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Vehicular Network Optimization via KESHTel Algorithm with Insights from Leabra Models
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In vehicular communication networks, optimizing connectivity and efficiency is paramount for ensuring seamless and reliable communication among vehicles. The identified problem centers on the inadequacies of traditional optimization approaches in addressing the dynamic and complex nature of vehicular networks. The absence of a comprehensive solution that combines the adaptive capabilities of the KESHTel algorithm with the cognitive insights gained from Leabra models. Existing methodologies often fall short in adapting to real-time changes and fail to capitalize on cognitive principles for efficient decision-making. This research addresses the need for enhanced vehicular network optimization by proposing the utilization of the KESHTel algorithm, coupled with insights derived from Leabra models. The method details the integration of the KESHTel algorithm, known for its adaptive learning capabilities, with insights from Leabra models, which are inspired by the neural architecture of the brain. This hybrid approach leverages machine learning and cognitive principles to optimize communication routes, minimize latency, and allocate resources intelligently within the vehicular network. Results from simulations and experiments demonstrate the effectiveness of the proposed approach in improving communication reliability, reducing congestion, and enhancing overall network performance. The findings indicate a significant advancement in vehicular network optimization, showcasing the potential of the KESHTel algorithm and cognitive insights from Leabra models in addressing the complex challenges inherent in dynamic vehicular environments.
Keywords
Vehicular Networks, Optimization, KESHTel Algorithm, Leabra Models, Cognitive Insights.
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- P. Kumar, R. Merzouki, B. Conrard and V. Coelen, “Multilevel Modeling of the Traffic Dynamic”, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 3, pp. 1066-1082, 2014.
- S. Djahel, R. Doolan, G.M. Muntean and J. Murphy, “A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches”, IEEE Communications Surveys and Tutorials, Vol. 17, No. 1, pp. 125-151, 2015.
- Y.S. Chia, Z.W. Siew, H.T. Yew, S.S. Yang and K.T.K. Teo, “An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks”, ICTACT Journal on Communication Technology, Vol. 3, No. 4, pp. 613-618, 2012.
- D.F. Allan and A.M. Farid, “A Benchmark Analysis of Open Source Transportation-Electrification Simulation Tools”, Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 1202-1208, 2015.
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, CRC Press, 2020.
- O. Csillik and M. Kelly, “Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery using Convolutional Neural Networks”, Drones, Vol. 2, No. 4, pp. 1-39, 2018.
- S. Kannan and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoTbased Bat Agents for Traffic Management”, Electronics, Vol. 10, No. 7, pp. 785-798, 2021.
- L. Liu, Y. Wang, J. Zhang and Q. Yang, “A Secure and Efficient Group Key Agreement Scheme for VANET”, Sensors, Vol. 19, No. 3, pp. 482-494, 2019.
- Y. Agarwal, K. Jain and O. Karabasoglu, “Smart Vehicle Monitoring and Assistance using Cloud Computing in Vehicular Ad Hoc Networks”, International Journal of Transportation Science and Technology, Vol. 7, No. 1, pp. 60-73, 2018.
- Nandhini Vineeth and H.S. Guruprasad, “Delay Analysis of Network Coded Video Streams in VANETs”, International Journal of Information Engineering and Electronic Business, Vol. 4, pp. 16-23, 2015.
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