Vehicular Network Optimization via KESHTel Algorithm with Insights from Leabra Models

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.