How to use Machine Learning for IoT analysis

Daniel Bishop
© Shutterstock / Venomous Vector

Many of the most exciting high-tech projects nowadays include bringing together knowledge from two or more well-established and fast-growing fields. One of the prominent examples is applying machine learning in order to filter and analyze the huge amount of data we obtain from the Internet of Things (IoT). But first, let’s see why IoT needs help from artificial intelligence in order to reach its full potential.

In essence, the IoT universe includes all sorts of sensors and smart devices that are plugged into the internet and capable of exchanging data with each other. This industry is growing at an enormous rate. It is expected that until 2022 we’ll have around 50 billion devices connected to the network, which is a 140 percent increase compared to 2018. And in 2035, this number could reach 1 trillion devices.

This massive upsurge also means a rise in the amount of exchanged data that will make this data impossible to analyze by using traditional methods. With 90 percent of online data being generated only in the last two years, this problem has already emerged, especially having in mind the reported shortage of data analysts worldwide. So how can machine learning help with sorting and analyzing this data?

Machine learning

Machine learning research is one of the most important efforts being made in the broader field of artificial intelligence. In short, machine learning scientists and engineers are trying to replicate the process of learning as it is displayed in humans. This project demands imagining the human brain as a very powerful computer, with the input being a combination of a number of external signals, and output being the summation of these signals, or in other terms, a concrete action or process in the human body that follows as a reaction to the input signals.

Now, for humans, learning means that the same input (signals) won’t always result in the same output (behavior, action or process), as physical neural pathways are changing and adapting according to the experience and feedback. Speaking in machine terms, learning means independently updating the algorithm that decides how input signals are calculated and how the output is determined.

And when we say that some software are capable of learning, that means that they are able to update this algorithm themselves, based on previous results and feedback (“experience”). In short, the software is given the objective and the raw data, while finding the right algorithm that will result in satisfying the objective is their job. With all this in mind, how can machine learning be used to help the IoT industry?

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Data analysis automation

The biggest benefit that machine learning brings to IoT is the automation of analysis of colossal amounts of generated and exchanged data. Instead of a human data analyst going through all these data manually, looking for patterns and anomalies, with properly implemented machine learning we can use a completely reversed top-down approach in analysis. In other words, given some desired outcome, the machine should find the factors and variables that are supposed to lead to this outcome.

Predictive analysis

By gradually recognizing regular patterns and updating the algorithm, software becomes able to predict incoming desired or undesired events. A system, though often supervised by a human scientist or engineer, is then automatically triggered by relevant input data, using a formula that it came up with basically by itself. It can easily recognize anomalies and inconsistencies that may have taken a human data analyst ages to discover just by looking at the raw data.

Furthermore, a machine-learning system is not there just to recognize abnormal behavior, but also to help us understand and establish long-term trends. This requires a huge job of selecting, recognizing, sorting, processing and associating a vast amount of collected data in order to make meaningful and comprehensive predictions.

Prescriptive power of machine learning

Finally, these systems don’t just have the predictive power, but prescriptive as well. Namely, the fact that they can predict future events based on the algorithms they build also means that they can help in making devices and systems at the edge of the IoT network more efficient. They can provide assistance not just with making predictions about what will happen, but also with determining which factors and parameters should be changed in order to get closer to the desired outcome.

Examples for this are numerous and there’s no doubt that more and more of the custom software development companies will turn to machine learning solutions to improve IoT analysis for their clients. One of the most well-known examples involves Google and their cooling system. The company’s engineers set up a number of sensors that monitored 120 different factors that could affect the cooling (pump speeds, fans, power, windows etc.). Successful analysis of this data was ensured by software equipped with machine learning abilities.

The result? An outright success. The system that monitored these variables came up with a model which was so well-optimized that it reduced Google’s cooling expenses by a staggering 40 percent. This task probably could’ve never been done so quickly and efficiently by a human data analyst using traditional methods.

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The need for collaboration

Still, today’s machine learning technology can’t do without human guidance and feedback, and this may not change for quite a while. Continual corrections and supervision is what makes these systems particularly effective in data analysis, especially when it comes to the amount of data generated by IoT.

Adding human experience and intuition to the self-learning systems is still vital to keeping them on the right track. As Andrej Karpathy, the director of AI at Tesla reckons, we may never get a 100 percent accurate and understandable model independently built by a machine. But working together and providing guidance to these machines is the only way to get to this goal as close as possible.


Daniel Bishop

Daniel Bishop started off as a content consultant for small SEO and web design companies. Eventually, he found his place as a junior editor for Design Rush, a B2B marketplace connecting brands with agencies. Always searching for new opportunities, he loves sharing ideas with other professionals in the digital community.

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