A Density Mapping Algorithm for Supporting Cyber Foraging Service Networks
Bibliographic record
Abstract
The finite wireless range and fixed location of service networks could render them unable to provide services in certain situations where a dense zone of users is created in the area. This could be in the event of a conference or a festival. Alternatively in extreme cases, the network may not be able to serve anyone at all in the case of a major power shutdown or network collapse. To deal with such situations, we propose to explore establishing a service network through cyber foraging when no service network is functioning properly in an area. To effectively establish a network based on cyber foraging, service nodes need to be placed at strategic points in the service area. This paper presents the first step in our approach. In this initial work, we present how to gather current information on users density in the area. The intended service area mapped with this information could help in identifying the strategic points to place service nodes to enable establishing a network through cyber foraging. We present a scanning algorithm that provides an approximate distribution of users within an area. This paper presents our approach, explains the algorithm in details and presents simulation results to show how the approach could be used.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".