Neighborhood Discovery Approach in WSN for Star Topology Using a Switched Beam Antenna
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are often used for gathering data collected by sensor nodes spread over large monitored areas. Star topologies, having a sink node at their center, are becoming a clear trend when the size of the monitored area allows it. The range of the radio links used, increases due to the decision to use a sub-GHz frequency and/or a particular signal coding that ensures a significant processing gain in the radio link budget. Even though such long-range radio links are beneficial in specific applications, in spite of the low data rate limit, often remains a weakness for application in many domains. Our overall objective is to consider combining the use of a switched-beam antenna only for the sink node of a star topology and omnidirectional antennas for wireless sensor nodes, in order to achieve a better balance between Range and Data Rate. When switched- beam antennas are used to equip some nodes, usual medium access methods have to be revised as does the discovery of the neighborhood of the sink. In this paper, we propose a scheme that allows the sink node to discover all its neighboring nodes within a limited timeframe, despite the hidden terminal problem effects worsen by antennas directivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".