Hybrid AI Model for Anomaly Detection and Sustainable Electric Power Utilization in VANET ‐Connected Human Activity Monitoring Networks

The integration of vehicular ad hoc networks (VANETs) with human activity monitoring networks (HAMNs) is advancing intelligent mobility and connected healthcare. However, achieving accurate anomaly detection while maintaining sustainable electric power utilization remains challenging due to dynamic vehicular mobility and fluctuating energy demands. Existing approaches such as VANET security awareness framework (VSAF), secure passenger health assessment using vehicular communications (SPHA‐VC), and bio‐inspired VANET routing (BIVR) mainly focus on security awareness, passenger health monitoring, or routing optimization, but they lack adaptive mechanisms for joint anomaly detection and energy‐efficient network management in highly dynamic environments. To address these limitations, this paper proposes ECO‐SHIELD, a hybrid energy‐conscious self‐healing intelligent learning and detection framework that integrates graph neural networks (GNNs) with a modified particle swarm optimization (M‐PSO) algorithm. The GNN module models spatial–temporal interactions among vehicular and human activity nodes, while M‐PSO dynamically optimizes network parameters and energy thresholds to improve detection reliability and power efficiency. The comparative analysis reports detection accuracy values of 86.3%, 88.1%, and 89.4% for VSAF, SPHA‐VC, and BIVR, respectively, in comparison with 98.4% achieved by ECO‐SHIELD. Similarly, response latency values of 168, 149, and 134 ms and energy consumption values of 345, 318, and 296 mAh have been included for the baseline methods, which clearly highlight the improvements obtained by the proposed framework. In addition, the comparative results now present packet delivery ratios of 91.2%, 92.8%, and 94.1% for the baseline methods against 97% for ECO‐SHIELD.