal routing for communication will assist to resolve these problems and provide the energy efficient and secure data transmission over FANET. Hence, in this paper, we proposed a whale optimization algorithm based optimized link state routing (WOA-OLSR) over FANET to provide optimal routing for energy efficient and secure FANET. The efficiency of OLSR is enhanced by using WOA and evaluated performance shows the better efficiency of WOA-OLSR in terms of some parameters such as a packet delivery ratio, end to end delay, energy utilization, throughput, and time complexity against the previous approaches OLSR, MP-OLSR, P-OLSR, ML-OLSR-FIFO and ML-OLSR-PMS.
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Sharma, V., Sabatini, R., & Ramasamy, S. (2016). UAVs assisted delay optimization in heterogeneous wireless networks. IEEE Communication Letters, 20, 2526–2529
Khan, N. A., Jhanjhi, N. Z., Brohi, S. N., & Nayyar, A. (2020). Emerging use of UAV’s: Secure communication protocol issues and challenges. In Drones in Smart-Cities (pp. 37-55). Elsevier.
Ahn, T., Seok, J., Lee, I., & Han, J. (2018). Reliable flying iot networks for UAV disaster rescue operations. Mobile Information Systems. https://doi.org/10.1155/2018/2572460
Erdelj, M., Uk, B., Konam, D., & Natalizio, E. (2018). From the eye of the storm: An IoT ecosystem made of sensors, smartphones and UAVs. Sensors, 18, 1–20. https://doi.org/10.3390/s18113814
Ferrera, E., Alcántara, A., Capitán, J., Castaño, A. R., Marrón, P. J., & Ollero, A. (2018). Decentralized 3D collision avoidance for multiple UAVs in outdoor environments. Sensors, 18, 1–20. https://doi.org/10.3390/s18124101
Bujari, A., Calafate, C. T., Cano, J. C., Manzoni, P., Palazzi, C. E., & Ronzani, D. (2018). A location-aware waypoint-based routing protocol for airborne DTNS in search and rescue scenarios. Sensors, 18, 1–14. https://doi.org/10.3390/s18113758
Hu, B., Wang, C., Chen, S., Wang, L., & Yang, H. (2018). Proactive coverage area decisions based on data field for drone base station deployment. Sensors, 18, 1–14. https://doi.org/10.3390/s18113917
Kim, B., Min, H., Heo, J., & Jung, J. (2018). Dynamic computation offloading scheme for drone-based surveillance systems. Sensors, 18, 1–10. https://doi.org/10.3390/s18092982
Popescu, D., Dragana, C., Stoican, F., Ichim, L., & Stamatescu, G. (2018). A collaborative UAV-WSN network for monitoring large areas. Sensors, 18, 1–25. https://doi.org/10.3390/s18124202
Huang, J., Fan, X., Xiang, X., Wan, M., Zhuo, Z., & Yang, Y. (2016). A clustering routing protocol for mobile ad hoc networks. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/5395894
Ganesan, R., Raajini, X. M., Nayyar, A., Sanjeevikumar, P., Hossain, E., & Ertas, A. H. (2020). BOLD: Bio-inspired optimized leader election for multiple drones. Sensors, 20, 1–20
Valentino, R., Jung, W. S., & Ko, Y. B. (2018). A design and simulation of the opportunistic computation offloading with learning-based prediction for unmanned aerial vehicle (UAV) clustering networks. Sensors, 18, 1–14. https://doi.org/10.3390/s18113751
Fahad, M., Aadil, F., Khan, S., Shah, P. A., Muhammad, K., Lloret, J., Wang, H., Lee, J. W., & Mehmood, I. (2018). Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Computers and Electrical Engineering, 70, 853–870
Aadil, F., Raza, A., Khan, M. F., Maqsood, M., Mehmood, I., & Rho, S. (2018). Energy aware cluster-based routing in flying ad-hoc networks. Sensors, 18, 1–16. https://doi.org/10.3390/s18051413
Albu-Salih, A. T., Seno, S. A. H., & Mohammed, S. J. (2018). Dynamic routing method over hybrid SDN for flying ad hoc networks. Baghdad Science Journal, 15(3), 361–368
Hong, J., Zhang, D., & Niu, X. (2017). Impact analysis of node motion on the performance of FANET Routing protocols. In: 14th International Conference on wireless Communications, Networking and Mobile Computing (WiCOM), pp. 147–162.
Zheng, X., & Qi, Q., Wang, Q., Li, Y. (2017). An adaptive density based routing protocol for flying Ad Hoc networks. In: 2nd International Conference on Materials Science, Resource and Environment Engineering (MSREE) AIP Conf. Proc., pp. 1–8. http://doi.org/https://doi.org/10.1063/1.5005315
Perez, A. G., & Cano, M. D. (2018). Flying ad hoc networks: A new domain for network communications. Sensors, 18, 1–23. https://doi.org/10.3390/s18103571
Sharma, V., Kumar, R., & Rathore, N. (2018). Topological broadcasting using parameter sensitivity-based logical proximity graphs in coordinated ground-flying ad hoc networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 6(3), 54–72
Khan, M. A., Khan, I. U., Safi, A., & Quershi, I. M. (2018). Dynamic routing in flying ad-hoc networks using topology-based routing protocols. Drones, 2, 1–15. https://doi.org/10.3390/drones2030027
Leonov, A. V., & Litvinov, G. A. (2018). Simulation-based packet delivery performance evaluation with different parameters in flying ad-hoc network (FANET) using AODV and OLSR. In: International Conference Information Technologies in Business and Industry, IOP Conf. Series, Journal of Physics, (pp. 1-16). Doi:https://doi.org/10.1088/1742-6596/1015/3/032178
Khan, M. A., Qureshi, I. M., & Khanzada, F. (2019). A hybrid communication scheme for efficient and low-cost deployment of future flying ad-hoc network (FANET). Drones, 3(16), 1–20. https://doi.org/10.3390/drones3010016
Yi, J., Adnane, H. A., David, S. & Parrein, B. (2018). Multipath optimized link state routing for mobile ad hoc network. HAL, 1–17.
Radu, D., Cretu, A., Parrein, B., Yi, J., Avram, C., & Astilean, A. (2018). Flying ad hoc network for emergency applications connected to a fog system. HAL, 1–13. (https://hal.archives-ouvertes.fr/hal-01763827)
Wen, S., & Huang, C. (2018). Delay-constrained routing based on stochastic model for flying ad hoc networks. Mobile Information Systems. https://doi.org/10.1155/2018/6056419
Arabi, S., Sabir, E., Elbiaze, H., & Sadik, M. (2018). Data gathering and energy transfer dilemma in UAV-assisted flying access network for IoT. Sensors, 18, 1–25. https://doi.org/10.3390/s18051519
Sharma, V., & Kumar, R. (2017). G-FANET: An ambient network formation between ground and flying ad hoc networks. Telecommunication Systems, 65(1), 31–54
Wei, Z., Liu, X., Han, C., & Feng, Z. (2018). Neighbor discovery for unmanned aerial vehicle networks. IEEE Access, 6, 68288–68301. https://doi.org/10.1109/ACCESS.2018.2871132
Li, J., Chen, M., Dai, F., & Wang, H. (2018). Prioritizing-based message scheduling for reliable unmanned aerial vehicles ad hoc network. International Journal Perform ability Engineering, 14(9), 2021–2029. https://doi.org/10.23940/ijpe.18.09.p10.20212029
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Namdev, M., Goyal, S. & Agarwal, R. An Optimized Communication Scheme for Energy Efficient and Secure Flying Ad-hoc Network (FANET). Wireless Pers Commun 120, 1291–1312 (2021). https://doi.org/10.1007/s11277-021-08515-y
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DOI: https://doi.org/10.1007/s11277-021-08515-y