Machine Learning: The Secret to IoT Success?

August 29, 2019 - 8 minutes read

The Internet of Things (IoT) has enormous potential; few technologies rival its ability to unlock new opportunities and innovation in nearly every space. As a result, businesses around the world are installing more sensors than ever before in an attempt to capitalize on the cutting-edge capabilities of IoT development.

But without proper data management strategies in place, these sensor implementations could backfire. Instead of improving efficiency and cutting costs, organizations could find themselves with an overwhelming amount of noise clogging up their servers — without much payoff to show for it.

With that said, the question must be asked: How can companies ensure their IoT data leads to actionable insights? The answer lies in machine learning.

The Main Obstacles Plaguing IoT Efforts

IoT covers a broad scope of applications. You can find traces of this technology present in your smart home lighting system as well as the manufacturing processes that created your smartphone. IoT is versatile, and nearly every industry now relies on it in some capacity.

But regardless of which IoT application we focus on, almost all of them face the same four primary concerns:


Privacy and security are top of mind when it comes to IoT concerns. Data is the lifeblood of IoT; without the ability to push, pull, and act upon data, IoT could not get anything done. But this back-and-forth exchange of data must be supported by rock-solid security. Keeping all communication safe is essential, especially when it comes to personal data like the kind collected by medical devices.


You can find IoT sensors anywhere nowadays. But that doesn’t necessarily mean you should put IoT sensors everywhere. Some conditions lend themselves better to IoT sensor implementation than others. If you choose to install your IoT sensors in a questionable or harsh environment, it’s likely you may experience faulty or no data — and this could seriously hamper the results you get from any algorithm you employ.

The 3Vs of Big Data

Volume, variety, and velocity are three defining traits of big data. Volume refers to the amount of data, variety refers to the data type quantity, and velocity refers to how fast your data is processed. Paying attention to each of these factors is imperative if you’re going to find the optimal algorithms for your data and the best solutions for your problem.


Obviously, one of IoT’s most revolutionary abilities is making disconnected items “talk” with one another. But because each item is created differently, this is often easier said than done. For instance, how do you get your fridge to talk to your espresso machine? Interactions between multiple devices require a common language or communication protocol to get things done.

Why Machine Learning?

As machine learning’s name implies, it focuses on trying to teach machines how to learn. Machine learning is essentially a data analysis method that automates analytical model building. By feeding a system the right data in the right manner, it learns from this information, identifies patterns, then makes decisions or acts off of it.

This is the core of every machine learning application. And it can help immensely with IoT endeavors. Let’s look at two main ways it could do so.

Automating Data Analysis

Just a few years ago, self-driving cars remained strictly in the realm of science fiction. Thanks to advancements in both IoT, and AI, this technology is finally arriving in reality. To function, autonomous vehicles require a careful orchestration of sensor communication and data analysis.

While the vehicle is moving, sensors are taking in thousands (if not millions) of data points. This information must be processed in a split second to prevent accidents and keep commutes convenient. There’s simply no way a human analyst could keep up with these lightning-fast demands, so automation is the only way this can be accomplished.

Machine learning equips a self-driving car’s computer with the capabilities it needs to filter through this abundance of information and focus on what matters most at that moment. Whether it’s speed, friction, a road obstruction, or another vehicle nearby, machine learning hones in on the most pertinent data and produces a solution seemingly instantaneously so you can get where you need to go safely.

Unparalleled Predictive Power

Besides identifying current obstacles, machine learning also helps IoT systems see more general patterns. In the case of cars, this subset of AI could help recognize room for improvement during certain maneuvers.

For example, let’s say you have trouble parallel parking. Your car’s computer could learn this insight after numerous iterations and then start providing additional guidance whenever it comes time for this dreaded parking procedure. The narrow streets of cities like New York City or San Francisco don’t seem so intimidating anymore, do they?

In the same vein, ML in IoT systems can also detect abnormal activity or outliers and trigger the appropriate measures and red flags for protection. This not only helps with security in the traditional sense but also many other matters not usually considered.

For example, imagine if your office’s air conditioning system was overworking unnecessarily and consuming more energy as a result. As Google has demonstrated with its HVAC system, machine learning could pick up on this over time and reduce your energy consumption substantially.

Transform IoT Data Into Smart Data

With machine learning, IoT can flawlessly function on both a detailed and holistic scale. Organizations across the globe are racing to leverage IoT’s abilities, but many of them get hung up on one or more of the obstacles we’ve discussed. No matter what problem you’re having, a combination of machine learning know-how and ingenuity can probably overcome it in a number of different ways.

Next time you encounter a problem with your IoT endeavors, consider machine learning. It’s the secret to making your IoT systems work smarter, not harder.

Tags: , , , , , , , , , , , , , , , , , ,