Landslide Prediction using Machine Learning on the Edge Node in Pervasive Internet of Things

Abstract

In today's world, everything is connected via internet, but Internet of Things will change our life in the future. For, this to happen, large amount of data has to be generated, processed and captured by IoT and are considered to be highly useful and contain valuable information. The critical role in making things smart is through Machine Learning techniques. Machine Learning will play important role in extracting data and knowledge from the connected things after that constructing smart system that provides convenient services. The data aggregated from the IoT nodes in an IoT environment has to be analyzed in a time bound manner by using different mining algorithms such as KNN, Naïve Bayes, LDA, SVM, C4.5. So, in this paper we focus on comparative analysis of data mining algorithm on internet of things data using MATLAB. We are using various data mining software because there are resource constraints in an IoT environment, and due to resource constraints we cannot use them at device level. In order to use them at edge level, we will analyze the data using MATLAB software tools as if they are to be used at the edge level. At last our preliminary results on real IoT datasets shows that C4.5 has better accuracy and have relatively higher processing speeds in all tools we used.