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Converging IoT, Cloud, and Big Data Technologies to Revolutionize the World

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Converging IoT, Cloud, and Big Data Technologies to Revolutionize the World

Lillian Pierson, founder of Data Mania, presents her view on the convergence of IoT, cloud, and big data technologies, with an example from the healthcare industry.

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Have you heard of medical radio-telemetry? Up until very recently, it’s been the closest thing that the medical field has had to remote, cloud-based intel on the human body. But now, with the convergence of IoT, cloud, and big data technologies, the health-care industry is primed for a revolutionary influx of new life-saving results. In this article, you’re going to see a plausible scenario from the healthcare industry, but keep in mind that such cloud-enabled IoT advancements are happening in every vertical–from utilities and transportation, to finance and retail, and everything in between.

Cloud-Enabled IoT in Action

Going back to the remote telemetry example, it works like this. Imagine your Uncle Jimmy recently had a heart attack and was implanted with a telemetry unit that continuously monitors his cardiac activity, and uses radio waves to stream data alerts back to his doctor when a disturbance occurs. Although this may sound cutting edge, it’s old technology. It’s basically an old school monitoring and alert system.

So, here’s how things have changed with the IoT. Imagine now that Uncle Jimmy has a FitBit (to track his daily level of physical activities) and is also taking sensor-enabled pills, like Helius, to capture and report data on his medication adherence, body temperature, heart rate, and rest patterns. Now imagine that each of these devices; the cardiac data streaming device, the FitBit, and the Helius pills are all connected to one another through a cloud-based network. Also consider that this cloud-based network is equipped with big data processing and analytics applications that work ceaselessly to derive insights from the data that’s collectively streaming in from all of these connected devices.

From this network, Uncle Jimmy gets real-time updates and suggestions about his health and wellness status. As a form of preventative medicine, the network is able to tell Jimmy when he forgot to take his heart meds, or should consider getting in a little exercise, or maybe should sleep in for a few extra hours, to protect his health in light of his underlying cardiac condition. These are predictive and prescriptive data insights based on real-time streaming data that is generated by data producing devices connected across a cloud-based network. Definitely a step up from the monitor and alert systems of yesteryear, that were only able to warn of problems after they had started. Now that you understand what the convergence of these technologies could look like in real-life, let’s take a gander at how it all works from the inside out.

Getting to Know the Lingo

First and foremost, to grasp this technology you need to know the vocabulary that’s used to describe it. An IoT cloud (also called the “fog”) is a network of cloud-based services that are connected to IoT-enabled devices. An IoT cloud supports the big data processing and analytics requirements of a broad IoT network, enabling it to make intelligent, adaptive, and autonomous decisions. The IoT-enabled devices that are connected to, and that sit on an IoT cloud are called edge devices.

Most of these edge devices come paired with their own device-embedded analytic applications, or device-embedded applications that are capable of processing and deriving insights from local data that’s captured by the device. One benefit of these device-embedded analytics applications is that, in many cases, they successfully bypass the need to send data back up into the IoT cloud for processing there.

Many device-embedded analytics applications are built on adaptive machine learning algorithms, called adaptive IoT applications. These adaptive IoT applications enable devices to adjust and adapt autonomously to the local conditions in which the device is operating. IoT cloud application developers are data scientists and engineers who focus exclusively on building adaptive IoT applications for deployment on local devices. The more general IoT developer, on the other hand, is responsible for building products and systems that serve the greater needs of the IoT cloud at-large, including all of its connected IoT devices, data sources, and cloud computing environments.

So once again referring to Uncle Jimmy and his network of connected health monitoring devices, in this example, the heart monitor, the FitBit, and the sensor-enabled pills are edge devices. They’re all connected across the fog, or IoT cloud. Since the FitBit can produce analytics outputs locally, without the need to stream data back to the cloud, it must have a local device-enabled analytics application, certain to have been developed by an IoT cloud application developer. The entire network of connected devices, their data streams, and the cloud-based applications that are used to process, store, and analyze all of this health data is built and maintained by IoT developers. Makes sense now, right?

Where People and Skillsets Fit In

All of this talk of autonomously functioning, adaptive devices may have you worried if there’s even going to be a place for people and their skillsets in the future. Rest assured, just looking at this from the technical perspective, it takes a whole army of knowledge workers to build, deploy, and maintain cloud-based IoT networks. Spark and Hadoop developers are required to design and execute the adaptive algorithms that are embedded on edge devices. Electrical and mechanical engineers are required to design many of the electromechanical devices that will be connected to IoT clouds. Legions of software engineers that code in Python, Java or C will be required to develop and maintain the IoT network. Data engineers will be required to configure and maintain cloud-based big data processing applications, and data scientists are required to build the device-embedded analytics applications.

This article, written by Lillian Pierson, first appeared on Data Science Central.

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Published at DZone with permission of Lillian Pierson. See the original article here.

Opinions expressed by DZone contributors are their own.

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