Blockchain-Anchored Adaptive Authentication for Real-Time Medical Data Streams in AI-Driven Smart Grid-IoMT Converged Networks

Abstract

The convergence of Smart Grids and the Internet of Medical Things (IoMT), termed Grid-IoMT, represents an emerging paradigm where healthcare facilities dynamically interact with energy grids to optimize both clinical operations and power consumption.Real-time medical data streams (e.g., continuous vital signs from wearable monitors, infusion pump logs, ventilatory parameters) traverse network infrastructure shared with grid telemetry, creating unprecedented attack surfaces where energy-demand manipulation can indirectly compromise patient safety, and conversely, medical data injection can destabilize grid frequency regulation.This paper presents BlockAuth-GridMed, a novel blockchain-anchored adaptive authentication framework specifically designed for real-time medical data streams in AI-driven Smart Grid-IoMT converged networks.The framework integrates three synergistic innovations:(1) A hierarchical blockchain architecture (local permissioned chains for clinical domains interconnected via a main chain for cross-domain trust) that anchors authentication proofs without introducing latency prohibitive for real-time medical applications (median latency 187ms),(2) An adaptive authentication engine powered by deep reinforcement learning (DRL) that dynamically adjusts authentication strength based on real-time risk assessment-escalating to multi-factor requirements during grid instability events or cyber-threat alerts while maintaining low-friction single-factor authentication during quiescent periods,(3) A zero-knowledge proof (ZKP) layer enabling mutual authentication between medical devices and grid nodes without revealing sensitive patient identifiers or clinical data patterns to energy system operators.We evaluate BlockAuth-GridMed using a realistic testbed emulating a 200-bed smart hospital integrated with an IEEE 13-bus distribution grid model, processing 15,000 real-time medical data streams per second across 6,500 IoMT devices and 12 grid sensors.The framework achieves 99.97% authentication success rate for latency-sensitive medical alerts (critical events requiring <100ms end-to-end latency) and maintains an average authentication overhead of 28ms, well within clinical requirements.Under adversarial conditions (simulated man-in-the-middle, replay, and grid-state injection attacks), BlockAuth-GridMed demonstrates 96.8% attack detection and 99.1% attack prevention rates, outperforming baseline certificate-based (83.4%/87.2%)and token-based (71.3%/74.6%)schemes.The DRL-driven adaptive authentication reduces unnecessary multi-factor challenges by 73% compared to static high-security policies, significantly improving clinical workflow efficiency.We also analyze blockchain gas costs (approx.\$0.012 per authentication), scalability under IoMT device churn (up to 15% daily device joins/leaves), and regulatory alignment with HIPAA, NERC CIP, and FDA pre-market guidance for medical device security.This work provides the first integrated authentication framework specifically tailored to the Grid-IoMT convergence, enabling secure, real-time, and adaptive protection for medical data streams in energy-aware healthcare infrastructures.