Preserving Temporal Relationships of Events for Wireless Sensor Actor Networks
Bibliographic record
Abstract
In this paper, we present the performance evaluation of an algorithm for preserving temporal relationships of events in Wireless Sensor Actor Networks (WSANs). The algorithm consists of two modules, which deal with the problems of temporal event ordering and time synchronization. These two problems are approached as a whole as they complement each other: in order to temporally order the events, the nodes must be synchronized. The goal of the proposed event ordering algorithm for WSANs is to reduce the overhead in terms of energy dissipation and delay. We also propose a tunable time synchronization algorithm employing a hybrid synchronization scheme suited for clustered topology. The proposed algorithm utilizes the message exchange necessary for event ordering and routing for synchronization purposes by piggybacking messages with synchronization pulses and replies to reduce the communication cost of synchronization. Simulation experiments showed that the event ordering algorithm is capable of reducing the overhead when compared to previously proposed algorithms. The synchronization algorithm demonstrated that the combination of synchronization techniques was well suited for the communication mode utilized in a clustered topology. The approach of piggybacking synchronization pulses and replies resulted in a considerable gain, which we demonstrated in the number of messages that were piggybacked for synchronization purposes.
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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 | 0.008 |
| 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 | 0.000 | 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".