Indoor geo-fencing and access control for wireless networks
Bibliographic record
Abstract
Having an idea of a user's location when he/she is using network services has been an area of interest ever since wireless networks became very popular. As the costs of wireless technologies decrease more and more, we observe the rise of an extremely diverse market of wireless capable devices. However, the field of indoor positioning is still wide open. In this field, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the requirements for users. In this research, we investigate the ways of coupling indoor geo-fencing with access control including authentication and registration. To achieve this, we apply a classification based geo-fencing approach using received signal strength indicator. Consequently, we are mainly focusing on associating accurate geo-fencing with secure communication and computing. Experimental results show that we have achieved considerable positioning accuracy while providing a secure way of communication. Favouring diversity, our implementation does not mandate users to undergo any system software modification or adding new hardware components.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".