Integrated LoRa Sensor Monitoring & Analytics System

Ottronic's field deployment of LoRa-connected environmental sensors produced a steady stream of telemetry, but the raw feed was hard to act on. Different sensor types reported very different physical signals - soil humidity versus tree-trunk expansion - and the readings arrived interleaved, without a reliable way to tell them apart automatically.

Without a pipeline to ingest, validate and visualise the data, the readings could not be turned into the two things the deployment actually needed: early warning of drought risk and a clear picture of vegetation growth over time and across locations.

I implemented an end-to-end real-time monitoring and analytics system for the LoRa sensor network. A data pipeline built on Node-RED ingests the incoming telemetry and writes it to InfluxDB, a time-series database well suited to high-frequency sensor readings.

Within the pipeline, numerical validation logic automatically distinguishes soil-humidity data from tree-expansion data, so each signal is routed, stored and interpreted correctly without manual tagging. This classification step is what makes the downstream analytics trustworthy.

On top of the stored data I designed interactive Grafana dashboards with geospatial visualisations, mapping sensor readings to their physical locations and layering on rule-based insights. The result is at-a-glance drought-risk and growth monitoring, with alerts that fire when readings cross meaningful thresholds.