Retraction Notice: Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems
Bibliographic record
Abstract
The evolution of the Internet of Things-enabled Maritime Transportation Systems (IoT-MTS) provides a sturdy data cornerstone for efficient maritime traffic scheduling and management. However, due to task heterogeneity and the limited computing power of individual vessels or stakeholders, although third-party clouds could provide powerful computing support for MTS, directly aggregating data from vessels to the cloud may cause privacy leakage and security concerns. Crowdsourcing as a newly distributed problem-solving paradigm could provide new solutions for conducting maritime big data computing, deep learning and sensing tasks by leveraging the crowd intelligence and computing power of vessels, but strong incentives are required to stimulate vessels to participate because of their selfishness and rationality. Nevertheless, existing incentives rarely consider the security problems caused by the man-in-the-middle attacks, honest-but-curious platform attacks and inference attacks simultaneously, and ignore the redundant winners and multi-attribute characteristics of participants. Toward this end, this paper proposes a tensor-based secure truthful incentive for IoT-MTS dubbed CrowdTensor to maximize the social welfare by eliminating redundant winners and meanwhile guaranteeing the desired economic properties, where the multi-attribute features and the complex association relationships of crowdsourcing systems are characterized by utilizing the tensor tool. A two-phase bid-preserving mechanism based on the cryptographic hash function and digital signature is introduced against malicious attacks. Both the rigorous theoretical analysis and extensive experimental results show that CrowdTensor outperforms other compared incentives and the desired properties can be achieved simultaneously.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".