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5G and artificial intelligence integration to improve drone routing

Roman Zaivyi*, Oleh Melnychok

roman.o.zaivyi@lpnu.ua

Received 01.11.2024, Revised 28.02.2025, Accepted 17.03.2025

Abstract

The development of 5G and artificial intelligence technologies creates new opportunities for improving the routing of unmanned aerial vehicles, which is particularly relevant for logistics, rescue operations, and monitoring of critical infrastructure. The purpose of the study was to analyse the prospects for implementing 5G and AI in drone routing, identify key challenges, and develop recommendations for their effective integration into Ukrainian airspace. The study used methods of theoretical analysis of scientific sources, comparative analysis of international experience, and systematisation of modern approaches to drone routing using 5G and AI. The architecture of 5G networks, route optimisation algorithms, and coordination mechanisms for swarms of drones was analysed. The main results of the study showed that the combination of 5G and AI provides a significant increase in the efficiency of autonomous unmanned systems, allowing helping to quickly adapt routes, optimise energy consumption, and improve the level of flight safety. Special attention was paid to comparing two popular route optimisation algorithms for UAVs: the particle swarm and the ant colony optimisation algorithms. The analysis showed that both algorithms effectively solve routing problems, but they have their advantages depending on the specifics of the application. The particle swarm algorithm proved to be more efficient for problems with a large number of variables, helping to optimise routes in real time under rapidly changing conditions. The ant colony optimisation algorithm, in turn, demonstrated an advantage in solving complex problems with a large number of obstacles. The practical significance of the study was to identify key technical and regulatory challenges associated with the integration of 5G and AI into drone routing, and to develop evidence-based approaches to solving them. The results obtained can be used to improve national regulations, promote the introduction of intelligent unmanned systems in logistics, infrastructure monitoring and rescue operations, and for further research in the field of autonomous aviation technologies

Keywords:

unmanned aerial vehicles; artificial intelligence; blockchain; neural network; drone swarms

https://doi.org/10.62660/bcstu/1.2025.80

Retrieved from Volume 30, No. 1, 2025

Pages 80-90

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References

[1] Aktas, F., Shayea, I., Ergen, M., & El-Saleh, A.A. (2023). Ai-enabled routing in next-gen networks: A brief overview. In Proceedings of the 10th international conference on wireless networks and mobile communications (pp. 1-6). Istanbul: IEEE. doi: 10.1109/WINCOM59760.2023.10322945.

[2] Alsamhi, S.H., Afghah, F., Sahal, R., Hawbani, A., Al-qaness, M.A., Lee, B., & Guizani, M. (2021). Green internet of things using UAVs in B5G networks: A review of applications and strategies. Ad Hoc Networks, 117, article number 102505. doi: 10.1016/j.adhoc.2021.102505.

[3] Ayass, T., Coqueiro, T., Carvalho, T., Jailton, J., Araújo, J., & Francês, R. (2022). Unmanned aerial vehicle with handover management fuzzy system for 5G networks: Challenges and perspectives. Intelligence & Robotics, 2(1), 20-36. doi: 10.20517/ir.2021.07.

[4] Bine, L.M., Boukerche, A., Ruiz, L.B., & Loureiro, A.A. (2023). IoDMix: A novel routing protocol for delay-tolerant internet of drones integration in intelligent transportation system. Ad Hoc Networks, 148, article number 103204. doi: 10.1016/j.adhoc.2023.103204.

[5] Biswas, A., & Wang, H.C. (2023). Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain. Sensors, 23(4), article number 1963. doi: 10.3390/s23041963.

[6] Caballero-Martin, D., Lopez-Guede, J.M., Estevez, J., & Graña, M. (2024). Artificial intelligence applied to drone control: A state of the art. Drones, 8(7), article number 296. doi: 10.3390/drones8070296.

[7] Dash, B., Ansari, M.F., & Swayamsiddha, S. (2023). Fusion of artificial intelligence and 5G in defining future UAV technologies – a review. In Proceedings of the international conference on device intelligence, computing and communication technologies (pp. 312-316). Dehradun: IEEE. doi: 10.1109/DICCT56244.2023.10110231.

[8] Du, P., He, X., Cao, H., Garg, S., Kaddoum, G., & Hassan, M.M. (2023). AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems. Computer Communications, 207, 46-55. doi: 10.1016/j.comcom.2023.04.032.

[9] European Aviation Safety Agency. (n.d.). Aviation safety reporting. Retrieved from https://www.easa.europa.eu/ en/domains/safety-management/aviation-safety-reporting.

[10] Garg, T., Gupta, S., Obaidat, M.S., & Raj, M. (2024). Drones as a service (DaaS) for 5G networks and blockchainassisted IoT-based smart city infrastructure. Cluster Computing, 27, 8725-8788. doi: 10.1007/s10586-024-04354-1.

[11] Gupta, R., Kumari, A., & Tanwar, S. (2021). Fusion of blockchain and artificial intelligence for secure drone networking underlying 5G communications. Transactions on Emerging Telecommunications Technologies, 32(1), article number e4176. doi: 10.1002/ett.4176.

[12] Haider, S.A., Zikria, Y.B., Garg, S., Ahmad, S., Hassan, M.M., & AlQahtani, S.A. (2022). Ai-based energy-efficient UAVassisted IoT data collection with integrated trajectory and resource optimization. IEEE Wireless Communications, 29(6), 30-36. doi: 10.1109/MWC.001.2200105.

[13] Han, T., Ribeiro, I.D., Magaia, N., Preto, J., Segundo, A.H., De Macedo, A.R., Muhammad, K., & De Albuquerque, V.H. (2021). Emerging drone trends for blockchain-based 5G networks: Open issues and future perspectives. IEEE Network, 35(1), 38-43. doi: 10.1109/MNET.011.2000151.

[14] Hashesh, A.O., Hashima, S., Zaki, R.M., Fouda, M.M., Hatano, K., & Eldien, A.S. (2022). AI-enabled UAV communications: Challenges and future directions. IEEE Access, 10, 92048-92066. doi: 10.1109/ ACCESS.2022.3202956.

[15] International Civil Aviation Organization. (n.d.). Safety reports. Retrieved from https://www.icao.int/safety/ pages/safety-report.aspx.

[16] International Telecommunication Union. (n.d.). Latest reports and publications. Retrieved from https://www.itu. int/en/action/environment-and-climate-change/Pages/latest-reports-and-publications.aspx.

[17] Khalid, M., Ali, J., & Roh, B.H. (2024). Artificial intelligence and machine learning technologies for integration of terrestrial in non-terrestrial networks. IEEE Internet of Things Magazine, 7(1), 28-33. doi: 10.1109/ IOTM.001.2300190.

[18] Khan, M.A., Kumar, N., Mohsan, S.A., Khan, W.U., Nasralla, M.M., Alsharif, M.H., Żywiołek, J., & Ullah, I. (2022). Swarm of UAVs for network management in 6G: A technical review. IEEE Transactions on Network and Service Management, 20(1), 741-761. doi: 10.1109/TNSM.2022.3213370.

[19] Khan, S.K., Naseem, U., Siraj, H., Razzak, I., & Imran, M. (2021). The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges. Transactions on Emerging Telecommunications Technologies, 32(7), article number e4241. doi: 10.1002/ett.4241.

[20] Kourtis, M.A., Xilouris, G., Oikonomakis, A., Batistatos, M.C., & Chochliouros, I. (2023). Integration of drone connectivity in 5G: An examination of the OASEES framework. In Proceedings of the IEEE future networks world forum (pp. 1-6). Baltimore: IEEE. doi: 10.1109/FNWF58287.2023.10520379.

[21] Lins, S., Cardoso, K.V., Both, C.B., Mendes, L., De Rezende, J.F., Silveira, A., Linder, N., & Klautau, A. (2021). Artificial intelligence for enhanced mobility and 5G connectivity in UAV-based critical missions. IEEE Access, 9, 111792-111801. doi: 10.1109/ACCESS.2021.3103041.

[22] Lu, Y., Wen, W., Igorevich, K.K., Ren, P., Zhang, H., Duan, Y., Zhu, H., & Zhang, P. (2023). UAV ad hoc network routing algorithms in space-air-ground integrated networks: Challenges and directions. Drones, 7(7), article number 448. doi: 10.3390/drones7070448.

[23] Majumdar, P., Mitra, S., Bhattacharya, D., & Bhushan, B. (2024). Enhancing sustainable 5G powered agriculture 4.0: Summary of low power connectivity, internet of UAV things, AI solutions and research trends. Multimedia Tools and Applications. doi: 10.1007/s11042-024-19728-1.

[24] Mohsen, B.M. (2024). Ai-driven optimization of urban logistics in smart cities: Integrating autonomous vehicles and IoT for efficient delivery systems. Sustainability, 16(24), article number 11265. doi: 10.3390/su162411265.

[25] Posvistak, V., & Miroshnychenko, D. (2024). Usage of mobile network for remote drone. Technologies and Engineering, 25(4), 73-84. doi: 10.30857/2786-5371.2024.4.7.

[26] Qasim, N.H., & Jawad, A.M. (2024). 5G-enabled UAVs for energy-efficient opportunistic networking. Heliyon, 10(12), article number e32660. doi: 10.1016/j.heliyon.2024.e32660.

[27] Rovira-Sugranes, A., Razi, A., Afghah, F., & Chakareski, J. (2022). A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook. Ad Hoc Networks, 130, article number 102790. doi: 10.1016/j. adhoc.2022.102790.

[28] Sarkar, N.I., & Gul, S. (2023). Artificial intelligence-based autonomous UAV networks: A survey. Drones, 7(5), article number 322. doi: 10.3390/drones7050322.

[29] Shayea, I., Dushi, P., Banafaa, M., Rashid, R.A., Ali, S., Sarijari, M.A., Daradkeh, Y.I., & Mohamad, H. (2022). Handover management for drones in future mobile networks – a survey. Sensors, 22(17), article number 6424. doi: 10.3390/s22176424.

[30] Solanki, A., Tarar, S., Singh, S.P., & Tayal, A. (2022). The internet of drones: AI applications for smart solutions. London: CRC Press.

[31] Sufyan, A., Khan, K.B., Khashan, O.A., Mir, T., & Mir, U. (2023). From 5G to beyond 5G: A comprehensive survey of wireless network evolution, challenges, and promising technologies. Electronics, 12(10), article number 2200. doi: 10.3390/electronics12102200.

[32] Tlili, F., Ayed, S., & Fourati, L.C. (2024). Advancing UAV security with artificial intelligence: A comprehensive survey of techniques and future directions. Internet of Things, 27, article number 101281. doi: 10.1016/j. iot.2024.101281.

[33] Trabelsi, N., Fourati, L.C., & Chen, C.S. (2024). Interference management in 5G and beyond networks: A comprehensive survey. Computer Networks, 239, article number 110159. doi: 10.1016/j.comnet.2023.110159.

[34] Warrier, A., Al-Rubaye, S., Inalhan, G., & Tsourdos, A. (2023). AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks. Aerospace, 10(10), article number 884. doi: 10.3390/ aerospace10100884.

[35] Wu, Q., Xu, J., Zeng, Y., Ng, D.W., Al-Dhahir, N., Schober, R., & Swindlehurst, A.L. (2021). A comprehensive overview on 5G-and-beyond networks with UAVs: From communications to sensing and intelligence. IEEE Journal on Selected Areas in Communications, 39(10), 2912-2945. doi: 10.1109/JSAC.2021.3088681.

[36] Yermolenko, R., Klekots, D., & Gogota, O. (2024). Development of an algorithm for detecting commercial unmanned aerial vehicles using machine learning methods. Machinery & Energetics, 15(2), 33-45. doi: 10.31548/ machinery/2.2024.33.

[37] Zaid, M., Kadir, M.K., Shayea, I., & Mansor, Z. (2024). Machine learning-based approaches for handover decision of cellular-connected drones in future networks: A comprehensive review. Engineering Science and Technology, an International Journal, 55, article number 101732. doi: 10.1016/j.jestch.2024.101732.

Suggested citation

Zaivyi, R., & Melnychok, O. (2025). 5G and artificial intelligence integration to improve drone routing. Bulletin of Cherkasy State Technological University, 30(1), 80-90. https://doi.org/10.62660/bcstu/1.2025.80