AN INDOOR GEO-FENCING BASED ACCESS CONTROL SYSTEM FOR WIRELESS NETWORKS
Bibliographic record
Abstract
Use of wireless network information for indoor positioning has been an area of interest since wireless networks became very popular. \nOn the other hand, the market started to grow in variety and production volumes leading to a variety of devices with many different hardware and software combinations. \nIn the field of indoor positioning, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the cost and requirements for both users and providers.\n\nThis thesis investigates possible methods of coupling indoor geo-fencing with access control including authentication, identification, and registration in a system. Moreover, various techniques are studied in order to improve the robustness and security of such a system. The focus of these studies is to improve the proposed system in such a way that gives it the ability to operate properly in noisy, heterogeneous, and less controlled environments where the presence of attackers is highly probable. To achieve this, a classification based geo-fencing approach using Received Signal Strength Indicator (RSSI) has been employed so that accurate geo-fencing is coupled with secure communication and computing. \nExperimental results show that considerable positioning accuracy has been achieved while providing high security measures for communication and transactions. \nFavouring diversity and generic design, the proposed 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".