Adaptive Power Control with Improved Genetic Algorithm to Enhance QoS Performance in Vehicular Communication Systems

Background: Vehicular Ad hoc Networks (VANETs) play a crucial role in intelligent transportation systems since they enable vehicles to communicate instantaneously. Elevated mobility and dynamic settings create difficulties for dependable data transmission, resulting in longer delays and packet loss. Method used: An Advanced Genetic Algorithm-based Vehicular Ad hoc Routing (AIGAV) model that leverages meta-heuristic optimization to enhance routing decisions in VANETs. It utilizes GPS for live navigation and modifies routes as required. This optimization reduces congestion and distributes network load more evenly for improved packet forwarding. Results: The AIGAV model exceeds the performance in crucial efficacy aspects. It enhanced data success rates (95.25%), remarkable throughput (521.41 Kbps), and minimal routing overhead (254.18 packets), and decreased data loss rates. Conclusion: The AIGAV model improves the efficiency of VANET routing through dependable and adaptable communication in mobile settings. It employs optimization to enhance safer, more intelligent transportation systems for upcoming uses.