Airfield lighting control (ALC) is critical for ensuring safe, efficient, and compliant airport operations, especially under low-visibility conditions. However, current centralized control architectures cannot adequately meet the real-time responsiveness, scalability, and reliability requirements of Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Level IV. To overcome these limitations, this paper proposes a novel cloud–edge–end collaborative architecture for a mobile ALC scenario, in which we formulate a joint task computing and energy consumption optimization problem to maximize long-term system utility under latency, computation, and communication constraints. In this way, the mobile airfield lighting (MAL) system can also quickly adapt its optimal formation pattern based on the airport environment, lighting conditions, and the type of aircraft taking off or landing via efficient computation, thereby achieving the best navigational assistance effect. For solving such an optimization problem, a framework that combines K-medoids with the Improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) is proposed to integrate the efficiency of clustering for rough allocation and the high-precision dynamic optimization capability of the improved TD3. The training depends on edge nodes and the cloud to achieve online performance. Finally, the extensive simulation proved that our novel algorithm is efficient.
Jiang et al. (2026) studied this question.