Using multi-agent geo-simulation techniques for intelligent sensor web management
Bibliographic record
Abstract
Sensor webs consist of a large collection of small nodes providing collaborative and distributed sensing ability in unpredictable environments. Nodes composing such sensor webs, are characterized by their resource restrictions, especially the energy, the processing, and the communication capacities. These nodes are also in constant interaction with each other and with their geographic environment. An efficient system aiming at managing sensor webs must take into account the evolution of the sensor nodes as well as the geographic environment. Such a management process involves coping with a variety of dynamic variables including the nodes characteristics, the environment properties as well as the sensed data. In this context, Multi-Agent Geo-Simulation (MAGS) provides a flexible approach that can be used to easily analyse complex systems such as sensor webs in large scale georeferenced environments. The purpose of this paper is to present SensorMAGS, an agent-based geo-simulation system which manages sensor nodes in virtual geographic environments. This system is applied in the context of a water resource monitoring project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".