The Internet of Things (IoT) presents many compelling opportunities for developers With the right tools and guidance, it doesn’t need to be intimidating. The conceptual and technical differences between IoT and traditional web and application are relatively easy to grasp. Plus, Microsoft offers a full range of managed services to make it even easier, while enabling you to experiment and scale. If your main skillset is in the Microsoft ecosystem, you can jump right in with Visual Studio Code, .NET, and Azure. If you prefer other languages and tools, our open-source SDKs will enable you to work with Azure solutions while staying in your comfort zone. Either way, you can get started quickly with our Azure IoT developer guide that will take you through common application patterns and tutorials that will have you up and running fast.
The IoT Application pattern
When it comes to knowing where to start, it can help to have a conceptual framework that organizes services and technologies in a logical way. We tend to think of IoT architecture as being divided into three main categories, each of which has considerations distinct from traditional development architectures:
Things: The devices and sensors in the field that are
Last week, the Microsoft Build conference brought developers lots of innovation and was action packed with in-depth sessions. During the event, my discussions in the halls ranged from containers to dev tools, IoT to Azure Cosmos DB, and of course, AI. The pace of innovation available to developers is amazing. And, in case there was simply too much for you to digest, I wanted to pull together some key highlights and top sessions to watch, starting with a great video playlist with highlights from the keynotes.
Empowering developers through the best tools
Build is for devs, and all innovation in our industry starts with code! So, let’s start with dev tools. Day one of Build marked the introduction of .NET Core 2.1 release candidate. .NET Core 2.1 improves on previous releases with performance gains and many new features. Check out all the details in the release blog and this great session from Build showing what you can use today:
.NET Overview & Roadmap: In this session, Scott Hanselman and Scott Hunter talked about all things .NET, including new .NET Core 2.1 features made available at Build.
Scott Hanselman and Scott Hunter sharing new .NET Core 2.1.
With AI being top
Since the release in 2016, developers are using our Azure IoT Python SDK to write device and back-end applications to connect to Azure IoT Hub and Device Provisioning Service, as well as writing modules for Azure IoT Edge (preview). Python is a popular choice for prototyping, and it is gaining traction in the embedded world.
If you decide to use the Python SDK for development, there are few things you should keep in mind: Python SDK is a wrapper on top of our Azure IoT C SDK, and we release binary packages on pip for Windows, Ubuntu, and Raspbian, all of which are compatible with Python 2 and Python 3. This approach has its ups and downs. On the upside, the features you see in C are available in Python with no functionality difference. On the downside, it is not a native Python SDK. Application Programming Interfaces (APIs) that we exposed may look different from typical Python APIs; and as a developer, you will need to pay attention the architecture of underlying platform, especially when you are using pip!
At a high level, there are three things that must align to reference the Python SDK properly: Python version (2 or
At Microsoft Build 2017 we shared our vision for Azure IoT Edge. At that time we demonstrated the idea of what Azure IoT Edge could do with Azure Stream Analytics job and Azure Machine Learning workloads by moving from Azure cloud to an IoT Edge device so that decisions could be made quickly and reliably without having to rely on the public Internet. This allowed million-dollar machines to operate reliably and safely with Azure IoT Edge, making intelligent decisions based on data from the machine. Deployment of these workloads can now be at a scale of thousands or millions of devices.
In November 2017, we shipped the public preview of Azure IoT Edge with the ability to move workloads from the Azure to Edge devices. These workloads included the AI Toolkit for Azure IoT Edge, Azure Machine Learning, Azure Stream Analytics, Azure Functions, and your own code. With these services on the edge, you could build rich and intelligent applications which operate locally, but can easily be deployed and managed from the Azure IoT Hub. We also articulated the security model for Azure IoT Edge and announced partnerships with NXP and Microchip for support of this model.
Since the public
This morning, at the Microsoft Build conference in Seattle, I talked about the key areas of new Azure innovation that enable the intelligent cloud and intelligent edge – spanning developer tools, DevOps, containers, serverless, Internet of Things (IoT) and artificial intelligence (AI).
Innovation starts with developers writing code. The effectiveness of your dev tools are at the heart of your ideas becoming reality. With this in mind, we continue to deliver new innovation and experiences with Visual Studio tools. Whether it is Visual Studio, VS Code or Visual Studio Team Services for DevOps, we are committed to providing the most productive developer experience end-to-end. Today, we announced a preview of Visual Studio IntelliCode, that brings AI to everyday development by providing intelligent suggestions that improve code quality and productivity. We also announced the preview of Live Share, which lets developers collaborate on their code and problem solve across Visual Studio and VS Code. Finally, building on our shared commitment to developers and open source, we also announced a fantastic partnership with GitHub where Visual Studio App Center will be natively available in GitHub via their marketplace. This means any GitHub developer building mobile apps for iOS, Android, Windows and macOS
It is hard these days to not walk past something which is connected to the Internet in some way. These things are everywhere – desks, pockets, wrists, walls, kitchens, vehicles, factories, traffic stops, grocery shops… the list goes on and on. These things perform useful operations, gather data, and most importantly have built-in connectivity. There are endless possibilities to what can be achieved when the data from these things is securely captured, processed, and analyzed using the processing power, availability, and intelligence of the cloud. We want to explore these possibilities, with YOU!
Which is why we are inviting you to participate in the Azure IoT on Serverless hackathon for your chance to win* a piece of the $20,000 prize pool.
This online competition will run over the next few months, is open to anyone who wants to participate. In addition to winning cash prizes, this competition gives you an opportunity to be featured on the Azure blog.
All ideas are welcome, whether you want to work on that sensors-driven smart-home project you have been putting off, build a remote monitoring solution for a healthcare facility, create an intelligent system to streamline the manufacturing process of your production plant,
Are you ready for Microsoft’s ultimate developer event? You probably already know Microsoft Build, happening May 7–9 in Seattle, Washington, is where you need to be to connect with the experts, discover new tools, and boost your skills around cloud technologies, AI, mixed reality, and more.
Sure, there will be great speakers and tech sessions galore—but did you know you could win a drone?
That’s right, we’re having a drone contest. Participants will compete against fellow conference go-ers on who’s drone can complete the outdoor search and rescue course designed specifically for Microsoft Build. You’ll get hands-on, end-to-end experience with Microsoft’s intelligent cloud platform, Azure IoT Edge, and be eligible to win a DJI Mavic Air drone.
How cool is that?
Here’s how it works
Contestants will create training images using AirSim, an open-source aerial informatics and robotics simulation platform. Then contestants will build and train a realistic AI drone model in the cloud using Custom Vision, and then create a container for AI deployment to Microsoft Azure IoT Edge. This intelligent cloud platform lets you run artificial intelligence at the edge of the cloud, perform analytics, deploy IoT solutions from cloud to edge-enabled devices, and manage them centrally
Corporate IT infrastructure has changed a lot in the past decade. From a relatively simple bounded space, with a defined “inside” and “outside,” IT networks have evolved to incorporate a wider range of devices, such as smartphones and tablets, and a growing amount of traffic from additional diverse networks, including the public Internet. However, nothing has the potential to disrupt traditional infrastructure topologies more than the Internet of Things (IoT). This has implications for infrastructure and operations (I&O) teams, as well as developers who are responsible for IoT solutions. A recent Forrester report titled “Edge Computing: IoT Will Spawn A New Infrastructure Market” highlights many of the changes and challenges that must be faced in this rapid evolution. Let’s take a look at a few of the highlights.
Consider the full breadth of devices: The “things” that are connected in IoT require new approaches to development and management, but these endpoints are not the only new hardware you have to consider. Diverse components, including field-located IoT gateways and micro-datacenters, will become part of the networked environments. The need for edge infrastructure will depend on how much latency can be tolerated in the system and the complexity of the operations that
We recently released a port of our Azure IoT Hub C SDK for iOS platform. Whether your iOS project is written in Swift or Objective-C, you can leverage our device SDK and service SDK directly and begin turning your iOS device into an IoT device! Our libraries are available on CocoaPod, a popular package manager for iOS, and the source code is available on GitHub.
iOS devices are traditionally not viewed as IoT devices, but recently, they are getting traction in the IoT space. Here are some of the interesting scenarios we gathered from our industry customers during the preview phase:
iOS device as the gateway for leaf devices or sensors on the factory floor. iOS device in a meeting room, which acts as an end IoT device to send and receive messages from Azure IoT Hub. iOS device to view the visualization of IoT telemetry. iOS device to manage IoT Hub operations.
So, what is in the box? If you have interacted with our Azure IoT Hub C SDK before, this would be familiar to you! Our C SDK is written in C99 for maximum portability to various platforms. The porting process involves writing a thin adoption layer for
Today we’re pleased to announce two key capabilities that Azure Time Series Insights will be delivering later this year:
A cost-effective long-term storage that enables a cloud-based solution to trend years’ worth of time series data pivoted on devices/tags. A device-based (also known industry-wide as “tag-based”) user experience backed by a time series model to contextualize raw time series data with device metadata and domain hierarchies.
Additionally, Time Series Insights will be integrating with advanced machine learning and analytics tools like Spark and Jupyter notebooks to help customers tackle time series data challenges in new ways. Data scientists and process engineers in industries like oil & gas, power & utility, manufacturing, and building management rely on time series data solutions for critical tasks like storage, data analysis, and KPI tracking and they’ll be able to do this using Time Series Insights .
Time series model and tag-centric experience
Time Series Insights’ current user interface is great for data scientists and analysts. However, process engineers and asset operators may not always find this experience natural to use. To address this, we are adding a device-based user experience to the Time Series Insights explorer. This new interface and the underlying time series