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Applications of Fog Computing and Internet of Things in Indian Smart Cities: An Empirical Study

Applications of Fog Computing and Internet of Things in Indian Smart Cities: An Empirical Study

Pragati Priyadarshinee
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 17
ISSN: 1947-8402|EISSN: 1947-8410|EISBN13: 9781683181989|DOI: 10.4018/IJSESD.302647
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MLA

Priyadarshinee, Pragati. "Applications of Fog Computing and Internet of Things in Indian Smart Cities: An Empirical Study." IJSESD vol.13, no.1 2022: pp.1-17. http://doi.org/10.4018/IJSESD.302647

APA

Priyadarshinee, P. (2022). Applications of Fog Computing and Internet of Things in Indian Smart Cities: An Empirical Study. International Journal of Social Ecology and Sustainable Development (IJSESD), 13(1), 1-17. http://doi.org/10.4018/IJSESD.302647

Chicago

Priyadarshinee, Pragati. "Applications of Fog Computing and Internet of Things in Indian Smart Cities: An Empirical Study," International Journal of Social Ecology and Sustainable Development (IJSESD) 13, no.1: 1-17. http://doi.org/10.4018/IJSESD.302647

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Abstract

Fog-computing and Internet of Things (IoT) together will work as a robust enabling technology for the success of a Smart city. Hence, the study focuses on the critical success factors based on Fog computing-IoT (FIoT) to bring success in Indian Smart cities by introducing a two-stage neural network that includes structural equation modeling and artificial neural network (SEM-ANN) hybrid approach. The research article introduces a new factor, Fog Computing (FC). IoT is again sub-divided into three more factors like the Internet of People (IoP), Internet of Services (IoS), and Internet of Energy (IoE) as the independent variables. 13 Smart cities and 379 respondents are involved in this study in India. The data analysis is done through two-stage SEM and ANN, which measures linear and non-linear relationships. From SEM output, it is identified that the IoE, Internet of People (IoP), and IoS have some significant positive effects on FIOT. The SEM accepted variables are considered the input for the next layer of ANN analysis that identified IoS as FIOT.

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