Optimizing Real-Time Data Analytics for Smart Grids via IoT Broker Extensions
Bibliographic record
Abstract
The growing adoption of Distributed Energy Resources (DERs) in smart grids has increased the need for efficient, low-latency data aggregation and control to support real-time grid services such as demand response (DR). This paper presents a comparative study of two Message Queuing Telemetry Transport (MQTT)-based data aggregation architectures: in-broker aggregation using custom broker extensions and downstream aggregation engines. We develop a custom MQTT extension for real-time analytics in smart grids and evaluate both approaches in a simulated high-concurrency DR setting, where DER data from multiple homes is published to a broker and aggregated within a defined window. Key metrics include round-trip latency, message delivery success rate, and memory usage. Results show that under QoS 1, the in-broker approach outperforms the downstream engine, achieving up to 200 ms lower latency and maintaining a 100% delivery rate at 100 concurrent clients, compared to 75% with the aggregation engine. While in-broker aggregation incurs higher memory usage under QoS 0, it becomes more efficient under QoS 1 due to reduced acknowledgment overhead. These findings show that broker-level aggregation provides a scalable, low-latency solution for real-time analytics in smart grids and is well-suited for large-scale IoT deployments in future energy systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".