WiFi Coverage Range Characterization for Smart Space Applications
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
Recently, humans are more and more dependent to communication technologies (CT) in their everyday life to get services, exchange information and communicate with their relatives. Hence, many researches have been made in order to propose convenient and low-cost solutions compatible with the context of smart spaces. This paper characterizes the range of WiFi for outdoor applications in comparison with most known empirical path loss models, and analyzes its impact for smart space services like Internet of Things (IoT). The obtained range is 550m tested with a Samsung Galaxy S5 smartphone, and the comparison with empirical model showed a good difference. Hence the validity and accuracy of those models will be examined for this context, in order to develop an empirical model taking into account environmental effect during our future research. As solutions based on WiFi are generally low cost, its technical characteristics are illustrated and a wide deployment scenario based on this technology is explained on light of obtained results.
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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 | 0.000 | 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 | 0.000 |
| 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 it