EdgePub: A Self-Adaptable Distributed MQTT Broker Overlay for the Far-Edge
Bibliographic record
Abstract
The MQTT protocol, based on a topic-based publish/subscribe paradigm, plays an important role in the Internet of Things (IoT), as it enables flexible and highly decoupled communications between the different entities of an IoT system. Further, several IoT applications require low latencies (e.g., tele-surgery, connected vehicles) – hence, a centralized MQTT (publish/subscribe) infrastructure can be impractical. In this paper, we present EdgePub, a dynamic, highly distributed, and self-adaptable edge-based publish/subscribe middleware that provides drop-in compatibility with existing MQTT-based client applications and brokers. EdgePub transparently builds a one-hop dissemination overlay over embedded MQTT brokers deployed at the far-edge (i.e., on the client devices themselves), and provides a load balancing strategy that continuously minimizes the average publication latency, while ensuring that the bandwidth constraints of the edge client devices are met. We provide an implementation through the form of an MQTT.js-compatible Node.JS library, and we evaluate EdgePub over different deployment scenarios (i.e., local to world-wide deployments), over a test-bed of Raspberry Pi devices. We report 18%-77% lower average latencies compared to centralized edge and cloud-based deployments, without exceeding the limited bandwidth constraints of the edge brokers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".