Clock synchronisation in WSN: simulation vs. implementation
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
A Wireless Sensor Network (WSN) consists of numerous nodes gathering observations and combining these observations. Often, the timing of these observations is of importance when processing sensor data. Thus, a need for clock synchronisation arises in WSNs. The Clock Sampling Mutual Network Synchronisation (CS-MNS) algorithm has been proposed to fulfil this role. This paper compares simulation results and testbed results for CS-MNS. The simulations were done using Matlab, the testbed implementation was done in TinyOS 2.1, running on a mix of TelosB and MCIAz motes. The results demonstrate good qualitative agreement between simulation and experimentation in most cases. Quantitatively, the testbed results converge slower and achieve less synchronisation accuracy, however. Using the testbed, we also compare CS-MNS against FTSP, the clock synchronisation protocol provided with TinyOS 2.1. In all scenarios, CS-MNS performans noticeably better than FTSP.
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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 | 0.001 |
| Open science | 0.002 | 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