Online Incentive Mechanisms for Socially-Aware and Socially-Unaware Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".