Topology Control of Drones Using Bio-Inspired Intelligent Firefly-Grasshopper Algorithm for Searching Intruder Unmanned Aerial Vehicle

References

• M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, and M. Debbah, “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE Commun. Surv. Tutorials, Vol. 21, no. 3, pp. 2334–60, third quarter 2019.
   Web of Science ®Google Scholar
• S. Park, H. T. Kim, S. Lee, H. Joo, and H. Kim, “Survey on anti-drone systems: Components, designs, and challenges,” IEEE. Access., Vol. 9, pp. 42635–59, 2021.
   Web of Science ®Google Scholar
• V. Chamola, P. Kotesh, A. Agarwal, N. Gupta, and M. Guizani, “A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques,” Ad. Hoc. Netw., Vol. 111, pp. 102324, 2021.
   PubMed Web of Science ®Google Scholar
• Available: https://indianexpress.com/article/explained/drone-attack-ied-indian-air-force-base-jammu-7385106/.
   Google Scholar
• L. Hong, H. Guo, J. Liu, and Y. Zhang, “Toward swarm coordination: topology-aware inter-UAV routing optimization,” IEEE Trans. Veh. Technol., Vol. 69, no. 9, pp. 10177–87, 2020.
   Web of Science ®Google Scholar
• J. Dai, Q. Hu, X. Liu, Y. Zhang, and J. Zhu, “Cluster head selection method of multiple UAVs under COVID-19 situation,” Comput. Commun., Vol. 196, pp. 141–7, 2022.
   PubMed Web of Science ®Google Scholar
• Z. M. Fadlullah, D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, “A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks,” IEEE Netw, Vol. 30, no. 1, pp. 100–5, 2016.
   Web of Science ®Google Scholar
• K.-K. Oh, M.-C. Park, and H.-S. Ahn, “A survey of multi-agent formation control,” Automatica, Vol. 53, no. 1, pp. 424–40, 2015.
   Google Scholar
• D. Kim, and J. Lee, “Integrated topology management in flying Ad Hoc networks: topology construction and adjustment,” IEEE. Access., Vol. 6, pp. 61196–211, 2018.
   Web of Science ®Google Scholar
• B.-W. Chen, and S. Rho, “Autonomous tactical deployment of the UAV array using self-organizing swarm intelligence,” IEEE Consum. Electron. Mag., Vol. 9, no. 2, pp. 52–6, 1 March 2020.
   Web of Science ®Google Scholar
• X. Dong, Y. Li, C. Lu, G. Hu, Q. Li, and Z. Ren, “Time-varying formation tracking for UAV swarm systems with switching directed topologies,” IEEE Trans. Neural Netw. Learn. Syst., Vol. 30, no. 12, pp. 3674–85, Dec. 2019.
   PubMed Web of Science ®Google Scholar
• J. Wu, C. Luo, Y. Luo, and K. Li, “Distributed UAV swarm formation and collision avoidance strategies over fixed and switching topologies,” IEEE Trans. Cybern., Vol. 52, no. 10, pp. 10969–79, 2021.
   Web of Science ®Google Scholar
• L. Zhang, F. Hu, Z. Chu, E. Bentley, and S. Kumar, “3D transformative routing for UAV swarming networks: A skeleton-guided, GPS-free approach,” IEEE Trans. Veh. Technol., Vol. 70, no. 4, pp. 3685–701, April 2021.
   Web of Science ®Google Scholar
• F. Dai, M. Chen, X. Wei, and H. Wang, “Swarm intelligence-inspired autonomous flocking control in UAV networks,” IEEE Access, Vol. 7, pp. 61786–96, 2019.
   Web of Science ®Google Scholar
• S. Nath, A. Paul, R. Banerjee, S. Bhaumik, J. K. Sing, and S. Kumar Sarkar, “Optimizing FANET routing using a hybrid approach of firefly algorithm and ACO-Lévy flight,” in 2020 IEEE VLSI Device Circuit and System (VLSI DCS), Kolkata, India, 2020, pp. 378–83. DOI:10.1109/VLSIDCS47293.2020.9179956.
   Google Scholar
• F. Aftab, A. Khan, and Z. Zhang, “Hybrid self-organized clustering scheme for drone based cognitive internet of things,” IEEE Access, Vol. 7, pp. 56217–27, 2019.
   Web of Science ®Google Scholar
• H. Okcu, and M. Soyturk, “Distributed clustering approach for UAV integrated wireless sensor networks,” Int. J. Ad Hoc Ubiquitous Comput, Vol. 15, no. 1/2/3, pp. 106–20, 2014.
   Web of Science ®Google Scholar
• F. Khelifi, A. Bradai, K. Singh, and M. Atri, “Localization and energy efficient data routing for unmanned aerial vehicles: Fuzzy-logic-based approach,” IEEE Commun. Mag, Vol. 56, no. 4, pp. 129–33, 2018.
   Web of Science ®Google Scholar
• A. Singha, A. K. Ray, and A. B. Samaddar, “Leader–follower based formation controller design for quadrotor UAVs,” Trans. Indian Natl. Acad. Eng., Vol. 7, no. 1, pp. 325–38, 2022.
   Google Scholar
• B. M. Albaker, and N. A. Rahim, “Signal acquisition and parameter estimation of radio frequency pulse radar using novel method,” IETE. J. Res., Vol. 55, no. 3, pp. 128–34, 2009.
   Web of Science ®Google Scholar
• S. Radha, and S. Shanmugavel, “Mobility models in mobile ad hoc network,” IETE. J. Res., Vol. 53, no. 1, pp. 3–12, 2007.
   Web of Science ®Google Scholar
• F. Pasandideh, T. D. E. Silva, A. A. S. D. Silva, and E. P. Freitas, “Topology management for flying ad hoc networks based on particle swarm optimization and software-defined networking,” Wireless Netw, Vol. 28, pp. 257–72, 2022.
   Web of Science ®Google Scholar
• D. Chen, Z. Fang, and S. Li, “A novel BSO algorithm for three-layer neural network optimization applied to UAV edge control,” Neural Process. Lett., 1–20, 2023.
   Web of Science ®Google Scholar
• V. Kumar, and D. Kumar, “A systematic review on firefly algorithm: past, present, and future,” Arch. Comput. Methods Eng., Vol. 28, no. 4, pp. 3269–91, 2021.
   Web of Science ®Google Scholar
• I. Fister, Jr., I. Fister, X. S. Yang, and J. Brest, “A comprehensive review of firefly algorithms,” Swarm. Evol. Comput., Vol. 13, pp. 34–46, 2013.
   Web of Science ®Google Scholar
• L. Abualigah, and A. Diabat, “A comprehensive survey of the grasshopper optimization algorithm: Results, variants, and applications,” Neural Computing and Applications, Vol. 32, no. 19, pp. 15533–56, 2020.
   Web of Science ®Google Scholar
• B. S. Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, and S. M. Sait, “Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems,” Eng. Comput., Vol. 38, no. 5, pp. 1–3, 2021.
   Web of Science ®Google Scholar
• Available: https://in.mathworks.com/help/uav/ref/uavmission.html.
   Google Scholar
• H. Ali, W. Shahzad, and F. A. Khan, “Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization,” Appl. Soft Comput, Vol. 12, no. 7, pp. 1913–28, 2012.
   Web of Science ®Google Scholar