Practical Byzantine Fault Tolerance Based Robustness for Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) has become a prominent paradigm to collect and share data based on sensing devices with built-in sensors in the Internet of Things era. Nevertheless, conventional MCS confronts various security and privacy vulnerabilities in terms of decentralized, openness, and non-dedicated properties. Currently, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure, which reduces the effectiveness and robustness of the MCS system. Meanwhile, fake task attack is a serious threat, as it would drain excessive resources from the participant devices and clog the MCS servers to disrupt the services offered by the MCS. To address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. Moreover, the distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain uses a practical Byzantine fault tolerance consensus that can tolerate one-third faulty nodes, making the implemented MCS system robust and sturdy. In addition, an ensemble learning approach is deployed in the blockchain for eliminating fake tasks by malicious requesters. The evaluation test is conducted under two different datasets representing a big city and a small one to have an MCS campaign. Numerical results show that the ensemble approach eliminates most of the fake tasks with a detection accuracy of up to 0.99. Furthermore, the ensemble learning integrated system outperforms individual learner based centralized systems, and non-fault tolerant systems in terms of Ratio of Legitimate Tasks (RoLT) saved and Ratio of Fake Tasks (RoFT).RoFTis low to 0.01, andRoLTis high up to 0.913 via the proposed MCS blockchain-driven framework.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".