Meeting IoT Users’ Preferences by Emerging Behavior at Run-Time
Internet of Things systems are increasing their importance in our lives. To provide their maximum benefit, they must be manually configured according to the users’ needs and routines. Thus, the increasing number of smart devices and systems being deployed will make this task completely unmanageable in the near future. This could limit the rise and penetration of IoT. Moreover, smartphones are standing out as the interface through which people interact with these systems. Due to their increasing capabilities they can also detect and analyze their users’ daily activities. Therefore, this research tries to address this situation by proposing an architecture that allows smartphones to learn from the habits of their users through automatic learning techniques, and a programming model that allows run-time adaptation of the IoT systems behavior to the detected needs through the invocation of the services provided by the smartphones.
KeywordsInternet of Things Context Smartphones Machine learning
This work was supported by the Spanish Ministry of Science and Innovation (TIN2014-53986-REDT and TIN2015-69957-R), by the Department of Economy and Infrastructure of the Government of Extremadura (GR15098), and by the European Regional Development Fund and by 4IE project (0045-4IE-4-P) funded by the Interreg V-A España-Portugal (POCTEP) 2014–2020 program.
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