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Original Article
Enhancing Communication, Collaboration, and Efficiency of the Tunicate-Based Swarm Algorithms to Effectively Solve Complex Optimization Problems Encountered in IOT environments
Jain Minal Mahendrakumar1
Dr. Khushbu2
1Research Scholar, Madhav University, Abu Road Pindwara, Rajesthan, India. 2Assistant Professor, Faculty of Computer Science and Application, Madhav University, Abu Road Pindwara, Rajestha, India.
Published Online: May-August 2025
Pages: 315-323
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250402041
References
Include citations from relevant literature that support your claims and the development of TSI, IoT optimization, and hybrid algorithm
approaches.
1. Herd, A., & Phelan, J. (2017). "Modeling Swarm Behavior Using Various Simulation Frameworks." Journal of Computation and Simulation.
2. Khan, S., & Kaur, S. (2020). "IoT Based Fuzzy Logic Control System for Smart Agriculture." Journal of Ambient Intelligence and Humanized
Computing.
3. Rojas, R., & Gonzalez, P. (2019). "Fuzzy Logic and Neural Networks in Swarm Intelligence." International Journal of Computational
Intelligence Systems.
4. Zhang, D., & Wu, Q. (2018). "A Hybrid Algorithm Based on Genetic Algorithm and Particle Swarm Optimization." IEEE Access.
5. Shahid, M., &Khusainov, R. (2020). "Optimization of IoT Applications: A Survey." Wireless Communications and Mobile Computing,
2020.
6. Dargie, W., &Poellabauer, C. (2010). "Fundamentals of Wireless Sensor Networks: Theory and Practice." Wiley.
7. Kennedy, J., &Eberhart, R. (1995). "Particle swarm optimization." Proceedings of the 1995 IEEE International Conference on Neural
Networks, 4, 1942-1948.
8. Dorigo, M., &Stützle, T. (2004). "Ant Colony Optimization." MIT Press.
9. J. Liu, D. Yang, M. Lian, and M. Li, ‘‘Research on intrusion detection based on particle swarm optimization in IoT,’’ IEEE Access, vol. 9,
pp. 38254–38268, 2021.
10. S. Tharewal, M. W. Ashfaque, S. S. Banu, P. Uma, S. M. Hassen, and M. Shabaz, ‘‘Intrusion detection system for industrial Internet of
Things based on deep reinforcement learning,’’ Wireless Commun. Mobile Comput., vol. 2022, pp. 1–8, Mar. 2022.
11. C. A. de Souza, C. B. Westphall, and R. B. Machado, ‘‘Two-step ensemble approach for intrusion detection and identification in IoT and
fog computing environments,’’ Comput. Electr. Eng., vol. 98, Mar. 2022, Art. no. 107694.
12. C. Meffert, D. Clark, I. Baggili, and F. Breitinger, ‘‘Forensic state acquisition from Internet of Things (FSAIoT): A general framework and
practical approach for IoT forensics through IoT device state acquisition,’’ in Proc. 12th Int. Conf. Availability, Rel. Secur., Aug. 2017, pp.
1–11.
13. M. Hossain, Y. Karim, and R. Hasan, ‘‘FIF-IoT: A forensic investigation framework for IoT using a public digital ledger.
approaches.
1. Herd, A., & Phelan, J. (2017). "Modeling Swarm Behavior Using Various Simulation Frameworks." Journal of Computation and Simulation.
2. Khan, S., & Kaur, S. (2020). "IoT Based Fuzzy Logic Control System for Smart Agriculture." Journal of Ambient Intelligence and Humanized
Computing.
3. Rojas, R., & Gonzalez, P. (2019). "Fuzzy Logic and Neural Networks in Swarm Intelligence." International Journal of Computational
Intelligence Systems.
4. Zhang, D., & Wu, Q. (2018). "A Hybrid Algorithm Based on Genetic Algorithm and Particle Swarm Optimization." IEEE Access.
5. Shahid, M., &Khusainov, R. (2020). "Optimization of IoT Applications: A Survey." Wireless Communications and Mobile Computing,
2020.
6. Dargie, W., &Poellabauer, C. (2010). "Fundamentals of Wireless Sensor Networks: Theory and Practice." Wiley.
7. Kennedy, J., &Eberhart, R. (1995). "Particle swarm optimization." Proceedings of the 1995 IEEE International Conference on Neural
Networks, 4, 1942-1948.
8. Dorigo, M., &Stützle, T. (2004). "Ant Colony Optimization." MIT Press.
9. J. Liu, D. Yang, M. Lian, and M. Li, ‘‘Research on intrusion detection based on particle swarm optimization in IoT,’’ IEEE Access, vol. 9,
pp. 38254–38268, 2021.
10. S. Tharewal, M. W. Ashfaque, S. S. Banu, P. Uma, S. M. Hassen, and M. Shabaz, ‘‘Intrusion detection system for industrial Internet of
Things based on deep reinforcement learning,’’ Wireless Commun. Mobile Comput., vol. 2022, pp. 1–8, Mar. 2022.
11. C. A. de Souza, C. B. Westphall, and R. B. Machado, ‘‘Two-step ensemble approach for intrusion detection and identification in IoT and
fog computing environments,’’ Comput. Electr. Eng., vol. 98, Mar. 2022, Art. no. 107694.
12. C. Meffert, D. Clark, I. Baggili, and F. Breitinger, ‘‘Forensic state acquisition from Internet of Things (FSAIoT): A general framework and
practical approach for IoT forensics through IoT device state acquisition,’’ in Proc. 12th Int. Conf. Availability, Rel. Secur., Aug. 2017, pp.
1–11.
13. M. Hossain, Y. Karim, and R. Hasan, ‘‘FIF-IoT: A forensic investigation framework for IoT using a public digital ledger.
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