Scalable and mobile context data retrieval and distribution for community response heterogeneous wireless networks
Bibliographic record
Abstract
Recent studies have indicated that community response networks, locally grouping both professional emergency responders and residents by using mobile and social networking technologies, can significantly improve disaster response. In particular, community response networks formed by mobile users/devices communicating by using only heterogeneous wireless ad hoc links, called herein CRHWNs, can exploit context awareness, defined as the capability of providing applications with full awareness of execution context. In fact, the correct and timely distribution of the current situation, such as health state and position of injured people, can substantially improve community coordination, thus increasing the possibility of saving human lives. Unfortunately, real-world context-aware services in disaster area scenarios require efficient, reliable, and scalable context data distribution and retrieval, and these properties clash with the limited resources usually supported by mobile devices and wireless communications. Along that direction, this article presents our context data distribution infrastructure for CRHWNs, which achieves data distribution efficiency and reliability by also exploiting useful quality indicators, such as data retrieval time and trustworthiness. We also show how our solution increases context data distribution/ retrieval scalability by dynamically self-adapting (a limited number of) data distribution paths and optimizing context data pushing to interested consumers. Experimental results validate our main assumptions and demonstrate how our solution introduces a limited runtime overhead.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".