Industrial IoT telemetry platform build

  • Services:
  • Industries: Energy
  • Engagement: Build
  • Timeline: Q1-Q2 2024

About the project

A European enterprise energy operator ran a network of several hundred field assets, each generating sensor data continuously. The existing SCADA integration polled devices on a slow cycle, which meant operators saw stale readings and alerts arrived minutes after a fault condition began. We designed and built an industrial IoT telemetry platform to ingest edge data in near real time, process it through a rules engine, and surface actionable alerts in an operations dashboard. Alert latency dropped from several minutes to under ten seconds and operators gained visibility across the full asset fleet from a single interface. This industrial iot case study shows what targeted data-architecture work can do for grid-side operations.

Background

The energy operator had SCADA hardware already in the field but no modern data layer sitting above it. Device readings were batched into a legacy historian at five-minute intervals, then exported to spreadsheets for daily review. When a sensor reading went out of range, an operator discovered it on the next refresh cycle at best. Regulatory requirements mandated tighter incident documentation, and the operations team was spending significant hours each week compiling manual reports. The brief was to modernize the telemetry layer without replacing field hardware.

Build your industrial IoT platform
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We help energy and industrial operators turn raw sensor data into real-time operational intelligence.

What we built

An on-premise industrial IoT telemetry stack with edge ingestion, stream processing, and a real-time operations dashboard.

Edge MQTT ingestion Icon

Edge MQTT ingestion

A containerized MQTT broker runs at the substation level and collects readings from Modbus and OPC-UA devices at one-second intervals. It buffers data locally during WAN outages and replays the backlog on reconnect, so no readings are lost.

Stream rules engine Icon

Stream rules engine

A Node.js stream processor evaluates each incoming data point against a configurable threshold table. When a reading crosses a boundary, it emits an alert event within about two seconds of the originating measurement. Operators configure thresholds through the dashboard without touching code.

Time-series historian Icon

Time-series historian

PostgreSQL with time-series table partitioning stores the full reading history at one-second granularity. Partition pruning keeps query latency low even as years of data accumulate. A nightly batch job generates the compliance summary reports the operator previously produced by hand.

Operations dashboard Icon

Operations dashboard

A React front end presents the live asset map, threshold violation timeline, and per-device trend charts. The dashboard updates over a WebSocket connection, so operators see new readings without refreshing the page. Role-based access controls separate read-only viewers from operators who can acknowledge alerts.

Takeaway

This industrial iot case study shows that the biggest gains in operational telemetry often come not from replacing field hardware but from adding a modern data layer above it. An MQTT-based edge collector, a stateless stream processor, and a time-series database together bridge the gap between legacy SCADA and the real-time visibility operations teams need. We approach every IoT engagement by mapping the data path end to end before writing any code, so the architecture fits the reliability and security constraints of industrial environments from the start.

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