HED_WISE> cat projects/industrial-iot-monitoring/README.md
Industrial IoT monitoring & analytics platform
End-to-end sensor data pipeline — from physical devices to a real-time monitoring dashboard with alerts and historical analytics.
- Client
- M.T. Industrial
- Year
- 2026
- Service
- IoT & Automation
- Tech stack
- PythonFastAPITimescaleDBMQTTGrafanaReactDockerRaspberry PiAWS IoT Core
> challenge
M.T. Industrial operates a network of industrial facilities across Romania where environmental conditions — temperature, humidity, air quality, vibration levels, and energy consumption — are critical to both equipment longevity and regulatory compliance. Before our engagement, monitoring was manual: technicians would physically visit each facility on a weekly rotation to read gauges and log values in spreadsheets. This approach meant problems were discovered days or weeks after they occurred, resulting in equipment failures that cost tens of thousands of euros in unplanned downtime. The company had already invested in installing industrial sensors (Modbus, MQTT-compatible) at key monitoring points, but had no software infrastructure to collect, transmit, store, or visualize the data these sensors produced.
> solution
We built a complete IoT data pipeline from edge to cloud. At the facility level, we deployed lightweight edge gateways (Raspberry Pi-based) that collect data from existing Modbus and MQTT sensors, perform local data validation and buffering, and transmit readings over secure MQTT channels to the cloud. The cloud backend ingests sensor data in real-time, stores it in a time-series database optimized for high-frequency writes and fast range queries, and processes it through a configurable alerting engine. Alerts are triggered by threshold breaches, rate-of-change anomalies, or sensor silence (no data received), and are delivered via email, SMS, and push notifications with full escalation chains. The monitoring dashboard provides facility managers with real-time views of all sensor readings across all locations, with drill-down capabilities by building, floor, zone, or individual sensor. Historical analytics include trend visualization, comparative analysis between time periods, automated daily/weekly reports, and exportable data for regulatory filings. We also built a predictive maintenance module that uses historical patterns to flag equipment likely to need servicing before a failure occurs.
> result
The platform now monitors over 340 sensors across 6 facilities in real-time. Mean time to detect critical anomalies dropped from 4.2 days (manual inspection cycle) to under 90 seconds. In the first year of operation, the early warning system prevented 12 potential equipment failures — estimated avoided cost: €180,000. Energy consumption optimization based on sensor analytics yielded a 9% reduction in utility costs across monitored facilities. The predictive maintenance module correctly flagged 87% of equipment issues at least 72 hours before failure. The system processes approximately 2.4 million data points per day with 99.99% data integrity.