Effective role-assignment for participatory sensing systems
Bibliographic record
Abstract
Mobile phones are equipped with a rich set of sensors which are useful in deploying various sensing activities. We focus on participatory sensing in which every participant carrying smartphone senses its environment and shares it with server. Most of the applications require location information to perform sensing activity. But, GPS drains considerable amount of energy if used for localization. So, a set of devices are chosen as broadcasters which turn on GPS, and the neighbouring devices rely on them to calculate their position. We propose an efficient energy model to minimize the power consumption of such a system. The existing scheme for finding optimal set of broadcasters is based on greedy algorithm. This is time efficient only when the number of participants are small. We propose a sorting based algorithm. This provides better time complexity for moderate and large data sets which is the actual case in real scenarios. We validate our work with extensive experiments on both real and synthetic datasets. Results demonstrate that our proposed approach effectively minimizes energy and saves 12-25% of the time for medium and large data sets.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".