Curiosity-Driven Energy-Aware Resource Allocation for Internet of Vehicles
Bibliographic record
Abstract
With the rapid advancement of the Internet of Vehicles (IoV), there arises an increasing demand for efficient connectivity and communication mechanisms between vehicles and infrastructures, wherein resource allocation assumes paramount importance. The primary objective of a resource allocation algorithm is to distribute limited resources, including power and spectrum, to users within the network while catering to the diverse requirements of users. In this paper, we introduce a novel approach called the Intrinsic Curiosity Module (ICM) based Double Q Learning (DQL) for resource allocation, denoted as ICM-DQRA, aimed at addressing resource allocation challenges in IoV network. We integrate the ICM into the DQL algorithm to incorporate an intrinsic reward to the agent. This intrinsic reward, absent in most reinforcement learning algorithms, serves to incentivize the agent to explore the environment further and make decisions conducive to better rewards. Through comprehensive simulations, it shows that our proposed method outperforms other approaches, such as the greedy method and DQL method. Specifically, the ICM-DQRA algorithm achieves a more efficient resource allocation result, leading to a substantial reduction in energy consumption across the vehicular network, ranging from 20% to 27%.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".