Trustworthiness and Comfort-Aware Participant Recruitment for Mobile Crowd-Sensing in Smart Environments
Bibliographic record
Abstract
Mobile crowd-sensing (MCS) has gained significant momentum in recent years for sensory data acquisition through non-dedicated sensors. Even though most of the participants are assumed to be willing to participate in the sensing campaigns, some smartphone users may be reluctant to grant access to particular sensors in their devices. Besides this, the presence of adversaries in the participant pool makes the participant selection problem further challenging. With these in mind, we introduce Reputation and Comfort Level Aware Participant Selection (RACLAPS)which allows participants to modify their available set of sensors that can be accessed by central platform/task publisher i.e. participants can turn-off sensors according to their comfort. To demonstrate the effect of RA-CLAPS, we utilize Selective and Reputation-aware Recruitment (SRR) in which participants are selective in choosing their task to sense solely to improve income. To enrich the discussion, Non-Selective and Reputation-aware Recruitment (NSR) is also considered in which participants are given no choice regarding selectiveness, sensor configuration. Simulation results show that RA-CLAPS improves average user utility by 7.6% compared to its predecessor, SRR. We also mention that average discomfort per participant is reduced by 6.4% under RA-CLAPS when compared to non-selective and selective recruitment approaches.
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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".