How Machine Learning Enhances The Value Of Industrial Internet of Things 

By October 10, 2017Product News

Industrial Internet of Things (IIoT) is already revolutionizing domains such as manufacturing, automobiles and healthcare. But the real value of IIoT will be realized only when Machine Learning (ML) is applied to the sensor data. This article attempts to highlight how ML augments IIoT solutions by bringing intelligent insights.

Cloud computing has been the biggest enabler of connected devices and enterprise IoT. Cheaper storage combined with ample computing power is the key driver behind the rise of IIoT. Though it was possible to capture data from various sensors and devices, customers found it prohibitively expensive to store massive datasets. Even after sufficient storage resources were allocated, the computing horse power required to process, query and analyze these datasets was missing in the enterprise data center. Much of the available resources were allocated to data warehouses and business intelligence systems that are critical to businesses. The acceptance of cloud as an extended data center changed the equation. Industry verticals such as manufacturing, automobile, healthcare and aviation are now capturing every possible data point generated by the sensors. They are taking advantage of cloud storage, Big Data and Big Compute capabilities offered by large public cloud providers. This has been the single most important factor in accelerating IIoT adoption in enterprises.

The first generation of IIoT is all about ingesting data and analyzing it. The data points originating from sensors go through multiple stages before transforming into actionable insights. IIoT platforms include extensible data processing pipelines capable of dealing with real-time data that demands immediate attention along with data that only makes sense over a period. The pipeline responsible for processing real-time data is called as Hot Path Analytics. For example, it may be too late before the IoT platform shuts down an LPG refilling machine after detecting an unusual combination of pressure and temperature thresholds. Instead, the anomaly should be detected within milliseconds followed by an immediate action triggered by a rule. The other scenario that demands near real-time processing is healthcare. Vital statistics of the patients are monitored in real time.

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