Edge Computing and IoT: Building Low-Latency Enterprise Applications

The cloud computing paradigm centralises computation in data centres thousands of miles from where data originates. For applications requiring sub-millisecond response times, operating in bandwidth-limited environments, or processing sensitive data that must not leave a facility, this model has fundamental limitations. Edge computing solves them.

What Edge Computing Means

Edge computing encompasses a spectrum:

• Device edge: Computation on the IoT device itself (microcontrollers, embedded systems)
• Near edge: Local gateways and servers within a facility (factory floor, retail store, hospital)
• Far edge: Telco edge nodes and CDN PoPs distributed globally

The right tier depends on latency requirements, available compute, and connectivity constraints.

Industrial IoT Use Cases

Predictive maintenance: Vibration, temperature, and current sensors on industrial equipment generate high-frequency time-series data. Anomaly detection models running at the near edge trigger alerts before failures occur—without the latency or bandwidth cost of sending all raw data to the cloud.

Quality control: Computer vision models running on edge GPU hardware inspect products at line speed (hundreds per minute), rejecting defects in real time. Cloud round-trip latency (50–200ms) makes cloud-based inspection infeasible at this speed.

Process control: Feedback control loops for manufacturing processes require actuation in milliseconds. Only device and near-edge computation can meet this requirement.

Architecture Patterns

Hierarchical edge: Raw data processed at device edge → aggregated insights to near edge → summary metrics and anomalies to cloud. Dramatically reduces cloud storage and bandwidth costs.

Offline-first: Edge applications must continue functioning when connectivity is lost. Design for eventual synchronisation, not real-time connectivity.

Model serving at edge: Deploy quantised, compressed ML models to edge hardware using frameworks like TensorFlow Lite, ONNX Runtime, or OpenVINO.