BACnet/MQ: A Message Queue-Integrated Communication Protocol for IoT-Enabled Building Automation Systems
Bibliographic record
Abstract
IoT technology plays a vital role in building automation systems(BAS), yet traditional BAS communication protocols face significant challenges in cross-subnet communication, device scalability, and secure dynamic addressing. Existing optimization solutions like Building Automation and Control Networks/Secure Connect (BACnet/SC) or Message Queuing Telemetry Transport (MQTT)-based approaches either increase specialized hardware costs or abandon BACnet’s object model, raising system complexity. To address these challenges, we propose BACnet/MQ, a novel building automation protocol integrating message queuing technology. By deeply embedding AMQP into the BACnet protocol stack, we construct a four-layer architecture. The system implements device UUID identification for cross-subnet addressing while maintaining backward compatibility through protocol gateways, and enhances dynamic addressing via an improved Ad-hoc On-demand Distance Vector Routing (AODV) algorithm for multipath transmission. We developed a prototype test platform for building automation communication and control systems. Experimental results demonstrate that the system fully supports standard BACnet object models and services, successfully integrates traditional protocols with modern message queuing technologies, and enables cross-subnet device communication. This research provides a feasible technical pathway for the evolution of building automation systems toward IoT-based smart building architectures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".