OppSense: Information sharing for mobile phones in sensing field with data repositories
Bibliographic record
Abstract
With the popularity and advancements of smart phones, mobile users can interact with the sensing facilities and exchange information with other wireless devices in the environment by short range communications. Opportunistic exchange has recently been suggested in similar contexts; yet we show strong evidence that, in our application, opportunistic exchange would lead to insufficient data availability and extremely high communication overheads due to inadequate or excessive human contacts in the environment. In this paper, we present OppSense, a novel design to provide efficient opportunistic information exchange for mobile phone users in sensing field with data repositories that tackles the fundamental availability and overhead issues. Our design differs from conventional opportunistic information exchange in that it can provide mobile phone users guaranteed opportunities for information exchange regardless the number of users and contacts in different environments. Through both analysis and simulations, we show that the deployment of data repositories plays a key role in the overall system optimization. We demonstrate that the placement of data repositories is equivalent to a connected K-coverage problem, and an elegant heuristic solution considering the mobility of users exists. We evaluate our proposed framework and algorithm with real mobile traces. Extensive simulations demonstrate that data repositories can effectively enhance the data availability up to 41% in low contact environment and significantly reduce the communication overheads to only 28% compared to opportunistic information exchange in high contact environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".