Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Accurate indoor positioning might not be viable by solely utilizing RF communications.
“On the other hand, there is a growing consensus that accurate indoor positioning might not be viable by sole utilizing RF communications [36].”
From What should 6G be? (2020) · cites this paper as [36]
The high intelligence of the 6G network directly reflects in communication services such as indoor/outdoor positioning, multi-device management, information search, e-health, surveillance, and cyber security.
“Furthermore, as a human-centric network, the high intelligence of 6G network also directly reflects in a plethora of communication services, e.g., indoor/outdoor positioning, multi-device management, information search, e-health, surveillance, cyber security [36], [71].”
From What should 6G be? (2020) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges
Pathak, P. H., Feng, X., Hu, P., & Mohapatra, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2476474 · Cited by 1,553 · see citing works
Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges
Ayyash, M., Elgala, H., Khreishah, A. A., Jungnickel, V., Little, T. D. C., Shao, S., Rahaim, M., Schulz, D., Hilt, J., & Freund, R. E. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7402263 · Cited by 466 · see citing works
Enabling smart cities through a cognitive management framework for the internet of things
Vlacheas, P. T., Giaffreda, R., Stavroulaki, V., Kelaidonis, D., Foteinos, V., Poulios, G., Demestichas, P., Somov, A., Biswas, A. R., & Moessner, K. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6525602 · Cited by 408 · see citing works
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
Mohammadi, M., & Al-Fuqaha, A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700298 · Cited by 377 · see citing works
Smart Home: Cognitive Interactive People-Centric Internet of Things
Feng, S., Setoodeh, P., & Haykin, S. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600682cm · Cited by 157 · see citing works
Retamal, J. R. D., Oubei, H. M., Janjua, B., et al. (2015) · Optics Express · doi:10.1364/oe.23.033656 · Cited by 110 · see citing works
QoE-Driven Big Data Architecture for Smart City
He, X., Wang, K., Huang, H., & Liu, B. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700231 · Cited by 106 · see citing works
Downlink Resource Allocation for Dynamic TDMA-Based VLC Systems
Abdelhady, A. M., Amin, O., Chaaban, A., et al. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2877629 · Cited by 79 · see citing works
Connectivity in IoT indoor lighting systems with visible light communications
Warmerdam, K., Pandharipande, A., & Caicedo, D. (2015) · IEEE Online Conference on Green Communications (OnlineGreenComm) · doi:10.1109/onlinegreencom.2015.7387378 · Cited by 34 · see citing works
Visible light networking and sensing
Zhou, X., & Campbell, A. T. (2014) · doi:10.1145/2643614.2643621 · Cited by 30 · see citing works
Robust Transceivers Design for Multi-Stream Multi-User MIMO Visible Light Communication
Sifaou, H., Kammoun, A., Park, K., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2777893 · Cited by 28 · see citing works
Indoor localization system using visible light communication
Cherntanomwong, P., & Chantharasena, W. (2015) · doi:10.1109/iciteed.2015.7408994 · Cited by 23 · see citing works
A light-based communication architecture for smart city applications
Boubakri, W., Abdallah, W., & Boudriga, N. A. (2015) · International Conference on Transparent Optical Networks · doi:10.1109/icton.2015.7193409 · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- M. B. Rahaim, T. Borogovac, and J. B. Carruthers, "Candles: Com- munication and lighting emulation software," in Proc. ACM WiNTECH, Chicago, USA, 2010, pp. 9-14.
- Shuping Dang [M'18] (shuping.dang@kaust.edu.sa) received a B.Eng (Hons) in Electrical and Electronic Engineering from the University of Manchester (with first class honors) and a B.Eng in Electrical Engineering and Automation from Beijing Jiaotong University in 2014 via a joint '2+2' dual-degree program, and a D.Phil in Engineering Science from University of Oxford in 2018. He is currently working as a Postdoctoral Fellow with the Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST).
- Guoqing Ma [S'18] (guoqing.ma@kaust.edu.sa) received B.Eng in South University of Science and Technology of China (SUSTC) in 2017. He is currently pursuing his Ph.D degree in the M.S/Ph.D program with the Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST). Basem Shihada [SM'12] (basem.shihada@kaust.edu.sa) is an associate and founding professor of computer science and electrical engineering in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST). Before joining KAUST in 2009, he was a visiting faculty at the Computer Sci- ence Department in Stanford University. His current research covers a range of topics in energy and resource allocation in wired and wireless communication networks, including wireless mesh, wireless sensor, multimedia, and optical networks. He is also interested in SDNs, IoT, and cloud computing. In 2012, he was elevated to the rank of Senior Member of IEEE.
Cited by
Sorted by citations
Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020) · Nature Electronics · doi:10.1038/s41928-019-0355-6 · Cited by 1,790 · see citing works
Self-citation
- Authors
- Dang, S., Ma, G., Shihada, B., & Alouini, M.-S.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Alouini, Mohamed-Slim
- Published
- 2019-11-12
- OpenAlex
- W4249507654
- Cited by
- 6
- References
- 13
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Physical Sciences › Engineering › Biomedical Engineering › Molecular Communication and Nanonetworks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power Line Communications and Noise
- Keywords
- smart buildings, indoor visible light communication, machine learning, data transmission networks, building intelligence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
