Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 59, Issue 2, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2022.102869
Keywords
Internet of things (IoT); Service design; IFTTT(if this then that); Graph embedding; Node2vec; Link prediction
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2019R1F1A1057071, 2021R1F1A1045787]
- National Research Foundation of Korea [2021R1F1A1045787] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study explores user-created automation applets to connect IoT devices and applications, and builds an IoT application network using data from the IFTTT platform. By analyzing the trigger-action relationships and clustering the embedded nodes, different IoT usage patterns are identified. Predicting the IoT application network and generating feasible service scenarios offer practical implications for enhancing user experiences and developing new services.
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.
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