A Flexible Architecture for Key Performance Indicators Assessment in Smart Cities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12292)


The concept of smart and sustainable city has been on the agenda for the last decade. Smart governance is about the use of innovation for supporting enhanced decision making and planning to make a city smart, by leveraging on Key Performance Indicators (KPIs) as procedural tools. However, developing processes and instruments able to evaluate smart cities is still a challenging task, due to the rigidity showed by the existing frameworks in the definition of KPIs and modeling of the subjects to be evaluated. Web-based platforms, spreadsheets or even Cloud-based applications offer limited flexibility, if the stakeholder is interested not only in using but also in defining the pieces of the puzzle to be composed. In this paper we present a flexible architecture supporting a model-driven approach for the KPIs assessment in smart cities. It identifies both required and optional components and functionalities needed for realizing the automatic KPIs assessment, while showing flexibility points allowing for different specification of the architecture, thus of the overall methodology.



This work was partially supported by the Centre for Urban Informatics and Modelling, National Project, GSSI as well as by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development.


  1. 1.
    Mutiara, D., Yuniarti, S., Pratama, B.: Smart governance for smart city. IOP Conf. Ser. Earth Environ. Sci. 126, 012–073 (2018)CrossRefGoogle Scholar
  2. 2.
    Directorate-General for Environment (European Commission): Intrasoft International, University of the West of England (UWE). Science Communication Unit. Indicators for sustainable cities, April 2018Google Scholar
  3. 3.
    European Commission: Europe 2020 A European strategy for smart, sustainable and inclusive growth, March 2010Google Scholar
  4. 4.
    International Telecommunication Union (ITU): Collection Methodology for Key Performance Indicators for Smart Sustainable Cities (2017).
  5. 5.
    Ferro, E., Caroleo, B., Leo, M., Osella M., Pautasso, E.: The role of ICT in smart city governance. In: International Conference for e-Democracy and Open Government (2013)Google Scholar
  6. 6.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017)Google Scholar
  7. 7.
    Mohagheghi, P., Aagedal, J.: Evaluating quality in model-driven engineering. In: International Workshop on Modeling in Software Engineering, p. 6. IEEE (2007)Google Scholar
  8. 8.
    da Silva, W.M., Alvaro, A., Tomas, G.H.R.P., Afonso, R.A., Dias, K.L., Garcia, V.C.: Smart cities software architectures: a survey. In: 28th Annual ACM Symposium on Applied Computing (SAC), pp. 1722–1727. ACM (2013)Google Scholar
  9. 9.
    Abu-Matar, M., Mizouni, R.: Variability modeling for smart city reference architectures. In: IEEE International Smart Cities Conference, pp. 1–8 (2018)Google Scholar
  10. 10.
    Voronin, D., Shevchenko, V., Chengar, O., Mashchenko, E.: Conceptual big data processing model for the tasks of smart cities environmental monitoring. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O., Musabirov, I. (eds.) DTGS 2019. CCIS, vol. 1038, pp. 212–222. Springer, Cham (2019). Scholar
  11. 11.
    Wenge, R., Zhang, X., Dave, C., Chao, L., Hao, S.: Smart city architecture: A technology guide for implementation and design challenges. China Commun. 11(3), 56–69 (2014)CrossRefGoogle Scholar
  12. 12.
    Simmhan, Y., Ravindra, P., Chaturvedi, S., Hegde, M., Ballamajalu, R.: Towards a data-driven IoT software architecture for smart city utilities. Softw. Pract. Exp. 48(7), 1390–1416 (2018)CrossRefGoogle Scholar
  13. 13.
    Santana, E.F.Z., Chaves, A.P., Gerosa, M.A., Kon, F., Milojicic, D.S.: Software platforms for smart cities: concepts, requirements, challenges, and a unified reference architecture. ACM Comput. Surv. 50(6), 1–37 (2017)CrossRefGoogle Scholar
  14. 14.
    Sinaeepourfard, A., Petersen, S.A., Ahlers, D.: D2C-SM: designing a distributed-to-centralized software management architecture for smart cities. In: Pappas, I.O., Mikalef, P., Dwivedi, Y.K., Jaccheri, L., Krogstie, J., Mäntymäki, M. (eds.) I3E 2019. LNCS, vol. 11701, pp. 329–341. Springer, Cham (2019). Scholar
  15. 15.
    Bettini, L., Di Ruscio, D., Iovino, L., Pierantonio, A.: Quality-driven detection and resolution of metamodel smells. IEEE Access 7, 16364–16376 (2019)CrossRefGoogle Scholar
  16. 16.
    Di Ruscio, D., Iovino, L., Pierantonio, A.: What is needed for managing co-evolution in MDE? In: International Workshop on Model Comparison in Practice, pp. 30–38. ACM (2011)Google Scholar
  17. 17.
    Brottier, E., Fleurey, F., Steel, J., Baudry B., Traon, Y.L.: Metamodel-based test generation for model transformations: an algorithm and a tool. In: International Symposium on Software Reliability Engineering, pp. 85–94 (2006)Google Scholar
  18. 18.
    Kolovos, D.S., Paige, R.F., Kelly, T., Polack, F.A.: Requirements for domain-specific languages. In: Workshop on Domain-Specific Program Development (2006)Google Scholar
  19. 19.
    Veisi, P., Stroulia, E.: AHL: model-driven engineering of android applications with BLE peripherals. In: Aïmeur, E., Ruhi, U., Weiss, M. (eds.) MCETECH 2017. LNBIP, vol. 289, pp. 56–74. Springer, Cham (2017). Scholar
  20. 20.
    Viyović, V., Maksimović, M., Perisić, B.: Sirius: a rapid development of DSM graphical editor. In: International Conference on Intelligent Engineering Systems (INES), pp. 233–238 (2014)Google Scholar
  21. 21.
    Bettini, L.: Implementing domain-specific languages with Xtext and Xtend. Packt Publishing, Birmingham (2016)Google Scholar
  22. 22.
    Kolovos, D.S., Paige, R.F., Polack, F.A.C.: The epsilon object language (EOL). In: Rensink, A., Warmer, J. (eds.) ECMDA-FA 2006. LNCS, vol. 4066, pp. 128–142. Springer, Heidelberg (2006). Scholar
  23. 23.
    Basciani, F., Di Rocco, J., Di Ruscio, D., Di Salle, A., Iovino, L., Pierantonio, A.: MDEForge: an extensible web-based modeling platform. In: CloudMDE@MoDELS, pp. 66–75 (2014)Google Scholar
  24. 24.
    Mellor, S.J., Balcer, M.: Executable UML: A Foundation for Model-Driven Architectures. Addison-Wesley, Boston (2002)Google Scholar
  25. 25.
    Rose, L.M., Kolovos, D.S., Paige, R.F.: EuGENia live: a flexible graphical modelling tool. In: Extreme Modeling Workshop, pp. 15–20 (2012)Google Scholar
  26. 26.
    Di Rocco, J., Di Ruscio, D., Iovino, L., Pierantonio, A.: Collaborative repositories in model-driven engineering. IEEE Softw. 32, 28–34 (2015)CrossRefGoogle Scholar
  27. 27.
    Basciani, F., Rocco, J.D., Ruscio, D.D., Iovino, L., Pierantonio, A.: Model repositories: will they become reality? In: CloudMDE@MoDELS (2015)Google Scholar
  28. 28.
    Jézéquel, J.-M., Barais, O., Fleurey, F.: Model driven language engineering with Kermeta. In: Fernandes, J.M., Lämmel, R., Visser, J., Saraiva, J. (eds.) GTTSE 2009. LNCS, vol. 6491, pp. 201–221. Springer, Heidelberg (2011). Scholar
  29. 29.
    Heidenreich, F., Johannes, J., Karol, S., Seifert, M., Wende, C.: Model-based language engineering with EMFText. In: Lämmel, R., Saraiva, J., Visser, J. (eds.) GTTSE 2011. LNCS, vol. 7680, pp. 322–345. Springer, Heidelberg (2013). Scholar
  30. 30.
    Strauch, C., Sites, U.-L.S., Kriha, W.: NoSQL databases. Lect. Notes Stuttg. Media Univ. 20, 24 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Gran Sasso Science InstituteL’AquilaItaly
  2. 2.CDL-MINTJohannes Kepler UniversityLinzAustria

Personalised recommendations