| Title |
Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey |
| ID_Doc |
40985 |
| Authors |
Sharma, H; Haque, A; Blaabjerg, F |
| Title |
Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey |
| Year |
2021 |
| Published |
Electronics, 10, 9 |
| DOI |
10.3390/electronics10091012 |
| Abstract |
Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author's knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications. |
| Author Keywords |
Internet of Things (IoT); sensor nodes; WSN-IoT; artificial intelligence; reinforcement learning; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000649994900001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied |
| Research Area |
Computer Science; Engineering; Physics |
| PDF |
https://www.mdpi.com/2079-9292/10/9/1012/pdf?version=1619342825
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