The Industry Experiences team has recently published a solution guide for extracting insights from existing IoT data. The solution consists of the following high-level components.
Ingest data Hot path processing Cold path processing Analytics clients
This is the first of a series of blogs that go through those components in detail. We start with ingesting data into the solution and creating a data stream.
The NIST data
The solution uses the data published by US National Institute of Standards and Technology Smart Manufacturing Systems test bed that exposes the manufacturing lab’s data. We use the data from the lab’s volatile data stream (VDS).
The VDS is implemented using the MTConnect standard. The standard offers a semantic vocabulary for manufacturing equipment. It defines a protocol to communicate with an MTConnect agent and the schema for the data returned.
The agent and its methods
The agent exposes an HTTP endpoint that supports the following API operations:
probe: Returns the list of devices, components, subcomponents and data items. current: Returns a snapshot of the data item’s most recent values or the state of the device at a point in time. sample: Returns Samples, Events and Condition in time series. asset: Most recent state
If you are a manufacturer who wants to take its first steps towards IoT, and you’re overwhelmed by the plethora of vendors and IoT platforms in the IoT space, you are not alone. IoT is still a new space, with many moving parts and products. This makes it hard for organizations to know exactly where and how to get started. In this blog, I will try to provide you a simplified overview and next steps, based on the conversations I have been having with many manufacturing organizations.
Components of an IoT solution
When it comes to deciding whether to build or buy your IoT solution it is important, of course, to understand exactly what you are building or buying. To that end, it helps to identify the main components of an IoT solution stack (Figure 1). From bottom to top:
Figure 1: Building Blocks of an IoT Solution
1. Cloud platform: a set of general-purpose PaaS services used by developers to develop cloud-based solutions. These services include messaging, storage, compute, security, and more. Cloud platforms (such as Microsoft Azure) also include analytics services and IoT services.
2. IoT platform: A set of IoT-specific PaaS and SaaS services and development
Technology is moving at an amazing pace. Manufacturers around the world are observing this first-hand. Additive manufacturing, robotics, and IoT are some of the technologies that directly influence the way manufacturing businesses operate. The manufacturing industry is not isolated from the huge leaps in computer technology. Software packages used for managing complex processes and fabrication machine tools (for example, Computer numerical control milling and CNC turning) are everywhere and are generating huge amounts of data.
I have heard from many customers in the manufacturing industry that they are not sure what steps they need to take to gain insights from all the data they have.
My recommendation is to start small. Do not make big investments right at the start, but first discover what can be gained from the available choices. Then bring it into a platform that can provide many other possibilities, such as machine learning and AI. The Azure platform features many application services. But important to manufacturers, Azure gives access to hardware resources, such as faster CPUs, field programmable gate arrays (FPGA), and graphics processing units (GPU) — all are easily accessible for state-of-the-art solutions.
What is starting small? What is possible? While seeking answers to
A year ago today we launched the public preview of the Azure IoT Hub Device Provisioning Service, and today we announce the public preview of the latest major wave of functionality to automate device provisioning! We’ve taken your feedback, made changes, built features, and are happy to make the following features available to you today via public preview:
Increased limit on the number of CA certificates stored (GA). Increased limit on the number of enrollments (GA). Symmetric key attestation support (preview). Re-provisioning support (preview). Enrollment-level allocation rules (preview). Custom allocation logic (preview).
All these features have support in the C device SDK and the Node service SDK, with full support to follow in general availability. Let’s dive deeper into each bullet.
As of today, all Device Provisioning Service instances have new max limits on the number of CA certificates stored and number of devices enrollments:
25 CA certificates which is up from 10. 500,000 enrollments, with more available if you contact support. This number is up from 10,000.
These limit increases are generally available.
Symmetric key attestation
Symmetric keys are one of the easiest ways to start off using the provisioning service and provide an easy “Hello world”
Azure IoT Hub message routing simplifies IoT solution development and enables you to send messages from your devices to cloud services in an automated, scalable, and reliable manner. You can also use routing queries to apply customized filters and send only the most relevant data to the service facing endpoints.
We are announcing a new capability in message routing that allows you to query on the device twin tags and properties, in addition it allows you to query on message properties and message body, which has been previously available. Moreover, the new Azure portal experience for message routing delivers simpler navigation between routes and endpoints making it an even better experience.
IoT solutions involve sending large amounts of device telemetry data that can rapidly become overwhelming. The new message routing capabilities in IoT Hub make it even more powerful and easier to set up automatic routes for messages from devices, implementing advanced filtering and sending huge amounts of data in real time. For example, if you are developing a connected factory solution, where you want to send data based on different device types such as motors and chillers to different services for computation of utilization and failure rates. You can
Electromobility has come a long way in recent years. Battery ranges have increased, charge times have decreased, and performance is continuously improving. With growing adoption of electric vehicles it is imperative for utility companies, municipalities, fleet managers, and electric vehicle drivers to closely manage, monitor, and optimize grid stability, energy supply, and charging sessions.
Using Azure Digital Twins, Allego has developed an intelligent electric vehicle charging solution. It models entities within the electric vehicle charging network such as regions, utility companies, charging stations, vehicles, and others to optimize charging schedules using real-time data like electric grid constraints, charging preferences, and more. As a result, Allego is providing electric vehicle drivers everywhere with flexible, easy-to-use, and environmentally friendly charging options.
Allego’s charging solution monitors and services charging points remotely and supports a variety of electric vehicles, charge speeds, and charging stations. These capabilities enable grid operators and energy suppliers to adapt to energy demand on the grid. For example, operators can reschedule electric vehicle charges to align to availability of renewable resources, lower energy rates, or to optimal power capacities. Additionally, electric vehicle drivers now have better visibility into their energy usage throughout the system, making it easier and more affordable
We recently announced the general availability of Azure IoT Edge, allowing you to deploy cloud workloads like AI and machine learning to run directly on your IoT devices. Now devices can act immediately on real-time data—whether it be recognizing a crack in a pipe from an aerial view or predicting equipment failure before it happens.
We are taking the next step to ensure our customers’ IoT solutions operate reliably in remote locations and harsh conditions—where cloud connectivity is intermittent at best. Today, we are happy to announce extended offline operation for Azure IoT Edge, now in public preview.
Existing capabilities have been enhanced with device restart and device-to-device communication even when disconnected from the cloud. After a one-time sync between the edge device and Azure IoT Hub, the edge device can function in offline mode indefinitely.
When using an edge device as a transparent gateway, we are releasing a new portal experience for assigning IoT devices as children of the gateway. Assigned children can connect and communicate with their parent or siblings even when there is no Azure IoT Hub connectivity.
These new features deliver frequently requested functionality by customers as a number of real-world scenarios require the power of
With the abundance of data coming from IoT devices and the global nature of business today, it’s essential to be able to understand correlations and track historical trends across your assets.
Imagine managing a fleet of trucks carrying items that need to be maintained at a specific temperature. Occasionally you see a low temperature alert triggered for some of your trucks during their daily scheduled delivery. As an operator, you will need to conduct a root cause analysis to understand why this is happening, if there are recurring patterns, and how to prevent it from happening in the future.
To help you with this, we’re excited to announce that we have now integrated Azure Time Series Insights into the Azure IoT Remote Monitoring solution accelerator. With Time Series Insights, you can gain deeper insights into your time-series sensor data by spotting trends, anomalies, and correlations across real-time and historical data in all your locations. New Remote Monitoring deployments (both Basic and Standard) will include Time Series Insights out-of-the-box* at no extra cost. All messages data from your IoT devices will be stored in Time Series Insights, but your alarms, rules, and configuration settings will remain in Cosmos DB.
In this article I discuss the biggest market opportunities for developers of IoT solutions. This is a topic of greatest interest to developers from independent software vendors (ISVs), and system integrators (SIs) who develop custom solutions for individual customers. For more resources, including a solution guide, podcasts, webinars, partner and customer highlights, explore Expanding business opportunities with IoT.
Opportunities for ISVs
The figure below shows the three layers in a typical IoT solution stack where an ISV could target their development.
Figure 1: IoT Solution Stack
Cloud platform: a set of PaaS services used to develop cloud-based solutions. Most cloud platforms also provide specialized analytics and IoT services. IoT platform: a set of PaaS and SaaS services for rapid development of IoT solutions. IoT platforms are usually built on top of a cloud platform. IoT solution: the end-user applications that help users in manufacturing companies to extract actionable insights from IoT data. IoT solutions can be built either on top of an IoT platform, or directly on top of a cloud platform.
For this article, the development of cloud platforms is out-of-scope. The cloud platform vendor ecosystem is already crowded, and the barriers to entry are extremely high. Only
In a previous blog article Extracting actionable insights from IoT data I discuss the value of collecting IoT data from machines, assets and products. The whole point of collecting IoT data is to extract actionable insights. Insights that will trigger some sort of action that will result in some business value such as optimized factory operations, improved product quality, better understanding of customer demand, new sources of revenue, and improved customer experience.
In this blog, I discuss the extraction of value out of IoT data by focusing on the analytics part of the story.
Generally speaking, data analytics comes in four types (Figure 1):
Descriptive, to answer the question: What’s happening? Diagnostic, to answer the question: Why’s happening? Predictive, to answer the question: What will happen? Prescriptive, to answer the question: What actions should we take?
Figure 1: IoT Analytics Flavors
Since IoT analytics is a subcase of data analytics, these types map nicely onto IoT analytics as follows:
Data Analytics Type
Monitor the status of machines, devices, products, and assets.
Assess if things are