Sampling and classifying interference patterns in a wireless sensor network
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
The low-powered transmissions in a wireless sensor network (WSN) are highly susceptible to interference from external sources. Our work is a step towards enabling WSN devices to better understand the interference in their environment so that they can adapt to it and communicate more efficiently. We extend our previous work in which we collected received signal strength traces using mote-class synchronized receivers at sample rates that are, to the best of our knowledge, higher than previously described in the literature. These traces contain distinct interference patterns, each with a different potential for being exploited by cognitive radio strategies. In order to exploit a pattern, however, a node must first recognize it. Given the energy and space constraints of a node, we explore succinct decision tree classifiers for the two most disruptive patterns. We expand on a basic feature set to incorporate attributes based on the dip statistic and the Lomb periodogram, both of which address specific, empirically observed behaviour, and we show their positive impact on both the decision tree structure and the overall classification performance. Moreover, we present an approximation of the periodogram that makes its construction feasible for mote-class devices, and we describe the simplification's impact on classification performance.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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