Energy-efficient gaming on mobile devices using dead reckoning-based power management
Bibliographic record
Abstract
We address the issue of how to reduce energy consumption of the wireless interface during multiplayer gaming sessions on mobile devices. Reducing energy consumed by the wireless interface is achieved by putting it in a low-energy state when it is not used. We leverage the dead reckoning technique used in existing games. In dead reckoning, future locations of objects in a game are estimated based on their current locations and velocities. The difference between the extrapolated and true locations is known as the dead reckoning error. We propose an algorithm that employs the dead reckoning error rate to dynamically control the state of the wireless interface. We implement our algorithm into a dead reckoning simulator that is based on a real open-source game. Our experimental results show that the proposed algorithm can achieve up to 36% energy savings for mobile devices. Our proposed algorithm is practical because it does not require much additional code and it allows easy integration with existing games.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".