Time Synchronization Based on Slow-Flooding in Wireless Sensor Networks
Bibliographic record
Abstract
The accurate and efficient operation of many applications and protocols in wireless sensor networks require synchronized notion of time. To achieve network-wide time synchronization, a common strategy is to flood current time information of a reference node into the network, which is utilized by the de facto time-synchronization protocol Flooding Time-Synchronization Protocol (FTSP). In FTSP, the propagation speed of the flood is slow because each node waits for a given period of time to propagate its time information about the reference node. It has been shown that slow-flooding decreases the synchronization accuracy and scalability of FTSP drastically. Alternatively, rapid-flooding approach is proposed in the literature, which allows nodes to propagate time information as quickly as possible. However, rapid flooding is difficult and has several drawbacks in wireless sensor networks. In this paper, our aim is to reduce the undesired effect of slow-flooding on the synchronization accuracy without changing the propagation speed of the flood. Within this context, we realize that the smaller the difference between the speeds of the clocks, the smaller the undesired effect of waiting times on the synchronization accuracy. In the light of this realization, our main contribution is to show that the synchronization accuracy and scalability of slow-flooding can drastically be improved by employing a clock speed agreement algorithm among the sensor nodes. We present an evaluation of this strategy on a testbed setup including 20 MICAz sensor nodes. Our theoretical findings and experimental results show that employing a clock speed agreement algorithm among the sensor nodes drastically improves the synchronization accuracy and scalability of slow-flooding.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".