New Approach Construction for Wireless ZigBee Sensor Based on Embedding Pancake Graphs
Bibliographic record
Abstract
Wireless Sensor Networks (WSN) based on the IEEE 802.15.4 standard are constantly expanding. Applications like production control, building control are more and more based on WSN because of their energy efficiency, self organizing capacity and protocol flexibility. However, the construction of Cluster-Tree networks based on the beacon mode Pancake graphs is still undefined by the IEEE 802.15.4 standard. In order to enable the construction of such topology, i.e., Beacon Cluster-Tree based on Pancake graphs, we present, in this paper, a new topology construction approach. The Pancake is one of the Cayley graphs that were proposed as alternative to the Hypercube for interconnecting processors in parallel computers. This network offers attractive and desirable properties: Vertex symmetry, small degree and diameter, extensibility, high connectivity, easy routing, regularity of topology, fault-tolerance, and embed ability of other topologies. We present in this work the many-to-one embedding of Multiply-Twisted Hypercube into the Pancake networks with dilation 5 as a new approach for wireless networks. The presented approach is based on the exploitation of RF front-end capabilities in treating multipath signals and, thus, avoiding the introduction of beacon or Super Frames scheduling algorithms. Avoiding the introduction of scheduling algorithms ensures a simple solution that could be easily implemented and executed by ZigBee sensor nodes.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".