A Testbed for Localizing Wireless LAN Devices Using Received Signal Strength
Bibliographic record
Abstract
This paper elaborates on the development of a wireless network testbed to measure the received signal strength indicator (RSSI) in different environments, as the first step for the application of fingerprinting-type localization algorithms of wireless LAN devices. Specifically, in the localization algorithm to the closest previously mapped sets of locations, the RSSI data collected first at known positions are then used to localize the mobile devices at random points. The localization algorithm tested is the minimum-distance algorithm in the RSSI feature space corresponding to the actual geographical points. This paper shows how the environment for RSSI measurement is built and what network configurations yield the most reliable measurements. In the first phase of building a testbed, configurations of off-the-shelf-equipment and the corresponding applications are explained. The second phase is to measure the RSSI in different propagation and physical environments. In this phase, different environments that have already been built in the first phase are examined. Firstly, RSSI is measured from access points' perspective. Secondly, RSSI measurements are taken from laptops' perspective. The third phase is to apply a localization algorithm using the collected data to verify the accuracy of the localization method and examine the characteristics of the collected data.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".