Sensor selection for mitigation of RSS-based attacks in wireless local area network positioning
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
Positioning in wireless networks has gained significant ground as an enabling technology for various applications such as event detection and context awareness. Since these positioning systems rely on radio features to locate a mobile, they are susceptible to non-cryptographic attacks resulting from malicious alteration of the propagation environment. This paper proposes a sensor selection scheme for increasing the resilience of fingerprinting-based positioning systems to RSS-based attacks in the context of wireless local area networks (WLAN). A distributed positioning scheme is proposed whereby an estimate is obtained from each WLAN access point (AP). Sensor selection is performed based on a nonparametric estimate of the Fisher information. Experimental results indicate superior performance compared to existing methods and graceful performance degradation in presence of RSS attacks.
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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