Journal Home > Volume 4 , issue 3

With the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.


menu
Abstract
Full text
Outline
About this article

A Survey on Algorithms for Intelligent Computing and Smart City Applications

Show Author's information Zhao Tong1( )Feng Ye1Ming Yan2Hong Liu1Sunitha Basodi3
College of Information Science and Engineering, Hunan Normal University, Changsha 410012, China
Agency for Science Technology and Research, Singapore 999002, Singapore
Department of Computer Science, Georgia State University, Atlanta 30302, GA, USA

Abstract

With the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.

Keywords:

cyber physical systems, Internet of Things (IoT), intelligent computing algorithm, Quality of Service (QoS), smart city
Received: 21 September 2020 Revised: 27 October 2020 Accepted: 08 December 2020 Published: 12 May 2021 Issue date: September 2021
References(71)
[1]
L. Zvolska, M. Lehner, P. Y. Voytenko, O. Mont, and A. Plepys, Urban sharing in smart cities: The cases of Berlin and London, Local Environment, vol. 24, no. 7, pp. 628-645, 2019.
[2]
G. D. Kuecker, K. Hartley, How smart cities became the urban norm: Power and knowledge in New Songdo City, Annals of the American Association of Geographers, vol. 100, no. 2, pp. 516-524, 2020.
[3]
Y. Song and C. R. Ding, Smart Urban Growth for China. Washington, DC, USA: Lincoln Institute of Land Policy Cambridge, 2009.
[4]
D. Schrank, T. Lomax, and S. Turner, TTI's 2012 urban mobility report powered by INRIX traffic data, Texas A and M Transportation Institute, vol. 83, no. 1, pp. 1-64, 2012.
[5]
L. L. Calderón-Garcidueñas, R. J. Kulesza, R. L. Doty, A. D’Angiulli, and R. Torres-Jardón, Megacities air pollution problems: Mexico City metropolitan area critical issues on the central nervous system pediatric impact, Environmental Research, vol. 137, pp. 157-169, 2015.
[6]
F. Halicioglu, A. R. Andrés, and E. Yamamura, Modeling crime in Japan, Economic Modelling, vol. 29, no. 5, pp. 1640-1645, 2012.
[7]
J. Y. He, H. J. Hu, R. W. Harrison, P. C. Tai, and Y. Pan, Rule generation for protein secondary structure prediction with support vector machines and decision tree, IEEE Transactions on Nanobioscience, vol. 5, no. 1, pp. 46-53, 2006.
[8]
L. Farhan and R. Kharel, Internet of things: Vision, future directions and opportunities, in Modern Sensing Technologies. Spriniger, 1991. pp. 331-347.
[9]
N. Sharma, M. Shamkuwar, and I. Singh, The history, present and future with IoT, in Internet of Things and Big Data Analytics for Smart Generation. Springer, 2019, pp. 27-51.
[10]
Z. Tong, H. J. Chen, X. M. Deng, K. L. Li, and K. Q. Li, A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization, Soft Computing, vol. 23, no. 21, pp. 11035-11054, 2019.
[11]
M. X. Duan, K. L. Li, X. K. Liao, and K. Q. Li, A parallel multiclassification algorithm for big data using an extreme learning machine, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2337-2351, 2017.
[12]
W. Zhong, J. Y. He, R. W. Harrison, P. C. Tai, and Y. Pan, Clustering support vector machines for protein local structure prediction, Expert Systems With Applications, vol. 32, no. 2, pp. 518-526, 2007.
[13]
A. Urbinati, M. Bogers, V. Chiesa, and F. Frattini, Creating and capturing value from big data: A multiple-case study analysis of provider companies, Technovation, vol. 84, pp. 21-36, 2019.
[14]
D. V. Gibson, G. Kozmetsky, and R. W. Smilor, The technopolis phenomenon: Smart cities, fast systems, Global Networks, vol. 38, no. 2, pp. 756-767, 1992.
[15]
C. Harrison, B. Eckman, R. Hamilton, P. Hartswick, J. Kalagnanam, J. Paraszczak, and P. Williams, Foundations for smarter cities, IBM Journal of Research and Development, vol. 54, no. 4, pp. 1-16, 2010.
[16]
R. Giffinger, H. Gudrun, Smart cities ranking: An effective instrument for the positioning of the cities?, IBM Journal of Research and Development, vol. 4, no. 12, pp. 7-26, 2010.
[17]
B. N. Silva, M. Khan, and K. J. Han, Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities, Sustainable Cities and Society, vol. 38, no. 1, pp. 697-713, 2018.
[18]
C. Balakrishna, Enabling technologies for smart city services and applications, in Proc. of 2012 Sixth International Conference on Next Generation Mobile Applications, Services and Technologies, Paris, France, 2012, pp. 223-227.
[19]
P. Liu, Z. H. Peng, China’s smart city pilots: A progress report, Computer, vol. 47, no. 10, pp. 72-81, 2013.
[20]
K. L. Li, C. B. Liu, K. Q. Li, and A. Y. Zomaya, A framework of price bidding configurations for resource usage in cloud computing, IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 8, pp. 2168-2181, 2015.
[21]
Z. Tong, X. M. Deng, H. J. Chen, J. Mei, and H. Liu, QL-HEFT: A novel machine learning scheduling scheme base on cloud computing environment, Neural Computing and Applications, vol. 32, no. 10, pp. 5553-5570, 2020.
[22]
W. S. Shi, J. Cao, Q. Zhang, Y. H. Li, and L. Y. Xu, Edge computing: Vision and challenges, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016.
[23]
R. P. Dameri, C. Benevolo, E. Veglianti, and Y. Y. Li, Understanding smart cities as a glocal strategy: A comparison between Italy and China, Technological Forecasting and Social Change, vol. 142, pp. 26-41, 2019.
[24]
Q. Li and S. F. Lin, Research on digital city framework architecture, in Proc. of 2001 International Conferences on Info-Tech and Info-Net, Beijing, China, 2001, pp. 30-36.
[25]
H. Chourabi, T. Nam, S. Walker, J. R. Gil-Garcia, S. Mellouli, K. Nahon, T. A. Pardo, and H. J. Scholl, Understanding smart cities: An integrative framework, presented at the 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 2012.
[26]
L. Sanchez, L. Muñoz, J. A. Galache, P. Sotres, J. R. Santana, V. Gutierrez, R. Ramdhany, A. Gluhak, S. Krco, and E. Theodoridis, SmartSantander: IoT experimentation over a smart city testbed, Computer Networks, vol. 61, pp. 217-238, 2014.
[27]
V. A. Memos, K. E. Psannis, Y. Ishibashi, B. G. Kim, and B. B. Gupta, An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework, Future Generation Computer Systems, vol. 83, pp. 619-628, 2018.
[28]
M. Al-Hader, A. Rodzi, A. R. Sharif, and N. Ahmad, Smart city components architicture, in Proc. of 2009 International Conference on Computational Intelligence, Modelling and Simulation, Brno, Czech Republic, 2009, pp. 93-97.
[29]
W. G. Rong, Z. Xiong, D. Cooper, C. Li, and H. Sheng, Smart city architecture: A technology guide for implementation and design challenges, China Communications, vol. 11, no. 3, pp. 56-69, 2014.
[30]
J. Bélissent, Getting clever about smart cities: New opportunities require new business models, Cambridge, MA, USA, vol. 193, no. 2, pp. 244-277, 2010.
[31]
S. Zygiaris, Smart city reference model: Assisting planners to conceptualize the building of smart city innovation ecosystems, Journal of the Knowledge Economy, vol. 4, no. 2, pp. 217-231, 2013.
[32]
S. H. Wang, H. Lu, Q. Huang, and J. Cao, Research on key technologies for smart transportation systems, (in Chinese), Geomatics & Spatial Information Technology, vol. 36, pp. 88-91, 2013.
[33]
P. Mannion, J. Duggan, and E. Howley, Parallel reinforcement learning for traffic signal control, Procedia Computer Science, vol. 52, no. 5, pp. 956-961, 2015.
[34]
P. Mannion, J. Duggan, and E. Howley, An experimental review of reinforcement learning algorithms for adaptive traffic signal control, Autonomic Road Transport Support Systems, vol. 48, no. 2, pp. 47-66, 2016.
[35]
H. Wei, G. J. Zheng, H. X. Yao, and Z. H. Li, Intellilight: A reinforcement learning approach for intelligent traffic light control, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA, 2018, pp. 2496-2505.
[36]
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2018.
[37]
M. Tahifa, J. Boumhidi, and A. Yahyaouy, Swarm reinforcement learning for traffic signal control based on cooperative multi-agent framework, in Proc. of 2015 Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2015, pp. 1-6.
[38]
T. S. Chu, J. Wang, L. Codecà, and Z. J. Li, Multi-agent deep reinforcement learning for large-scale traffic signal control, IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1086-1095, 2019.
[39]
C. H. Wan, M. C. Hwang, Value-based deep reinforcement learning for adaptive isolated intersection signal control, IET Intelligent Transport Systems, vol. 12, no. 9, pp. 1005-1010, 2018.
[40]
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, Nature, vol. 518, no. 7540, pp. 529-533, 2015.
[41]
Z. Tong, H. J. Chen, X. M. Deng, K. L. Li, and K. Q. Li, A scheduling scheme in the cloud computing environment using deep Q-learning, Information Sciences, vol. 512, pp. 1170-1191, 2020.
[42]
L. X. Zhang, K. L. Li, C. Y. Li, and K. Q. Li, Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems, Information Sciences, vol. 379, pp. 241-256, 2017.
[43]
H. Lund, P. A. Østergaard, D. Connolly, and B. V. Mathiesen, Smart energy and smart energy systems, Energy, vol. 137, pp. 556-565, 2017.
[44]
T. Atasoy, H. E. Akınç, and Ö. Erçin, An analysis on smart grid applications and grid integration of renewable energy systems in smart cities, in Proc. of 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Palermo, Italy, 2015, pp. 547-550.
[45]
X. S. Dong, L. J. Qian, and L. Huang, Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach, in Proc. of 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, South Korea, 2017, pp. 119-125.
[46]
S. Hosein and P. Hosein, Load forecasting using deep neural networks, in Pooc. of 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2017, pp. 1-5.
[47]
J. G. Chen, K. L. Li, K. Bilal, K. Q. Li, and S. Y. Philip, A bi-layered parallel training architecture for large-scale convolutional neural networks, IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 5, pp. 965-976, 2018.
[48]
K. L. Li, X. Y. Tang, and K. Q. Li, Energy-efficient stochastic task scheduling on heterogeneous computing systems, IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 11, pp. 2867-2876, 2013.
[49]
S. Y. Kim, H. Lim, Reinforcement learning based energy management algorithm for smart energy buildings, Energies, vol. 11, no. 8, pp. 2010-2028, 2018.
[50]
S. Y. Zhou, Z. J. Hu, W. Gu, M. Jiang, and X. P. Zhang, Artificial intelligence based smart energy community management: A reinforcement learning approach, CSEE Journal of Power and Energy Systems, vol. 5, no. 1, pp. 1-10, 2019.
[51]
Y. Liu, C. Yang, L. Jiang, S. L. Xie, and Y. Zhang, Intelligent edge computing for IoT-based energy management in smart cities, IEEE Network, vol. 33, no. 2, pp. 111-117, 2019.
[52]
E. Mocanu, D. C. Mocanu, P. H. Nguyen, A. Liotta, M. E. Webber, M. Gibescu, and J. G. Slootweg, On-line building energy optimization using deep reinforcement learning, IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3698-3708, 2018.
[53]
Y. Wang, K. L. Li, H. Chen, L. G. He, and K. Q. Li, Energy-aware data allocation and task scheduling on heterogeneous multiprocessor systems with time constraints, IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 2, pp. 134-148, 2014.
[54]
H. C. Hua, Y. C. Qin, C. T. Hao, and J. W. Cao, Optimal energy management strategies for energy internet via deep reinforcement learning approach, Applied Energy, vol. 239, pp. 598-609, 2019.
[55]
S. U. Amin, M. S. Hossain, G. Muhammad, M. Alhussein, and M. A. Rahman, Cognitive smart healthcare for pathology detection and monitoring, IEEE Access, vol. 7, pp. 10745-10753, 2019.
[56]
J. G. Chen, K. L. Li, Q. Y. Deng, K. Q. Li, and S. Y. Philip, Distributed deep learning model for intelligent video surveillance systems with edge computing, IEEE Transactions on Industrial Informatics, .
[57]
X. L. Xiao, T. B. Mudiyanselage, C. Y. Ji, J. Hu, and Y. Pan, Fast deep learning training through intelligently freezing layers, IEEE Green Computing and Communications, presented at the 2019 International Conference on Internet of Things (iThings), Atlanta, GA, USA, 2019.
[58]
M. Chen, W. Li, Y. X. Hao, Y. F. Qian, and I. Humar, Edge cognitive computing based smart healthcare system, Future Generation Computer Systems, vol. 86, no. 3, pp. 403-411, 2018.
[59]
Z. M. Fadlullah, A. S. K. Pathan, and H. Gacanin, On delay-sensitive healthcare data analytics at the network edge based on deep learning, in Proc. of 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, Cyprus, 2018, pp. 388-393.
[60]
S. Shukla, M. F. Hassan, L. T. Jung, and A. Awang, Architecture for latency reduction in healthcare Internet-of-Things using reinforcement learning and fuzzy based fog computing, presented at International Conference of Reliable Information and Communication Technology, Bandung, Indonesia, 2018.
[61]
S. Boudko and H. Abie, Adaptive cybersecurity framework for healthcare Internet of Things, in Proc. of 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway, 2019, pp. 1-6.
[62]
H. H. Tseng, Y. Luo, S. N. Cui, J. T. Chien, R. K. T. Haken, and I. E. Naqa, Deep reinforcement learning for automated radiation adaptation in lung cancer, Medical Physics, vol. 44, no. 12, pp. 6690-6705, 2017.
[63]
S. Basodi, C. Ji, H. Zhang, and Y. Pan, Gradient amplification: An efficient way to train deep neural networks, Big Data Mining and Analytics, vol. 3, no. 3, pp. 196-207, 2020.
[64]
F. Y. Bu, X. Wang, A smart agriculture IoT system based on deep reinforcement learning, Future Generation Computer Systems, vol. 99, pp. 500-507, 2019.
[65]
W. G. Ding, G. Taylor, Automatic moth detection from trap images for pest management, Computers and Electronics in Agriculture, vol. 123, pp. 17-28, 2016.
[66]
X. Cheng, Y. H. Zhang, Y. Q. Chen, Y. Z. Wu, and Y. Yue, Pest identification via deep residual learning in complex background, Computers and Electronics in Agriculture, vol. 141, pp. 351-356, 2017.
[67]
J. Lu, J. Hu, G. N. Zhao, F. H. Mei, and C. S. Zhang, An in-field automatic wheat disease diagnosis system, Computers and Electronics in Agriculture, vol. 142, pp. 369-379, 2017.
[68]
T. T. Huong, N. H. Thanh, N. T. Van, N. T. Dat, N. van Long, and A. Marshall, Water and energy-efficient irrigation based on markov decision model for precision agriculture, in Proc. of 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE), Hue, Vietnam, 2018, pp. 51-56.
[69]
S. A. M. Varman, A. R. Baskaran, S. Aravindh, and E. Prabhu, Deep learning and IoT for smart agriculture using WSN, in Proc. of 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017, pp. 1-6.
[70]
L. J. Sun, Y. X. Yang, J. Hu, D. Porter, T. Marek, and C. Hillyer, Reinforcement learning control for water-efficient agricultural irrigation, in Proc. of 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), Guangzhou, China, 2017, pp. 1334-1341.
[71]
A. Martínez-Ballesté, P. A. Pérez-Martínez, and A. Solanas, The pursuit of citizens’ privacy: A privacy-aware smart city is possible, IEEE Communications Magazine, vol. 51, no. 6, pp. 136-141, 2013.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 21 September 2020
Revised: 27 October 2020
Accepted: 08 December 2020
Published: 12 May 2021
Issue date: September 2021

Copyright

© The author(s) 2021

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 62072174), the National Natural Science Foundation of Hunan Province, China (No. 2020JJ5370), and Scientific Research Fund of Hunan Provincial Education Department, China ( Nos. 17C0959 and 18C0016).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Reprints and Permission requests may be sought directly from editorial office.

Return