Behavior of clock‐sampling mutual network synchronization in wireless sensor networks: convergence and security
Bibliographic record
Abstract
Abstract Clock synchronization is an important component of wireless sensor networks (WSNs) both for co‐ordination of node communications and for time stamping sensor data. The previously presented clock sampling mutual network synchronization (CS‐MNS) algorithm is simple, has low communication and processing overhead, and allows fully decentralized operation. We present some simulation results that indicate the potential of CS‐MNS to achieve high clock synchronization accuracy in mobile multi‐hop wireless networks. Past work has shown clock convergence under specific conditions in single‐hop networks. We show analytically that in the absence of offset errors, the network clocks converge. In the presence of offset errors, we present conditions on the degree of clock asynchrony under which the network clock rates show convergent behavior. The analysis is applicable as long as the network topology is connected and, thus, is of interest in both single‐hop and multi‐hop environments. As a side result, we also show how a network designer can use these conditions to add a bias term to the CS‐MNS algorithm and, thus, improve the start‐up dynamics of the algorithm. Furthermore, we discuss the algorithm from a security standpoint. Finally, we propose a method for adding external reference synchronization that is compatible with our security discussion. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".