Participant Comfort Adaptation in Dependable Mobile Crowdsensing Services
Bibliographic record
Abstract
Mobile Crowdsensing (MCS) is a ubiquitous sensing concept under the Internet of Things (IoT) ecosystem where builtin sensors in smart mobile devices are utilized as users join in sensing campaigns launched by the crowdsensing platform. The pervasive and non-dedicated nature of the sensing instruments in MCS raises the trustworthiness issue. On the other hand, due to granting access to the hardware on their devices, user comfort -which is directly related to the information revealed or the type of sensor activation by user- is also another barrier against wide adoption of MCS in the IoT Era. In this article, we present an adaptive mechanism to manage user comfort in an adaptive manner while ensuring the trustworthiness of the crowdsensed data through auction based reputation maintenance at the MCS platform. The proposed mechanism allows the users to adaptively switch their sensory allocation that are made available to the MCS platform based on historical tracking of the changes in their utility. Through simulations, we show that adaptive management of sensory selection in the auction-based MCS campaign can result in up to >3% increase in user comfort and up to >2% improvement in platform utility when compared to the fixed configuration of sensory arrays based on constant comfort levels used in user recruitment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".