Monitoring the Smart City Sensor Data Using Thingsboard and Node-Red
Bibliographic record
Abstract
The ubiquitous use of smartphones, smart devices, and low-cost sensors, has fostered much attention around the Internet of things (IoT). The Internet of Things has led to the manufacturing of more IoT-related products ranging from devices to applications. The sensors in an IoT network provide data on the environment in which they are deployed and interact with other devices and the applications in the broader IoT ecosystem. IoT components do not necessarily adhere to the same standard and so can be very heterogeneous in a particular environment. This can create challenges in the collection and monitoring of heterogeneous data in real-time as well as monitoring the infrastructure itself. Such data collection is not only important for the analysis of the environment but also for monitoring the health of the devices and operational components, such as software elements. In this paper, we introduce an architecture aimed at collecting, visualizing, and monitoring streams of data from sensors and from infrastructure components. The ThingsBoard IoT platform is used for data collection and visualization. Node-Red is used to categorize the data on the sensor name. Experiments using sensor data sets are provided to illustrate the approach, processing, and visualization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".