Opinion on Different Classification Algorithms Used in Internet of Things Environment for Large Data Set

Abstract

Nowadays, IoT is an emerging technique and has evolved in many areas such as healthcare, smart homes, agriculture, smart city, education, industries, automation, etc. Many sensor and actuator-based devices deployed in these areas collect data or sense the environment. This data is further used to classify the complicated problem related to the particular environment around us, which also increases efficiency, productivity, accuracy and the economic benefit of the devices. The main aim of this survey article is how the data collected by these sensors in the Internet of Things-based applications are handled and classified by classification algorithms. This survey article also identifies various classification algorithms such as KNN, Random forest logistic regression, SVM with different parameters, such as accuracy cross validation, etc., applied on the large dataset generated by sensor-based devices in various IoT-based applications to classify it. In addition, this article also gives a brief review on advance IoT called CIoT.