Bibliographic record
Abstract
WiFi backscatter communication has the potential to enable battery-free sensors which can transmit data using a WiFi network. In order for WiFi backscatter systems to be practical they should be compatible with existing WiFi networks without any hardware or software modifications. Moreover, they should work with networks that use encryption. In this paper, we present WiTAG which achieves these requirements, making the implementation and deployment of WiFi backscatter communication more practical. In contrast with existing systems which utilize the physical layer for backscatter communication, we take a different approach by leveraging features of the MAC layer to communicate. WiTAG is designed to send data by selectively interfering with subframes (MPDUs) in an aggregated frame (A-MPDU). This enables standard compliant communication using modern, open or encrypted 802.11n and 802.11ac networks without requiring hardware or software modifications to any devices. We implement WiTAG using off-the-shelf components and evaluate its performance in line-of-sight and non-line-of-sight scenarios. We show that WiTAG achieves a throughput of up to 4 Kbps without impacting other devices in the network.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.207 | 0.166 |
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".