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25 août 2020
- handle: 10670/1.fcr5l4
- hal: hal-03414757
- doi: 10.1007/978-3-030-57321-8_30
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-57321-8_30
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
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Amin Anjomshoaa et al., « Inter-space Machine Learning in Smart Environments », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-030-57321-8_30
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Résumé
Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.