Hybrid Machine Learning–Based Trust Management Approach to Secure the Mobile Crowdsourcing
Bibliographic record
Abstract
The Internet of Things (IoT) has become a cornerstone in modern automation and data exchange, with IoT devices increasingly embedded into daily life. This development coincides with a rapid growth in mobile device usage and the proliferation of interactive mobile applications, catalyzing the evolution of mobile crowdsourcing. Such applications offer a range of functionalities, from automated data collection through sensor-driven and location-aware services to manual input via user surveys and feedback. For mobile crowdsourcing, particularly in anonymous and ever changing environments, modern trust management systems rarely deal with the problem of participants credibility and reliability. This article presents a hybrid approach that integrates sophisticated trust management techniques with Support Vector Machine (SVM) to improve the security of mobile crowdsourcing platforms. The system considerably enhances the reliability of trust evaluations, successfully protecting against malicious actors. It provides a thorough trustworthiness score to contributors by utilizing a variety of variables, including social networking site data, reputation measures, and user behavior patterns. The high efficacy of our model is demonstrated by an exploratory evaluation of a real mobile crowdsourcing platform, which achieved an accuracy rate of approximately 99.85%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".