A Reputation-aware Mobile Crowd Sensing Scheme for Emergency Detection
Bibliographic record
Abstract
The unforeseen proliferation of smart devices has set in motion research efforts aimed at building Smart Cities (SCs) that improve the well-being of their citizens. One of the key technologies to achieve a SC is Mobile Crowd Sensing (MCS). In MCS, data is collected from the environment surrounding the smart device owners and utilized in the provision of a wide array of SC services. A prevalent class of services which is attracting increasing attention is smart emergency services, where MCS is leveraged to facilitate the detection and mitigation operations of crises. In this paper, we study the problem of an emergency situation detection based on MCS-provided data from heterogeneous participants. Specifically, we formulate our problem based on Detection Theory and underline its computational complexity. We present a greedy algorithm that aims to balance the trade-off between the decision time and the quality of the final decision. We perform extensive simulation experiments that show how our scheme improves the correct detection rate compared to a naive reputation-unaware baseline.
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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".