What IoT devices and applications should be connected? Predicting user behaviors of IoT services with node2vec embedding. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Kim, Seonghee; Suh, Yongyoon; Lee, Hakyeon
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: Internet of things (IoT) -- Service design -- IFTTT(if this then that) -- Graph embedding -- Node2vec -- Link prediction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102869
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We build an IoT application network to grasp user behavior in IoT services. The trigger-action relationships of the user-created applets on IFTTT are used. We predict the network to identify promising connections between IoT applications. Node2vec-based link prediction is conducted with ML classifiers. The proposed approach can be used for enhancing user experiences in IoT services. Abstract: User-created automation applets to connect IoT devices and applications have become popular and widely available. Exploring those applets enables us to grasp the patterns of how users are utilizing and maximizing the power of connection by themselves, which can deliver practical implications for IoT service design. This study builds an IoT application network with the data of the IFTTT(if this then that) platform which is the most popular platform for self-automation of IoT services. The trigger-action relationships of the IFTTT applets currently activated are collected and used to construct an IoT application network whose nodes are IoT service channels, and links represent their connections. The constructed IoT network is then embedded by the node2vec technique, an algorithmic framework for representational learning of nodes in networks. Clustering the embedded nodes produces the four clusters of IoT usage patterns: Smart Home, Activity Tracking, Information Digest, and Lifelogging & Sharing. We also predict the IoT application network using node2vec-based link prediction with several machine learning classifiers to identify promising connections between IoT applications. Feasible service scenarios are then generated from predicted links between IoT applications. The findings and the proposed approach can offer IoT service providers practical implications for enhancing user experiences and developing new services.