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D2C-DM: Distributed-to-Centralized Data Management for Smart Cities Based on Two Ongoing Case Studies

  • Amir SinaeepourfardEmail author
  • John Krogstie
  • Sobah Abbas Petersen
Conference paper
  • 289 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1038)

Abstract

Smart city environments follow different technological management strategies (such as resource management, data management, and so on) between end-users to city planners and technological devices (e.g., sensor, camera surveillance, etc.) to enhance citizens’ quality of life through the variety of the smart services. Data management is one of the most critical issues in smart cities because data is a core resource in the smart city. Without proper data, no smart services in the smart cities exist to make a connection between end-users and technological devices. A few numbers of distributed-to-centralize data management architectures have been proposed. In addition, there are several different distributed schema by several technological options exist (e.g., cloudlet, fog, etc.) but almost all of the studies used a distributed-to-centralized data management architecture based on fog to cloud technologies. Therefore, the fog-to-cloud data management architecture can use both potentials of fog and cloud technologies, including the decrease in communication latencies, organizing distinct policies (e.g., data filtering, data compression, etc.) and so on. In this paper, first, previous studies of distributed-to-centralized data management architectures through two different smart city scenarios have been revisited. Afterward, the easy use and adaptation of the distributed-to-centralized data management architecture to any smart city scenario has been shown. In addition, the advantages of this data management architecture have been highlighted including efficiency rates for the data collection and data storage, and reducing data and network traffic. Finally, a number of the lesson learned from previous case studies has been addressed.

Keywords

IoT Smart city Data management Distributed data management Centralized data management Fog-to-cloud data management Sensor data management 

Notes

Acknowledgment

This paper has been written within the Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME ZEN). The authors gratefully acknowledge the support from the Research Council of Norway, the Norwegian University of Science and Technology (NTNU), SINTEF, the municipalities of Oslo, Bergen, Trondheim, Bodø, Bærum, Elverum and Steinkjer, Sør-Trøndelag county, Norwegian Directorate for Public Construction and Property Management, Norwegian Water Resources and Energy Directorate, Norwegian Building Authority, ByBo, Elverum Tomteselskap, TOBB, Snøhetta, ÅF Engineering AS, Asplan Viak, Multiconsult, Sweco, Civitas, FutureBuilt, Hunton, Moelven, Norcem, Skanska, GK, Caverion, Nord-Trøndelag Elektrisitetsverk - Energi, Numascale, Smart Grid Services Cluster, Statkraft Varme, Energy Norway and Norsk Fjernvarme.

In addition, the first author would like to express my very great appreciation to the Advanced Network Architecture Lab (https://craax.upc.edu/) in UPC university of Barcelona, Spain because of their support for his Ph.D. thesis under the FI-DGR scholarship 2015FI_B100186 (https://upcommons.upc.edu/handle/2117/114435).

References

  1. 1.
    Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics: a technology tutorial. J. Mag. IEEE Access 2, 652–687 (2014)CrossRefGoogle Scholar
  2. 2.
    Almeida, F.L.F., Calistru, C.: The main challenges and issues of big data management. Int. J. Res. Stud. Comput. 2, 11–20 (2012)Google Scholar
  3. 3.
    Henry, S., Hoon, S., Hwang, M., Lee, D., DeVore, M.D.: Engineering trade study: extract, transform, load tools for data migration. In: IEEE Conference on Design Symposium, Systems and Information Engineering, pp. 1–8 (2005)Google Scholar
  4. 4.
    Jin, J., Gubbi, J., Marusic, S., Palaniswami, M.: An information framework for creating a smart city through Internet of Things. IEEE Internet Things J. 1, 112–121 (2014)CrossRefGoogle Scholar
  5. 5.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. J. Future Gener. Comput. Syst. 29, 1645–1660 (2013)CrossRefGoogle Scholar
  6. 6.
    Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., Heinzelman, W.: Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. In: IEEE symposium on Computers and communications (ISCC), pp. 000059–000066. IEEE (2012)Google Scholar
  7. 7.
    Ali, M., Riaz, N., Ashraf, M.I., Qaisar, S., Naeem, M.: Joint cloudlet selection and latency minimization in fog networks. IEEE Trans. Ind. Inform. 14(9), 4055–4063 (2018)CrossRefGoogle Scholar
  8. 8.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marin-Tordera, E.: Data preservation through Fog-to-Cloud (F2C) data management in smart cities. In: IEEE 2nd International Conference on Fog and Edge Computing (ICFEC), pp. 1–9. IEEE (2018)Google Scholar
  9. 9.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marin-Tordera, E.: Fog-to-Cloud (F2C) data management for smart cities. In: Future Technologies Conference (FTC) (2017). http://saiconference.com/Downloads/FTC2017/Proceedings/21_Paper_396-Fog-to-Cloud_F2C_Data_Management.pdf
  10. 10.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X.: Hierarchical distributed fog-to-cloud data management in smart cities. Doctoral thesis, Departament d’Arquitectura de Computadors, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain (2017)Google Scholar
  11. 11.
    Sinaeepourfard, A., Krogstie, J., Petersen, S.A., Gustavsen, A.: A zero emission neighbourhoods data management architecture for smart city scenarios: discussions toward 6Vs challenges. In: International Conference on Information and Communication Technology Convergence (ICTC). IEEE (2018)Google Scholar
  12. 12.
    Rao, T.V.N., Khan, A., Maschendra, M., Kumar, M.K.: A paradigm shift from cloud to fog computing. Int. J. Sci. Eng. Comput. Technol. 5, 385 (2015)Google Scholar
  13. 13.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)Google Scholar
  14. 14.
    Masip, X., Marín, E., Jukan, A., Ren, G.J., Tashakor, G.: Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud (F2C) computing systems. J. IEEE Wirel. Commun. Mag. 23, 120–128 (2016)CrossRefGoogle Scholar
  15. 15.
    Patil, P., Kulkarni, U.: Delay efficient distributed data aggregation algorithm in wireless sensor networks. Int. J. Comput. Appl. 69, 48–55 (2013)Google Scholar
  16. 16.
    He, T., Gu, L., Luo, L., Yan, T., Stankovic, J.A., Son, S.H.: An overview of data aggregation architecture for real-time tracking with sensor networks. In: 20th IEEE International Parallel and Distributed Processing Symposium, p. 8-pp. IEEE (2006)Google Scholar
  17. 17.
    Rathore, M.M., Ahmad, A., Paul, A., Rho, S.: Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Comput. Netw. 101, 63–80 (2016)CrossRefGoogle Scholar
  18. 18.
    Karthick, N., Kalrani, X.A.: A survey on data aggregation in big data and cloud computing. Int. J. Comput. Trends Technol. (IJCTT) 17, 28–32 (2014)Google Scholar
  19. 19.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marin-Tordera, E.: A novel architecture for efficient fog to cloud data management in smart cities. In: IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 2622–2623. IEEE (2017)Google Scholar
  20. 20.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marin-Tordera, E., Yin, X., Wang, C.: A data lifeCycle model for smart cities. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 400–405. IEEE (2016)Google Scholar
  21. 21.
    Hussain, F., Al-Karkhi, A.: Big data and fog computing. In: Internet of Things, pp. 27–44. Springer (2017)Google Scholar
  22. 22.
  23. 23.
    Gea, T., Paradells, J., Lamarca, M., Roldan, D.: Smart cities as an application of Internet of Things: experiences and lessons learnt in Barcelona. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 552–557. IEEE (2013)Google Scholar
  24. 24.
    Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marín-Tordera, E., Cirera, J., Grau, G., Casaus, F.: Estimating smart city sensors data generation. In: The 15th IFIP Annual Mediterranean Ad Hoc Networking Workshop, pp. 1–8. IEEE (2016)Google Scholar
  25. 25.
    Sinaeepourfard, A., Krogstie, J., Petersen, S.A.: A big data management architecture for smart cities based on fog-to-cloud data management architecture. In: Proceedings of the 4th Norwegian Big Data Symposium (NOBIDS) (2018, in press)Google Scholar
  26. 26.
    Kahvazadeh, S., Souza, V.B., Masip-Bruin, X., Marn-Tordera, E., Garcia, J., Diaz, R.: Securing combined fog-to-cloud system through SDN approach. In: Proceedings of the 4th Workshop on CrossCloud Infrastructures and Platforms, p. 2. ACM (2017)Google Scholar
  27. 27.
    Mehdipour, F., Javadi, B., Mahanti, A.: FOG-engine: towards big data analytics in the fog. In: IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 640–646. IEEE (2016)Google Scholar
  28. 28.
    Han, W., Xiao, Y.: Big data security analytic for smart grid with fog nodes. In: International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, pp. 59–69. Springer (2016)Google Scholar
  29. 29.
    Clemente, J., Valero, M., Mohammadpour, J., Li, X., Song, W.: Fog computing middleware for distributed cooperative data analytics. In: IEEE Fog World Congress (FWC), pp. 1–6. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amir Sinaeepourfard
    • 1
    Email author
  • John Krogstie
    • 1
  • Sobah Abbas Petersen
    • 1
  1. 1.Department of Computer ScienceNorwegian University of Science and Technology (NTNU)TrondheimNorway

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