Novel Distribution-Aware and Learning-Based Dynamic Scheme for Efficient User Incentivization in Edge Sensing Systems
Bibliographic record
Abstract
Edge sensing (ES) systems employ users’ owned smart devices with built-in sensors to gather data from users’ surrounding environments and use their processors to carry out edge computing tasks. Therefore, ES is emerging as a potential solution for remote sensing challenges. Additionally, ES systems are recognized for their favorable characteristics, including efficient time and cost management, scalability, and the ability to gather real-time data. To improve the performance of ES systems, enormous efforts have been made to enhance the quality of data (QoD) and the systems’ spatiotemporal coverage. Moreover, the research community has focused on developing better incentive schemes, as user incentivization is essential for enhancing system performance. In this study, we assess the impact of users’ mobility and availability on the spatiotemporal coverage and QoD of ES systems, taking into account the heterogeneity of users. We propose a distribution-aware and learning-based dynamic incentive scheme. Specifically, we consider the randomness of users’ mobility and velocity using a 2-dimensional random waypoint (RWP) model and support the learning-based incentive scheme with a long short-term memory (LSTM) model. The LSTM model utilizes the users’ historical data to predict their availability to perform the sensing tasks. The learning-based incentive scheme is further used to enhance system performance and effectively manage the trade-off between quality and cost, by recruiting users based on the required quality and cost constraints, to meet the minimum quality requirement within a constrained incentivization budget.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".