| Title |
A review on smart city - IoT and deep learning algorithms, challenges |
| ID_Doc |
39242 |
| Authors |
Rajyalakshmi, V; Lakshmanna, K |
| Title |
A review on smart city - IoT and deep learning algorithms, challenges |
| Year |
2022 |
| Published |
International Journal Of Engineering Systems Modelling And Simulation, 13, 1 |
| DOI |
|
| 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. |
| Author Keywords |
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 |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000792823300001 |
| WoS Category |
Engineering, Multidisciplinary |
| Research Area |
Engineering |
| PDF |
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