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Machine Learning and IoT

Machine learning in conjunction with IoT will play an increasingly important role in our lives as the days go by. Both falls under Computer Science field that is currently in a rapid state of development.

IoT has really exploded over the past three years- demonstrating its potential in applications ranging from wearables and automated cars to smart homes and smart cities, creating an impact everywhere.

All connected devices generate a deluge of information that needs to be monitored and analyzed. Then can they learn continuously from the available sets of data and improve themselves without any manual intervention. That’s how IoT devices are becoming smarter.

Different ML techniques such as decision trees, clustering, neural and Bayesian networks help the devices to identify patterns in different types of data sets coming from diverse sources. It takes appropriate decisions based on the analysis. Such challenges are faced, especially in the case of embedded systems. The most important thing is that there is no programming or coding support given to these devices all through this process. Without implementing ML, it would really be difficult for smart devices and the IoT to make smart decisions in real-time, severely limiting their capabilities.

The main purpose behind ML is to automate the development of different analytical models to enable algorithms to continuously learn with the help of available data. Google’s self-driving vehicle is one such development that uses different ML techniques with IoT to create a completely autonomous vehicle. It combines the advanced features of different modern cars (like speech recognition, lane assistance, adaptive cruise control, parking assistants and navigators).

Application of ML in IoT

Energy sector: Arduino MEGA is used to reduce the energy cost for a coffee machine. This is one of the simpler implementations of ML with IoT. But these techniques are also being implemented in lights and air conditioners connected to the IoT.

Routing traffic: The combination of different sensors and ML algorithms is widely implemented for routing traffic. One such implementation is in a system using the LarKC platform. The data from the traffic and weather can suggest several routes to the same destination.

Home: IoT applications are extremely suitable for the home and are being widely used in home automation. They are implemented in apartments for light and humidity control, and temperature sensors. IoT is also applied in heart rate sensors.

Industry: There are several companies and organizations that use IoT and ML algorithms for health care, traffic management, etc. In manufacturing industries, considerable human resources can be saved with the help of an IoT system that uses cameras and controllers.