Data collection from wireless sensor networks using a hybrid mobile agent-based approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lack of foresight into the potential future uses of long-term deployments, and limited resources of wireless sensor networks preclude placing all codes on the nodes at run time. Software mobile agents ease this problem by carrying encapsulated code for specific measurement, aggregation and data processing actions. Agent itinerary schemes usually pose agent routing as a variant of the classic Travelling Salesman Problem. This leads to significant inefficiencies because built-up data inflate the agent. We propose a hybrid technique, harnessing the nature of wireless transmission, mobile agent features, and data aggregation, to provide the efficiency of traditional response path optimization techniques in mobile agent itineraries. Simulations demonstrate that the algorithm outperforms traditional mobile agent scheduling approaches in wireless sensor networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it