Analyzing IoT Sensor Data and Building Predictive Algorithms

You can use MATLAB®  to analyze your IoT sensor data. As IoT solutions emerge, the amount of available sensor data is growing, but developing insight into that data can be difficult. Analyzing historical data is often the first step in understanding data you intend to use in real time. You may want to perform some basic statistics on your data to find anomalies, and you may also need to clean up your data by removing bad data points or filtering out noise.

MATLAB also makes it easy to work with time-stamped data that one typically finds in IoT applications. You can use timetable to organize and manipulate your time-stamped data. Timetable is designed to help you combine datasets with different timestamps in a meaningful way.

As you get more familiar with your data, you may try to predict future data points. To do this, you can apply machine learning techniques. Machine learning algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.

MATLAB, Signal Processing Toolbox™, Statistics and Machine Learning Toolbox™, and Neural Network Toolbox™  provide functions for filtering and resampling data, applying basic statistics on data, and for using machine learning techniques including those needed to perform classification, regression and clustering analyses - common tasks in IoT. If you are dealing with large data sets, MATLAB also allows you to work with your big data.