Machine Learning and Big Data for Smart Generation

  • Anandakumar Haldorai
  • Arulmurugan Ramu
  • Suriya Murugan
Part of the Urban Computing book series (UC)


The rapid development and deployment of smart cities result in huge data generation at an increased rate. This huge volume of data is exhausted without extracting potential knowledge due to insufficient algorithms and data analytics mechanisms. This dynamic nature of smart cities demands for advanced machine learning algorithm that supports efficient data processing and adaptive learning from real-time data. Intelligent and adaptive learning structure for smart urban areas is investigated that utilize numerous levels of enormous information created by smart urban areas in order to provide diverse level of knowledge abstractions. This learning framework can manage data as labeled and unlabeled data based on user feedback, thereby utilizing all available data and is capable of managing the scalable requirements of smart generation. The challenges of big data and the importance of machine learning algorithms for intelligent generation are highlighted here. Various challenges and future research scope for consolidating AI and abnormal state knowledge for smart age administrations and also the cognitive nature of smart city are explored in this chapter, thereby improving their performance.


Big data Machine learning Deep learning IoT Smart data Smart city 


  1. 1.
    Pal, D., et al.: Big data in smart-cities: current research and challenges. IJEEI. 6, 351–360 (2018). Scholar
  2. 2.
    Kaltenrieder, P., et al.: Digital personal assistant for cognitive cities: a paper prototype. In: Towards Cognitive Cities: Advances in Cognitive Computing and its Application to the Governance of Large Urban Systems. Springer, New York (2016). Scholar
  3. 3.
    Vlacheas, P., et al.: Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun. Mag. 51, 13539039 (2013). Scholar
  4. 4.
    Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., Long, K.: Cognitive internet of things: a new paradigm beyond connection. IEEE Internet Things J. 1(2), 129–143 (2014). Scholar
  5. 5.
    Feng, S., Setoodeh, P., Haykin, S.: Smart home: cognitive interactive people-centric internet of things. IEEE Commun. Mag. 55(2), 34–39 (2017). Scholar
  6. 6.
    Yaqoob, I., et al.: Enabling communication technologies for smart cities. IEEE Commun. Mag. 55(1), 112–120 (2017)CrossRefGoogle Scholar
  7. 7.
    Dwevedi, R., et al.: Environment and big data: role in smart cities of India. Resources. 7, 64 (2018). Scholar
  8. 8.
    Abaker, I., Hashem, T., et al.: The role of big data in smart city. Int. J. Inf. Manag. 36(5), 748–758 (2016). Scholar
  9. 9.
    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). Scholar
  10. 10.
    Lopez, D., et al.: Spatial big data analytics of influenza epidemic in Vellore, India. In: IEEE Xplore (2015).
  11. 11.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, New York (2011)Google Scholar
  12. 12.
    Saeid, M., et al.: Machine learning for internet of things data analysis: a survey. Dig. Commun. Netw. 4, 161 (2017). Scholar
  13. 13.
    Cecchinel, C., Jimenez, M., Mosser, S., Riveill, M.: An Architecture to Support the Collection of Big Data in the Internet of Things. In: IEEE World Congress on Services (2014).
  14. 14.
    Tang, B., Chen, Z., Hefferman, G., Pei, S., Tao, W., He, H., Yang, Q.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Industr. Inform. PP(99), 1–11 (2017)Google Scholar
  15. 15.
    Qiu, J., et al.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 2016, 67 (2016). Scholar
  16. 16.
    Muhammed, T., Mehmood, R., Albeshri, A., Katib, I.: UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access. 6, 32258–32285 (2018)CrossRefGoogle Scholar
  17. 17.
    Rudin, C., Waltz, D., Anderson, R., Boulanger, A., Salleb-Aouissi, A., Chow, M., Dutta, H., Gross, P., Huang, B., Ierome, S., et al.: Machine learning for the New York City power grid. IEEE Trans. Pattern Anal. Mach. Intell. 34, 328–345 (2012)CrossRefGoogle Scholar
  18. 18.
    Jurado, S., Nebot, À., Mugica, F., Avellana, N.: Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques. Energy. 86, 276–291 (2015). Scholar
  19. 19.
    Belhajem, I., Ben Maissa, Y., Tamtaoui, A.: Improving vehicle localization in a smart city with low cost sensor networks and support vector machines. Mob. Netw. Appl. 23, 854–863 (2018)CrossRefGoogle Scholar
  20. 20.
    Idowu, S., Saguna, S., Åhlund, C., Schelén, O.: Applied machine learning: forecasting heat load in district heating system. Energ. Buildings. 133, 478–488 (2016). Scholar
  21. 21.
    Gomede, E., Gaffo, F.H., Brigano, G.U., de Barros, R.M., de Mendes, L.S.: Application of computational intelligence to improve education in smart cities. Sensors. 18, 267 (2018)CrossRefGoogle Scholar
  22. 22.
    You, H., Yang, X.: Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy. Land Use Policy. 68, 531–551 (2017)CrossRefGoogle Scholar
  23. 23.
    Abbasi, M., El Hanandeh, A.: Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag. 56, 13–22 (2016). Scholar
  24. 24.
    Kosmides, P., Adamopoulou, E., Demestichas, K., Theologou, M., Anagnostou, M., Rouskas, A.: Socially aware heterogeneous wireless networks. Sensors. 15(6), 13705–13724 (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Computer Science and EngineeringPresidency UniversityYelahanka, BengaluruIndia
  3. 3.Department of Computer Science and EngineeringKPR Institute of Engineering and TechnologyCoimbatoreIndia

Personalised recommendations