Adaptive priority-based edge-centric resource management for the internet of vehicles

Vehicle Fog Computing (VFC) enables increased processing capacity and intelligent transportation support services. VFC has become increasingly important for delay-sensitive applications due to its low latency. Vehicles, on the other hand, face difficulties in combining essential services and executi...

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Veröffentlicht in:Future generation computer systems Jg. 175; S. 108094
Hauptverfasser: Ehsan, Mohaimin, Lieira, Douglas D., Meneguette, Rodolfo I., De Grande, Robson E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2026
Schlagworte:
Fuzzy algorithm
Prioritization
Q-Learning
Resource management
Vehicular fog computing
Prioritization
Q-Learning
Fuzzy algorithm
Vehicular fog computing
Resource management
ISSN:0167-739X
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Zusammenfassung:Vehicle Fog Computing (VFC) enables increased processing capacity and intelligent transportation support services. VFC has become increasingly important for delay-sensitive applications due to its low latency. Vehicles, on the other hand, face difficulties in combining essential services and executing tasks appropriately. Many approaches to pooling idle vehicle resources have been proposed, but few prioritize requests. This proposed approach focuses on hierarchical resource allocation based on priorities, taking into account factors such as deadlines, distances, and mobility issues. Prioritization is achieved through the use of a priority queue, which distributes managed resources based on requests and availability. The hierarchy is implemented synchronously. An iterative ranking mechanism for vehicle resource requests is introduced based on fuzzy membership functions. A Q-learning-based method selects a fog, while the dynamic prioritization technique chooses the vehicle to be served. The technique seeks to reduce the time a service request remains in the system queue, while maintaining good throughput and meeting the criteria for service. QoS. Simulations were performed with realistic mobility models and real maps, including various densities and times, different maps, and varied parameters. In large-scale urban situations, simulated evaluations demonstrate improved response times and overall costs for service requests.
ISSN:0167-739X
DOI:10.1016/j.future.2025.108094