Why IoT Needs Machine Learning to Thrive

There's an unceasing buzz around big data and AI, the opportunities and threats of these technologies and concerns about their future. Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights?

The simple answer is yes, and it lies in machine learning (ML).

Machine Learning Capabilities

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.

To learn, a computer is presented with raw data which it tries to make sense of. As it progresses, it gets more and more experienced, producing ever more sophisticated feedback.

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

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 ML

Coming back to the car example, the real power of IoT lies not only in detecting current dangers but identifying more general patters. For example, the system could learn about the driver who takes turns too tightly or has difficulties with parallel parking, and help him or her by providing additional guidance in these matters.

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.

How Should It Work?

It's vital to understand that, when an IoT system depends on human input, it can fail miserably. It needs the support of machine learning to become a perfectly aligned system resistant to human errors.

In an interconnected world, human mistakes are quickly corrected by algorithms. This helps optimize the entire process through feedback mechanisms. The predictive component of the system can identify the correct input to get the expected output.

When powered by ML, IoT can work flawlessly both at an individual level, so that you don’t mess up your morning routine, for example, and at a collective level. The latter case can be illustrated with interconnected cars that can communicate with each other and perform dynamic rerouting to avoid traffic jams.

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.