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Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning
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  • Shuping Dang ,
  • Guoqing Ma ,
  • Basem Shihada ,
  • Mohamed-Slim Alouini
Shuping Dang
King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology (KAUST)

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Guoqing Ma
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Basem Shihada
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Mohamed-Slim Alouini
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The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers. The SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptation accordingly. With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services. To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities. In this regard, we envision an SB framework enabled by indoor VLC and ML in this article.