Task Assignment in Extreme Edge Sensing: Balancing Response Time and Incentives
Bibliographic record
Abstract
Extreme Edge Sensing (EES) offers an enhanced approach to efficient remote sensing by utilizing the computational capabilities of user devices for immediate data processing. In contrast to traditional Mobile Crowd Sensing (MCS), EES provides both data collection and local data processing to accelerate decision-making. However, due to the variability in participant capabilities and task requirements, the complexity of task assignments becomes challenging. This complexity necessitates a mechanism that balances incentives and response time, ensuring tasks are completed within predefined budget and time limits. This paper presents a new task assignment strategy that categorizes participants based on their capabilities and task needs. Using the Hungarian algorithm, our methodology optimizes task assignments with an objective function aiming to minimize both monetary and time costs. We then evaluate the minimum budget needed for successful task completion and its dependency on objective function parameters. A comparison of our method's performance against a standard greedy approach demonstrates its effectiveness. The results suggest that our method enhances the efficiency and reliability of task assignment in EES systems, with potential applications in smart cities, environmental monitoring, and other areas requiring efficient remote sensing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".