Improving Ultra-Wideband Positioning Security Using a Pseudo-Random Turnaround Delay Protocol
Bibliographic record
Abstract
Ultra-wideband (UWB) technology is very suitable for indoor wireless localization and ranging. IEEE 802.15.4a is the first physical layer standard specifically developed for wireless ranging and positioning. While malicious devices are not typically present, snoopers, impostors and jammers can exist. The data link and network layers in standards such as Wi-Fi, IEEE 802.15.4 and 802.11 mainly provide authentication and encryption support, but security about ranging or location is rarely considered. Ranging can be achieved using just the preamble and start of frame delimiter (SFD), so in this case malicious devices can easily obtain position information. Therefore, the security of ranging or positioning protocols is very important, which differs from the case with data exchange protocols. To provide secure location services, a protocol is presented which is based on a pseudo-random turnaround delay. In this protocol, devices use different turnaround times so that it is difficult for a snooper to figure out the location of sensor devices in protected areas. At the same time, in the period of Hello frame transmission, together with the authentication mechanism of IEEE 802.15.4, an impostor cannot easily engages its deception attack.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".