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Big Data Mining and Analytics  2021, Vol. 4 Issue (3): 155-172    DOI: 10.26599/BDMA.2020.9020029
A Survey on Algorithms for Intelligent Computing and Smart City Applications
Zhao Tong1,*(),Feng Ye1(),Ming Yan2(),Hong Liu1(),Sunitha 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
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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.

Key wordscyber physical systems      Internet of Things (IoT)      intelligent computing algorithm      Quality of Service (QoS)      smart city     
Received: 21 September 2020      Published: 20 May 2021
Fund:  National Natural Science Foundation of China(62072174);National Natural Science Foundation of Hunan Province, China(2020JJ5370);Scientific Research Fund of Hunan Provincial Education Department, China(17C0959)
Corresponding Authors: Zhao Tong     E-mail:;;;;
About author: Zhao Tong received the PhD degree in computer science from Hunan University, China in 2014. He was a visiting scholar at the Georgia State University from 2017 to 2018. He is currently an associate professor at the College of Information Science and Engineering of Hunan Normal University. His research interests include modeling and scheduling for parallel and distributed computing systems. He has published more than 15 research papers in international conferences and journals, such as IEEE Transactions on Parallel and Distributed Systems, Information Sciences, Neural Computing and Applications, and Journal of Parallel and Distributed System. He is a member of CCF.|Feng Ye received the BE degree from Hunan Institute of Science and Technology, Yueyang, China in 2018. He is currently a master student at the College of Information Science and Engineering, Hunan Normal University, Changsha, China. His research interests include cloud computing, mobile edge computing, objective optimization, task scheduling, machine learning, and artificial intelligence.|Ming Yan received the PhD degree from Sichuan University, Chengdu, China in 2018. He was a visiting PhD at Georgia State University in 2018. He is currently a scientist at Agency for Science Technology and Research, Singapore. His research interests include neural networks and natural language processing. He has published several papers on ACL, KBS, etc.|Hong Liu received the MS degree from National University of Defense Technology, Changsha, China in 1988. He is currently a full professor at the College of Information Science and Engineering, Hunan Normal University. His research interests include artificial intelligence and distribute system. He has published more than 10 research papers in international conferences and journals, such as the Information Sciences, Neural Computing and Applications, Journal of Parallel and Distributed System, and Soft Computing.|Sunitha Basodi received the BS degree from the Jawaharlal Nehru Technological University, Hyderabad, India in 2010 and the PhD drgree in computer science from Georgia State University, Atlanta, USA in 2019. She has worked in Amazon Inc., India, as a software developer. Her research focuses on improving machine learning and deep learning methods, and their applications in neuroimaging, bioinformatics, and smart grid domains.
Cite this article:

Zhao Tong,Feng Ye,Ming Yan,Hong Liu,Sunitha Basodi. A Survey on Algorithms for Intelligent Computing and Smart City Applications. Big Data Mining and Analytics, 2021, 4(3): 155-172.

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Fig. 1 Number of smart cities under construction across the world.
Fig. 2 Smart city framework.
Fig. 3 Compound annual growth rates of smart city segments.
Fig. 4 Adaptive control traffic signal workflow.
Fig. 5 Framework of reinforcement learning.
Fig. 6 Energy management framework for smart cities.
Fig. 7 Structure of the CNN model.
Fig. 8 Smart healthcare framework based on edge computing.
Fig. 9 Smart agriculture framework based on edge computing.
[1]   Zvolska L., Lehner M., Voytenko P. Y., Mont O., and Plepys A., Urban sharing in smart cities: The cases of Berlin and London, Local Environment, vol. 24, no. 7, pp. 628-645, 2019.
[2]   Kuecker G. D., Hartley K., 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]   Song Y. and Ding C. R., Smart Urban Growth for China. Washington, DC, USA: Lincoln Institute of Land Policy Cambridge, 2009.
[4]   Schrank D., Lomax T., and Turner S., 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]   Calderón-Garcidue?as L. L., Kulesza R. J., Doty R. L., D’Angiulli A., and Torres-Jardón R., 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]   Halicioglu F., Andrés A. R., and Yamamura E., Modeling crime in Japan, Economic Modelling, vol. 29, no. 5, pp. 1640-1645, 2012.
[7]   He J. Y., Hu H. J., Harrison R. W., Tai P. C., and Pan Y., 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]   Farhan L. and Kharel R., Internet of things: Vision, future directions and opportunities, in Modern Sensing Technologies. Spriniger, 1991. pp. 331-347.
[9]   Sharma N., Shamkuwar M., and Singh I., The history, present and future with IoT, in Internet of Things and Big Data Analytics for Smart Generation. Springer, 2019, pp. 27-51.
[10]   Tong Z., Chen H. J., Deng X. M., Li K. L., and Li K. Q., 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]   Duan M. X., Li K. L., Liao X. K., and Li K. Q., 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]   Zhong W., He J. Y., Harrison R. W., Tai P. C., and Pan Y., Clustering support vector machines for protein local structure prediction, Expert Systems With Applications, vol. 32, no. 2, pp. 518-526, 2007.
[13]   Urbinati A., Bogers M., Chiesa V., and Frattini F., Creating and capturing value from big data: A multiple-case study analysis of provider companies, Technovation, vol. 84, pp. 21-36, 2019.
[14]   Gibson D. V., Kozmetsky G., and Smilor R. W., The technopolis phenomenon: Smart cities, fast systems, Global Networks, vol. 38, no. 2, pp. 756-767, 1992.
[15]   Harrison C., Eckman B., Hamilton R., Hartswick P., Kalagnanam J., Paraszczak J., and Williams P., Foundations for smarter cities, IBM Journal of Research and Development, vol. 54, no. 4, pp. 1-16, 2010.
[16]   Giffinger R., Gudrun H., 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]   Silva B. N., Khan M., and Han K. J., 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]   Balakrishna C., 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]   Liu P., Peng Z. H., China’s smart city pilots: A progress report, Computer, vol. 47, no. 10, pp. 72-81, 2013.
[20]   Li K. L., Liu C. B., Li K. Q., and Zomaya A. Y., 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]   Tong Z., Deng X. M., Chen H. J., Mei J., and Liu H., 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]   Shi W. S., Cao J., Zhang Q., Li Y. H., and Xu L. Y., Edge computing: Vision and challenges, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016.
[23]   Dameri R. P., Benevolo C., Veglianti E., and Li Y. Y., 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]   Li Q. and Lin S. F., 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]   Chourabi H., Nam T., Walker S., Gil-Garcia J. R., Mellouli S., Nahon K., Pardo T. A., and Scholl H. J., Understanding smart cities: An integrative framework, presented at the 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 2012.
[26]   Sanchez L., Mu?oz L., Galache J. A., Sotres P., Santana J. R., Gutierrez V., Ramdhany R., Gluhak A., Krco S., and Theodoridis E., SmartSantander: IoT experimentation over a smart city testbed, Computer Networks, vol. 61, pp. 217-238, 2014.
[27]   Memos V. A., Psannis K. E., Ishibashi Y., Kim B. G., and Gupta B. B., 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]   Al-Hader M., Rodzi A., Sharif A. R., and Ahmad N., Smart city components architicture, in Proc. of 2009 International Conference on Computational Intelligence, Modelling and Simulation, Brno, Czech Republic, 2009, pp. 93-97.
[29]   Rong W. G., Xiong Z., Cooper D., Li C., and Sheng H., Smart city architecture: A technology guide for implementation and design challenges, China Communications, vol. 11, no. 3, pp. 56-69, 2014.
[30]   Bélissent J., Getting clever about smart cities: New opportunities require new business models, Cambridge, MA, USA, vol. 193, no. 2, pp. 244-277, 2010.
[31]   Zygiaris S., 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]   Wang S. H., Lu H., Huang Q., and Cao J., Research on key technologies for smart transportation systems, (in Chinese), Geomatics & Spatial Information Technology, vol. 36, pp. 88-91, 2013.
[33]   Mannion P., Duggan J., and Howley E., Parallel reinforcement learning for traffic signal control, Procedia Computer Science, vol. 52, no. 5, pp. 956-961, 2015.
[34]   Mannion P., Duggan J., and Howley E., 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]   Wei H., Zheng G. J., Yao H. X., and Li Z. H., 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]   Sutton R. S. and Barto A. G., Reinforcement Learning: An Introduction. Cambridge, MA, USA: MIT Press, 2018.
[37]   Tahifa M., Boumhidi J., and Yahyaouy A., 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]   Chu T. S., Wang J., Codecà L., and Li Z. J., 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]   Wan C. H., Hwang M. C., Value-based deep reinforcement learning for adaptive isolated intersection signal control, IET Intelligent Transport Systems, vol. 12, no. 9, pp. 1005-1010, 2018.
[40]   Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., Graves A., Riedmiller M., Fidjeland A. K., Ostrovski G., et al., Human-level control through deep reinforcement learning, Nature, vol. 518, no. 7540, pp. 529-533, 2015.
[41]   Tong Z., Chen H. J., Deng X. M., Li K. L., and Li K. Q., A scheduling scheme in the cloud computing environment using deep Q-learning, Information Sciences, vol. 512, pp. 1170-1191, 2020.
[42]   Zhang L. X., Li K. L., Li C. Y., and Li K. Q., Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems, Information Sciences, vol. 379, pp. 241-256, 2017.
[43]   Lund H., ?stergaard P. A., Connolly D., and Mathiesen B. V., Smart energy and smart energy systems, Energy, vol. 137, pp. 556-565, 2017.
[44]   Atasoy T., Ak?n? H. E., and Er?in ?., 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]   Dong X. S., Qian L. J., and Huang L., 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]   Hosein S. and Hosein P., 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]   Chen J. G., Li K. L., Bilal K., Li K. Q., and Philip S. Y., 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]   Li K. L., Tang X. Y., and Li K. Q., 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]   Kim S. Y., Lim H., Reinforcement learning based energy management algorithm for smart energy buildings, Energies, vol. 11, no. 8, pp. 2010-2028, 2018.
[50]   Zhou S. Y., Hu Z. J., Gu W., Jiang M., and Zhang X. P., 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]   Liu Y., Yang C., Jiang L., Xie S. L., and Zhang Y., Intelligent edge computing for IoT-based energy management in smart cities, IEEE Network, vol. 33, no. 2, pp. 111-117, 2019.
[52]   Mocanu E., Mocanu D. C., Nguyen P. H., Liotta A., Webber M. E., Gibescu M., and Slootweg J. G., On-line building energy optimization using deep reinforcement learning, IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3698-3708, 2018.
[53]   Wang Y., Li K. L., Chen H., He L. G., and Li K. Q., 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]   Hua H. C., Qin Y. C., Hao C. T., and Cao J. W., Optimal energy management strategies for energy internet via deep reinforcement learning approach, Applied Energy, vol. 239, pp. 598-609, 2019.
[55]   Amin S. U., Hossain M. S., Muhammad G., Alhussein M., and Rahman M. A., Cognitive smart healthcare for pathology detection and monitoring, IEEE Access, vol. 7, pp. 10745-10753, 2019.
[56]   Chen J. G., Li K. L., Deng Q. Y., Li K. Q., and Philip S. Y., Distributed deep learning model for intelligent video surveillance systems with edge computing, IEEE Transactions on Industrial Informatics, .
doi: 10.1109/TII.2019.2909473
[57]   Xiao X. L., Mudiyanselage T. B., Ji C. Y., Hu J., and Pan Y., 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]   Chen M., Li W., Hao Y. X., Qian Y. F., and Humar I., Edge cognitive computing based smart healthcare system, Future Generation Computer Systems, vol. 86, no. 3, pp. 403-411, 2018.
[59]   Fadlullah Z. M., Pathan A. S. K., and Gacanin H., 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]   Shukla S., Hassan M. F., Jung L. T., and Awang A., 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]   Boudko S. and Abie H., 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]   Tseng H. H., Luo Y., Cui S. N., Chien J. T., Haken R. K. T., and Naqa I. E., Deep reinforcement learning for automated radiation adaptation in lung cancer, Medical Physics, vol. 44, no. 12, pp. 6690-6705, 2017.
[63]   Basodi S., Ji C., Zhang H., and Pan Y., Gradient amplification: An efficient way to train deep neural networks, Big Data Mining and Analytics, vol. 3, no. 3, pp. 196-207, 2020.
[64]   Bu F. Y., Wang X., A smart agriculture IoT system based on deep reinforcement learning, Future Generation Computer Systems, vol. 99, pp. 500-507, 2019.
[65]   Ding W. G., Taylor G., Automatic moth detection from trap images for pest management, Computers and Electronics in Agriculture, vol. 123, pp. 17-28, 2016.
[66]   Cheng X., Zhang Y. H., Chen Y. Q., Wu Y. Z., and Yue Y., Pest identification via deep residual learning in complex background, Computers and Electronics in Agriculture, vol. 141, pp. 351-356, 2017.
[67]   Lu J., Hu J., Zhao G. N., Mei F. H., and Zhang C. S., An in-field automatic wheat disease diagnosis system, Computers and Electronics in Agriculture, vol. 142, pp. 369-379, 2017.
[68]   Huong T. T., Thanh N. H., Van N. T., Dat N. T., van Long N., and Marshall A., 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]   Varman S. A. M., Baskaran A. R., Aravindh S., and Prabhu E., 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]   Sun L. J., Yang Y. X., Hu J., Porter D., Marek T., and Hillyer C., 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]   Martínez-Ballesté A., Pérez-Martínez P. A., and Solanas A., The pursuit of citizens’ privacy: A privacy-aware smart city is possible, IEEE Communications Magazine, vol. 51, no. 6, pp. 136-141, 2013.
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