IoT strategy: 3 examples show CIOs must think bigger than sensors
Blog: The Enterprise Project - Enterprise Technology
The term “Internet of Things” (IoT) means many different things to many different people. But for most, IoT describes the broad deployment of low-cost sensors and computing power to capture big data and to enable smart thermostats or add functionality to connected vehicles.
There’s significant value in these applications, but an emphasis on very simple sensors is constraining and tends to limit people’s concept of what’s possible with the combination of the IoT components – sensors, data, computation, and algorithms.
In this era of big data, we also forget that data is derived from sensors and instruments – from wearable and non-contact physiological monitors, environmental sensors, from your interactions with your devices. In a class I teach at MIT, “Beyond IoT: Sensory Intelligence & Smart Technology,” we learn how the same concepts that drive IoT can be applied much more broadly and deeply than people typically think.
[ How do edge computing and IoT fit together? Read also: Edge computing: 4 common misconceptions, explained. ]
An understanding of what’s possible today via IoT – and where the technology is headed – opens up a world of possibilities for CIOs and other IT leaders.
IoT’s principles suit bigger, badder tech problems
To understand how organizations can leverage the underlying principles of IoT for “bigger and badder” technology deployments, it’s important to first understand what these principles are. At their heart, IoT systems are characterized by the convergence of micro-sensing, computation, and communication. This convergence allows IoT systems to do the following:
- Acquire (or “sense”) data from the environment
- Pre-process data locally
- Communicate data to servers
- Combine data from multiple sensor types
- Perform signal processing both locally and in the cloud
- Draw inferences and provide insights about the world from the data, using computational techniques such as machine learning
- Make decisions and control actions in the environment
When we picture these principles in action, we tend to think of “traditional” IoT use cases, such as passive environmental monitoring: A sensor collects information about air or water quality, this data is pre-processed locally and transmitted to a central location, and then it is used to inform decision-making (often with the help of machine learning tools).
3 IoT examples that tackle intensive computing problems
But it doesn’t take a great leap of imagination to apply these same concepts and processes to more intensive computing problems. And it’s in these cases where we can see the true potential of applications that extend beyond IoT.
Here are three use cases that illustrate what I mean:
Although the solution is not yet commercially available, my lab at MIT recently validated the first fully non-contact ultrasound imaging process: A pulsed laser shines on a person’s body, tiny amounts of local heating create a sound wave that propagates into the body and back to the body surface, where it is detected with another laser — imaging algorithms then create a non-contact ultrasound image.
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While the result is similar to that of a traditional contact ultrasound, both the patient experience and the computational requirements are vastly different. Unlike a traditional ultrasound, the contactless system must “figure out” the shape of the patient’s body through computation before it can construct an image. The technology has potential applications for treatments on the battlefield, in surgical suites, for burn victims, and other settings in which applying a goop-slathered probe to a person’s body is not a practical option.
Most people would never think of such a system as an IoT application, and yet it is – it’s a system that collects data, transmits it to a processor, and uses complex algorithms to assist with real-time analysis and decision-making. It’s a great example of how the competencies necessary for IoT can translate to other settings.
2. Environmental monitoring
The use of sensors to monitor air or water quality is a classic example of IoT use. But by applying the concepts of sensing, computation, and communication more broadly, researchers can go beyond IoT to gain a more robust picture of what’s happening in the environment.
Rather than using only local environmental sensors, a monitoring organization might also use satellite imagery to determine the amount of light reflecting off of large bodies of water. And of course, we can obtain direct information about water quality: taking samples and looking at them under a microscope.
By connecting information from all of these approaches, we can gain a more robust picture of environmental quality than by using a simple IoT solution alone. What’s important isn’t the IoT sensors themselves (the “Things” in “Internet of Things”), but rather the data. And data can be gathered in a number of different ways and combined in many different ways.
Much has been made about the use of connected sensors and IoT systems to perform predictive maintenance in manufacturing facilities – one area of smart manufacturing. By monitoring equipment for signs of wear, the thinking goes, manufacturing companies can maximize the life of their machinery and proactively replace parts before they break – potentially preventing costly unplanned downtime.
The problem is, not every component of every machine can be sensed directly. Let’s take the example of a blade used to cut plastic packaging material. Over time, that blade is going to wear and will produce cuts that aren’t quite as clean as when it was new. But there isn’t a direct way to measure how sharp the blade is or the cut plastic.
Instead, we collect process data, power, speed, torque being applied to the rest of the machinery, and then detect tiny changes in the features in those signals that correlate to the degradation of the blade. It’s another use case that relies on the fundamental principles of IoT but creates value by applying them to a new context.
These three use cases represent only a sliver of what will be possible in the coming years. It is critical for CIOs and IT leaders to look for boundary-pushing applications within their own industries.
Those who do will quickly uncover use cases that they never thought possible. Those who don’t will risk being left behind.
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