A Low-Code Containerized Edge Architecture for IIoT Telemetry Orchestration: Mitigating Cloud API Rate Limits Through Dual-Path Routing

This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which a Hot Path persists all telemetry locally, while a Cold Path selectively forwards only anomalous or summary events to cloud services. The architecture is implemented as a lightweight containerized stack based on n8n, Eclipse Mosquitto, InfluxDB, and Grafana, and evaluated on a Raspberry Pi 4 under baseline, cloud-only saturation, and edge-filtered stress scenarios. Under the cloud-only condition, the external endpoint is throttled to approximately 60 requests/min, yielding a rejection rate of 98.0% (95% Wilson confidence interval: 97.43–98.44%). Under the dual-path condition, the same inbound load is fully retained locally while outbound cloud traffic is reduced by 98.0%, thereby avoiding throttling without sacrificing edge-side data fidelity. The measured Hot Path processing latency remains around 5 ms on average, with observed peaks below 10 ms, which is compatible with soft real-time monitoring workloads. Compared with more established low-code tools such as Node-RED, the novelty of the study is not the existence of visual orchestration itself, but the combination of containerized deployment, explicit hot/cold decoupling, and an empirical rate-limit mitigation analysis focused on low-cost edge hardware.