Efficient Data Uploading for Mobile Crowdsensing via Team Collaborating and Matching
Bibliographic record
Abstract
With the proliferation of mobile devices and crowdsensing applications, mobile crowdsensing (MCS) has been an appealing sensing paradigm as an alternative to the traditional sensor networks. Instead of deploying static and expensive sensors in sensing areas, MCS leverages sensors embedded in mobile devices and intelligence of mobile users to sense their surroundings, which utilizes the existing communication infrastructure. In a typical sensing cycle in MCS, recruited mobile users as workers collect data according to specified requirements and upload them to an MCS platform. A challenging problem of MCS is data uploading, which requires workers to upload their collected data in a cost-effective manner. A promising solution is to integrate edge computing and exploit the redundant resources of various edge nodes to facilitate data uploading. In this paper, we investigate such a data uploading problem in MCS, which incorporates collaborations among multiple edge nodes and properly matches a team of edge nodes with a sensing worker according to various constraints. Notably, we ensure that the demand of a worker for data uploading is fully satisfied even if served by multiple edge nodes. As we prove that the problem is NP-hard, we propose an efficient solution based on Lagrangian relaxation. Extensive numerical results show that our approach achieves a high approximation ratio and performs stably in various experiment settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".