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The Role of Artificial Intelligence in Smart Cities: Systematic Literature Review

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Data and Information in Online Environments (DIONE 2021)

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Abstract

The increase in urban population has brought climate, technological and economic changes that may negatively affect the quality of life in cities. In response, the concept of a smart city has emerged referring to use of novel ICTs to reduce the adverse effects on cities and its inhabitants. Among other technologies, Artificial Intelligence (AI) is used in that context, evolving rapidly and playing an essential role in supporting intelligent city-wide systems in different domains. It is thus beneficial to identify current research advances and get a better understanding of the role the AI plays in this particular context. Consequently, there is a need to systematically study the connection between AI and smart cities, by focusing on the findings that uncover its role, possible applications, but also challenges to using the concepts and technologies branded as AI in smart cities. Therefore, the paper presents a systematic literature review and provides insights into the achievements and advances of AI in smart cities pertaining to the mentioned aspects.

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References

  • Abbas, S., et al.: Modeling, simulation and optimisation of power plant energy sustainability for IoT enabled smart cities empowered with deep extreme learning machine. IEEE Access 8, 39982–39997 (2020)

    Article  Google Scholar 

  • Ahmed, U., Mumtaz, R., Anwar, H., Mumtaz, S., Qamar, A.: Water quality monitoring: from conventional to emerging technologies. Water Supply 20(1), 28–45 (2019)

    Article  Google Scholar 

  • Ajerla, D., Mahfuz, S., Zulkernine, F.: A Real-time patient monitoring framework for fall detection. Wirel. Commun. Mobile Comput. (2019). https://doi.org/10.1155/2019/9507938

  • Al Zamil, M.G.H., Samarah, S., Rawashdeh, M., Karime, A., Hossain, M.S.: Multimedia-oriented action recognition in Smart City - based IoT using multilayer perceptron. Multimedia Tools Appl. 78, 30315–30329 (2019)

    Article  Google Scholar 

  • Alhussein, M., Muhammad, G.: Automatic voice pathology monitoring using parallel deep models for smart healthcare. IEEE Access 7, 46474–46479 (2019)

    Article  Google Scholar 

  • Alhussein, M., Muhammad, G., Shamim Hossain, M.: EEG pathology detection based on deep learning. IEEE Access 7, 27781–27788 (2019)

    Article  Google Scholar 

  • Almeida, A., Azkune, G.: Predicting human behaviour with recurrent neural networks. Appl. Sci. 8, 305 (2018). https://doi.org/10.3390/app8020305

    Article  Google Scholar 

  • Almeshaiei, E., Al Habaibeh, A., Shakmak, B.: Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers. J. Clean. Prod. 244, 118788 (2019). https://doi.org/10.1016/j.jclepro.2019.118788

    Article  Google Scholar 

  • Amin, S.U., Shamim Hossain, M., Muhammad, G., Alhussein, M., Abdur Rahman, M.D.: Cognitive smart healthcare for pathology detection and monitoring. IEEE Access 7, 10745–10753 (2019)

    Article  Google Scholar 

  • Anthony Jnr, B.: A case-based reasoning recommender system for sustainable smart city development. AI Soc. 22, 1–25 (2020). https://doi.org/10.1007/s00146-020-00984-2

    Article  Google Scholar 

  • Asad, M.S., Ahmad, J., Hussain, S., Zoha, A., Abbasi, Q.H., Imran, M.A.: Mobility prediction-based optimisation and encryption of passenger Traffic-Flows using machine learning. Sensors 20, 2629 (2020)

    Article  Google Scholar 

  • Austin, M., Delgoshaei, P., Coelho, M., Heidarinejad, M.: Architecting smart city digital twins: combined semantic model and machine learning approach. J. Manag. Eng. 36(4) (2020). https://doi.org/10.1061/(asce)me.1943-5479.0000774

  • Aydin, G., Hallac, I.R., Karakus, B.: Architecture and implementation of a scalable sensor data storage and analysis system using cloud computing and big data technologies. J. Sens. (2015). https://doi.org/10.1155/2015/834217. paper 834217

  • Aymen, F., Mahmoudi, C.: A novel energy optimization approach for electrical vehicles in a smart city. Energies 12(929) (2019). https://doi.org/10.20944/preprints201901.0214.v1

  • Cai, B.Y., Alvarez, R., Sit, M., Duarte, F., Ratti, C.: Deep learning-based video system for accurate and real-time parking measurement. IEEE Internet Things J. 6(5), 7693–7701 (2019)

    Article  Google Scholar 

  • Calvo, P.: The ethics of Smart City (EoSC): moral implications of hyperconnectivity, algorithmisation and the datafication of urban digital society. Ethics Inf. Technol. 22, 141–149 (2020)

    Article  Google Scholar 

  • Cao, J., Cao, M., Wang, J., Yin, C., Wang, D., Vidal, P.P.: Urban noise recognition with convolutional neural network. Multimedia Tools Appl. 78, 29021–29041 (2019)

    Article  Google Scholar 

  • Castelli, M., Sormani, R., Trujillo, L., Popovič, A.: Predicting per capita violent crimes in urban areas: an artificial intelligence approach. J. Ambient Intell. Humaniz. Comput. 8, 29–36 (2017)

    Article  Google Scholar 

  • Chen, M., Miao, Y., Jian, X., Wang, X., Humar, I.: Cognitive-LPWAN: towards intelligent wireless services in hybrid low power wide area networks. IEEE Trans. Green Commun. Networking 2(3), 402–417 (2019a)

    Google Scholar 

  • Chen, Q., Wang, W., Wu, F., De, S., Zhang, B., Huang, X.: A survey on an emerging area: deep learning for smart city data. IEEE Trans. Emerg. Top. Comput. Intell. 3(5), 392–410 (2019b)

    Article  Google Scholar 

  • Choudhury, S. K., Padhy, R. P., Sangaiah, A. K., Sa, P. K., Muhammad, K., Bakshi, S.: Scale aware deep pedestrian detection. Trans. Emerg. Telecommunications Technol. 30(3552) (2018)

    Google Scholar 

  • De Paz, J.F., Bajo, J., Rodriguez, S., Villarrubia, G., Corchado, J.M.: Intelligent system for lighting control in smart cities. Inf. Sci. 372, 241–255 (2016)

    Article  Google Scholar 

  • Dennis, L.A., Slavkovik, M.: Machines that know right and cannot do wrong: the theory and practice of machine ethics. IEEE Intell. Inform. Bull. 19(1), 8–11 (2018)

    Google Scholar 

  • Dilawar, N., et al.: Understanding citizen issues through reviews: a step towards data informed planning in smart cities. Appl. Sci. 8(1589) (2018). https://doi.org/10.3390/app8091589

  • Diro, A.A., Chilamkurti, N.: Distributed Attack detection scheme using deep learning approach for internet of things. Future Generation Computer Systems 82, 761–768 (2017). https://doi.org/10.1016/j.future.2017.08.043

    Article  Google Scholar 

  • Eldrandaly, K.A., Abdel-Basset, M., Abdel-Fatah, L.: PTZ-Surveillance coverage based on artificial intelligence for smart cities. Int. J. Inf. Manage. 49, 520–532 (2019)

    Article  Google Scholar 

  • El-Wakeel, A.S., Li, J., Noureldin, A., Hassanein, H.S., Zorba, N.: Towards a practical crowdsensing system for road surface conditions monitoring. IEEE Internet Things J. 5(6), 4672–4685 (2018)

    Article  Google Scholar 

  • Etzioni, A., Etzioni, E.: AI assisted ethics. Ethics Inf. Technol. 18, 149–156 (2016)

    Article  Google Scholar 

  • Falco, G., VisWanathan, A., Caldera, C., Shrobe, H.: A master attack methodology for an AI-Based automated attack planner for smart cities. IEEE Access 6, 48360–48378 (2018)

    Article  Google Scholar 

  • Ferrara, E., et al.: An AI approach to collecting and analysing human interactions with urban environments. IEEE Access 7, 141476–141486 (2019)

    Article  Google Scholar 

  • Gomede, E., de Barros, R.M., de Suoza Mendes, L.: Use of deep multi-target prediction to identify learning styles. Appl. Sci. 10(5), 1756 (2020). https://doi.org/10.3390/app10051756

    Article  Google Scholar 

  • Gomede, E., Gaffo, F.H., Brigano, G.U., de Barros, R.M., de Suoza Mendes, L.: Application of computational intelligence to improve education in smart cities. Sensors 18(1), 267 (2018). https://doi.org/10.3390/s18010267

    Article  Google Scholar 

  • Gomez, C.A., Shami, A., Wang, X.: Machine learning aided scheme for load balancing in dense IoT networks. Sensors 18(11), 3779 (2018). https://doi.org/10.3390/s18113779

    Article  Google Scholar 

  • Gong, Z., Li, X., Liu, J., Gong, Y.: Machine learning in explaining nonprofit organisations’ participation: a driving factors analysis approach. Neural Comput. Appl. 31, 8267–8277 (2019)

    Article  Google Scholar 

  • Hossen, M.I., Michael, G.K.O., Connie, T., Lau, S.H., Hossain, F.: Smartphone-Based context flow recognition for outdoor parking system with machine learning approaches. Electronics 8(7), 784 (2019). https://doi.org/10.3390/electronics8070784

    Article  Google Scholar 

  • Huang, J., Deng, Y., Yang, Q., Sun, J.: An Energy-Efficient train control framework for smart railway transportation. IEEE Trans. Comput. 65(5), 1407–1417 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  • Huang, Z., Tang, J., Shan, G., et al.: An efficient passenger-hunting recommendation framework with multitask deep learning. IEEE Internet Things J. 6(5), 7713–7721 (2019)

    Article  Google Scholar 

  • Hurst, W., Montanez, C.A.C., Shone, N., Al-Jumeily, D.: An ensemble detection model using multinomial classification of stochastic gas smart meter data to improve wellbeing monitoring in smart cities. IEEE Access 8, 7877–7898 (2020)

    Article  Google Scholar 

  • Hwang, S., Lee, Z., Kim, J.: Real-time pedestrian flow analysis using networked sensors for a smart subway system. Sustainability 11(23), 6560 (2019). https://doi.org/10.3390/su11236560

    Article  Google Scholar 

  • Idowu, S., Saguna, S., Ahlund, C., Schelen, O.: Applied machine learning: Forecasting heat load in district heating system. Energy Build. 133, 478–488 (2016)

    Article  Google Scholar 

  • Iqbal, R., Doctor, F., More, B., Mahmu, S., Yosuf, U.: Big data analytics: computational intelligence techniques and application areas. Technol. Forecast. Soc. Chang. 153, (2020). https://doi.org/10.1016/j.techfore.2018.03.024

    Article  Google Scholar 

  • Jung, D., Tuan, V.T., Tran, D.Q., Park, M., Park, S.: Conceptual framework of an intelligent decision support system for smart city disaster management. Appl. Sci. 10(2), 666 (2020). https://doi.org/10.3390/app10020666

    Article  Google Scholar 

  • Ke, R., Zhung, Y., Pu, Z., Wang, Y.: A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Trans. Intell. Transp. Syst. (2020). https://doi.org/10.1109/TITS.2020.2984197

    Article  Google Scholar 

  • Ke, X., Zhang, Y.: Fine-grained vehicle type detection and recognition based on dense attention network. Neurocomputing 399, 247–257 (2020). https://doi.org/10.1016/j.neucom.2020.02.101

    Article  Google Scholar 

  • Khan, S., Muhammad, K., Mumtaz, S., Baik, S.W., de Albuquerque, V.H.C.: Energy-efficient deep CNN for smoke detection in foggy IoT environment. IEEE Internet Things J. 6(6), 9237–9245 (2019)

    Article  Google Scholar 

  • Kim, H., Ben-Othman, J.: Toward integrated virtual emotion system with AI applicability for secure CPS-Enabled smart cities: AI-Based research challenges and security issues. IEEE Network 34(3), 30–36 (2020). https://doi.org/10.1109/MNET.011.1900299

    Article  Google Scholar 

  • Kofod-Petersen, A.: How to do a Structured Literature Review in computer science (2015). https://research.idi.ntnu.no/aimasters/files/SLR_HowTo2018.pdf

  • Le, L.T., Nguyen, H., Dou, J., Zhou, J.: A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl. Sci. 9(13), 2630 (2019a). https://doi.org/10.3390/app9132630

    Article  Google Scholar 

  • Le, L.T., Nguyen, H., Zhou, J., Dou, J., Moayedi, H.: Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl. Sci. 9(13), 2714 (2019b). https://doi.org/10.3390/app9132714

    Article  Google Scholar 

  • Li, D., Deng, L., Cai, Z., Franks, B., Yao, X.: Intelligent transportation system in macao based on deep self-coding learning. IEEE Trans. Industr. Inf. 14(7), 3253–3260 (2018)

    Article  Google Scholar 

  • Liang, F., Hatcher, W.G., Liao, W., Gao, W., Yu, W.: Machine learning for security and the internet of things: the good, the bad, and the Ugly. IEEE Access 7, 158126–158147 (2019)

    Article  Google Scholar 

  • Lin, W., et al.: Analysing the relationship between human behavior and indoor air quality. Sens. Actuator Networks 6(13) (2017). https://doi.org/10.3390/jsan6030013

  • Liu, C.H., Chen, Z., Zhan, Y.: Energy-efficient distributed mobile crowd sensing: a deep learning approach. IEEE J. Sel. Areas Commun. 37(6), 1262–1276 (2019a)

    Article  Google Scholar 

  • Liu, Y., et al.: DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings. IEEE Internet Things J. 6(4), 7319–7333 (2019b)

    Article  Google Scholar 

  • Liu, Y., Yang, C., Jang, L., Xie, S., Zhang, Y.: Intelligent edge computing for IoT-based energy management in smart cities. IEEE Network 33(2), 111–117 (2019c). https://doi.org/10.1109/MNET.2019.1800254

    Article  Google Scholar 

  • Liu, Y., Zhang, W., Pan, S., Li, Y., Chen, Y.: Analysing the robotic behaviour in a smart city with deep enforcement and imitation learning using IoRT. Comput. Commun. 150, 346–356 (2020)

    Article  Google Scholar 

  • Mannion, P., Duggan, J., Howley, E.: Parallel reinforcement learning for traffic signal control. Procedia Comput. Sci. 52, 956–961 (2015)

    Article  Google Scholar 

  • Manzanilla-Salazar, O.G., Malandra, F., Mellah, H., Wette, C., Sanso, B.: A machine learning framework for sleeping cell detection in a smart-city IoT telecommunications infrastructure. IEEE Access 8, 61213–61225 (2020)

    Article  Google Scholar 

  • Martinez Garcia, J., Zoeke, D., Vossiek, M.: MIMO-FMCW Radar-based parking monitoring application with a modified convolutional neural network with spatial priors. IEEE Access 6, 41391–41398 (2018)

    Article  Google Scholar 

  • Medapati, P.K., Murthy, P.H.S.T., Sridhar, K.P.: LAMSTAR: for IoT-based face recognition system to manage the safety factor in smart cities. Trans Emerging Tel Tech. e3843 (2019). https://doi.org/10.1002/ett.3843

  • Miraftabzadeh, S.A., Rad, P., Choo, K.K.R., Jamshidi, M.: A privacy-aware architecture at the edge for autonomous real-time identity reidentification in crowds. IEEE Internet Things J. 5(4), 2936–2946 (2018)

    Article  Google Scholar 

  • Mo, X., Zhang, L., Li, H., Qu, Z.: A novel air quality early-warning system based on artificial intelligence. Environ. Res. Public Health 16(19), 3505 (2019). https://doi.org/10.3390/ijerph16193505

    Article  Google Scholar 

  • Mohanta, B., Das, P., Pantaik, S.: Healthcare 5.0: A paradigm shift in digital healthcare system using Artificial Intelligence, IOT and 5G Communication. In: 2019 International Conference on Applied Machine Learning (ICAML), Bhubaneswar, pp. 191–196 (2019). https://doi.org/10.1109/icaml48257.2019.00044

  • Niu, X., Zhu, Y., Cao, Q., Zhang, X., Xie, W., Zheng, K.: An online-traffic-prediction based route finding mechanism for smart city. Int. J. Distrib. Sens. Networks 11(8) (2015). https://doi.org/10.1155/2015/970256

  • Obinikpo, A.A., Kantarci, B.: Big sensed data meets deep learning for smarter health care in smart cities. Sens. Actuator Network 6(4), 26 (2017). https://doi.org/10.3390/jsan6040026

    Article  Google Scholar 

  • Palka, D., Brodny, J., Rizaoglu, T., Bagci, U., Maščeník, J.: Literature research in the field of technology assessment using a tool of a systematic literature review. Multidisciplinary Aspects Prod. Eng. 1, 109–115 (2018)

    Article  Google Scholar 

  • Park, S., Lee, S., Park, S., Park, S.: AI-based physical and virtual platform with 5-layered architecture for sustainable smart energy city development. Sustainability 11(16) (2019). https://doi.org/10.3390/su11164479

  • Ping, P., Xu, G., Kumala, E., Gao, J.: Smart street litter detection and classification based on faster R-CNN and edge computing. Int. J. Softw. Eng. 30(4), 537–553 (2020)

    Google Scholar 

  • Ponce, H., Gutierrez, S.: An indoor predicting climate conditions approach using Internet-of-Things and artificial hydrocarbon networks. Measurement 135, 170–179 (2018). https://doi.org/10.1016/j.measurement.2018.11.043

    Article  Google Scholar 

  • Qin, L., Yu, N., Zhao, D.: Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. Tech. Gazzette 25, 528–535 (2018)

    Google Scholar 

  • Qiu, J., Du, L., Zhang, D., Su, S., Tian, Z.: Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Trans. Industr. Inf. 16(4), 2659–2666 (2020)

    Article  Google Scholar 

  • Rjab, A.B., Mellouli, S.: Smart cities in the era of artificial intelligence and internet of things: literature review from 1990 to 2017. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, pp. 1–10 (2018). https://doi.org/10.1145/3209281.3209380. Article No. 81

  • Rjab, A.B., Mellouli, S.: Artificial intelligence in smart cities: systematic literature network analysis. In: Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance, pp. 259–269 (2019). https://doi.org/10.1145/3326365.3326400

  • Rojek, I., Studzinski, J.: Detection and localisation of water leaks in water nets supported by an ICT system with artificial intelligence methods as a way forward for smart cities. Sustainability 11(2), 518 (2019). https://doi.org/10.3390/su11020518

    Article  Google Scholar 

  • Roldan, J., Boubeta-Puig, J., Martinez, L.J., Ortiz, G.: Integrating complex event processing and machine learning: an intelligent architecture for detecting IoT security attacks. Expert Syst. Appl. 149, (2020). https://doi.org/10.1016/j.eswa.2020.113251

    Article  Google Scholar 

  • Sanam, T.F., Godrich, H.: A multi-view discriminant learning approach for indoor localization using amplitude and phase features of CSI. IEEE Access 8, 55947–55959 (2020)

    Article  Google Scholar 

  • Serban, A.C., Lytras, A.M.D.: Artificial intelligence for smart renewable energy sector in Europe - Smart energy infrastructures for next generation smart cities. IEEE Access 8, 77364–77377 (2020)

    Article  Google Scholar 

  • Shen, M., Tang, X., Zhu, L., Du, X., Guizani, M.: Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet Things J. 6(5), 7702–7712 (2019)

    Article  Google Scholar 

  • Sholla, S., Mir, R.N., Chishti, M.A.: A neuro fuzzy system for incorporating ethics in the internet of things. J. Ambient Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-02217-2

    Article  Google Scholar 

  • Shousong, C., Xiaomin, G., Xiaoguang, W., Ying, C.: Research on urban land price assessment based on artificial neural network model. IEEE Access 7, 180738–180748 (2019)

    Article  Google Scholar 

  • Shu, W., Cai, K.: A SVM multi-class image classification method based on DE and KNN in smart city management. IEEE Access 7, 132775–132785 (2019)

    Article  Google Scholar 

  • Talamo, M., Valentini, F., Dimitri, A., Allegrini, I.: Innovative technologies for cultural heritage. Tattoo sensors and AI: the new life of cultural assets. Sensors 20(7), 1909 (2020). https://doi.org/10.3390/s20071909

    Article  Google Scholar 

  • Vazquez-Canteli, J., Ulyanin, S., Kampf, J., Nagy, Z.: Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities. Sustain. Cities Soc. 45, 243–257 (2018). https://doi.org/10.1016/j.scs.2018.11.021

    Article  Google Scholar 

  • Venkatesh, J., Aksanli, B., Chan, C.S., Akyurek, S.A., Simunic Rosing, T.: Modular and personalised smart health application design in a smart city environment. IEEE Internet Things J. 5(2), 614–623 (2018)

    Article  Google Scholar 

  • Wan, C.H., Hwang, M.C.: Value-based deep reinforcement learning for adaptive isolated intersection signal control. IET Intell. Transp. Syst. 12(9), 1005–1010 (2018)

    Article  Google Scholar 

  • Wang, H., Xue, Q., Cui, T., Li, Y., Zeng, H.: Cold start problem of vehicle model recognition under CrossScenario based on transfer learning. Comput. Mater. Continua. 63(1), 337–351 (2020)

    Article  Google Scholar 

  • Wang, S.: Smart data mining algorithm for intelligent education. J. Intell. Fuzzy Syst. 37(1), 9–16 (2019)

    Article  Google Scholar 

  • Webster, J., Watson, R.: Analysing the past to prepare for the future: writing a literature review. MIS Q. 26(2) (2002). http://www.jstor.org/stable/4132319

  • Wolfswinkel, J.F., Furtmueller, E., Wilderom, C.P.M.: Using grounded theory as a method for rigorously reviewing literature. Eur. J. Inf. Syst. 22(1), 45–55 (2013)

    Article  Google Scholar 

  • Wu, X., Park, Y., Li, A., Huang, X., Xiao, F., Usmani, A., Huang, X.: Smart detection of fire source in tunnel based on the numerical database and artificial intelligence. Fire Technol. (2020). https://doi.org/10.1007/s10694-020-00985-z

    Article  Google Scholar 

  • Xiong, M., et al.: Person re-identification with multiple similarity probabilities using deep metric learning for efficient smart security applications. J. Parallel Distrib. Comput. 132, 230–241 (2017). https://doi.org/10.1016/j.jpdc.2017.11.009

  • Xu, R., Cheng, Y., Liu, Z., Xie, Y., Yang, Y.: Improved long short-term memory based anomaly detection with concept drift adaptive method for supporting IoT services. Future Gener. Comput. Syst. 112, 228–242 (2020). https://doi.org/10.1016/j.future.2020.05.035

    Article  Google Scholar 

  • Yao, H., Gao, P., Wang, J., Zhang, P., Jiang, C., Han, Z.: Capsule network assisted IoT traffic classification mechanism for smart cities. IEEE Internet Things J. 6(5), 7515–7525 (2019)

    Article  Google Scholar 

  • Yassine, A., Singh, S., Alamri, A.A.: Mining human activity patterns from smart home big data for health care applications. IEEE Access 5, 13131–13141 (2017)

    Article  Google Scholar 

  • Zhang, P., Zhao, Q., Gao, J., Li, W., Lu, J.: Mining human activity patterns from smart home big data for health care applications. IEEE Access 7, 63550–63563 (2019)

    Article  Google Scholar 

  • Zhang, Q., Zhou, D., Zeng, X.: HeartID: a multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications. IEEE Access 5, 11805–11816 (2017)

    Article  Google Scholar 

  • Zhao, B., Teo, Y.S., Ng, W.S., Ng, H.H.: Data-driven next destination prediction and ETA improvement for urban delivery fleets. IET Intell. Transp. Syst. 13(11), 1624–1635 (2019a)

    Article  Google Scholar 

  • Zhao, L., Wang, J., Liu, J., Kato, N.: Routing for crowd management in smart cities: a deep reinforcement learning perspective. IEEE Commun. Mag. 57(4), 88–93 (2019b). https://doi.org/10.1109/MCOM.2019.1800603

    Article  Google Scholar 

  • Zhou, T., Shen, J., He, D., Vijayakumar, P., Kumar, N.: Human-in-the-loop-aided privacy-preserving scheme for smart healthcare. IEEE Trans. Emerg. Top. Comput. Intell. (2020). https://doi.org/10.1109/TETCI.2020.2993841

    Article  Google Scholar 

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Acknowledgment

This work has been supported by the Croatian Science Foundation (project No. IRP-2017-05-7625).

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  1. Faculty of Economics, Business and Tourism, University of Split, Cvite Fiskovica 5, 21000, Split, Croatia

    Ivana Dominiković, Maja Ćukušić & Mario Jadrić

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  1. Ivana Dominiković
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  2. Maja Ćukušić
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Correspondence to Maja Ćukušić .

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  1. Federal University of Santa Catarina, Florianópolis, Brazil

    Edgar Bisset Álvarez

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Dominiković, I., Ćukušić, M., Jadrić, M. (2021). The Role of Artificial Intelligence in Smart Cities: Systematic Literature Review. In: Bisset Álvarez, E. (eds) Data and Information in Online Environments. DIONE 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-77417-2_5

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