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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Almeida, F.L.F., Calistru, C.: The main challenges and issues of big data management. Int. J. Res. Stud. Comput. 2, 11–20 (2012)
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)
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)
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)
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)
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)
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)
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
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)
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)
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)
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)
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)
Patil, P., Kulkarni, U.: Delay efficient distributed data aggregation algorithm in wireless sensor networks. Int. J. Comput. Appl. 69, 48–55 (2013)
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)
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)
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)
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)
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)
Hussain, F., Al-Karkhi, A.: Big data and fog computing. In: Internet of Things, pp. 27–44. Springer (2017)
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)
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)
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)
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)
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)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sinaeepourfard, A., Krogstie, J., Petersen, S.A. (2020). D2C-DM: Distributed-to-Centralized Data Management for Smart Cities Based on Two Ongoing Case Studies. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)