Internet of Things Applications

The Internet of Things (IoT) promises to change everything by enabling “smart” environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond “connected” to “smart” in a blog titled “Internet of Things: Connected Does Not Equal Smart”. The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct.



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Question #1: What Is “At The Edge”?

“At the edge” refers to the multitude of devices or sensors that are scattered across any network or embedded throughout a product (car, jet engine, CT Scan) that is generating data about the operations and performance of that specific device or sensor.

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Question #2: Are We Really Developing Analytics “At The Edge”?

Are we really “performing analytics” (collecting the data, storing the data, preparing the data, running analytic algorithms, validating the analytic goodness of fit and then acting on the results) at the edges, or are we just “executing the analytic models” at the edges? It’s one thing to “execute the analytic models” (e.g., scores, rules, recommendations) at the edges, but something entirely different to actually “perform analytics” at the edges.

[...] Question #3: What Sorts Of Analytics Are We Performing At The Edge?

In our airplane example with 6,000 sensors on the plane generating over 2.5 Tb of data per day, how are we performing the analytics at the end?

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Question #4: Where Are The Analytic Models Actually Being Built?

Okay, so we “execute” the pre-built modes at the edge, but we actually build (test, refine, test, refine) the analytic models by bringing the detailed sensor data back to a central data and analytics environment (a.k.a. the Data Lake). Figure 3, courtesy of Joel Dodd of Pivotal, shows the data flow and the supporting analytics execution.

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You might want to quantify the impact of these economic changes on your network demand and performance. That would eventually require 8 to 14 years of data. And that’s why you are going to want a data lake as the foundation of the transition from a “connected” IoT world to a “smart” IoT world.

See more at: infocus.emc.com