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A Low-Code Containerized Edge Architecture for IIoT Telemetry Orchestration: Mitigating Cloud API Rate Limits Through
1UCASE Software Engineering Research Group, Department of Computer Science and Engineering, University of Cadiz, Avenida de la Universidad de Cádiz 10, 11519 Puerto Real, Cádiz, Spain.
A novel dual-path architecture using a low-code workflow engine effectively manages Industrial Internet of Things (IIoT) data at the edge. This approach overcomes cloud API rate limits by processing telemetry locally, reducing cloud traffic by 98.0%.
Area of Science:
- Computer Science
- Industrial Engineering
- Edge Computing
Background:
- Cloud API rate limits pose challenges for direct telemetry uploads in Industrial Internet of Things (IIoT) systems.
- Existing low-code tools may not be optimized for edge deployments facing connectivity constraints.
Purpose of the Study:
- To evaluate a low-code workflow engine as practical IIoT middleware at the edge.
- To introduce and validate a dual-path architecture for mitigating cloud API throttling.
Main Methods:
- Implementation of a containerized stack (n8n, Mosquitto, InfluxDB, Grafana) on a Raspberry Pi 4.
- Evaluation under baseline, cloud-only saturation, and edge-filtered stress scenarios.
- Comparison of cloud-only direct upload versus a dual-path (Hot Path/Cold Path) approach.
Main Results:
- Cloud-only uploads experienced a 98.0% rejection rate due to rate limiting.
- The dual-path architecture retained all local telemetry while reducing cloud traffic by 98.0%.
- Hot Path processing latency averaged ~5 ms, suitable for soft real-time monitoring.
Conclusions:
- The proposed low-code dual-path architecture is a viable solution for edge IIoT middleware under API rate constraints.
- Containerized deployment, hot/cold path decoupling, and edge-focused analysis offer novel contributions.
- This approach ensures edge-side data fidelity while optimizing cloud communication.