Artificial Intelligence in the Smart City — A Literature Review
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References
- Abbas, S., Khan, M. A., Falcon-Morales, L. E., Rehman, A., Saeed, Y., Zareei, M., Zeb, A., & Mohamed, E. M. (2020). Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered with Deep Extreme Learning Machine. IEEE Access, 8, 39982-39997. doi: 10.1109/ACCESS.2020.2976452
- Abuga, D., & Raghava, N. S. (2021). Real-time smart garbage bin mechanism for solid waste management in smart cities. Sustainable Cities and Society, 75. doi: 10.1016/j.scs.2021.103347
- Adel, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. Journal of Cloud Computing-Advances Systems and Applications, 11(1). doi: 10.1186/s13677-022-00314-5
- Aguilera, U., Peña, O., Belmonte, O., & López-de-Ipiña, D. (2017). Citizen-centric data services for smarter cities. Future Generation Computer Systems, 76, 234-247. doi: 10.1016/j.future.2016.10.031
- Alam, F., Mehmood, R., Katib, I., Albogami, N. N., & Albeshri, A. (2017). Data Fusion and IoT for Smart Ubiquitous Environments: A Survey. IEEE Access, 5, 9533-9554. doi: 10.1109/ACCESS.2017.2697839
- Alifi, M. R., & Supangkat, S. H. (2016). Information extraction for traffic congestion in social network: Case study: Bekasi city. 2016 International Conference on ICT for Smart Society, ICISS 2016, 53-58. doi: 10.1109/ICTSS.2016.7792848
- Ali, R., Zikria, Y. B., Kim, B.-S., & Kim, S. W. (2020). Deep reinforcement learning paradigm for dense wireless networks in smart cities. In EAI/Springer Innovations in Communication and Computing (pp. 43-70). Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-030-14718-1_3
- Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91. doi: 10.1016/j.cities.2019.01.032
- Allam, Z., & Jones, D. S. (2020). On the coronavirus (Covid-19) outbreak and the smart city network: Universal data sharing standards coupled with artificial intelligence (ai) to benefit urban health monitoring and management. Healthcare, 8(1). doi: 10.3390/ healthcare8010046
- Allam, Z., & Newman, P. (2018). Redefining the Smart City: Culture, Metabolism and Governance. Smart Cities, 1(1), 4-25. doi: 10.3390/smartcities1010002
- Allam, Z., Tegally, H., & Thondoo, M. (2019). Redefining the use of big data in urban health for increased live-ability in smart cities. Smart Cities, 2(2), 259-268. doi: 10.3390/smartcities2020017
- Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on collaborative smart drones and internet of things for improving smartness of smart cities. IEEE Access, 7, 128125-128152. doi: 10.1109/ACCESS.2019.2934998
- Al-Turjman, F., & Baali, I. (2022). Machine learning for wearable IoT-based applications: A survey. Transactions on Emerging Telecommunications Technologies, 33(8). doi: 10.1002/ett.3635
- Al-Turjman, F., Nayyar, A., Devi, A., & Shukla, P. K. (2021). Intelligence of things: AI-IoT based critical-applications and innovations. In Intelligence of Things: AI-IoT Based Critical-Applications and Innovations. Springer International Publishing. doi: 10.1007/978-3-030-82800-4
- Amoroso, S., Aristodemou, L., Criscuolo, C., Dechezleprete, A., Dernis, H., Grassano, N., Moussiegt, L., Napolitano, L., Nawa, D., Squicciarini, M., & Tuebke, A. (2021). World Corporate Top R&D investors: Paving the way for climate neutrality. Publications Office of the European Union, Luxembourg, JRC126788, EUR 30884 EN.
- Ang, K. L.-M., Seng, J. K. P., Ngharamike, E., & Ijemaru, G. K. (2022). Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches. ISPRS International Journal of Geo-Information, 11(2). doi: 10.3390/ijgi11020085
- Anthopoulos, L., & Kazantzi, V. (2022). Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers. Sustainable Cities and Society, 76. doi: 10.1016/j. scs.2021.103492
- Anuradha, M., Jayasankar, T., Prakash, N. B., Sikkandar, M. Y., Hemalakshmi, G. R., Bharatiraja, C., & Britto, A. S. F. (2021). IoT enabled cancer prediction system to enhance the authentication and security using cloud computing. Microprocessors and Microsystems, 80. doi: 10.1016/j.micpro.2020.103301
- Aqib, M., Mehmood, R., Alzahrani, A., & Katib, I. (2020). In-memory deep learning computations on gpus for prediction of road traffic incidents using big data fusion. In EAI/Springer Innovations in Communication and Computing (pp. 79-114). Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-030-13705-2_4
- Atitallah, S. B., Driss, M., Boulila, W., & Ghezala, H. B. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38. doi: 10.1016/j.cosrev.2020.100303
- Augustine, P. (2020). The industry use cases for the Digital Twin idea. In P. Raj & P. Evangeline (Eds.), Advances in Computers (pp. 79-105). Academic Press Inc. doi: 10.1016/bs.adcom.2019.10.008
- Aymen, F., & Mahmoudi, C. (2019). A novel energy optimization approach for electrical vehicles in a smart city. Energies, 12(5). doi: 10.3390/en12050929
- Badura, D. (2017). Urban traffic modeling and simulation. Forum Scientiae Oeconomia, 5(4), 85-97. doi: 10.23762/FSO_VOL5NO4_17_7
- Bilan, S., Šuleř, P., Skrynnyk, O., Krajňáková, E., & Vasilyeva, T. (2022). Systematic bibliometric review of artificial intelligence technology in organizational management, development, change and culture. Business: Theory and Practice, 23(1), 1-13. doi: 10.3846/ btp.2022.13204
- Bornmann, L., & Haunschild, R. (2017). Quality and impact considerations in bibliometrics: a reply to Ricker. Scientometrics, 111(3), 1857-1859. doi: 10.1007/ s11192-017-2373-3
- Boulos, M. N. K., Wilson, J. T., & Clauson, K. A. (2018). Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. International Journal of Health Geographics, 17. doi: 10.1186/ s12942-018-0144-x
- Braun, T., Fung, B. C. M., Iqbal, F., & Shah, B. (2018). Security and privacy challenges in smart cities. Sustainable Cities and Society, 39, 499-507. doi: 10.1016/j. scs.2018.02.039
- Bucchiarone, A., Battisti, S., Marconi, A., Maldacea, R., & Ponce, D. C. (2021). Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and Social Implications. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3790-3799. doi: 10.1109/TITS.2020.3025670
- Castelli, M., Sormani, R., Trujillo, L., & Popovič, A. (2017). Predicting per capita violent crimes in urban areas: an artificial intelligence approach. Journal of Ambient Intelligence and Humanized Computing, 8(1), 29-36. doi: 10.1007/s12652-015-0334-3
- Chang, C.-Y., Ko, K.-S., Guo, S.-J., Hung, S.-S., & Lin, Y.-T. (2020). CO multi-forecasting model for indoor health and safety management in smart home. Journal of Internet Technology, 21(1), 273-284. doi: 10.3966/160792642020012101023
- Chen, J., Ramanathan, L., & Alazab, M. (2021). Holistic big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities. Microprocessors and Microsystems, 81. doi: 10.1016/j.micpro.2020.103722
- Chen, M., Liu, W., Wang, T., Liu, A., & Zeng, Z. (2021). Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach. Computer Networks, 195. doi: 10.1016/j.comnet.2021.108186
- Chen, Y., Lu, Y., Bulysheva, L., & Kataev, M. Y. (2022). Applications of Blockchain in Industry 4.0: a Review. Information Systems Frontiers. doi: 10.1007/s10796-022-10248-7
- Choudhary, P., & Sarthy, P. (2022). Transforming Cities for Sustainability: Role of Standards on Smart City. 2022 2nd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control, PARC 2022. doi: 10.1109/PARC52418.2022.9726674
- Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11). doi: 10.3390/en11112869
- Communication from The Commission to The European Parliament, The European Council, The Council, The European Economic and Social Committee and The Committee of the regions. The European Green Deal. COM (2019) 640 Final. (2019).
- Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62. doi: 10.1016/j.techsoc.2020.101257
- Cui, Q., Wang, Y., Chen, K.-C., Ni, W., Lin, I.-C., Tao, X., & Zhang, P. (2019). Big data analytics and network calculus enabling intelligent management of autonomous vehicles in a smart city. IEEE Internet of Things Journal, 6(2), 2021-2034. doi: 10.1109/ JIOT.2018.2872442
- David, M., Mbabazi, E. S., Nakatumba-Nabende, J., & Marvin, G. (2023). Crime Forecasting using Interpretable Regression Techniques. 7th International Conference on Trends in Electronics and Informatics, ICOEI 2023 - Proceedings, 1405-1411. doi: 10.1109/ ICOEI56765.2023.10126071
- De Giovanni, P. (2023). Sustainability of the Metaverse: A Transition to Industry 5.0. Sustainability, 15(7). doi: 10.3390/su15076079
- Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768. doi: 10.1016/j.future.2017.08.043
- Dong, Y., & Yao, Y.-D. (2021). IoT platform for covid-19 prevention and control: A survey. IEEE Access, 9, 49929-49941. doi: 10.1109/ACCESS.2021.3068276
- Ejdys, J., & Gulc, A. (2020). Trust in Courier Services and Its Antecedents as a Determinant of Perceived Service Quality and Future Intention to Use Courier Service. Sustainability, 12, 1-18. doi: 10.3390/ su12219088
- Elghaish, F., Matarneh, S. T., Edwards, D. J., Pour Rahimian, F., El-Gohary, H., & Ejohwomu, O. (2022). Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework. Construction Innovation, 22(3), 647-670. doi: 10.1108/CI-03-2022-0062
- Espina-Romero, L., Guerrero-Alcedo, J., Goñi Avila, N., Noroño Sánchez, J. G., Gutiérrez Hurtado, H., & Quiñones Li, A. (2023). Industry 5.0: Tracking Scientific Activity on the Most Influential Industries, Associated Topics, and Future Research Agenda. Sustainability, 15(6). doi: 10.3390/su15065554
- Ferdowsi, A., Challita, U., & Saad, W. (2019). Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview. IEEE Vehicular Technology Magazine, 14(1), 62-70. doi: 10.1109/ MVT.2018.2883777
- Fernández, C., Manyà, F., Mateu, C., & Sole-Mauri, F. (2014). Modeling energy consumption in automated vacuum waste collection systems. Environmental Modelling and Software, 56, 63-73. doi: 10.1016/j.envsoft.2013.11.013
- Frey, C., Hertweck, P., Richter, L., & Warweg, O. (2022). Bauhaus.MobilityLab: A Living Lab for the Development and Evaluation of AI-Assisted Services. Smart Cities, 5(1), 133-145. doi: 10.3390/smartci-ties5010009
- Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952-108971. doi: 10.1109/ACCESS.2020.2998358
- Gaber, H., Othman, A. M., & Fahad, A. H. (2020). Future of connected autonomous vehicles in smart cities. In Solving Urban Infrastructure Problems Using Smart City Technologies: Handbook on Planning, Design, Development, and Regulation (pp. 599-611). Elsevier. doi: 10.1016/B978-0-12-816816-5.00027-9
- Gad, A. G. (2022). Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering, 29(5), 2531-2561. doi: 10.1007/s11831-021-09694-4
- Galindo, F. (2014). Methods for law and ICT: An approach for the development of smart cities. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8929, 26-40. doi: 10.1007/978-3-662-45960-7
- Gams, M., Gu, I. Y.-H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86. doi: 10.3233/AIS-180508
- Garcia-Retuerta, D., Chamoso, P., Hernández, G., Guzmán, A. S. R., Yigitcanlar, T., & Corchado, J. M. (2021). An efficient management platform for developing smart cities: Solution for real-time and future crowd detection. Electronics, 10(7). doi: 10.3390/electronics10070765
- Gaska, K., & Generowicz, A. (2020). SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study. Energies, 13(13). doi: 10.3390/ en13133338
- Ghadami, N., Gheibi, M., Kian, Z., Faramarz, M. G., Naghedi, R., Eftekhari, M., Fathollahi-Fard, A. M., Dulebenets, M. A., & Tian, G. (2021). Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods. Sustainable Cities and Society, 74. doi: 10.1016/j.scs.2021.103149
- Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021). IoT for smart cities: Machine learning approaches in smart healthcare—A review. Future Internet, 13(8). doi: 10.3390/fi13080218
- Glińska, E., & Siemieniako, D. (2018). Binge drinking in relation to services - Bibliometric analysis of scientific research directions. Engineering Management in Production and Services, 10(1), 45-54. doi: 10.1515/ emj-2018-0004
- Gohari, A., Ahmad, A. B., Rahim, R. B. A., Supa’at, A. S. M., Razak, S. A., & Gismalla, M. S. M. (2022). Involvement of Surveillance Drones in Smart Cities: A Systematic Review. IEEE Access, 10, 56611-56628. doi: 10.1109/ACCESS.2022.3177904
- Golinska-Dawson, P., & Sethanan, K. (2023). Sustainable Urban Freight for Energy-Efficient Smart Cities— Systematic Literature Review. Energies, 16(6). doi: 10.3390/en16062617
- Gudanowska, A. E. (2017). A Map of Current Research Trends within Technology Management in the Light of Selected Literature. Management and Production Engineering Review, 8(1), 78-88. doi: 10.1515/mper-2017-0009
- Gupta, S., Modgil, S., Lee, C.-K., Cho, M., & Park, Y. (2022). Artificial intelligence enabled robots for stay experience in the hospitality industry in a smart city. Industrial Management and Data Systems, 122(10), 2331-2350. doi: 10.1108/IMDS-10-2021-0621
- Halicka, K. (2017). Main Concepts of Technology Analysis in the Light of the Literature on the Subject. Procedia Engineering, 182, 291-298. doi: 10.1016/j.proeng.2017.03.196
- Hantrais, L., Allin, P., Kritikos, M., Sogomonjan, M., Anand, P. B., Livingstone, S., Williams, M., & Innes, M. (2021). Covid-19 and the digital revolution. Contemporary Social Science, 16(2), 256-270. doi: 10.1080/21582041.2020.1833234
- Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1). doi: 10.1186/s40537-019-0206-3
- Hu, S., & Jiang, T. (2019). Artificial Intelligence Technology Challenges Patent Laws. Proceedings - 2019 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2019, 241-244. doi: 10.1109/ICITBS.2019.00064
- Hu, Y.-C., Lin, Y.-H., & Gururaj, H. L. (2021). Partitional clustering-hybridized neuro-fuzzy classification evolved through parallel evolutionary computing and applied to energy decomposition for demand-side management in a smart home. Processes, 9(9). doi: 10.3390/pr9091539
- Javed, A. R., Shahzad, F., Rehman, S. U., Bin Zikria, Y., Razzak, I., Jalil, Z., & Xu, G. D. (2022). Future smart cities requirements, emerging technologies, applications, challenges, and future aspects. Cities, 129. doi: 10.1016/j.cities.2022.103794
- Jiang, Y., Xiao, W., Wang, R., & Barnawi, A. (2020). Smart Urban Living: Enabling Emotion-Guided Interaction with Next Generation Sensing Fabric. IEEE Access, 8, 28395-28402. doi: 10.1109/ACCESS.2019.2961957
- Kaginalkar, A., Kumar, S., Gargava, P., & Niyogi, D. (2021). Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective. Urban Climate, 39. doi: 10.1016/j.uclim.2021.100972
- Kakderi, C., Oikonomaki, E., & Papadaki, I. (2021). Smart and Resilient Urban Futures for Sustainability in the Post COVID-19 Era: A Review of Policy Responses on Urban Mobility. Sustainability, 13(11). doi: 10.3390/su13116486
- Kamel Boulos, M. N., Peng, G., & Vopham, T. (2019). An overview of GeoAI applications in health and health-care. International Journal of Health Geographics, 18(1). doi: 10.1186/s12942-019-0171-2
- Keathley-Herring, H., Van Aken, E., Gonzalez-Aleu, F., Deschamps, F., Letens, G., & Orlandini, P. C. (2016). Assessing the maturity of a research area: bibliometric review and proposed framework. Scientometrics, 109(2), 927-951. doi: 10.1007/s11192-016-2096-x
- Khan, N., Haq, I. U., Khan, S. U., Rho, S., Lee, M. Y., & Baik, S. W. (2021). DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems. International Journal of Electrical Power and Energy Systems, 133. doi: 10.1016/j.ijepes.2021.107023
- Khatoon, S., Rahman, S. M. M., Alrubaian, M., & Alamri, A. (2019). Privacy-Preserved, Provable Secure, Mutually Authenticated Key Agreement Protocol for Healthcare in a Smart City Environment. IEEE Access, 7, 47962-47971. doi: 10.1109/ACCESS.2019.2909556
- Khoa, T. A., Nhu, L. M. B., Son, H. H., Trong, N. M., Phuc, C. H., Phuong, N. T. H., Van Dung, N., Nam, N. H., Chau, D. S. T., & Duc, D. N. M. (2020). Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study. Wireless Communications and Mobile Computing, 2020. doi: 10.1155/2020/8896637
- Kim, K., Kim, J. S., Jeong, S., Park, J.-H., & Kim, H. K. (2021). Cybersecurity for autonomous vehicles: Review of attacks and defense. Computers and Security, 103. doi: 10.1016/j.cose.2020.102150
- Kozłowska, J., Benvenga, M. A., & de Alencar Nääs, I. (2023). Investment Risk and Energy Security Assessment of European Union Countries Using Multicriteria Analysis. Energies, 16, 1-28. doi: 10.3390/ en16010330
- Ktari, J., Frikha, T., Hamdi, M., Elmannai, H., & Hmam, H. (2022). Lightweight AI Framework for Industry 4.0 Case Study: Water Meter Recognition. Big Data and Cognitive Computing, 6(3). doi: 10.3390/ bdcc6030072
- Kummitha, R. K. R. (2020). Smart technologies for fighting pandemics: The techno-and human-driven approaches in controlling the virus transmission. Government Information Quarterly, 37(3). doi: 10.1016/j. giq.2020.101481
- Kuru, K. (2021). Planning the Future of Smart Cities with Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework. IEEE Access, 9, 6571-6595. doi: 10.1109/ACCESS.2020.3049094
- Kuźmicz, K., Ryciuk, U., Glińska, E., Kiryluk, H., & Rollnik-Sadowska, E. (2022). Perspectives of mobility development in remote areas attractive to tourists. Ekonomia i Środowisko, 80, 150-188. doi: 10.34659/ eis.2022.80.1.440
- Laamarti, F., Badawi, H. F., Ding, Y., Arafsha, F., Hafidh, B., & Saddik, A. E. (2020). An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities. IEEE Access, 8, 105950-105961. doi: 10.1109/ACCESS.2020.2999871
- Le, L. T., Nguyen, H., Dou, J., & Zhou, J. (2019). 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. Applied Sciences, 9(13). doi: 10.3390/app9132630
- Leung, C. K., Braun, P., & Cuzzocrea, A. (2019). AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning. Sensors, 19(6). doi: 10.3390/ s19061345
- Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., Feng, L., Chen, T., Yu, H., & Yang, Q. (2020). FedVision: An online visual object detection platform powered by federated learning. In R. Puri & N. Yorke-Smith (Eds.), Proceedings of the 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020 (pp. 13172-13179). The AAAI Press.
- Liu, Y., Ma, X., Shu, L., Yang, Q., Zhang, Y., Huo, Z., & Zhou, Z. (2020). Internet of things for noise mapping in smart cities: State of the art and future directions. IEEE Network, 34(4), 112-118. doi: 10.1109/ MNET.011.1900634
- Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities. IEEE Network, 33(2), 111-117. doi: 10.1109/MNET.2019.1800254
- Li, W., Yigitcanlar, T., Liu, A., & Erol, I. (2022). Mapping two decades of smart home research: A systematic scientometric analysis. Technological Forecasting and Social Change, 179. doi: 10.1016/j.techfore.2022.121676
- Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). Computer Methods and Programs in Biomedicine, 226. doi: 10.1016/j. cmpb.2022.107161
- López-Blanco, R., Martín, J. H., Alonso, R. S., & Prieto, J. (2023). Time Series Forecasting for Improving Quality of Life and Ecosystem Services in Smart Cities. In V. Julián, J. Carneiro, R. S. Alonso, P. Chamoso, & P. Novais (Eds.), Lecture Notes in Networks and Systems: Vol. 603 LNNS (pp. 74-85). Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-031-22356-3_8
- Lourenco, V., Mann, P., Guimaraes, A., Paes, A., & De Oliveira, D. (2018). Towards Safer (Smart) Cities: Discovering Urban Crime Patterns Using Logic-based Relational Machine Learning. Proceedings of the International Joint Conference on Neural Networks, 2018-July. doi: 10.1109/IJCNN.2018.8489374
- Lv, Z., Qiao, L., Singh, A. K., & Wang, Q. (2021). AI-empowered IoT Security for Smart Cities. ACM Transactions on Internet Technology, 21(4), 99. doi: 10.1145/3406115
- Łasak, P., & Wyciślak, S. (2023). Blockchain and cloud platforms in banking services: A paradox perspective. Journal of Entrepreneurship, Management, and Innovation, 19(4), 12-47. doi: 10.7341/20231941
- Ma, M., Stankovic, J. A., & Feng, L. (2018). CityResolver: A Decision Support System for Conflict Resolution in Smart Cities. Proceedings - 9th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2018, 55-64. doi: 10.1109/ICCPS.2018.00014
- Ma, Y., Ping, K., Wu, C., Chen, L., Shi, H., & Chong, D. (2020). Artificial Intelligence powered Internet of Things and smart public service. Library Hi Tech, 38(1), 165-179. doi: 10.1108/LHT-12-2017-0274
- Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I. (2018). How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management? Communications of the Association for Information Systems, 297-320. doi: 10.17705/1CAIS.04319
- Muhammad, K., Lloret, J., & Baik, S. W. (2019). Intelligent and energy-efficient data prioritization in green smart cities: Current challenges and future directions. IEEE Communications Magazine, 57(2), 60-65. doi: 10.1109/MCOM.2018.1800371
- Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31(3), 553-574. doi: 10.1007/s12525-020-00442-3
- Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms. Technologies, 10(1). doi: 10.3390/technologies10010005
- Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021). Federated Learning for Internet of Things: A Comprehensive Survey. IEEE Communications Surveys and Tutorials, 23(3), 1622-1658. doi: 10.1109/COMST.2021.3075439
- Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability (Switzerland), 12(7), 1-19. doi: 10.3390/su12072789
- Niñerola, A., Sánchez-Rebull, M.-V., & Hernández-Lara, A.-B. (2019). Tourism research on sustainability: A bibliometric analysis. Sustainability (Switzerland), 11(5). doi: 10.3390/su11051377
- O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019). Smart energy systems for sustainable smart cities: Current developments, trends and future directions. Applied Energy, 237, 581-597. doi: 10.1016/j.apenergy.2019.01.024
- Ortega-Fernández, A., Martín-Rojas, R., & García-Morales, V. J. (2020). Artificial intelligence in the urban environment: Smart cities as models for developing innovation and sustainability. Sustainability (Switzerland), 12(19). doi: 10.3390/SU12197860
- Paiva, S., Ahad, M. A., Tripathi, G., Feroz, N., & Casalino, G. (2021). Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges. Sensors, 21(6), 1-45. doi: 10.3390/s21062143
- Park, S., Park, S., Choi, M. I., Lee, S., Lee, T., Kim, S., Cho, K., & Park, S. (2020). Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization. Sensors, 20(17). doi: 10.3390/s20174918
- Perc, M., Ozer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5(1). doi: 10.1057/s41599-019-0278-x
- Pramod, M. S., Balodi, A., Pratik, A., Satya Sankalp, G., Varshita, B., & Amrit, R. (2023). Energy-Effcient Reinforcement Learning in Wireless Sensor Networks Using 5G for Smart Cities. In Applications of 5G and Beyond in Smart Cities (pp. 63-86). CRC Press. doi: 10.1201/9781003227861-4
- Ragab, A., Osama, A., & Ramzy, A. (2023). Simulation of the Environmental Impact of Industries in Smart Cities. Ain Shams Engineering Journal, 14(6). doi: 10.1016/j.asej.2022.102103
- Rani, S., Mishra, R. K., Usman, M., Kataria, A., Kumar, P., Bhambri, P., & Mishra, A. K. (2021). Amalgamation of advanced technologies for sustainable development of smart city environment: A review. IEEE Access, 9, 150060-150087. doi: 10.1109/ACCESS.2021.3125527
- Reebadiya, D., Rathod, T., Gupta, R., Tanwar, S., & Kumar, N. (2021). Blockchain-based Secure and Intelligent Sensing Scheme for Autonomous Vehicles Activity Tracking Beyond 5G Networks. Peer-to-Peer Networking and Applications, 14(5), 2757-2774. doi: 10.1007/s12083-021-01073-x
- Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3). doi: 10.1007/s42979-021-00592-x
- Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2022). Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions. Mobile Networks and Applications. doi: 10.1007/s11036-022-01937-3
- Serban, A. C., & Lytras, M. D. (2020). Artificial intelligence for smart renewable energy sector in Europe - Smart energy infrastructures for next generation smart cities. IEEE Access, 8, 77364-77377. doi: 10.1109/ACCESS.2020.2990123
- Shankar, K., Perumal, E., Elhoseny, M., Taher, F., Gupta, B. B., & El-Latif, A. A. A. (2021). Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities. ACM Transactions on Internet Technology, 22(3). doi: 10.1145/3453168
- Shi, J., Liu, S., Zhang, L., Yang, B., Shu, L., Yang, Y., Ren, M., Wang, Y., Chen, J., Chen, W., Chai, Y., & Tao, X. (2020). Smart Textile-Integrated Microelectronic Systems for Wearable Applications. Advanced Materials, 32(5). doi: 10.1002/adma.201901958
- Shi, X., Luo, J., Luo, J., Li, X., Han, K., Li, D., Cao, X., & Wang, Z. L. (2022). Flexible Wood-Based Tribo-electric Self-Powered Smart Home System. ACS Nano, 16(2), 3341-3350. doi: 10.1021/acsnano.1c11587
- Siderska, J., Alsqour, M., & Alsaqoor, S. (2023). Employees’ attitudes towards implementing robotic process automation technology at service companies. Human Technology, 19(1), 23-40. doi: 10.14254/1795-6889.2023.19-1.3
- Siderska, J., & Jadaan, K. S. (2018). Cloud manufacturing: A service-oriented manufacturing paradigm. A review paper. Engineering Management in Production and Services, 10(1), 22-31. doi: 10.1515/emj-2018-0002
- Singh, S. K., Rathore, S., & Park, J. H. (2020). BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence. Future Generation Computer Systems, 110, 721-743. doi: 10.1016/j. future.2019.09.002
- Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63. doi: 10.1016/j.scs.2020.102364
- Skouby, K. E., & Lynggaard, P. (2014). Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services. Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014, 874-878. doi: 10.1109/IC3I.2014.7019822
- Szpilko, D. (2017). Tourism Supply Chain – Overview of Selected Literature. Procedia Engineering, 182, 687-693. doi: 10.1016/j.proeng.2017.03.180
- Szpilko, D., & Ejdys, J. (2022). European Green Deal — research directions. A systematic literature review. Ekonomia i Środowisko - Economics and Environment, 81(2), 8-38. doi: 10.34659/eis.2022.81.2.455
- Szpilko, D., Budna, K., Drmeyan, H., & Remiszewska, A. (2023). Sustainable and smart mobility — research directions. A systematic literature review. Ekonomia i Środowisko - Economics and Environment, 86(3). doi: 10.34659/eis.2023.86.3.584
- Szpilko, D., Szydło, J., & Winkowska, J. (2020). Social Participation of City Inhabitants Versus Their Future Orientation. Evidence From Poland. WSEAS Transactions on Business and Economics, 17, 692-702. doi: 10.37394/23207.2020.17.67
- Szum, K. (2021). IoT-based smart cities: A bibliometric analysis and literature review. Engineering Management in Production and Services, 13(2), 115-136. doi: 10.2478/emj-2021-0017
- Tian, Y., & Pan, L. (2015). Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network. 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), 153-158. doi: 10.1109/SmartCity.2015.63
- Toglaw, S., Aloqaily, M., & Alkheir, A. A. (2018). Connected, autonomous and electric vehicles: The optimum value for a successful business model. 2018 5th International Conference on Internet of Things: Systems, Management and Security, IoTSMS 2018, 303-308. doi: 10.1109/IoTSMS.2018.8554391
- Tomaszewska, E. J., & Florea, A. (2018). Urban smart mobility in the scientific literature - Bibliometric analysis. Engineering Management in Production and Services, 10(2), 41-56. doi: 10.2478/emj-2018-0010
- Ullah, Z., Al-Turjman, F., Moatasim, U., Mostarda, L., & Gagliardi, R. (2020). UAVs joint optimization problems and machine learning to improve the 5G and Beyond communication. Computer Networks, 182. doi: 10.1016/j.comnet.2020.107478
- Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications, 154, 313-323. doi: 10.1016/j.comcom.2020.02.069
- United Nation. (2015). Transforming our world: the 2030 Agenda for Sustainable Development. In United Nation: Vol. A/RES/70/1.
- van Eck, N. J., & Waltman, L. (2018). VOSviewer Manual. Manual for VOSviewer version 1.6.11 software documentation.
- Vázquez-Canteli, J. R., Ulyanin, S., Kämpf, J., & Nagy, Z. (2019). Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities. Sustainable Cities and Society, 45, 243-257. doi: 10.1016/j.scs.2018.11.021
- Wang, K., Zhao, Y. F., Gangadhari, R. K., & Li, Z. X. (2021). Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China. Sustainability, 13(19). doi: 10.3390/su131910983
- Wences, P., Martinez, A., Estrada, H., & Gonzalez, M. (2017). Decision-making intelligent system for passenger of urban transports. In P. Singh, J. Bravo, & S. F. Ochoa (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS (pp. 128-139). Springer Verlag. doi: 10.1007/978-3-319-67585-5_14
- Winkowska, J., Szpilko, D., & Pejić, S. (2019). Smart city concept in the light of the literature review. Engineering Management in Production and Services, 11(2), 70-86. doi: 10.2478/emj-2019-0012
- Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596-615. doi: 10.1080/01900692.2018.1498103
- Wu, T.-Y., Meng, Q., Chen, Y.-C., Kumari, S., & Chen, C.-M. (2023). Toward a Secure Smart-Home IoT Access Control Scheme Based on Home Registration Approach. Mathematics, 11(9). doi: 10.3390/ math11092123
- Wu, Y. (2021). Cloud-Edge Orchestration for the Internet of Things: Architecture and AI-Powered Data Processing. IEEE Internet of Things Journal, 8(16), 12792-12805. doi: 10.1109/JIOT.2020.3014845
- Wu, Z., & Chu, W. (2021). Sampling Strategy Analysis of Machine Learning Models for Energy Consumption Prediction. 2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021, 77-81. doi: 10.1109/SEGE52446.2021.9534987
- Yamakami, T. (2017). An organizational coordination model for IoT: A case study of requirement engineering of city-government in Tokyo in city platform as a service. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, 2017-December, 259-263. doi: 10.1109/ICTC.2017.8190982
- Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6). doi: 10.3390/en13061473
- Yuan, T. T., Da RochaNeto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022). Machine learning for next-generation intelligent transportation systems: A survey. Transactions on Emerging Telecommunications Technologies, 33(4). doi: 10.1002/ett.4427
- Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2022). Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Science, 34(1), 1-28. doi: 10.1080/09540091.2021.1936455
- Zhi-Xian, Z., & Zhang, F. (2022). Image Real-Time Detection Using LSE-Yolo Neural Network in Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes. Wireless Communications and Mobile Computing, 2022. doi: 10.1155/2022/2608798
- Zhou, H., Liu, Q., Yan, K., & Du, Y. (2021). Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT. Wireless Communications and Mobile Computing, 2021. doi: 10.1155/2021/9249387
Language: English
Page range: 53 - 75
Submitted on: May 1, 2023
Accepted on: Nov 10, 2023
Published on: Dec 29, 2023
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2023 Danuta Szpilko, Felix Jimenez Naharro, George Lăzăroiu, Elvira Nica, Antonio de la Torre Gallegos, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.