Generative AI-Driven Context-Aware BDI-Based Smart Routing Protocol for Intelligent Transportation Systems

This research introduces a newly proposed Belief-Desire-Intention (BDI) agent-based Smart Routing Protocol for Intelligent Transportation Systems (ITS) that leverages context-aware decision-making inspired by Generative Artificial Intelligence (GAI) reasoning models. The protocol enhances routing decisions by considering real-time traffic conditions, weather, road blockages, peak-hour dynamics, and AI-generated predictions of future congestion. By incorporating BDI reasoning, the system dynamically adjusts routes based on both current and forecasted conditions, thereby improving the overall performance of vehicular networks. Our proposed system aims to optimise key performance indicators such as Packet Delivery Ratio (PDR), End-to-End Delay, Throughput, Control Overhead, Traffic Flow, Routing Decision Accuracy, and Energy Consumption. Simulation results demonstrate that the BDI-based system outperforms traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), showing significant improvements in PDR, Throughput, and Energy Efficiency. For example, the BDI-based system achieved a PDR of 98%, compared to 85% for AODV and 80% for DSR. Additionally, the BDI protocol reduced end-to-end delay by 25% and energy consumption by 30% compared to the baseline protocols. These findings underscore the BDI-based system’s capability for real-time, adaptive routing in ITS, enabling optimal network performance and resource efficiency through context-aware and predictive routing decisions.