WSN05-5: On Node Population in a Multi-Level 802.15.4 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
We consider the problem of maintaining the prescribed event sensing reliability while maximizing cluster and network lifetime in a multi-cluster 802.15.4 sensor network. Clusters are connected through bridges which also act as cluster coordinators; both ordinary nodes and bridges resolve contention using the CSMA-CA algorithm. Cluster lifetime is maximized through the use of redundant sensors which are periodically sent to sleep using a simple distributed activity management algorithm. Network lifetime is maximized by equalizing lifetimes of individual clusters through the adjustment of the number of nodes. We model this problem analytically and derive the probability distribution of the network lifetime. We also derive the expression for node count that compensates for the increased load due to contention caused by the bridge. Experiments show that this technique easily equalizes cluster lifetimes.
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 | 0.000 |
| Open science | 0.001 | 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