IoT service classification and clustering for integration of IoT service platforms

Title : IoT service classification and clustering for integration of IoT service platforms
Journal : The Journal of Supercomputing
Authors : Daewon Lee, HwaMin Lee
Corresponding author : HwaMin Lee

With the rapid development of sensors, wireless communication, and cloud computing, information technology today focuses on service environments created by the Internet of Things (IoT). IoT technologies have become widely used in various contexts including smart homes, building management, surveillance services, and smart farms. Some IoT applications such as Siri are popular in everyday life. IoT requires communication and interaction between various devices and services. To solve the various complex problems associated with IoT services, earlier research focused on IoT service platforms such as gateways and mobile edge computing services. However, the similarities and reusabilities of IoT services have received little attention. In this paper, we develop an IoT service classification and clustering system. We classify the operation of an IoT service into four steps that differ in their characteristics. Based on this classification, we extend the classic EM (expectation–maximization) algorithm to cluster IoT services in terms of their similarities. To validate our proposed classification and clustering system, we divide over 100 commercial IoT services into five clusters, showing that such services are well clustered by similarity and purpose.

This research was supported by Next-Generation Information Computing Development Program and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7083417 & NRF-2016R1C1B1008330).