Skip to main content
SpringerLink
Log in

Smart City: Recent Advances and Research Issues

  • Conference paper
  • First Online:
Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

Abstract

Smart cities use technological innovations to improve urban services and people’s livelihoods to develop sustainably. Different technology solutions and technologies like IoT sensors, big data analytics, communication networks, and applications are being used to collect and analyze data to boost various services in smart cities, including public services, transport, and various other utilities. The article intends to discuss the state-of-the-art technologies of smart cities and their roles and applications. It also seeks to analyze the current research trend in the smart city domain and its key enabling technologies. It also pursues to identify some of the open issues and challenges facing efficient use of energy, smart decision-making systems, privacy and security of data, and effective and secure communication technologies in smart cities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Z. Khan, A. Anjum, S.L. Kiani, Cloud based big data analytics for smart future cities, in Proceedings of the 2013 IEEE/ACM 6th International Conference (IEEE, 2013)

    Google Scholar 

  2. I. Yaqoob, V. Chang, A. Gani, S. Mokhtar, I. Abaker, et al., WITHDRAWN: information fusion in social big data: foundations, state-of-the-art, applications, challenges, and future research directions (2016)

    Google Scholar 

  3. F. Facchinei, S. Simone, S. Gesualdo, Flexible parallel algorithms for big data optimization, in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2014)

    Google Scholar 

  4. M. Boukhechba, A. Bouzouane, S. Gaboury, C. Gouin-Vallerand, S. Giroux, B. Bouchard, A novel Bluetooth low energy based system for spatial exploration in smart cities. Exp. Syst. Appl. 77, 71–82 (2017)

    Article  Google Scholar 

  5. A. Kramers, M. Höjer, N. Lövehagen, J. Wangel, Smart sustainable cities—exploring ICT solutions for reduced energy use in cities. Environ. Model Softw. 56, 52–62 (2014)

    Article  Google Scholar 

  6. P. Neirotti, A. De Marco, A.C. Cagliano, G. Mangano, F. Scorrano, Current trends in smart city initiatives: some stylised facts. Cities 38, 25–36 (2014)

    Article  Google Scholar 

  7. A.H. Alavi, P. Jiao, W.G. Buttlar, N. Lajnef, Internet of Things—enabled smart cities: state-of-the-art and future trends. Measurement 129, 589–606 (2018)

    Article  Google Scholar 

  8. J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013)

    Article  Google Scholar 

  9. I. Ruiz-Mallén, Co-production and resilient cities to climate change, in Participatory Research and Planning in Practice, ed. by J. Nared, D. Bole. (SpringerOpen, Cham, Switzerland, 2020)

    Google Scholar 

  10. L. Cilliers, S. Flowerday, Factors that influence the usability of a participatory IVR crowdsourcing system in a smart city. South African Comput. J. 29, 16–30 (2017)

    Article  Google Scholar 

  11. K.L. Terence, R. Hui, S. Sherratt, D.D. Sánchez, Major requirements for building smart homes in smart cities based on Internet of Things technologies. Future Gener. Comput. Syst. 76, 358–369 (2017)

    Article  Google Scholar 

  12. T. Kim, C. Ramos, S. Mohammed, Smart city and IoT. Future Gener. Comput. Syst. 76, 159–162 (2017)

    Article  Google Scholar 

  13. N. Kumar, A.V. Vasilakos, J.P.C. Rodrigues, A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities. IEEE Commun. Mag. 55, 14–21 (2017)

    Article  Google Scholar 

  14. M. Boukhechba, A. Bouzouane, S. Gaboury, C. Gouin-Vallerand, S. Giroux, B. Bouchard, A novel Bluetooth low energy based system for spatial exploration in smart cities. Exp. Syst. Appl. 77, 71–82 (2017)

    Article  Google Scholar 

  15. S. Ortega, J.M. Santana, J. Wendel, A. Trujillo, S.M. Murshed, Generating 3D city models from open LiDAR point clouds: advancing towards smart city applications, in Open Source Geospatial Science for Urban Studies. (Springer, 2020), pp. 97-116

    Google Scholar 

  16. L. Pantoli, G. Barile, A. Leoni, M. Muttillo, V. Stornelli, Electronic interface for lidar system and smart cities applications. J. Commun. Softw. Syst. 15, 118–125 (2019)

    Google Scholar 

  17. S. Joshi, U.K. Singh, S. Yadav, Smart dustbin using GPS tracking. Int. Res. J. Eng. Technol. 6, 165–170 (2019)

    Google Scholar 

  18. J. Zhang, Y. Zheng, D. Qi, R. Li, X. Yi, Deep spatio-temporal residual networks for citywide crowd flows prediction, in Thirty-First AAAI Conference on Artificial Intelligence, vol. 259 (2017), pp. 147-166

    Google Scholar 

  19. B.P. Bhattarai, S. Paudyal, Y. Luo, M. Mohanpurkar, K. Cheung, R. Tonkoski, R. Hovsapian, K.S. Myers, Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions. IET Smart Grid. 2, 141–154 (2019)

    Article  Google Scholar 

  20. M.A. Ferrag, L. Maglaras, Deepcoin: a novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. 67, 1285–1297 (2019)

    Article  Google Scholar 

  21. E.J. Topol, High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019)

    Article  Google Scholar 

  22. A.S. Elmaghraby, M. Losavio, Cyber security challenges in smart cities: safety, security and privacy. J. Adv. Res. 5, 491–497 (2014)

    Article  Google Scholar 

  23. L. Yibin, W. Dai, Z. Ming, M. Qiu, Privacy protection for preventing data over-collection in smart city. IEEE Trans. Comput. 65,1339-1350 (2015)

    Google Scholar 

  24. Y. Duan, Z. Lu, Z. Zhou, X. Sun, J. Wu, Data privacy protection for edge computing of smart city in a DIKW architecture. Eng. Appl. Artif. Intell. 81, 323–335 (2019)

    Article  Google Scholar 

  25. B. Jia, et al., A blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Sensors 18(11) (2018)

    Google Scholar 

  26. L. Qi, C. Hu, X. Zhang, M.R. Khosravi, S. Sharma, S. Pang, T. Wang, Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Trans. Ind. Inform. (2020)

    Google Scholar 

  27. D. Puthal, X. Wu, S. Nepal, R. Ranjan, J. Chen, SEEN: a selective encryption method to ensure confidentiality for big sensing data streams. IEEE Trans. Big Data 5, 379–392 (2017)

    Article  Google Scholar 

  28. D.J. Power, R. Sharda, Model-driven decision support systems: concepts and research directions. Decis. Supp. Syst. 43, 1044–1061 (2007)

    Google Scholar 

  29. D. Jung, et al., Conceptual framework of an intelligent decision support system for smart city disaster management. Appl. Sci. 10 (2020)

    Google Scholar 

  30. K. Dorgham, et al., A decision support system for smart health care, in IoT and ICT for Healthcare Applications. (Springer, Cham, 2020), pp. 85-98

    Google Scholar 

  31. K.S. Gayathri, K.S. Easwara Kumar, Intelligent decision support system for dementia care through smart home. Procedia Comput. Sci. 93, 947–955 (2016)

    Article  Google Scholar 

  32. J. Siryani, B. Tanju, T.J. Eveleigh, A machine learning decision-support system improves the Internet of Things’ smart meter operations. IEEE Internet Things J. 4, 1056-1066 (2017)

    Google Scholar 

  33. K. Abdelghany, H. Hashemi, M.E. Khodayar, A decision support system for proactive-robust traffic network management. IEEE Trans. Intell. Transp. Syst. 20, 297-312 (2018)

    Google Scholar 

  34. R. Lu, S.H. Hong, Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Appl. Energ. 236, 937–949 (2019)

    Article  Google Scholar 

  35. H. Kumar, P.M. Mammen, K. Ramamritham, Explainable AI: deep reinforcement learning agents for residential demand side cost savings in smart grids (2019)

    Google Scholar 

  36. Q. Huang et al., Rapid Internet of Things (IoT) prototype for accurate people counting towards energy efficient buildings. J. Inform. Technol. Constr. 24, 1–13 (2019)

    Google Scholar 

  37. S. Azri, U. Ujang, A. Abdul Rahman, 3D geo-clustering for wireless sensor network in smart city, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42.4/W12 (2019)

    Google Scholar 

  38. S. Ma et al., Energy-cyber-physical system enabled management for energy-intensive manufacturing industries. J. Clean. Prod. 226, 892–903 (2019)

    Article  Google Scholar 

  39. M.M. Rathore et al., Real-time secure communication for smart city in high-speed big data environment. Future Gener. Comput. Syst. 83, 638–652 (2018)

    Article  Google Scholar 

  40. G. Pasolini, et al., Smart city pilot projects using LoRa and IEEE802. 15.4 technologies. Sensors 18(4) (2018)

    Google Scholar 

  41. K. Biswas, V. Muthukkumarasamy, Securing smart cities using blockchain technology, in IEEE 18th International Conference on High Performance Computing and Communications (IEEE, 2016)

    Google Scholar 

  42. A. Rashed, et al., Integrated IoT medical platform for remote healthcare and assisted living, in 2017 Japan-Africa Conference on Electronics, Communications and Computers (JAC-ECC) (IEEE, 2017)

    Google Scholar 

  43. T. Zaheer, et al., A vehicular network-based intelligent transport system for smart cities. Int. J. Distrib. Sens. Netw. 15(11) (2019)

    Google Scholar 

  44. S. Zeadally, F. Siddiqui, Z. Baig, A. Ibrahim, Smart healthcare: challenges and potential solutions using Internet of Things (IoT) and big data analytics. PSU Rev. J. 1-17 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, St. Mary’s College, Shillong, India

    Bonani Paul

  2. Department of Computer Applications, Assam Don Bosco University, Guwahati, India

    Sarat Kr. Chettri

Authors
  1. Bonani Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarat Kr. Chettri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bonani Paul .

Editor information

Editors and Affiliations

  1. Research and Industry Incubation Center, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

    V. Suma

  2. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  3. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

  4. GoPerception Laboratory, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paul, B., Chettri, S.K. (2021). Smart City: Recent Advances and Research Issues. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_7

Download citation

Publish with us

Policies and ethics

Search

Navigation