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Power Consuming Activity Recognition in Home Environment

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Book cover Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10602))

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Abstract

This work proposed an activity recognition model which focus on the power consuming activity in home environment, to help residents modify their behavior. We set the IoT system with lower number of sensors. The key data for identifying activity comes from widely used smart sockets. It first took residents’ acceptability into consideration to set the IoT system, then used a seamless indoor position system to get residents’ position to help recognize the undergoing activities. Based on ontology, it made use of domain knowledge in daily activity and built an activity ontology. The system took real home situation into consideration and make full use of both electric and electronic appliances’ data into the context awareness. The knowledge helps improve the performance of the data-driven method. The experiment shows the system can recognize the common activities with a high accuracy and have a good applicability to real home scenario.

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References

  1. Sarah, D.: The effectiveness of feedback on energy consumption. In: A Review for DEFRA of the Literature on Metering, Billing and Direct Displays, vol. 486 (2006)

    Google Scholar 

  2. Chen, C., Das, B., Cook, D.J.: A data mining framework for activity recognition in smart environments. In: The 6th International Conference on Intelligent Environments, pp. 80–83 (2010)

    Google Scholar 

  3. Poppe, R.: A survey on vision-based human action recognition. Image Vision Comput. 28(6), 976–990 (2010)

    Article  Google Scholar 

  4. Nappi, M., Piuri, V., Tan, T., Zhang, D.: Introduction to the special section on biometric systems and applications. IEEE Trans. Syst. Man Cybern. Syst. 44(11), 1457–1460 (2014)

    Article  Google Scholar 

  5. Peetoom, K.K.B., Lexis, M.A.S., Joore, M., Dirksen, C.D., De Witte, L.P.: Literature review on monitoring technologies and their outcomes in independently living elderly people. Disab. Rehabil. Assist. Technol. 10(4), 271–294 (2015)

    Article  Google Scholar 

  6. Liu, Q., Cai, W., Shen, J., Fu, Z., Liu, X., Linge, N.: A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur. Commun. Netw. 9(17), 4002–4012 (2016)

    Article  Google Scholar 

  7. Wilson, D.H., Atkeson, C.: Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In: Gellersen, H.W., Want, R., Schmidt, A. (eds.) Pervasive 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005). doi:10.1007/11428572_5

    Chapter  Google Scholar 

  8. Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24646-6_10

    Chapter  Google Scholar 

  9. Fu, Z., Huang, F., Sun, X., Vasilakos, A.V., Yang, C.-N.: Enabling semantic search based on conceptual graphs over encrypted outsourced data. IEEE Trans. Serv. Comput. (2016)

    Google Scholar 

  10. Shoaib, M., Bosch, S., Scholten, H., Havinga, P.J.M., Incel, O.D.: Towards detection of bad habits by fusing smartphone and smartwatch sensors. In: IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 591–596 (2015)

    Google Scholar 

  11. De, D., Bharti, P., Das, S.K., Chellappan, S.: Multimodal wearable sensing for fine-grained activity recognition in healthcare. IEEE Internet Comput. 19, 26–35 (2015)

    Article  Google Scholar 

  12. Xia, Z., Wang, X., Sun, X., Wang, B.: Steganalysis of least significant bit matching using multi-order differences. Secur. Commun. Netw. 7(8), 1283–1291 (2014)

    Article  Google Scholar 

  13. Abbate, S.: A smartphone-based fall detection system. Pervasive Mob. Comput. 8(6), 883–899 (2012)

    Article  Google Scholar 

  14. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newslett. 12(2), 74–82 (2010)

    Article  Google Scholar 

  15. Zhang, Y., Sun, X., Baowei, W.: Efficient algorithm for K-barrier coverage based on integer linear programming. China Commun. 13(7), 16–23 (2016)

    Article  Google Scholar 

  16. Chen, M.: Towards smart city: M2M communications with software agent intelligence. Multimedia Tools Appl. 67, 167–178 (2012)

    Article  Google Scholar 

  17. Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the ACM MOBICOM, pp. 27–38 (2013)

    Google Scholar 

  18. Adib, F., Katabi, D.: See through walls with WiFi!. In: Proceedings of the ACM SIGCOMM, pp. 75–86 (2013)

    Google Scholar 

  19. Lai, C.F., Lai, Y.X., Yang, L.T., Chao, H.C.: Integration of IoT energy management system with appliance and activity recognition. In: IEEE International Conference on Green Computing and Communications, pp. 66– 71 (2012)

    Google Scholar 

  20. Cho, W.T., Lai, Y.X., Lai, C.F., Huang, Y.M.: Appliance-aware activity recognition mechanism for IoT energy management system. Comput. J. 56(8), 1020–1033 (2013)

    Article  Google Scholar 

  21. Granovsky-Grisaru, S., Shaya, M., Diamant, Y.Z.: Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Humaniz. Comput. 1(1), 57–63 (2010)

    Article  Google Scholar 

  22. Nazerfard, E., Das, B., Holder, L.B., Cook, D.J.: Conditional random fields for activity recognition in smart environments. In: ACM International Health Informatics Symposium, pp. 282–286 (2010)

    Google Scholar 

  23. Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24, 961–974 (2012)

    Article  Google Scholar 

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Acknowledgments

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 701697.

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Authors and Affiliations

  1. School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK

    Xiaodong Liu & Qi Liu

Authors
  1. Xiaodong Liu
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  2. Qi Liu
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Corresponding author

Correspondence to Qi Liu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Liu, X., Liu, Q. (2017). Power Consuming Activity Recognition in Home Environment. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_31

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  • DOI: https://doi.org/10.1007/978-3-319-68505-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68504-5

  • Online ISBN: 978-3-319-68505-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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