The ability to collect and analyze data from manufacturing equipment, buildings, vehicles and various other corporate assets can deliver tremendous business value. Sensors can provide data on temperature, pressure, fuel levels and more, and send alerts if equipment malfunctions or is in need of routine maintenance. Fleet managers can track the location and speed of tractor-trailers, monitor driver behavior, and modify routes based upon real-time conditions.
These are just a few of the limitless applications of the Internet of Things (IoT). Billions of Internet-connected devices are capturing data that can be used to automatically track, manage and maintain assets, maximize productivity, improve safety, and minimize unplanned downtime. Because of these benefits, 60 percent of enterprises are looking at IoT use cases, according to a recent report from Bain & Company. The management consulting firm expects the market for IoT hardware, software and services to reach $520 billion by 2021, more than double the $235 billion spent in 2017.
However, reaping the rewards of the IoT is proving to be no small undertaking. Given the large volumes of data generated by IoT devices, simply transporting the data from the endpoint to a data center or the cloud leads to latency that greatly reduces the value of the information.
Organizations need to move processing power to the edge of the network so that data can be analyzed closer to the source. Edge computing also helps reduce bandwidth requirements by minimizing the amount of data that is sent over the network to the cloud.
Microsoft is bringing computing and artificial intelligence (AI) to IoT devices through its Azure IoT Edge service. Previewed in 2017, Azure IoT Edge became generally available on June 27, 2018. Microsoft also open-sourced the IoT Edge V1 codebase and made it available on GitHub.
Azure IoT Edge enables developers to containerize workloads and run them locally on IoT devices. The platform supports Windows and Linux operating systems and a broad array of programming languages and uses the same programming model as other Azure IoT services. Programmers can develop applications using familiar toolsets, leverage existing business logic, and run the same code on devices and in the cloud.
Machine Learning, Cognitive Services and other Azure products can also be extended to IoT devices, making it possible to implement image recognition and other complex AI and analytics applications without writing custom code. Azure Stream Analytics can reduce IoT costs by processing data locally and using trained models to determine what data needs further analysis.
The device management capabilities available within Azure IoT Hub ensures secure and reliable operation even when devices are offline or have intermittent connectivity by automatically syncing the latest state of the device with the cloud. Azure IoT Edge also features deep integration with the Azure IoT Device Provisioning Service. Thousands of devices can be securely provisioned without operator intervention, making IoT edge deployments truly scalable.
To take full advantage of the IoT from both an operational and strategic business perspective, computing and AI must move to the network edge. Let us show you how the Azure IoT Edge service can help you optimize, manage and scale your IoT environment.
Written and composed by Principal, Steve Soper