A comparison between different FHSS techniques for use in a multiple access secure wireless sensor network
Bibliographic record
Abstract
A wireless sensor network is composed of a set of small sensor nodes deployed in an ad hoc fashion that cooperate for sensing a physical phenomenon (e.g, temperature, humidity, luminosity). Each sensor node plays a dual role as a data originator and a data router. Therefore a node is likely to communicate with a varying number of its peers. Communications receivers in these peer-to-peer networks must offer robust performance when receiving signals from multiple nodes and must offer resistance to hostile jamming and interferences. In this paper, we analyse the performance of frequency hopping spread spectrum for use in a multiple access secure wireless sensor networks. We use different type of detectors. The first is based on the fuzzy rank order detector (FROD), and the others are a conventional detector using the maximum rank sum receiver (MRSR) and the hard decision majority vote (HDMV) detector. We make a quantitative comparison between the three detectors in terms of error probability for wireless sensor networks.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".