A Task Bundling based Multi-Platform Cooperation Mechanism for Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".