Solution ideas
This article describes a solution idea. Your cloud architect can use this guidance to help visualize the major components for a typical implementation of this architecture. Use this article as a starting point to design a well-architected solution that aligns with your workload's specific requirements.
This solution describes how buildings and campuses can securely and reliably connect, and scale their on-premises Internet of Things (IoT) devices to the cloud.
Architecture
Download a Visio file of this architecture.
Workflow
Hospital buildings use various connected devices to monitor both patient health and facility performance.
- Health-tracking devices include patient monitors, CT scanners, and blood pressure monitors.
- Building safety and quality devices include air quality and building temperature sensors.
The patient health and building monitoring devices send data to LTE or 5G Radio Access Network (RAN) devices.
The 5G or LTE radios in the hospitals forward the data to the 5G or LTE packet cores running on the edge servers. The edge servers can be Azure Stack Edge or any Azure Arc-enabled servers.
On the edge servers, the IoT Edge runtime can preprocess data before sending it to Azure for further analysis.
In the cloud, Azure IoT Hub ingests data quickly and securely, and sends it to Azure Machine Learning.
Azure Machine Learning incorporates the new data to further optimize the model that controls the smart building settings.
Data from Azure IoT Hub also feeds into Azure Digital Twins, which provides a map of the hospitals' networked IoT devices as a virtual simulation.
Data also feeds into Azure Time Series Insights, which can analyze patient health over a period of time, or treatment efficacy over several hospitals. Time Series Insights also offers a visualization layer to aid in decision-making.
All the data is stored in Azure Data Lake Storage, which can store data of any format and size.
Components
This solution uses the following Azure components:
- Azure Stack Edge is a portfolio of devices that bring compute, storage, and intelligence to the IoT Edge. Azure Stack Edge acts as a cloud storage gateway that enables data transfers to Azure, while retaining local access to files.
- Azure Arc-enabled Kubernetes connects Kubernetes clusters running inside or outside of Azure.
- Azure Sphere is a comprehensive IoT security solution that includes hardware, OS, and cloud components for IoT device security.
- Azure IoT Edge deploys cloud intelligence locally on IoT devices.
- Azure IoT Hub is a cloud-based managed service for bidirectional communication between IoT devices and Azure.
- Azure Machine Learning is an integrated data science solution for data scientists and developers to build, train, and deploy machine learning models.
- Azure Digital Twins is an IoT platform that creates digital representations of real-world things, places, processes, and people in the cloud.
- Azure Time Series Insights is an end-to-end IoT analytics platform to monitor, analyze, and visualize industrial IoT analytics data at scale.
- Azure Data Lake Storage is a scalable and secure data lake for high-performance analytics workloads.
Scenario details
Cloud services can store and analyze the IoT data to diagnose anomalies and take corrective or preventive actions. Azure services can further analyze and store the data, and use machine learning to optimize building settings.
Potential use cases
In this solution, a healthcare facility uses LTE or 5G-enabled IoT devices to track both patient health and building performance. The devices use built-in Azure Sphere certified chips to stream data to on-premises edge servers, which communicate with the Azure cloud. On-premises network administrators can view network health through the packet cores on the edge servers.
Other examples of this approach include:
- Predictive maintenance for machines in a coffeehouse.
- Safety and compliance monitoring for perishable food and drink temperatures in a food manufacturing plant.
- Detecting the optimal point for resource extraction in the energy sector, based on data collected by autonomous exploration vehicles.
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal author:
- Nikhil Ravi | Production Management Leader