However, this vision isn't exactly new, we can look back to 1926, when Nikola Tesla remarked:
"When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole. We shall be able to communicate with one another instantly, irrespective of distance."
In recent years, this opportunity has led many sectors and industries to adopt IoT technologies, with the primary goal of saving costs, improving asset utilization, making processes more efficient, and improving productivity.
Some of the key IoT adopting industries are currently:
However, the list of IoT examples keeps growing as new capabilities and benefits are being discovered.
Combining the Data
Using IoT technologies certainly has its challenges, some of which include data and information management issues, privacy and security concerns, organizational inability to manage the complexities of IoT, and finally, the lack of standards and interoperable technologies.
Gladly enough, big industry players like Microsoft have stepped in to create universal IoT platforms [namely Azure IoT, ] that support many different devices, protocols, programming languages, and data managing methods.
This aims to satisfy the needs of the most demanding commercial IoT applications.
Microsoft Azure IoT Suite: Step-by-step
Microsoft’s Azure Cloud provides a full set of interoperable cloud services that can be combined to create extremely powerful and scalable IoT systems in a matter of hours.
Naturally, the first step of implementing any IoT system, is getting the raw sensor data from the devices to the system for analysis, processing, storage, and of course action.
While the focus of this example is wearables, you can see there is a big list of Microsoft approved IoT devices.
1. Beginning with the Azure IoT hub
The first endpoint of our IoT system is a new Azure service called the Azure IoT Hub – it’s a fully managed service that enables reliable and secure bidirectional communications between millions of IoT devices and a solution back end.
Amongst other features, Microsoft’s Azure IoT hub provides reliable device-to-cloud and cloud-to-device messaging at scale, enables secure communications, provides extensive monitoring for device connectivity and identity management.
Finally it includes device libraries for the most popular languages and platforms.
The Hub receives all the constant streams of raw sensor data, but does not perform any action on it. This is the job of Stream Analytics.
2. Stream Analytics
Secondly we have stream analytics whose role is to monitor the Azure IoT Hub and perform operations on it. This ranges from reformatting and transferring the data, to looking for specific patterns and triggers that should result to some sort of action, depending on the event.
For more details on stream analytics, you might find this video helpful:
For example: in our solution, there is a particular Stream Analytics job that constantly monitors the raw sensor data of the IoT Hub that is related to a user’s heartrate.
Should a sudden change occur, action is taken (in our case, calling a person next of kin or even notifying an ambulance).
Such simple events can be identified using hard-coded rules through a SQL-like language Microsoft has developed for the Stream Analytics jobs.
3. Machine Learning Techniques
However, detecting more complicated patterns and events, though, can be more challenging and the use of Machine Learning techniques is necessary; Machine Learning can allow the system to recognise patterns derived from previous trained use-case examples.
Luckily, the people at Microsoft have also taken care of that, providing seamless integration with Azure’s Machine Learning Studio.
Therefore, it is now possible to create statistical models of sensor use-cases using previous historical data, deploy them, and then use them as functions on the Stream Analytics service to help easily recognise new similar events that match the patterns of the trained models.
This can be extremely helpful in many IoT cases, for example predictive monitoring, i.e. predicting when an engine will break before it does, based on similar sensor data patterns of engines that broke in the past.
When those patterns are spotted, Stream Jobs can interact with other Azure IoT Services such as Event Hubs and Web Jobs, to create triggering events and perform the necessary actions.
4. Storage and Visualisation
Finally, he last pieces of the IoT and Microsoft Azure puzzle would be storing the data and visualizing it.
However, in these matters, Microsoft Azure already provides more than enough solutions like SQL Database, SQL Data Warehouse, and DocumentDB for storage, and PowerBI for visualization.
The verdict: Succeeding with the Azure IoT Suite
The strongest advantage of the Microsoft Azure IoT architecture is that all the services can interact with each other seamlessly, with no delay, availability or security issues, since they all reside on the same data centres used to host the entire solution.
Just to remind you...
4 key steps to an Azure IoT Solution
Azure IoT hub
Machine Learning Techniques
Storage and visualisation
To the outside world, your IoT solution can seem like a black-box that takes in all the raw sensor data, stores it, and performs the necessary actions depending on the application.
Presuming that one understands the high-level architecture and requirements of their system - the Azure IoT suite can automatically handle a large percentage of the complicated technicalities, allowing the engineers to focus on the system’s bigger picture.
We’d love to find out which of these Azure IoT stages were most informative – make sure to comment below. You can start digital transformation yourself with this guide on IT Partners ⇓