TL;DR
Experimental results show that the proposed P-LLM framework outperforms traditional scheduling methods and existing reinforcement learning baselines, and maintains stable and consistent performance across different real-world scenarios, time periods, fleet sizes, and order volumes.
The incorporation of Mobile Edge Computing into satellite systems is a highly promising approach to enabling large-scale intelligent Internet of Things services in remote areas. However, the high-speed mobility of satellites and the extreme scarcity of on-board resources pose significant challenges, as traditional reac...
The scheduling efficiency of airport special vehicles directly determines ground-handling quality and flight punctuality. Conventional methods rely heavily on human experience and static rules, lacking adaptability to dynamic uncertainties involving flights, vehicles, environments, and human-machine interactions. This...
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet management, all under tight requirements on accuracy, latency, cost, and reliability. Meeting these requirements is challenging because vehicl...
The rapid growth of wireless devices and the emergence of dynamic traffic hotspots have increased the need for intelligent trajectory planning in unmanned aerial vehicle base stations (UAV-BSs) operating in complex urban environments, where conventional reactive methods relying only on current system states cannot anti...
Federated learning (FL) has become a promising paradigm for privacy-preserving and communication-efficient model training in vehicular networks. With the continuous expansion of vehicular networks, multiple FL tasks are often initiated concurrently by moving vehicles, which poses substantial challenges to the conventio...
Cloud computing has emerged as a new paradigm, which entrusts task scheduling to ensure the satisfaction of stringent constraints on latency, energy, and resources for sustainably running real-time applications. State-of-the-art natural DRL-based scheduling solutions mainly rely heavily on DRL techniques and are either...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
