Reputation-based sensing-as-a-service for crowd management over the cloud
Bibliographic record
Abstract
Cloud computing model can enable provisioning of sensing services through mobile phones, namely Sensing-as-a-Service (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> aaS). In this paper, we study S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> aaS over social networking services for crowd management problem where malicious users report false sensor readings leading to severe disinformation at the crowd control platform. To this end, we propose Trustworthy Sensing for Crowd Management (TSCM) which is a reputation-based crowd management scheme over the cloud platform where sensing data is collected from smart phones based on an auction mechanism. TSCM periodically runs an auction in order to assign dynamically arriving sensing task requests to the smart phone users forming a crowd connected through a social network. User bids, task values and user reputation values are taken as the inputs whereas the outputs are the utility of the crowd management platform and the average utility per user while reputation of a user is a function of the accuracy of the sensed data. Through simulations, we show that TSCM significantly improves the platform utility while degrading the ratio of the maliciously crowdsourced task by 75%. Furthermore, we also show that under TSCM, reputation of malicious users converge to a low value at the order of 40% following a few auctions.
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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 | 0.000 | 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".