Edge Computing Notes (IoT)

WIP

Edge Computing is where computing is done close to IoT devices and machines/PLCs.  Basically, if it happens on the "edge" of your network.  The processing occurs on local devices, gateways, or edge servers near IoT sensors, cameras, or machines.

Low Latency: Ideal for applications like autonomous vehicles, industrial automation, and AR/VR.

Bandwidth Efficiency: Only relevant data is sent to the cloud, reducing costs.

Reliability: Systems can continue functioning even with intermittent connectivity.

Over the past few weeks, I ordered a Raspberry Pi, which I intend to use for processing data from remote IoT sensors, namely Cameras, LiDAR, and temperature.

MQTT is the programming language to ...

Node-RED provides a web browser-based flow editor, which can be used to create JavaScript functions (Wikipedia)

Azure IoT Edge is a cloud-to-edge computing platform that extends Azure services and custom logic to IoT devices. It allows you to deploy containerised workloads (such as AI models, analytics, and business logic) directly on edge devices, enabling local data processing and reducing reliance on continuous cloud connectivity. This improves latency, bandwidth efficiency, and resilience in offline scenarios.

IoT Edge Modules are Docker-compatible containers running Azure services, third-party apps, or custom code. Examples: Azure Stream Analytics, Azure Machine Learning models. [learn.microsoft.com]

IoT Edge Runtime must be installed on each edge device. Handles module deployment, security, and communication between modules, devices, and the cloud.

Includes:

IoT Edge Agent: Deploys and monitors modules.

IoT Edge Hub: Manages messaging between modules and cloud. [learn.microsoft.com]

Azure IoT Hub is a cloud service for managing IoT Edge devices, configurations, and monitoring.