الملخص
Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook network contention among co-located services with heterogeneous and dynamic latency requirements. While time-sensitive networking (TSN) provides bounded-latency communication, conventional and reinforcement learning-based schedulers struggle to adapt to highly dynamic vehicular environments and inter-queue dependencies. To address these limitations, we propose a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC. Each TSN queue is assigned an autonomous agent that jointly learns the queue service order and time-slot duration to minimize deadline misses under speed-dependent latency requirements. We employ multi-agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions. Evaluation against single-agent, multi-agent, and non-learning-based baselines shows that MAPPO provides robust performance across different traffic profiles. Compared with centralized single-agent methods, it reduces service latency by up to 66.2% and improves reliability by up to 271.8%. Furthermore, unlike urgency-based heuristics, MAPPO ensures balanced scheduling while achieving lower inference times compared to other MARL methods.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Pereira, B. A. C., Carvalho, M., Temiz, F., Salehi, S., Erol-Kantarci, M., Gavrielides, A., Marquez-Barja, J. M., & Macedo, D. F. (2026). Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks. https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks
MLA 9
Pereira, Bernardo A. C., et al. "Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks." https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks.
شيكاغو (المؤلف–التاريخ)
Pereira, Bernardo A. C., Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Andreas Gavrielides, Johann M. Marquez-Barja, and Daniel F. Macedo. 2026. "Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks." https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks.
هارفارد
Pereira, B. A. C., Carvalho, M., Temiz, F., Salehi, S., Erol-Kantarci, M., Gavrielides, A., Marquez-Barja, J. M. and Macedo, D. F. (2026) 'Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks', Available at: https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks.
فانكوفر
Pereira BAC, Carvalho M, Temiz F, Salehi S, Erol-Kantarci M, Gavrielides A, et al. Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks. https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks
IEEE
B. A. C. Pereira, M. Carvalho, F. Temiz, S. Salehi, M. Erol-Kantarci, A. Gavrielides, J. M. Marquez-Barja, and D. F. Macedo, "Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks," https://omanscience.com/ar/articles/deadline-aware-multi-agent-reinforcement-learning-for-tsn-based-vehicular-edge-networks.