Mobility-aware trustworthy crowdsourcing in cloud-centric Internet of Things
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Bibliographic record
Abstract
In the Internet of Things (IoT) era, smart devices that are equipped with various types of sensors can enable access to the IoT architecture through a cloud-inspired service model, 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). S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> aaS can provide crowdsourced data to an application running on a cloud platform. The crowdsourced data can be used for several purposes such as public safety. One of the biggest challenges here is the incentive mechanisms for the users who are requested to provide 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 propose mobility-aware trustworthy crowdsourcing (MATCS) framework in a cloud-centric IoT architecture which adopts and extends a previous scheme, Trustworthy Sensing for Crowd Management (TSCM) [1] by incorporating user mobility-awareness in the presence of maliciously altered sensing data. MATCS employs a user-centric incentive mechanism which collects sensing data based on an auction procedure. In the auction procedure, MATCS uses users' reputations, bids, current location and their estimated dislocation during crowdsourcing process. Furthermore, in order to investigate the benefits of reputation-awareness, we also propose reputation-unaware Mobility-Aware Crowdsourcing (MACS). Performance of MATCS is evaluated via simulations, and it is compared to MACS and a benchmark scheme, which aims at making a compromise between the utilities of the users and the platform by considering neither mobility nor trustworthiness. Simulation results confirm that mobility-awareness improves the utility of the platform significantly whereas combining reputation-awareness and mobility-awareness by MATCS can triple the improvement. Besides, user incomes are not significantly impacted by MACS or MATCS when users are mobile. Furthermore, maliciously altered data ratio can be degraded by 20%~55% by reputation-awareness in MATCS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it