Data Classification in Internet of Things for Smart Objects Framework

Over the last two decades, we have seen an enormous amount of growth in data. In the meantime, billions of sensors and objects generate a massive amount of data every single day. Processing this wealth of data is indeed a daunting task and it pushes us to adopt different data classification approaches that are intelligent and accurate. Therefore, the computational limitation in Internet of Things (IoT) devices and the utmost importance of the processing time in real-time scenarios, requires algorithms that should have high classification performance even with limited computational resources and in the minimum of time. Motivating by this, in this paper we present an experimental study of three efficient data classification approaches in order to identify the most efficient classifier for our smart objects framework ”SADM-SmartObject” previously proposed. Comparative study was conducted based on a set of performance evaluation metrics. The experiment has shown that the best approach is Decision Tree. It performed the best results.