Performance comparison of machine learning algorithms for data aggregation in social internet of things
J Meghana, J. Hanumanthappa, S. P. Shiva Prakash · Global Transitions Proceedings · 2021
Social Internet of Things (SIoT) is a paradigm of IoT where in objects are able to build social relationship among each other based on user preferences there by creating social platform. To establish relationship, diversified IoT devices interact and setup a connection between them. The relationship is built by considering same features, attribute, device type etc. The data generated by the heterogeneous devices of SIoT devices are huge and it has to be efficiently used. There exist few works related to data aggregation in SIoT in the literature. Hence, in this work a method is proposed to aggregate the SIoT data and conditions used to classify the relationship between the devices. The performance of Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Artificial Neural Network (ANN) machine learning algorithms are tested on the dataset. The experimental results show that DT and ANN algorithms performs well compared to other ML algorithms.