ChainSensing: A Novel Mobile Crowdsensing Framework With Blockchain
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a promising paradigm of large-scale sensing. A group of mobile users is recruited with their smart devices to accomplish various sensing tasks in specific areas. The mobility and intelligence of mobile users enable MCS to achieve a sufficient coverage ratio of sensing tasks or areas. Currently, MCS is generally proposed and implemented in a centralized way under a platform’s control. However, this centralized structure is vulnerable to a single point of failure. The platform’s failure leads to a shutdown of the entire system. In addition, there is a trust issue between the platform and mobile users because of computational transparency and financial security. It is possible that the platform manipulates the working process of MCS to obtain an improper gain. To overcome these problems, we propose a decentralized MCS framework, named ChainSensing, by leveraging blockchain. In ChainSensing, mobile users interact with blockchain via smart contracts to complete their operations, e.g., publishing sensing tasks and submitting collected data. Since there are computationally intensive problems in ChainSensing, e.g., path planning, path selection, and reward determination, it is significantly expensive to solve such problems in blockchain. Therefore, we propose to leverage smart devices and computing oracles to solve these problems. Specifically, we propose a heuristic algorithm to solve the path planning problem in smart devices of mobile users; we employ computing oracles to solve the path selection and reward determination problems. Finally, we conduct numerical simulations based on Ethereum to evaluate the performance of ChainSensing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".