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The Internet of Things-Enabled Smart City: An In-Depth Review of Its Domains and Applications | IEEE Conference Publication | IEEE Xplore

The Internet of Things-Enabled Smart City: An In-Depth Review of Its Domains and Applications


Abstract:

The Internet of Things (IoT) refers to a comprehensive system that integrates diverse devices and technologies, thereby eliminating the need for human involvement. IoT ha...Show More

Abstract:

The Internet of Things (IoT) refers to a comprehensive system that integrates diverse devices and technologies, thereby eliminating the need for human involvement. IoT has played a pivotal role in advancing the creation of Smart City (SC) systems, which aim to promote sustainable living, enhance citizen comfort, and boost productivity. IoT-enabled SC encompasses multiple sectors and relies on diverse underlying systems to facilitate its functioning. This article begins with an overview of the IoT-enabled SC. It proceeds to comprehensively investigate seven primary sections and over thirty sub-sections, drawing insights from a thorough analysis of one hundred credible papers. The purpose of this paper is to provide a thorough comprehension of the importance of SCs in both individual and societal contexts.
Date of Conference: 01-02 November 2023
Date Added to IEEE Xplore: 27 November 2023
ISBN Information:

ISSN Information:

Conference Location: Mashhad, Iran, Islamic Republic of
References is not available for this document.

I. Introduction

By 2050, 68% of the world's population will live in urban regions, a growth of more than 10% during the next three decades. Data revealed a dramatic growth in urbanization from 751 million in 1950 to 4.2 billion in 2018 [1]. By 2040, the world's energy consumption is projected to increase by 56% due to the difficulties brought on by the accelerated development of digital technologies [2]. Over the next 30 years, urbanization rises by more than 10%, reflecting the world's rapidly expanding population. 70% of the world's use of natural resources occurs in urban areas, exacerbating pollution, ecosystem degradation, and energy constraints. Due to their focus on reducing costs and increasing employment opportunities while also tackling climate change and providing access to clean water, urban areas face a substantial challenge from the limited resource accessibility [3].

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1.
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2.
K. M. Al-Obaidi et al., "A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective", Energies, vol. 15, no. 16, pp. 5991, Aug. 2022.
3.
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4.
M. Whaiduzzaman et al., "A Review of Emerging Technologies for IoT-Based Smart Cities", Sensors, vol. 22, no. 23, pp. 9271, Nov. 2022.
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J. Souza et al., "Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018", Sustainability, 2019.
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7.
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8.
A. Meydani, A. Meidani and S. Shahablavasani, "Implementation of the Internet of Things Technology in the Smart Power Grid", Proc. 10th Iranian Conf. Renewable Energy Distrib. Gener, Mar. 2023.
9.
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10.
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11.
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12.
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13.
Z. Liu et al., "Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization", Big Data, vol. 7, no. 2, Jun. 2019.
14.
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15.
M. Dong and L. Grumbach, "A hybrid distribution feeder long-term load forecasting method based on sequence prediction", IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 470-482, Jan. 2020.
16.
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17.
A. Rosato et al., "A neural network based prediction system of distributed generation for the management of microgrids", IEEE Trans. Ind. Appl, vol. 55, no. 6, pp. 7092-7102, Nov. 2019.
18.
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19.
P. Bagheri and W. Xu, "Model-free Volt-Var control based on measurement data analytics", IEEE Trans. Power Syst, vol. 34, Mar. 2019.
20.
H. Liu et al., "Complex power quality disturbances classification via curvelet transform and deep learning", Electr. Power Syst, vol. 163, 2018.
21.
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22.
J. J. Q. Yu et al., "Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks", IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 1694-1703, Mar. 2019.
23.
O. Bouachir et al., "FederatedGrids: Federated learning and blockchain-assisted P2P energy sharing", IEEE Trans. Green Commun. Netw, vol. 6, no. 1, pp. 424-436, Mar. 2022.
24.
M. Foti, C. Mavromatis and M. Vavalis, "Decentralized blockchain-based consensus for Optimal Power Flow solutions", Appl. Energy, vol. 283, pp. 116100, Feb. 2021.
25.
J. Faraji et al., "Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting", IEEE Access, vol. 8, pp. 157284-157305, 2020.
26.
N. Koltsaklis et al., "A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques", Energies, vol. 14, Mar. 2021.
27.
A. A. Abdelsalam, A. A. Salem, E. S. Oda and A. A. Eldesouky, "Islanding detection of microgrid incorporating inverter based DGs using long short-term memory network", IEEE Access, vol. 8, pp. 106471-106486, 2020.
28.
C. Goumopoulos and N. Potha, "Mental fatigue detection using a wearable commodity device and machine learning", J. Ambient Intell. Humanized Comput, vol. 13, pp. 1-19, Jan. 2022.
29.
J. Tang et al., "Seizure detection using wearable sensors and machine learning: Setting a benchmark", Epilepsia, vol. 62, Aug. 2021.
30.
F. Tabei et al., "Cuffless blood pressure monitoring system using smartphones", IEEE Access, vol. 8, pp. 11534-11545, 2020.

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References

References is not available for this document.