A 6G-ENABLED IOT FRAMEWORK WITH AI AND BLOCKCHAIN FOR ULTRA-LOW LATENCY VEHICULAR COORDINATION

This study presents a unified framework for ultra-low latency autonomous vehicle coordination by integrating sixth-generation (6G) communication technologies with Internet of Things (IoT) sensing, artificial intelligence/machine learning (AI/ML), and blockchain-based trust management. Leveraging 6G enablers such as terahertz spectrum, massive MIMO, reconfigurable intelligent surfaces (RIS), and ultra-reliable low-latency communication (URLLC+), the proposed framework achieves sub-millisecond responsiveness with high reliability. AI paradigms including federated learning (FL) and deep reinforcement learning (DRL) are employed to enable adaptive and distributed intelligence while reducing communication overhead and preserving data privacy. Mobile edge computing (MEC) further enhances real-time decision-making by localizing computation closer to vehicles. To evaluate performance, synthetic datasets were developed and validated against literature benchmarks for key indicators including latency, throughput, energy consumption, packet delivery ratio (PDR), model accuracy, and coverage probability. Results demonstrate that the framework reduces latency by over 90% compared to 5G, sustains throughput under high vehicle density, and improves energy efficiency through distributed AI deployment. Additionally, blockchain-based mechanisms coupled with AI-driven intrusion detection enhance trust and resilience against cyberattacks. Overall, the study highlights the transformative role of 6G-enabled IoT and AI/ML integration in building intelligent, secure, and scalable transportation ecosystems suitable for next-generation mobility.