Leveraging Machine Learning in IoT Environments

It can be fascinating to watch two revolutionary yet distinct technology segments converge. That’s what’s happening in the worlds of AI and machine learning (ML) as they couple their value with the Internet of Things (IoT). Today, there are approximately 10 billion connected IoT devices deployed throughout the world, and their task is to monitor, track, and collect information in real-world environments so that it can be used for positive business and personal outcomes. 

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The real key to IoT environments is gathering and analyzing data. The question then becomes: what do you do with that data? Once it has been transmitted to some type of repository, it must be analyzed quickly (often immediately) and with impactful results to be useful. That’s where machine learning technologies come into play. 

Machine learning is a type of artificial intelligence (AI) that allows systems to learn automatically and improve from experience without being specifically programmed. At the core of machine learning is the ability to access data, look for patterns, learn from it, and make future decisions based on examples it’s provided — and doing so automatically without human intervention. It can process information and make those decisions at breakneck speed, so it is invaluable across almost any conceivable technology, including IoT environments. 

Machine learning has two key advantages over human decision-making for IoT environments:

  1. It is good at managing huge datasets. Humans hunting for data amid seas of data must first filter out the unrelated or irrelevant data, which means that as much as 73 percent of the data goes unanalyzed. Machine learning can sort through all the data at once.
  2. It’s much faster. IoT data is collected instantly, which means the data has to be analyzed in real time to be valuable. Humans simply can’t process the data fast enough to make the insights and analyses useful. 

The more data it gathers, the easier it is for it to detect patterns and apply them to future analyses. When used properly, IoT data can help businesses and people improve everything from operational efficiency and safety to predicting equipment failures, routing energy optimally or providing health data to physicians in real time. 

Industrial IoT Environments and Machine Learning

IoT is powering a new era for smart factories and manufacturing, known as industrial IoT (IIoT). IoT sensors and monitors can be connected to production equipment, where they continually track operational performance, usage patterns, downtime, and impending equipment failures. 

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Here’s how machine learning helps make the process happen: 

  1. IoT sensors connect to machinery and observe discrete variables like vibration, noise, heat, and temperature. 
  2. Real-time data is uploaded to the cloud, where the machine learning model resides to perform analysis. 
  3. ML parses the equipment information into data that is used for training and verification. 
  4. The ML model scans huge volumes of records for anomalies, correlations and projections, and then creates a hypothesis. 
  5. The ML model goes through the process of testing the hypothesis, and once validated, the result is published as an executable endpoint. 
  6. Finally, the streaming data makes inferences for the health of each piece of equipment, knowing what it has been trained to look for. 

This area of “predictive maintenance” is one of the most important segments of IIoT as it can help keep costs down, improve safety, and increase equipment longevity.

Vehicles Autonomous 

Everyone is excited by the prospect of autonomous vehicles driving us from one place to another, and machine learning is a key ingredient. Today’s cars are loaded with IoT devices, and they play a huge part in managing how a car runs, avoids dangerous situations, knows when to be fixed, and even what type of music to play. 

Machine learning is helping autonomous vehicles in the following ways:

  1. Navigation: Machine learning algorithms can automatically monitor a car’s navigation system and assign the fastest or shortest route based on conditions of the road, including traffic.
  2. Safety: IoT devices in a car’s systems send real-time data on the immediate surroundings, and ML algorithms can perform safety maneuvers faster than a driver can react, such as avoiding collisions and keeping pedestrians and bikers safe.
  3. Parking: ML is able to analyze surroundings and park a care itself in a space that might be hard for a driver to accomplish.
  4. Entertainment: There is a huge market for providing user-based entertainment options for drivers. ML keeps a constant watch on which stations you listen to, when you skip a song, and can tailor the experience to fit your liking. 

And as exciting as autonomous vehicles are, integrated mobility that includes coordinated mass transportation systems, traffic control using surveillance cameras, and smart parking is another big area that will be improved by ML and IoT environments. 

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IoT Thrives on Machine Learning

The segments of IoT and machine learning are converging quickly, and it’s an exciting development that will impact businesses and people in profound ways in the coming years. The more technologists can learn about machine learning techniques, the better they’ll be positioned to contribute to this brave new world. 

About the Author

Stuart RauchStuart Rauch

Stuart Rauch is a 25-year product marketing veteran and president of ContentBox Marketing Inc. He has run marketing organizations at several enterprise software companies, including NetSuite, Oracle, PeopleSoft, EVault and Secure Computing. Stuart is a specialist in content development and brings a unique blend of creativity, linguistic acumen and product knowledge to his clients in the technology space.

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