BlazeFlow: a Multi-Layer Communication Middleware for Real-Time Distributed IoT Applications
Bibliographic record
Abstract
The Internet of Things landscape has grown steadily over the last decade, fueling digital transformation. Data is now regarded as a precious asset. As a result, many companies have shifted their operations to the cloud to avoid maintaining massive infrastructure to hold and process all the data. However, in many settings, sending all the raw data to the cloud for processing and storage is not possible due to the unreliability of wide-area connectivity, coupled with the high costs of data transfer, storage, and processing. Also, some applications exhibit stringent response-time requirements. Edge computing can mitigate some of these issues, but it also has some drawbacks. This paper presents BlazeFlow, our vision of a multilayer data flow solution that can transport data to and from any layer of the cloud-to-device continuum based on publish-subscribe abstractions. BlazeFlow handles data flows between different services deployed onto the same node, between different devices of the same layer, and between different layers (edge, fog, and cloud). This is realized by a cross-layer common bridging solution that can automatically aggregate various protocols that are appropriate for each layer/device (e.g., ROS2, MQTT, Redis, and Kafka). BlazeFlow is designed to support bandwidth-intensive and time-critical services deployed at the various layers of the system. We evaluate a preliminary design of BlazeFlow over an autonomous robotics use case, and we show that it can sustain multi-layer data lows at a high frequency and with a low latency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".