2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 196-208, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mahendra Sahare 1 * , Priti Maheshwary 2 , Vinay Kumar Dwivedi 3
Doi: https://doi.org/10.54216/JISIoT.140115
In the recent era of communication technology, flying ad hoc networks are gaining popularity because of their flexibility and broad area of application to gather data from environmental sources with limited infrastructure. FANET nodes, or unmanned aerial vehicles (UAVs), are heterogeneous devices, and coordination between the UAVs is an important part of communication with limited battery power sources. In ad hoc networks, devices have limited battery power, so proper battery utilization is critical to maintaining network connectivity. In order to establish a network without congestion, it is vital to have inter-UAV and IoT wireless communication for cooperation and collaboration among many UAVs. UAV connections may experience frequent disconnections. Another obstacle is the limited distance allowed between the stations. The routing algorithm selects only the nodes that are specifically requested by the source node based on its requirements and maintains the source node no longer needs the route until it. IoT devices have limited processing capability and memory. A single mobile device controls the IoT devices, or users can use the concept of automation to control the functioning of smart IoT devices. This research proposes a fuzzy-based congestion control scheme (MCPFB) to control the congestion between UAVs and IoT devices. UAVs are faster, and IoT devices can collect information from UAVs and forward it to other devices. The UAV’s can store limited and sufficient types of information, but during routing, only a single path is available, which causes congestion in the FANET-IoT network. The fuzzy based load prediction and balancing routing is able to handle the problem of congestion in FANET-IoT. In order to overcome the problem of congestion with improper energy utilisation, this paper presents fuzzy rule-based congestion control techniques for a flying ad hoc network. We focus on the efforts to reduce congestion in the FANET-IoT network. Routing is a critical issue in FANET-IoT and hence the focus of this research is on the performance improvement of routing in FANET-IoT. Packets dropping on the nodes show congestion occurrence in the network, and the possibility of lost connectivity with other nodes is high. Unlike the aforementioned works, the proposed MCPFB routing shows better performance compared to the conventional BARS scheme in FANET-IoT.
Congestion , Fuzzy , FANET , IoT , Routing , UAV
[1] Kaur, M.; Prashar, D.; Rashid, M.; Khanam, Z.; Alshamrani, S.S.; AlGhamdi, A.S. An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network. Electronics 2022, 11, 252. https://doi.org/10.3390/electronics11020252
[2] Shaojie Wen, Lianbing Deng, Yuhang Liu, “Distributed optimization via primal and dual decompositions for delay-constrained FANETs” ELSEVIER Ad Hoc Networks 109(2020).
[3] O.S. Oubbatia, A. Lakas, F. Zhou, M.G. s, M.B. Yagoubi, A survey on position-based routing protocols for flying adhoc networks (FANETs), Veh. Commun. 10 (2017) 29–56.
[4] V. Sharma, R. Kumar, N. Kumar, DPTR: Distributed priority tree-based routing protocol for FANETs, Comput. Commun. 122 (2018) 129–151.
[5] H. Chao, Y. Cao, Y. Chen, Autopilots for small fixed-wing unmanned air vehicles: a survey, in: International Conference on Mechatronics and Automation, 2007 (ICMA 2007), 2007, pp. 3144–3149.
[6] B.S. Morse, C.H. Engh, M.A. Goodrich, UAV video coverage quality maps and prioritized indexing for wilderness search and rescue, in: Proceedings of the 5th ACM/IEEE International Conference on Human–Robot Interaction, HRI ’10, Piscataway, NJ, USA, 2010, pp. 227–234.
[7] E. Yanmaz, C. Costanzo, C. Bettstetter, W. Elmenreich, A discrete stochastic process for coverage analysis of autonomous UAV networks, in: Proceedings of IEEE Globecom-WiUAV, IEEE, 2010.
[8] L. To, A. Bati, D. Hilliard, Radar cross-section measurements of small unmanned air vehicle systems in non-cooperative field environments, in: 3rd European Conference on Antennas and Propagation, 2009 (EuCAP 2009), IEEE, 2009,pp.3637–3641.
[9] Mehdi Hosseinzadeh, Saqib Ali, Liliana Ionescu-Feleaga, Bogdan-Stefan Ionescu, Mohammad Sadegh Yousefpoor, Efat Yousefpoor, Omed Hassan Ahmed, Amir Masoud Rahmani, Asif Mehmood, A novel Q-learning-based routing scheme using an intelligent filtering algorithm for flying ad hoc networks (FANETs), Journal of King Saud University - Computer and Information Sciences, Volume 35, Issue 10, 2023.
[10] Lansky, J., Rahmani, A.M., Zandavi, S.M., Chung, V., Yousefpoor, E., Yousefpoor, M.S.,Khan, F., Hosseinzadeh, M., 2022c. A Q-learning-based routing scheme for smart air quality monitoring system using flying Ad Hoc networks. Sci. Rep. 12 (1), 20184.
[11] Arafat, M.Y., Moh, S., 2021. A Q-learning-based topology-aware routing protocol for flying Ad Hoc networks. IEEE Internet Things J. 9 (3), 1985–2000.
[12] Jung, W.S., Yim, J., Ko, Y.B., 2017. QGeo: Q-learning-based geographic Ad Hoc routing protocol for unmanned robotic networks. IEEE Commun. Lett. 21 (10), 2258–2261.
[13] Qiu, X., Xie, Y., Wang, Y., Ye, L., Yang, Y., 2021. QLGR: A Q-learning-based geographic FANET routing algorithm based on multiagent reinforcement learning. KSII Trans. Internet Inform. Syst. 15 (11)
[14] Alam, M.M., Moh, S., 2023. Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms. Veh. Commun. 40, 100572.
[15] Rahmani, A.M., Ali, S., Yousefpoor, E., Yousefpoor, M.S., Javaheri, D., Lalbakhsh, P., Ahmed, O.H., Hosseinzadeh, M., Lee, S.W., 2022. OLSR+: A new routing method based on fuzzy logic in flying Ad-Hoc networks (FANETs). Veh. Commun. 36, 100489.
[16] Lansky, J., Rahmani, A.M., Malik, M.H., Yousefpoor, E., Yousefpoor, M.S., Khan, M.U., Hosseinzadeh, M., 2023. An energy-aware routing method using firefly algorithm for flying Ad Hoc networks. Sci. Rep. 13 (1), 1323.
[17] Lansky, J., Rahmani, A.M., Zandavi, S.M. et al. A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks. Sci Rep 12, 20184 (2022).
[18] Arafat, M. Y. & Moh, S. A Q-learning-based topology-aware routing protocol for flying ad hoc networks. IEEE Internet Things (2021).
[19] Ahmed N. Al-Masri , Manal Nasir, A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment, Fusion: Practice and Applications, Vol. 6 , No. 1 , (2021) : 08-16 (Doi : https://doi.org/10.54216/FPA.060102)
[20] Mohammed Abdul J. Maktoof,Anwar Ja’afar M. Jawad,Hasan M. Abd,Ahmed Husain,Ali Majdi, Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking, Journal of Fusion: Practice and Applications, Vol. 11 , No. 1 , (2023) : 114-128 (Doi : https://doi.org/10.54216/FPA.110109)
[21] K. Shankar, Recent Advances in Sensing Technologies for Smart Cities, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 05-15 (Doi : https://doi.org/10.54216/IJWAC.010101)
[22] A. Sariga , J. Uthayakumar, Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 33-46 (Doi : https://doi.org/10.54216/IJWAC.010102)
[23] Ajith Krishna R , Ankit Kumar , Vijay K, An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT, Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 37-45 (Doi : https://doi.org/10.54216/JCHCI.010105)
[24] Sonia Jenifer Rayen, Survey On Smart Cane For Visually Impaired Using IOT, Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 2 , (2021) : 81 - 85 (Doi : DOI: https://doi.org/10.54216/JCHCI.010205)
[25] M. Elhoseny , X. Yuan, Modeling of Optimal Adaptive Weighted Clustering Protocol for Vehicular Ad hoc Networks, Journal of Cybersecurity and Information Management, Vol. 2 , No. 2 , (2020) : 68-76 (Doi : https://doi.org/10.54216/JCIM.020204)
[26] Mohamed Elhoseny , X. Yuan, An Energy Efficient Clustering Protocol using Enhanced Rain Optimization Algorithm in Mobile Adhoc Networks, Journal of Cybersecurity and Information Management, Vol. 7 , No. 2 , (2021) : 85-94 (Doi : https://doi.org/10.54216/JCIM.070201)
[27] Nihal N. Mostafa, Esmeralda Kazia, Smart Sensor Networks for Industrial IoT Applications, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 45-53 (Doi : https://doi.org/10.54216/JISIoT.080204)
[28] Waleed Abd Elkhalik, AI-Driven Smart Homes: Challenges and Opportunities, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 54-62 (Doi : https://doi.org/10.54216/JISIoT.080205)
[29] Ismail Eyad Samara, Intelligent systems and AI techniques: Recent advances and Future directions, Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 1 , No. 2 , (2022) : 30-45 (Doi : https://doi.org/10.54216/IJAACI.010202)
[30] Myvizhi M., Neutrosophic MCDM Model for Evaluation and Selection best 5G Network Architecture, International Journal of Advances in Applied Computational Intelligence, Vol. 4 , No. 1 , (2023) : 08-18 (Doi : https://doi.org/10.54216/IJAACI.040101)
