Proactive Constrained Adaptive Preemptive Scheduling for Age-of-Information Minimization and Safety in Mobile Edge Computing Environments

Optimizing information freshness (Age-of-Information, AoI) in Mobile Edge Computing (MEC) for low-latency Internet of Things (IoT) applications presents a significant challenge due to the need for strict adherence to operational safety and resource constraints. Existing methods often struggle with robust constraint handling or fine-grained dynamic scheduling. This paper proposes Proactive Constrained Scheduling with Adaptive Preemption (PCSAP), a novel hybrid optimization framework. PCSAP integrates proactive constraint handling from Safe Reinforcement Learning with adaptive preemption for dynamic task scheduling in multi-user, heterogeneous MEC environments. It models the problem as a Constrained Markov Decision Process, incorporating a proactive constraint sensing term to guide violation avoidance and an Adaptive Preemption Module that dynamically calculates urgency indices for intelligent resource allocation. A multi-layer decision framework separates high-level strategic policy learning from low-level index-based scheduling. Extensive simulations demonstrate PCSAP's superior performance, achieving significantly lower average AoI and dramatically reduced constraint violation rates compared to state-of-the-art baselines. It also maintains high task completion and efficient energy utilization. An ablation study confirms the critical roles of both core components. Further analyses validate PCSAP's robustness, practical applicability, and ability to deliver a superior user experience, confirming its viability for real-time deployment.