Determining Per-Mode Battery Usage within Non-trivial Mobile Device Apps
Bibliographic record
Abstract
The impact on battery life has become a key design criteria for mobile device applications (apps). Poor user perceptions about an app's energy use can now quickly lead to negative social media reviews and the resulting adverse impact son the app's marketability. Traditionally, assessments of an app's energy use have been done by simply measuring the total current draw experienced by the device while the app is on. Such approaches have become insufficient within modern devices due to the prevalence of energy management features within their hardware and operating systems (OSes) and the increased complexity of modern apps. This leads to: (i) non-stationary time domain current signals, due to the devices' dynamics power management, and (ii) different operational "modes" within an app having distinct energy use profiles. Within this work, an approach based on matched filters is developed to allow per-mode energy profiles to be correctly identified and characterized under(i) and (ii). This approach is then applied to quantitatively assess the energy use profile for a dual-mode multi-platform commercial Android quality of experience (QoE) assessment app across a number of mobile devices and OS variants, where it is shown that such differences can lead to significant differences in the app-level energy profiles that are produced.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".