An Efficient Algorithm for Preserving Events' Temporal Relationships in Wireless Sensor Actor Networks
Bibliographic record
Abstract
This paper proposes an event ordering algorithm for wireless sensor actor networks (WSANs) that could be applied for monitoring critical conditions (such as fires, explosions, toxic gas leaks etc.) in order to ensure the correct interpretation of events. We propose modifications to the ordering by confirmation event ordering protocol for WSANs by introducing clustering into the network's topology. The objectives of the proposed modifications are a reduced number of messages, energy efficiency, scalability and reduced latency. At the same time, we propose a hybrid synchronization scheme for the clustered topology, in which local time scales are used at the level of clusterheads. The clusterheads are synchronized with each other by the actor node using the reference broadcast synchronization technique, while the nodes inside clusters are synchronized with the round trip synchronization technique. The synchronization scheme aims at preserving energy and reducing network delay and it is better suited for the resource sparseness of wireless sensor networks as opposed to methods that use global time scales. Moreover, our proposed algorithm uses the message exchange necessary for event ordering and routing protocols for time synchronization purposes by piggybacking synchronization pulses and replies on these messages, thus reducing the additional traffic needed for time synchronization. In this paper, we present our protocol, discuss its implementation and provide its proof of correctness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".