iCoMe: A novel incentivized cooperative mobile resource management mechanism
Bibliographic record
Abstract
In this paper, we present a novel cooperative resource management mechanism in mobile cloud computing environment. This mechanism is based on cooperation between mobile devices using their short range radio technology such as WiFi with the goal of maximizing the revenue of the cellular service provider. Users with poor cellular link quality connect with nearby devices through their WiFi interface. The service provider provides incentives to mobile devices to motivate them to contribute in such cooperative scheme. We first formulate the resource management problem as a mixed integer linear programming model. The optimal solution has an NP-hard complexity. To tackle the complexity of the problem, we then propose iCoMe, which is an Incentivized Cooperative MobilE resource management mechanism. The resource management problem in iCoMe is solved distributively by the service provider and mobile devices. We prove that iCoMe has a polynomial time computational complexity. Simulation results confirm the close to optimal performance of iCoMe. Results also show that our proposed mechanism considerably increases the revenue of the service provider compared to non-cooperative schemes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".