QoE-Aware Computational Resource Allocation for Connected Vehicles in Smart Urban Environments
Bibliographic record
Abstract
Smart urban environments aim to enhance living standards by delivering effective and responsive services to residents. The growing number of connected objects, however, places increasing demands on computational efficiency and service provisioning. Leveraging the advancements in Information and Communications Technology (ICT), Connected and Autonomous Vehicles (CAVs) can serve as valuable computational resources to support service delivery. These vehicles can play the role of Vehicles as Computational Resources (VaCRs) by sharing their computational resources within smart cities. However, ensuring Quality of Experience (QoE) for service requesters, based on their diverse preferences, poses significant challenges in selecting and allocating resources. This paper presents a QoE-aware computational resource allocation system for Connected Vehicles (CVs), aimed at enhancing service delivery and computational efficiency in dynamic urban settings. The system models user requests based on key QoE factors and employs Performance Evaluation (PE) and QoE models developed using Multi-Criteria Decision-Making (MCDM) and machine learning techniques. A hierarchical multiagent architecture supports system deployment and coordination, while a QoE-aware game-theoretic model guides fair and efficient resource allocation. Compared to prior work, the proposed system demonstrates significantly improved performance in simulations, achieving higher classification accuracy (up to 96.5%) and lower average costs for service delivery. These results confirm the system’s effectiveness in harnessing vehicular computational resources and optimizing QoE in smart city environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".