Empowering V2X Security: Integration of PoAh 2.0 and Edge LLM in Context-Aware Blockchain Ecosystems

Vehicle-to-Everything (V2X) networks require secure, low-latency data exchanges under dynamic mobility and evolving threats. Conventional blockchain consensus mechanisms, though effective for decentralized trust, lack real-time adaptive authentication capabilities essential for heterogeneous V2X environments. To address this, we introduce Proof of Authentication 2.0 (PoAh 2.0), an adaptive blockchain consensus integrated with an Edge Large Language Model (Edge LLM) and a Random Forest (RF) classifier. Edge LLM semantically analyzes transaction contexts, while RF processes numerical metadata, collectively classifying vehicular transactions into normal, sensitive, or critical, dynamically adjusting cryptographic authentication intensity accordingly. Our approach ensures real-time contextual adaptability, robust resistance against Sybil, replay, and 51% attacks, minimal communication overhead, and data privacy by localized processing. Comprehensive theoretical security analyses with formal proofs underscore the resilience of PoAh 2.0. Empirical validations through realistic V2X scenarios are earmarked as critical future work.