Survey on Transit Edge: Dynamic Task Management in Collaborative Rail Computing

The growing adoption of Intelligent Transportation Systems (ITS) demands reliable real-time monitoring, predictive maintenance, and safety-critical responses in smart rail networks. However, embedded devices such as the ESP32 are constrained by limited computation, memory, and energy efficiency, restricting their suitability for large-scale deployments. To address these challenges, we propose TRANSIT EDGE, a Train–Edge–Cloud (TEC) collaborative framework that distributes sensing, computation, and decision-making tasks across train nodes, edge servers, and the cloud. Multi-sensor data—including driver health metrics, obstacle detection, fire alertsare dynamically prioritized using a Q-learning reinforcement learning (RL) scheduler, while a quantum-inspired optimization layer accelerates convergence.The framework’s modular and scalable design further ensures adaptability to future extensions such as predictive maintenance, multi-train coordination, and integration with next-generation 5G/6G communication networks. These results establish TRANSIT EDGE as a cost-effective, intelligent, and deployment-ready solution for enhancing safety, efficiency, and resilience in modern rail systems.