Nested IoT Edge Devices and Offline Dashboards

Nested IoT Edge Devices and Offline Dashboards

Azure IoT Edge devices can now be nested to securely collect data across networks organized in hierarchical layers. Industrial customers commonly use layered networks to isolate and secure their most critical assets, as recommended by the ISA-95 standard.

With this new IoT Edge capability, customers can quickly overcome the challenges of collecting data from each layer and gain local insights to help reduce unplanned downtime, increase equipment efficiency, and reduce product defects while fully complying with strict industry standards.

Learn more at https://aka.ms/iotshow/IoTEdgeForIIot

Project Showcase: Machine Learning with RP2040

Project Showcase: Machine Learning with RP2040

SparkFun Electronics highlights the RP2040 for use in machine learning projects.

The RP2040 is supported with both C/C++ and MicroPython cross-platform development environments, including easy access to runtime debugging. It has UF2 boot and floating-point routines baked into the chip. The built-in USB can act as both device and host. It has two symmetric cores and high internal bandwidth, making it useful for signal processing and video. While the chip has a large amount of internal RAM, the board includes an additional 16MB external QSPI flash chip to store program code.

Building a Balancing Robot with Azure Sphere

Building a Balancing Robot with Azure Sphere

Mike Hall shows Scott Hanselman how his team built a self-balancing robot using Azure Sphere and Azure RTOS that can be controlled and updated remotely from Azure IoT Central. In addition, the robot supports deferred updates for the OS and applications, which ensures the robot is in a safe position before initiating an update.

Related links:

Proactive, Always-on Service in Dynamics 365 with Chatbots, IVA, and IoT

Proactive, Always-on Service in Dynamics 365 with Chatbots, IVA, and IoT

Microsoft Mechanics shows you how to set up a proactive and always-on service organization with Dynamics 365, from self–service automated actions using intelligent and conversational chatbots and IoT, to high touch customer agent and frontline technician support.

Expert Deanna Sparks joins host Jeremy Chapman to share how to combine automation, intelligence and live personnel engagement to take customer support to the next level.

How to Update Disconnected IoT Devices

How to Update Disconnected IoT Devices

Here is how to update your disconnected IoT devices with Azure Update for IoT Hub.

The Microsoft Connected Cache Preview IoT Edge module will provide Device Update for Azure IoT Hub customers with the capability of an intelligent in-network cache, which enables image-based and package-based updates of Linux OS-based devices behind and IoT Edge gateway (downstream IoT devices).

In cases where downstream Azure IoT Edge devices do not have internet connectivity or may not be allowed to download content from the internet, this capability is important.

  • IoT
  • Closed
  • 1 min read

Device Update for IoT Hub

Device Update for IoT Hub

Device Update for IoT Hub is a comprehensive platform that customers can use to publish, distribute, and manage over-the-air updates for everything from tiny sensors to gateway-level devices. This platform is now in preview in the Western US, Asia, and Europe to keep devices and applications secure and updated.

Keeping a network of IoT devices secure and up to date can be complex and costly, especially for organizations with limited technology staffing. Rather than take on the cost and work of building and maintaining custom update solutions, Device Update for IoT Hub customers can take advantage of a platform that is tailored for IoT devices and built on the experience Microsoft has developed through regularly updating hundreds of millions of Windows devices.   

Learn more by reading the blog post at https://aka.ms/iot-hub-device-update-blog

  • IoT
  • Closed
  • 59 sec read

Smart Cities Ontology for Digital Twins

Smart Cities Ontology for Digital Twins

See how the Azure IoT team is working with the Smart Cities ecosystem to enable interoperability by providing DTDL-based ontologies.

Common representation of places, infrastructure, and assets will be paramount for interoperability and enabling data sharing between multiple domains. It is our goal to provide a DTDL-based ontology definition to provide common ground for modeling environments leveraging well-established industry standards, accelerate developers time to results, and enable interoperability between DTDL-based solutions from different solution providers. In this episode we present the work we have been doing with our partners Sirus/ OASC to provide DTDL based Smart Cities ontology starting with ETSI CIM NGSI-LD models.

Learn more about the Smart Cities Ontology for Digital Twins: https://aka.ms/iotshow/SmartCitiesOntologyForDigitalTwins

Getting Started with IoT Devices on Azure

Getting Started with IoT Devices on Azure

Getting started with IoT devices is not trivial.

It usually involves lots of tooling, complex connectivity, and more.

Ryan Winter from the Azure IoT Developers and Devices team is creating Getting Started Guide that will walk you through connecting a new device to Azure IoT using the Azure RTOS, and the Azure RTOS IoT middleware in under 30 minutes.

Ryan will even demo one of these guides and will build Azure RTOS on an STMicroelectronics Discovery Kit and connect it to Azure IoT Central in about 5 minutes!

Check out all the Getting Started Guide at https://aka.ms/gsg

ML at the edge and in the Cloud with Azure Sphere

ML at the edge and in the Cloud with Azure Sphere

See how Azure Sphere allows to run ML at the edge combined with Cloud AI in this demo on the IoT Show.

IoT devices can work with cognitive services in the cloud for ML tasks such as face verification. However, it is often useful to have ML at the edge as well, to avoid streaming up data all the time – like a “wake word”. We will show you how Azure Sphere enables you to easily build ML at the edge that works with ML in the cloud, in the context of a face detection/recognition scenario.

Learn more reading the blog post at https://aka.ms/iotshow/MLOnAzureSphere