Utilizing parallelization and embedded multicore architectures for scheduling large-scale wireless mesh networks
Bibliographic record
Abstract
WirelessHART™ was released in September 2007 and became an IEC standard in April 2010 (IEC 62591). It is the first open wireless communication standard specifically designed for process measurement and control applications deployed in harsh and noisy environments. WirelessHART distinguishes itself from other public standards by maintaining a central Network Manager. The Network Manager is responsible for maintaining up-to-date routes and communication schedules for the network, thus guaranteeing the reliable and real-time network communications. To deal with the intensive computation requirement in a centralized WirelessHART Network Manager, particularly of middle or large scale network sizes, in this article, we utilize parallelization techniques to implement the Network Manager on embedded multicore architectures. By leveraging the multicore capabilities of the AMD Embedded G-Series Dual-Core processor, we utilize the Texas Multicore Technologies' (TMT) SequenceL™ language and runtime environment to parallelize the algorithms proposed in our previous work for constructing reliable routing graphs and real-time communication schedule. Our experiments show that AMD Embedded G-Series is an ideal platform for medium to large size WirelessHART network and SequenceL™ language can help significantly reduce the development cycle and further improve the algorithm efficiency and system performance in embedded multicore architectures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".