How IoT uses Machine Learning(ML)

The scope of ML is to mimic the way the human brain processes inputs to generate logical responses. If people rely on learning, training or experience, machines need an algorithm. Also, as each of us learns more, we adapt our reactions, become more skilled and start to apply our efforts selectively. Replicating this self-regulatory behavior in machines is the finish line of ML development.

IoT Challenges

Under the broad umbrella of the Internet of Things (IoT), we can find anything ranging from your smartphone to a smart fridge to sensors monitoring industrial processes.

Yet, there are at least four essential concerns related to IoT implementation, which need to be addressed:

• Security and Privacy: Any algorithm that processes this kind of data needs to embed ways to keep all communication safe, especially if we’re talking about personal data such as that collected by medical sensors.
• Accuracy of Operation: Sensors implemented in harsh conditions can send faulty data, or no data, disrupting the algorithm.
• The 3 Vs of Big Data: Most IoT devices generate what can be classified as big data because it checks the 3Vs: volume, velocity, and variety. Tackling the 3Vs means finding the best algorithms for the type of data you’re using and the problem you’re trying to solve.
• Interconnectivity: The value of IoT is in making disconnected items and tools “talk” to each other. However, since these are all created differently, they need to have a common language, which is usually the smallest common denominator. If computers already have protocols like TCP/IP, how would your fridge talk to your coffee machine?

Why Use Machine Learning for IoT?

There are at least two main reasons why machine learning is the appropriate solution for the IoT universe. The first has to do with the volume of data and the automation opportunities. The second is related to predictive analysis.

Data Analysis Automation by Machine Learning

Let’s take car sensors as an example. When a car is moving, the sensors record thousands of data points which need to be processed in real time to prevent accidents and offer comfort to passengers. There’s no way for a human analyst to perform such a task for each car, so automation is the only solution.

Through machine learning, the central computer of the vehicle can learn about dangerous situations, like speed and friction parameters, which can be hazardous to the driver, and engage safety systems on the spot.

The Predictive Power of Machine Learning

The most useful feature of ML for IoT is that it can detect outliers and abnormal activity and trigger the necessary red flags. As it learns more and more about a phenomenon, it becomes more accurate and efficient. A great example is what Google did with its HVAC system, reducing energy consumption significantly.

Last but not least, there’s also the opportunity to create models which predict future events very accurately by identifying the factors leading to a particular result. This offers a chance to play with the inputs and control results.

From Big Data to Smart Data

The “work smarter, not harder” advice is a good fit for managing IoT-generated data and turning it into useful insights. While big data is all about overcoming the challenges posed by the 3 Vs, smart data can refer to:

• Clean-up of sensor data on the spot before sending it to the cloud for analysis
• Pre-processed batches of sensor information, ready to be turned into actionable insights

The added value of machine learning in both cases is that it can take smart data and make ML models work faster and more accurately.