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IoT Big Data Analytics with Fog Computing for Household Energy Management in Smart Grids

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Abstract

Smart homes generate a vast amount of data measurements from smart meters and devices. These data have all the velocity and veracity characteristics to be called as Big Data. Meter data analytics holds tremendous potential for utilities to understand customers’ energy consumption patterns, and allows them to manage, plan, and optimize the operation of the power grid efficiently. In this paper, we propose a unified architecture that enables innovative operations for near real-time processing of large fine-grained energy consumption data. Specifically, we propose an Internet of Things (IoT) big data analytics system that makes use of fog computing to address the challenges of complexities and resource demands for near real-time data processing, storage, and classification analysis. The design architecture and requirements of the proposed framework are illustrated in this paper while the analytics components are validated using datasets acquired from real homes.

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References

  1. Chhabra, A.S., Choudhury, T., Srivastava, A.V., Aggarwal, A.: Prediction for big data and IoT in 2017. In: International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, pp. 181-187 (2017)

    Google Scholar 

  2. Ge, Y., Liang, X., Zhou, Y.C., Pan, Z., Zhao, G.T., Zheng, Y.L.: Adaptive analytic service for real-time internet of things applications. In: IEEE International Conference on Web Services (ICWS), San Francisco, CA, pp. 484–491 (2016)

    Google Scholar 

  3. El-Sayed, H., et al.: Edge of Things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment. IEEE Access 6, 1706–1717 (2018)

    Article  Google Scholar 

  4. Pouladzadeh, P., Kuhad, P., Peddi, S.V.B., Yassine, A., Shirmohammadi, S.: Mobile cloud based food calorie measurement. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Chengdu, pp. 1–6 (2014)

    Google Scholar 

  5. Peddi, S.V.B., Kuhad, P., Yassine, A., Pouladzadeh, P., Shirmohammadi, S., Shirehjini, A.A.N.: An intelligent cloud-based data processing broker for mobile e-health multimedia applications. Future Generat. Comput. Syst. J. 66, 71–86 (2017)

    Article  Google Scholar 

  6. Mebrek, A., Merghem-Boulahia, L., Esseghir, M.: Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing. In: IEEE 16th International Symposium on Network Computing and Applications (NCA), Cambridge, pp. 1-4 (2017)

    Google Scholar 

  7. Al-Ali, A.R., Zualkernan, I.A., Rashid, M., Gupta, R., Alikarar, M.: A smart home energy management system using IoT and big data analytics approach. IEEE Trans. Consum. Electron. 63(4), 426–434 (2017)

    Article  Google Scholar 

  8. Berouine, A., Lachhab, F., Malek, Y.N., Bakhouya, M., Ouladsine, R.: A smart metering platform using big data and IoT technologies. In: 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat, pp. 1-6 (2017)

    Google Scholar 

  9. Yassine, A.: Implementation challenges of automatic demand response for households in smart grids. In: 3rd International Conference on Renewable Energies for Developing Countries (REDEC), Zouk Mosbeh, pp. 1–6 (2016)

    Google Scholar 

  10. Sultan, M., Ahmed, K.N.: SLASH: self-learning and adaptive smart home framework by integrating IoT with big data analytics. In: Computing Conference, London, pp. 530–538 (2017)

    Google Scholar 

  11. Yang, S.: IoT stream processing and analytics in the fog. IEEE Commun. Mag. 55(8), 21–27 (2017)

    Article  Google Scholar 

  12. Singh, S., Yassine, A.: Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 11, 452 (2018)

    Article  Google Scholar 

  13. Singh, S., Yassine, A.: Mining energy consumption behavior patterns for households in smart grid. IEEE Transactions on Emerging Topics in Computing (2017). https://doi.org/10.1109/TETC.2017.2692098

  14. Cai, H., Xu, B., Jiang, L., Vasilakos, A.V.: IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet Things J. 4(1), 75–87 (2017)

    Google Scholar 

  15. He, J., Wei, J., Chen, K., Tang, Z., Zhou, Y., Zhang, Y.: Multi-tier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet Things J. 5(2), 677–686 (2018). https://doi.org/10.1109/JIOT.2017.2724845

    Article  Google Scholar 

  16. Taneja, M., Davy, A.: Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm. In: IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, pp. 1222-1228 (2017)

    Google Scholar 

  17. Minh, Q.T., Nguyen, D.T., Van Le, A., Nguyen, H.D., Truong, A.: Toward service placement on Fog computing landscape. In: 4th NAFOSTED Conference on Information and Computer Science, Hanoi, pp. 291–296 (2017)

    Google Scholar 

  18. Gonzalez, N.M., et al.: Fog computing: data analytics and cloud distributed processing on the network edges. In: 35th International Conference of the Chilean Computer Science Society (SCCC), Valparaiíso, pp. 1-9 (2016)

    Google Scholar 

  19. Cao, H., Wachowicz, M., Cha, S.: Developing an edge computing platform for real-time descriptive analytics. In: IEEE International Conference on Big Data (Big Data), Boston, MA, pp. 4546–4554 (2017)

    Google Scholar 

  20. Yassine, A., Nazari Shirehjini, A.A., Shirmohammadi, S.: Smart meters big data: game theoretic model for fair data sharing in deregulated smart grids. IEEE Access vol. 3, no, 2743–2754 (2015)

    Article  Google Scholar 

  21. Yassine, A., Shirmohammadi, S.: Measuring user’s privacy payoff using intelligent agents. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009, pp 169–174 (2009)

    Google Scholar 

  22. Han, J., Pei, J., kamber, M.: Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods. Data mining: Concepts and techniques, Chap. 6, 3rd edn, pp. 243–278. Morgan Kaufmann, Waltham (2011). http://www.sciencedirect.com/science/book/9780123814791. ISBN: 9780123814791

    Google Scholar 

  23. Paverd, A., Martin, A., Brown, I.: Security and privacy in smart grid demand response systems. In: Cuellar, J. (ed.) SmartGridSec 2014. LNCS, vol. 8448, pp. 1–15. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10329-7_1

    Chapter  Google Scholar 

  24. Yassine, A., Shirmohammadi, S.: Privacy and the market for private data: a negotiation model to capitalize on private data. In: IEEE/ACS International Conference on Computer Systems and Applications, Doha, pp. 669-678 (2008)

    Google Scholar 

  25. Yassine, A., Shirehjini, A.A., Shirmohammadi, S., Tran, T.: Knowledge-empowered agent information system for privacy payoff in ecommerce. Knowl. Inf. Syst. 32(2), 445–473 (2012)

    Article  Google Scholar 

  26. Makonin, S., Ellert, B., Bajic, I.V., Popowich, F.: AMPds2 - Almanac of minutely power dataset : electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci. Data 3, 1–12 (2015). https://doi.org/10.1038/sdata.2016.37

    Article  Google Scholar 

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Correspondence to Abdulsalam Yassine .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Singh, S., Yassine, A. (2019). IoT Big Data Analytics with Fog Computing for Household Energy Management in Smart Grids. In: Pathan, AS., Fadlullah, Z., Guerroumi, M. (eds) Smart Grid and Internet of Things. SGIoT 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-030-05928-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-05928-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05927-9

  • Online ISBN: 978-3-030-05928-6

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