Efficient and Resilient Data Synchronization with Timestamp Compression in IoT Systems
Bibliographic record
Abstract
Cohesive collaboration in distributed Internet of Things (IoT) systems relies exclusively on accurate time synchronization. However, traditional time synchronization methods over the Internet require frequent timestamp exchange, inevitably increasing the related network overhead and energy consumption. Furthermore, resource-constrained IoT devices and varying Internet conditions can easily lead to unreliable timestamp-based latency measurement during synchronization. In this paper, we propose both a resilient timestamping process for data synchronization over the Internet to retain accurate temporal relationships among data samples as well as a supporting timestamp compression technique to reduce the related overhead. The new timestamp design can support energy-efficient and reliable data synchronization over dynamic Internet conditions. Specifically, by measuring the clock cycles between two sampling instants using the local clocks, an accurate timestamp can be provided for each data sample while eliminating the unreliability induced by network uncertainties during data transmission. Moreover, MAC-layer timestamps are employed to estimate clock drifts inherent to each IoT device in comparison to the universal time reference, where redundant timestamps are compressed to minimize the communication and computation burden of energy-constrained IoT devices. Additionally, a check digit is derived from the compressed segments to validate timestamps, further enhancing synchronization reliability. Simulation results demonstrate the superiority of the proposed scheme with increased synchronization accuracy and prolonged network lifespan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".