A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Space-Air-Ground Integrated Network (SAGIN) as an efficient newly integration network could provide more comprehensive network services to meet the multifarious quality of service requirements in different Intelligent Transportation Systems (ITS). By taking advantage of SAGIN, Space-Air-Ground Integrated Vehicular Crowdsensing (SAGI-VCS) would have great potential and the services regarding ITS could be facilitated. However, centralized SAGI-VCS is usually vulnerable to malicious attacks and the trust issues are one of the main reasons that hinder its further development. Blockchain as a distributed hyperledger shows a vital potential to solve the trust problem of multiple participants who do not trust each other and tackle the security issues in SAGI-VCS. Additionally, selfishness is another factor that prevents vehicles from participating in SAGI-VCS. The vast majority of existing incentives for vehicular crowdsensing only focus on the terrestrial networks which cannot be directly used in SAGI-VCS. Meanwhile, the redundant winner phenomenon and the multi-attributes of participants are less considered by them. Toward this end, we first illustrate a blockchain-enabled service architecture for SAGI-VCS and then construct a unified representation model. Afterwards, a tensor computing based truthful incentive mechanism TensorBC for blockchain-enabled SAGI-VCS is proposed to motivate vehicles to participate in completing tasks, ensure the security of the whole process and maximize the social welfare. TensorBC not only can eliminate the redundant winner phenomenon, but also can guarantee the economic properties such as truthfulness, individual rationality and profitability. Finally, both the rigorous theoretical analysis and extensive experimental results show that TensorBC could achieve a better performance.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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