Abstract

Flying Ad-hoc Networks (FANETs), formed by Unmanned Aerial Vehicles (UAVs), represent an emerging and promising communication paradigm. These networks face unique challenges due to UAVs high mobility, limited energy resources, and dynamic topology. In this work, we propose a novel multi-hop clustering algorithm aimed at creating stable, energy-efficient clusters in FANET environments. The proposed solution enhances cluster longevity and communication efficiency through mobility-aware clustering, energy-centric cluster head (CH) selection, and a ground station(GS)-assisted cluster maintenance management mechanism. First, steady multi-hop clusters are constructed, having CHs with not only high stability and high energy but also with steady and high-energy neighboring areas, and then a proper GS-assisted cluster maintenance mechanism is applied. Experimental results, based on extended simulations, demonstrate that our approach outperforms existing schemes significantly, in terms of cluster stability, communication overhead, and security resilience.

References

• S. Yang, T. Li, D. Wu, T. Hu, W. Deng, H. Gong, Bio-inspired multi-hop clustering algorithm for FANET, Ad Hoc Networks 154 (2024).
• H. Duan, H. Qiu, L. Chen, et al., Prospects on unmanned aerial vehicle autonomous swarm technology, Sci. Technol. Rev. 36 (21) (2018) 90–98.
• I. Bekmezci, O.K. Sahingoz, and S. Temel, Flying ad-hoc networks (FANETs): a survey, Ad Hoc Netw. 11 (3) (2013) 1254–1270.
• O. T. Abdulhae, J. S. Mandeep, and M. T. Islam, Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs), IEEE Access, Vol. 10, 2022.
• H. Ali, S. U. Islam, H. Song, and K. Munir, ‘‘A performance-aware routing mechanism for flying ad hoc networks,’’ Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, p. e4192, 2021.
• J. Liu, Q. Wang, C. T. He, K. Jaffrès-Runser, Y. Xu, Z. Li, and Y. Xu, ‘‘QMR: Q-learning based multi-objective optimization routing protocol for flying ad hoc networks,’’ Comput. Commun., vol. 150, pp. 304–316, Jan. 2020.
• H. Yang and Z. Liu, ‘‘An optimization routing protocol for FANETs,’’ EURASIP J. Wireless Commun. Netw., vol. 2019, no. 1, pp. 1–8, Dec. 2019.
• O. S. Oubbati, M. Mozaffari, N. Chaib, P. Lorenz, M. Atiquzzaman, and A. Jamalipour, ‘‘ECaD: Energy-efficient routing in flying ad hoc networks,’’ Int. J. Commun. Syst., vol. 32, no. 18, p. e4156, 2019.
• M. A. Khan, A. Safi, I. M. Qureshi, and I. U. Khan, ‘‘Flying ad-hoc networks (FANETs): A review of communication architectures, and routing protocols,’’ in Proc. 1st Int. Conf. Latest Trends Electr. Eng. Comput. Technol. (INTELLECT), Nov. 2017, pp. 1–9.
• S. Wen and C. Huang, ‘‘Asynchronous distributed optimization via dual decomposition for delay-constrained flying ad hoc networks,’’ Comput. Commun., vol. 137, pp. 70–80, Mar. 2019.
• M. Mohamed Syed Ibrahim and P. Shanmugaraja, ‘‘Optimized link state routing protocol performance in flying ad-hoc networks for various data rates of un manned aerial network,’’ Mater. Today: Proc., vol. 37, pp. 3561–3568, 2021.
• E. I. Basri, M. T. H. Sultan, M. Faizal, A. A. Basri, M. F. Abas, M. S. A. Majid, J. S. Mandeep, and K. A. Ahmad, ‘‘Performance analysis of composite ply orientation in aeronautical application of unmanned aerial vehicle (UAV) NACA4415 wing,’’ J. Mater. Res. Technol., vol. 8, no. 5, pp. 3822–3834, 2019.
• M. Y. Arafat and S. Moh, ‘‘Location-aided delay tolerant routing protocol in UAV networks for post-disaster operation,’’ IEEE Access, vol. 6, pp. 59891–59906, 2018.
• A. H. Wheeb, R. Nordin, A. A. Samah, M. H. Alsharif, and M. A. Khan, ‘‘Topology-based routing protocols and mobility models for flying ad hoc networks: A contemporary review and future research directions,’’ Drones, vol. 6, no. 1, p. 9, 2021.
• S. Jiang, W. Min, L. Liu, and Z. Luo, ‘‘Multi-scale multi-view deep feature aggregation for food recognition,’’ IEEE Trans. Image Process., vol. 29, pp. 265–276, 2020.
• H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, ‘‘Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,’’ IEEE Access, vol. 7, pp. 48572–48634, 2019.
• H. Said, R. Nordin, and N. Abdullah, ‘‘Energy models of zigbee-based wireless sensor networks for smart-farm,’’ Jurnal Kejuruteraan, vol. 31, no. 1, pp. 77–83, 2019.
• J. Sánchez-García, D. G. Reina, and S. L. Toral, ‘‘A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario,’’ Future Gener. Comput. Syst., vol. 90, pp. 129–148, Jan. 2019.
• D. Radu, A. Cretu, B. Parrein, J. Yi, C. Avram, and A. Aştilean, ‘‘Flying ad hoc network for emergency applications connected to a fog system,’’ in Proc. Int. Conf. Emerg. Internetworking, Data Web Technol. Springer, 2018, pp. 675–686.
• F. Fabra, W. Zamora, J. Masanet, C. T. Calafate, J.-C. Cano, and P. Manzoni, ‘‘Automatic system supporting multicopter swarms with manual guidance,’’ Comput. Electr. Eng., vol. 74, pp. 413–428, Mar. 2019.
• O. S. Oubbati, M. Atiquzzaman, P. Lorenz, M. H. Tareque, and M. S. Hossain, ‘‘Routing in flying ad hoc networks: Survey, constraints, and future challenge perspectives,’’ IEEE Access, vol. 7, pp. 81057–81105, 2019.
• L. Meriño, ‘‘Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface,’’ Brain-Comput. Interfaces, vol. 4, nos. 1–2, pp. 122–135, 2017.
• H. Abualola, H. Otrok, H. Barada, M. Al-Qutayri, and Y. Al-Hammadi, ‘‘Matching game theoretical model for stable relay selection in a UAV-assisted Internet of Vehicles,’’ Veh. Commun., vol. 27, Jan. 2021, Art. no. 100290.
• A. Rovira-Sugranes, A. Razi, F. Afghah, and J. Chakareski, ‘‘A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook,’’ 2021, arXiv:2104.01283.
• N. R. Gans and J. G. Rogers, ‘‘Cooperative multirobot systems for military applications,’’ Current Robot. Rep., vol. 2, pp. 105–111, Jan. 2021.
• H. A. Alobaidy, J. Mandeep, M. Behjati, R. Nordin, and N. F. Abdullah, ‘‘Wireless transmissions, propagation and channel modelling for IoT technologies: Applications and challenges,’’ IEEE Access, 2022.
• Y. Wen, D. Liang, and Y. Wu, ‘‘Study on the key technology and application of UAV surveying and mapping data processing,’’ in Proc. J. Phys., Conf., 2021, vol. 1744, no. 2, Art. no. 022089.
• A. M. Jawad, R. Nordin, H. M. Jawad, S. K. Gharghan, A. Abu-Samah, M. J. Abu-Alshaeer, and N. Abdullah, ‘‘Wireless drone charging station using class-E power amplifier in vertical alignment and lateral misalignment conditions,’’ Energies, vol. 15, no. 4, p. 1298, 2022.
• C. Pu, ‘‘Jamming-resilient multipath routing protocol for flying ad hoc networks,’’ IEEE Access, vol. 6, pp. 68472–68486, 2018.
• M. Behjati, A. B. M. Noh, H. A. Alobaidy, M. A. Zulkifley, R. Nordin, and N. F. Abdullah, ‘‘LoRa communications as an enabler for Internet of Drones towards large-scale livestock monitoring in rural farms,’’ Sensors, vol. 21, no. 15, p. 5044, 2021.
• A. Chriki, H. Touati, H. Snoussi, et al., FANET: communication, mobility models and security issues, Comput. Netw. 163 (2019), 106877.
• S. Bhandari, X. Wang, R Lee, Mobility and location-aware stable clustering scheme for UAV networks, IEEE Access 8 (2020) 106364–106372.
• M. Gerla, J. Tzu-Chieh Tsai, Multicluster, mobile, multimedia radio network, Wirel. Netw. 1 (3) (1995) 255–265.
• A. Ephremides, J.E. Wieselthier, D.J Baker, A design concept for reliable mobile radio networks with frequency hopping signaling, in: Proceedings of the IEEE 75, 1987, pp. 56–73.
• M. Chatterjee, S.K. Das, D. Turgut, WCA: a weighted clustering algorithm for mobile ad hoc networks, Cluster Comput. 5 (2) (2002) 193–204.
• P. Basu, N. Khan, T.D.C. Little, A mobility-based metric for clustering in mobile ad hoc networks, in: Proceedings 21st international conference on distributed computing systems workshops. IEEE, 2001, pp. 413–418.
• C. Zang, S. Zang, Mobility prediction clustering algorithm for UAV networking, in: 2011 IEEE Globecom Workshops (GC Wkshps), IEEE, 2011, pp. 1158–1161.
• K. Singh, A.K. Verma, TBCS: a trust based clustering scheme for secure communication in flying ad-hoc networks, Wirel. Person. Commun. 114 (2020) 3173–3196.
• Y. Yan, X. Xia, L. Zhang, et al., A clustering scheme based on the binary Whale Optimization Algorithm in FANET, Entropy 24 (10) (2022) 1366.
• R. Zhang, Y. Gao, Y. Ding, Clustering optimization algorithm for UAV cluster network, Comput. Eng. Des. 43 (07) (2022) 1848–1855.
• J. Shu, Y. Ge, L. Liu, et al., Mobility prediciton clustering routing in UAVs, in: Proceedings of 2011 International Conference on Computer Science and Network Technology 3, IEEE, 2011, pp. 1983–1987.
• Q. Wang, J. Nan, J. Huang, et al., Clustering routing protocol for cluster networks based on mobile prediction, J. Air Force Eng. Univer. 19 (6) (2018) 90–96.
• S. Pathak, S. Jain, An optimized stable clustering algorithm for mobile ad hoc networks, EURASIP J. Wirel. Commun. Netw. 2017 (2017) 1–11.
• J. Mei, J Nan, Weighted stable clustering algorithm for flying ad hoc network, Applic. Res. Comput. 38 (11) (2021) 3411–3416.
• X.Y. Wang, Y.Y. Zhang, Z.Y. Liu, et al., Structure management scheme of flying ad-hoc networks (FANET): a dynamic weight values weighted clustering algorithm, in: Journal of Physics: Conference Series, IOP Publishing, 2022, 012004, 2330.
• M. Aissa, M. Abdelhafidh, A.B Mnaouer, EMASS: a novel energy, safety and mobility AWARE-BASED clustering algorithm for FANETs, IEEE Access 9 (2021) 105506–105520.
• J. Guo, Z. Ren, J. Qiu, et al., Fast and stable weighted clustering algorithm for unmanned aerial vehicle ad hoc networks [J/OL], Applic. Res. Comput. 41 (1) (2023).
• Y. Yu, L. Ru, K. Fang, Bio-inspired mobility prediction clustering algorithm for Ad Hoc UAV networks, Eng. Lett. 24 (3) (2016).
• X. Li, J Nan, Clustering routing algorithm based on IK-means clustering, Applic. Res. Comput. 38 (04) (2021) 1149–1153. +1164.
• F. Aftab, A. Khan, Z. Zhang, Hybrid self-organized clustering scheme for drone based cognitive Internet of Things, IEEE Access 7 (2019) 56217–56227.
• Y. Liu, H. Zhao, J. Zhang, et al., Topology optimization based on adaptive hummingbird algorithm in flying ad hoc networks, J. Electron. Inform. Technol. 45 (6) (2023) 1–9.
• S. Bharany, S. Sharma, S. Bhatia, et al., Energy efficient clustering protocol for FANETS using moth flame optimization, Sustainability 14 (10) (2022) 6159.
• A. Khan, S. Khan, A.S. Fazal, et al., Intelligent cluster routing scheme for flying ad hoc networks, Sci. China Inform. Sci. 64 (8) (2021), 182305.
• A. Khan, F. Aftab, Z. Zhang, BICSF: bio-inspired clustering scheme for FANETs, IEEE Access 7 (2019) 31446–31456.
• M.Y. Arafat, S. Moh, Localization and clustering based on swarm intelligence in UAV networks for emergency communications, IEEE Internet of Thing. J. 6 (5) (2019) 8958–8976.