Performance Analysis of Data Mining Techniques in IoT

Abstract

Internet of Things (IoT) accumulates bulk of data from heterogeneous devices implanted with sensors. This data is accumulated over a period of time from sensory devices and is maintained on a server. To take optimal decisions in real time, meaningful information need to be extracted out from the data accumulated. Numerous data mining (DM) techniques are available for analyzing the data and then to make future predictions based on the discovered information. The number of devices connections in IoT is expected to reach 25 to 30 billion by 2020 and so as the new applications of IoT are going to emerge. Therefore, an efficient and fast DM technique is required to make predictions and take decisions in real time to maintain the goodwill of IoT in society. This paper first discusses three different DM techniques: classification, clustering, and association based mining and their possible combinations. Later, this work highlights the applications of using a particular DM technique in IoT. Finally, a comparative analysis is made among each DM technique on the basis of its precision, accuracy and recall value. This will lead to identity the best DM technique that can be applied in IoT.