By name: domain-iot:edge-computing description: Edge computing patterns for IoT including gateway architecture, local vs cloud processing decisions, edge ML inference, containerized edge workloads with K3s and Azure IoT Edge, edge-cloud data synchronization, and offline-resilient operation. allowed-tools: Read, Grep, Glob, Bash
Edge Computing for IoT
When to use
- Designing edge gateway architecture and selecting hardware (Raspberry Pi to industrial)
- Deciding what to process at the edge vs offload to the cloud
- Deploying ML inference models on constrained edge hardware
- Containerizing edge workloads with K3s or Azure IoT Edge modules
- Implementing edge-cloud data sync and conflict resolution strategies
- Building systems that must function during internet outages
Core principles
- Intermittent connectivity is the default — design store-and-forward from day one; connectivity is the exception, not the guarantee
- Edge runs inference, cloud runs training — push optimized models down, pull raw data and drift metrics up
- Protocol translation lives at the gateway boundary — one cloud-side identity, many device-side protocols; canonical data model applied at the seam
- Pin container versions on edge devices —
latestin a factory is a production incident waiting to happen - 100ms is the latency ceiling for control loops — anything safety-critical or real-time stays local regardless of network quality
Reference Files
references/gateway-and-processing.md— gateway responsibilities, hardware selection guide, edge vs cloud processing decision criteria, hybrid patternreferences/edge-ml-and-containers.md— TensorFlow Lite / ONNX / TensorRT frameworks, ML deployment pipeline, K3s, Azure IoT Edge, container best practicesreferences/sync-and-offline.md— data synchronization strategies, conflict resolution, offline operation with local storage and RTC drift handling