Impact of Users' Mobility on the Quality of Edge Sensing Systems
Bibliographic record
Abstract
Edge sensing (ES) is rising as a potential solution for remote sensing challenges, as it exploits the proliferation of smartphones, leverages their embedded sensors to collect data from users' surrounding environments and uses their processors to perform edge computing tasks. Moreover, it is characterized by its low cost and time efficiency. Tremendous efforts have been dedicated to ES systems' quality of data (QoD) and coverage to enhance its performance. Since users incentivization plays a crucial role in enhancing the system's performance, the research community concentrated on improving incentives schemes. In this paper, we evaluate the effect of users' mobility on ES systems' quality of data and coverage, and propose a users' distribution-based dynamic-incentive scheme. In particular, we use a 2-dimensional random waypoint (RWP) model to emulate the randomness of users' mobility and velocity. The proposed incentive scheme aims to eliminate the negative impact of mobility on the QoD; by considering different factors to determine users' incentives and creating users' attraction areas in the targeted cells.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".