A review on smart city - IoT and deep learning algorithms, challenges. (29th April 2022)

• Record Type: Journal Article
• Authors: Rajyalakshmi, Vankadhara; Lakshmanna, Kuruva
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 1(2022)
• Page Start: 3
• Page End: 26
• Publication Date: 2022-04-29
• Subjects: convolutional neural networks -- CNNs -- deep belief networks -- DBNs -- deep Boltzmann machines -- DBMs -- deep learning -- DL -- internet of things -- IoT -- recurrent neural networks -- RNNs -- smart building -- SB -- smart city -- SC -- smart parking -- SP -- smart waste management -- SWM -- stacked auto encoders -- SAEs; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Recent improvements in the IoT are giving rise to the explosion of interconnected devices, empowering many smart applications. IoT devices engender massive data that requires intellectual processing and data analysis, especially the DL algorithms applied in the SC applications such as SP, SWM, traffic, healthcare, SB, energy, and others. Inspired by these plentiful applications, we present the key abilities of DL in IoT-related smart applications. First, we discussed the main motivation behind the SC and reviewed the use of CNNs, RNNs, SAEs, DBMs, and DBNs. We studied and tabulated several DL practices and use cases of SC. Finally, we categorise many research challenges regarding the operative strategy, implementation of DL-IoT, future research directions, and further research challenges. We proposed promising future directions for DL-IoT in SC environments. The overall idea of this survey is to utilise the few available resources more smartly by incorporating DL-IoT.