Personalized Primary Port: Analysis of User View for the Smart Environment

2006 ◽  
pp. 989-997
Author(s):  
Wei-jin Park ◽  
Junhwan Kim ◽  
Doo-Hwan Bae
Keyword(s):  
Smart Environment ◽  
Primary Port

A SMART ENVIRONMENT INFRASTRUCTURE

2016 ◽  
Vol 15 (10) ◽  
pp. 2285-2300
Author(s):  
Mihai Horia Zaharia
Keyword(s):  
Smart Environment

Creating a smart environment using optical wireless

2010 ◽  
Author(s):  
Charusluk Viphavakit ◽  
Mohammed Sohail ◽  
Natasha Shrestha ◽  
Poompat Saengudomlert ◽  
Waleed S. Mohammed
Keyword(s):  
Smart Environment ◽  
Optical Wireless

A Smart Environment for Children with Autism

2015 ◽  
Vol 14 (2) ◽  
pp. 42-50 ◽  
Author(s):  
Monica Tentori ◽  
Lizbeth Escobedo ◽  
Gabriela Balderas
Keyword(s):  
Children With Autism ◽  
Smart Environment

Trade-offs in Augmented Reality User Interfaces for Controlling a Smart Environment

2021 ◽  
Author(s):  
Connor Daniel Flick ◽  
Courtney J Harris ◽  
Nikolas T Yonkers ◽  
Nahal Norouzi ◽  
Austin Erickson ◽  
...  
Keyword(s):  
Augmented Reality ◽  
User Interfaces ◽  
Smart Environment ◽  
Trade Offs

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

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