Content-based Filter Publish Subscribe Model for Real-time WSN applications
Bibliographic record
Abstract
In the recent years, the publish/subscribe (pub/sub) communication model has emerged as a suitable communication paradigm for large-scale distributed systems. That is due to its effective decoupling properties for the network’s participants in time, space, and synchronization. These properties are well-suited for Wireless Sensor/Actuator Networks (WSAN) applications. Data Distribution Service (DDS) is a well-known standard in the academic and industrial communities for supporting real-time distributed systems based on the pub/sub model. TinyDDS is a light weight and partial porting of DDS middleware to WSN platforms. The main objective of this paper is to use TinyDDS standard-based solution to minimize the energy consumption and maximize throughput of WSANs when applying the pub/sub interaction scheme, while maintaining the content-based filter QoS support. Adding content-based filter to the default TinyDDS (DTDDS) enable the WSAN to gain high performance in terms of packet delivery ratio and reduce the power consumption and we called this addition as CFTDDS. The Experiments s conducted in this work prove the efficiency of our proposal CFTDDS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".