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AuthorVarlamis, Iraklis
AuthorSardianos, Christos
AuthorChronis, Christos
AuthorDimitrakopoulos, George
AuthorHimeur, Yassine
AuthorAlsalemi, Abdullah
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:42Z
Publication Date2022
Publication NameInternational Journal of Data Science and Analytics
ResourceScopus
URIhttp://dx.doi.org/10.1007/s41060-022-00331-2
URIhttp://hdl.handle.net/10576/37806
AbstractInternet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliver immediate actions to sets of actuators that are installed locally. In this work, we present the (EM)3 project (Consumer Engagement towards Energy Saving Behaviour by Means of Exploiting Micro Moments and Mobile Recommendation Systems), which combines data collection, information abstraction, timed recommendations for energy saving actions and automations that promote energy saving in a household or office setup. The advantage of the (EM)3 project is that each room or office setup is controlled locally on an edge device, thus removing the need to share data to the cloud. The current article details on the data and information processing aspects of the (EM)3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. It also demonstrates the scalability of the solution by simulating a simple scenario of distributed data collection and processing on the edge nodes, which takes advantage of federated learning in order to adapt to the needs of multiple users without exposing their privacy. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
SponsorThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectBig data collection
Edge information processing
Habit change
Information abstraction
Timed recommendations
TitleUsing big data and federated learning for generating energy efficiency recommendations
TypeArticle
dc.accessType Abstract Only


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