Alkhalidy, Muhsen, Bany Taha, Mohammad, Chowdhury, Rasel, Talhi, Chamseddine, Ould-Slimane, Hakima and Mourad, Azzam.
2024.
« Optimizing CP-ABE decryption in urban VANETs: A hybrid reinforcement learning and differential evolution approach ».
IEEE Open Journal of the Communications Society, vol. 5.
pp. 6535-6545.
Compte des citations dans Scopus : 2.
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Abstract
In urban environments, efficiently decrypting CP-ABE in VANETs is a significant challenge due to the dynamic and resource-constrained nature of these networks. VANETs are critical for ITS that improve traffic management, safety, and infotainment through V2V and V2I communication. However, managing computational resources for CP-ABE decryption remains difficult. To address this, we propose a hybrid RL-DE algorithm. The RL agent dynamically adjusts the DE parameters using real-time vehicular data, employing Q-learning and policy gradient methods to learn optimal policies. This integration improves task distribution and decryption efficiency. The DE algorithm, enhanced with RL-adjusted parameters, performs mutation, crossover, and fitness evaluation, ensuring continuous adaptation and optimization. Experiments in a simulated urban VANET environment show that our algorithm significantly reduces decryption time, improves resource utilization, and enhances overall efficiency compared to traditional methods, providing a robust solution for dynamic urban settings.
| Item Type: | Peer reviewed article published in a journal |
|---|---|
| Professor: | Professor Talhi, Chamseddine |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date Deposited: | 22 Nov 2024 21:27 |
| Last Modified: | 02 Dec 2024 20:42 |
| URI: | https://espace2.etsmtl.ca/id/eprint/29908 |
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