Share Sens: An Approach to Optimizing Energy Consumption of Continuous Mobile Sensing Workloads
Bibliographic record
Abstract
Smartphones and a growing number of wearable devices are equipped with a variety of powerful sensors. This has led to increased interest in developing of applications across a wide variety of domains including health-care, entertainment, environmental monitoring and transportation, which use sensor feeds to offer services. However, most of these applications require continuous sensing, which places a heavy demand on the device's typically limited battery power. This problem is further amplified as multiple applications attempt to monitor multiple sensors simultaneously. In this paper, we present ShareSens, our approach to opportunistically merge independent sensing requirements of applications. We achieve this using sensing schedulers for sensors, which determine the lowest sensing rate which would satisfy all requests, and then use custom filters to send out only the needed data to each application. Sensing requests made through the ShareSens API (which we have implemented for Android) are forwarded to the relevant schedulers which determine the optimum sensing rates to satisfy all requests. The paper presents the design and implementation of ShareSens, as well as results from our experimental work on the power savings that can be achieved by using it.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".