Edge computing brings data processing close to where data is generated: the plant floor. Instead of sending every sensor reading to the cloud, edge nodes process, filter, and analyse data locally, sending only actionable insights upstream. This reduces bandwidth, improves latency, and enables local autonomy when cloud connectivity is unavailable.

IIoT EDGE ARCHITECTURE

Edge vs Cloud Processing Decision

PROCESS AT EDGE WHEN:
  • ? Latency < 50ms required
  • ?? Intermittent connectivity
  • ?? Data sovereignty rules apply
  • ?? High-frequency raw data
  • ?? Safety-critical decisions
SEND TO CLOUD WHEN:
  • ?? Long-term trend analysis
  • ?? ML model training
  • ?? Enterprise reporting
  • ?? Cross-site benchmarking
  • ?? Non-real-time analytics

< 10ms
Edge Response
100-500ms
Cloud Round-trip
70-90%
Bandwidth Reduction

What Problems Edge Computing Solves

  • Bandwidth constraints: A refinery may have 50,000+ sensor points. Streaming all data at 1-second resolution to the cloud would require enormous bandwidth. Edge aggregation reduces this by 90%+ by sending only exceptions, averages, and key performance indicators.
  • Latency: Real-time analytics (vibration anomaly detection, predictive maintenance triggers) cannot wait for a cloud round-trip. Edge processing enables sub-second response.
  • Resilience: Edge nodes continue operating during WAN outages, ensuring local monitoring and control continuity.

Common Edge Hardware

  • Industrial PCs (IPCs): Ruggedised x86 platforms (Beckhoff, Moxa, Advantech) running Windows 10 IoT or Linux
  • ARM-based gateways: Lower power consumption, suitable for remote sites. Raspberry Pi Compute Module in hardened enclosures.
  • PLC-integrated edge: Siemens SIMATIC ET 200SP Open Controller, Rockwell Logix with FactoryTalk Edge, Emerson PACSystems with edge analytics

Edge Software Stack

A typical industrial edge node runs:

  • OPC UA server/client: Collects data from PLCs and field devices
  • MQTT broker: Local message routing (Mosquitto)
  • Stream processing: Node-RED, Apache Kafka, or AVEVA Edge for data transformation
  • Container runtime: Docker or K3s for deploying microservices
  • Time-series database: InfluxDB or TimescaleDB for local storage

Ignition Edge

Inductive Automation’s Ignition Edge is the most widely deployed industrial edge platform. It runs on any hardware capable of running Java, collects data from any OPC UA or legacy device, and syncs data to a central Ignition server using Store and Forward – ensuring no data is lost during connectivity outages.

Security at the Edge

Edge nodes are often more exposed than centralised systems. Security requirements:

  • Hardware security module (TPM 2.0) for key storage
  • Signed software images and verified boot
  • Minimal attack surface: disable unused ports, services, and user accounts
  • Out-of-band management for remote access to edge nodes