Telemetry Processing and Dashboard Layer
Built the Node-RED, InfluxDB, and Grafana layer that transforms MQTT telemetry into stored metrics and operator-facing dashboards.
Case Snapshot
Role
Product-minded implementation: data model, interface behavior, integration path, and maintainable implementation artifacts.
Scope
I implemented a local analytics stack in which Node-RED subscribes to MQTT, reshapes telemetry into a stable measurement schema, writes it to InfluxDB, and exposes dashboards through Grafana. Token handoff between InfluxDB,...
Constraints
InfluxDB tokens are generated inside the stack, encrypted before being shared through mounted volumes, and decrypted only inside the services that need them. Provisioned dashboards, local persistence, and decoupled processing...
Result
Added operator-facing visibility to the BLE monitoring stack
Architecture
Node
Sensor events arrive from the BLE and MQTT path as the input stream for downstream processing.
Edge
Raspberry Pi 5 runs Node-RED, InfluxDB, and Grafana as the local analytics and dashboard layer of the monitoring platform.
Cloud
The stack is designed to stay useful without external cloud dependencies, while leaving room for later alerting or remote visualization if needed.
Node
Sensor events arrive from the BLE and MQTT path as the input stream for downstream processing.
Edge
Raspberry Pi 5 runs Node-RED, InfluxDB, and Grafana as the local analytics and dashboard layer of the monitoring platform.
Cloud
The stack is designed to stay useful without external cloud dependencies, while leaving room for later alerting or remote visualization if needed.
Architecture Views
Concise system views summarize the project boundary, deployment path, and data flow without adding implementation claims.
System overview diagram
Node
Deployment diagram
Edge
Data flow diagram
Cloud
Technical Decisions
- Node-RED flow for normalization and routing of MQTT telemetry
- InfluxDB setup and token creation during container initialization
- Grafana datasource and dashboard provisioning from versioned files
- Encrypted token exchange between analytics services
Challenges
- MQTT messages alone do not provide operational visibility. The platform still needs schema normalization, durable storage, dashboard provisioning, and a clean way to share access tokens...
- InfluxDB tokens are generated inside the stack, encrypted before being shared through mounted volumes, and decrypted only inside the services that need them.
- Provisioned dashboards, local persistence, and decoupled processing make the analytics layer repeatable after rebuilds and easier to inspect during troubleshooting.
Lessons Learned
- Added operator-facing visibility to the BLE monitoring stack
- Made dashboards and datasources reproducible from source control
- Turned a classroom dashboard task into a reusable observability component
Future Improvements
- Keep architecture views aligned with the implementation.
- Keep documentation concise: align README, architecture decisions, and screenshots.
Tech Stack
Artifacts
Related Project
Follow the adjacent case study to see how this project connects with the rest of the work.
Secure BLE MQTT Monitoring Platform
Built a Raspberry Pi 5 monitoring platform that ingests BLE sensor data, secures transport with TLS-enabled MQTT, processes events in Node-RED, stores metrics in InfluxDB, and visualizes them in Grafana.