A Pattern-Driven Middleware Architecture for IoT Data
Bibliographic record
Abstract
Sensors in IoT systems are vital when it comes to collecting data across various applications automatically. However, there is no standardised software architecture for collecting and performing operations on data coming from IoT sensors, while also being able to aggregate other data sources. This is particularly evident in the context of managing asynchronous operations and streaming data, which are common in sensor-based systems. To address these issues, this paper proposes PD-MidI, a design pattern-based IoT middleware architecture that provides not only essential data collection, processing, and aggregation capabilities, but also incorporates comprehensive security, privacy, and anonymization features. We demonstrate how the middleware handles asynchronous data flows by implementing the Observer and Producer-Consumer design patterns, while also addressing access control requirements. To validate our proof-of-concept, we developed two use cases. The first use case demonstrated our middleware’s effectiveness in monitoring energy data from Hydro-Québec by collecting data from their energy meters and computing average energy consumption across various parameterisable time intervals. The second use case illustrated the middleware’s capability to collect and process real-time data from a Fitbit watch through a Firebase database. Our implementation results are promising and demonstrate the ability of our middleware to successfully provide flexibility, heterogeneity, and abstraction in data handling.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".