A New Design Prospective for User Specific Intelligent Control of Devices in a Smart Environment

  • Vaskar DekaEmail author
  • Shikhar Kumar Sarma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)


With the rapid growth of ICT, almost all the entities exist in this world are moving towards computing enabled digital space. At present, the computing infrastructure is not only an infrastructure for scientific computation and communication, but also it has been considered as a platform for computation of anything appearing at anytime and anywhere. One of such computing platform is smart environment (SE); which is an intelligent physical environment equipped with different types of hardware and network tools & technologies in order to sense and interpret the present context of the environment so that proactive services can be delivered to the users/inhabitants for making the inhabitants lives more comfortable. Researches in different directions at different levels in the domain of SE are currently becoming very popular in the minds of researchers. This work is also such kind of research work wherein the mechanism for intelligent control of the devices through learning has been considered. Learning is the major attribute to be incorporated in a SE through which the environment will be able to understand about the preferences of the status of the devices for a specific inhabitant at a given time. In this regard, this work tried to find out the mechanism to enable the learning feature by proposing a data structure. Working concept of the proposed data structure and running time analysis of the same also have been placed in this paper along with its required procedures. Experiments also have been performed for evaluating the proposed mechanism.


ICT SE RFID Bipartite graph 



The expenditure for participating in the conference including travel allowance is supported through the faculty and staff development grant under TEQIP III project of GUIST. We are thankful for the support.


  1. 1.
    Weiser, M.: The computer for the twenty-first century. Sci. Am. 94–100 (1991)Google Scholar
  2. 2.
    Cook, D.J., Das, S.K.: Smart Environments: Technology, Protocols and Applications. (Wiley Series on Parallel and Distributed Computing). Wiley, Hoboken (2004)CrossRefGoogle Scholar
  3. 3.
    Kidd, C: The aware home: a living laboratory for ubiquitous computing research. In: Proceedings of 2nd International Workshop on Cooperative Buildings, pp. 190–197 (1999)CrossRefGoogle Scholar
  4. 4.
    Kientz, J.A., Patel, S.N., Jones, B., Price, E., Mynatt, E.D., Abowd, G.D.: The Georgia Tech aware home. In: Extended Abstracts of the Conference on Human Factors in Computing Systems (CHI 2008), pp. 3675–3680 (2008)Google Scholar
  5. 5.
    Holmes, A., Duman, H., Pounds-Cornish, A.: The iDorm: Gateway to heterogeneous networking environments. In: Proceedings of International Test and Evaluation Association (ITEA) Workshop on Virtual Home Environments, pp. 30–37 (2002)Google Scholar
  6. 6.
    Cook, D., Youngblood, M., Heierman, E., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: an agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), pp. 521–524 (2003)Google Scholar
  7. 7.
    Youngblood, G., Cook, D.J., Holder, L.: The MavHome architecture. In: Technical Report CSE-2004-18, University of Texas at Arlington (2004)Google Scholar
  8. 8.
    Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., Jansen, E.: The Gator Tech Smart House: a programmable pervasive space. Computer 38(3), 50–60 (2005)CrossRefGoogle Scholar
  9. 9.
    Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2013)CrossRefGoogle Scholar
  10. 10.
    Heierman, E.O., Cook, D.J.: Improving home automation by discovering regularly occurring device usage patterns. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), FL, USA (2003)Google Scholar
  11. 11.
    Coble, J., Cook, D., Holder, L., Rathi, R.: Structure discovery from sequential data. In: Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, USA (2004)Google Scholar
  12. 12.
    Jakkula, V. R., Cook, D. J.: Using temporal relations in smart environment data for activity prediction. In: Proceedings of the 24th International Conference on Machine Learning (2007)Google Scholar
  13. 13.
    Hussain, S., Schaffner, S., Moseychuck, D.: Applications of wireless sensor networks and RFID in a smart home environment. In: Proceedings of Seventh Annual Communications Networks and Services Research Conference (2009)Google Scholar
  14. 14.
    Hsu, H., Lee, C., Chen, Y.: An RFID-based reminder system for smart home. In: Proceedings of the IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 264–269, Singapore (2011)Google Scholar
  15. 15.
    Kumar, S., Qadeer, M.A.: Application of AI in home automation. Int. J. Eng. Technol. 4(6), 803–807 (2012)CrossRefGoogle Scholar
  16. 16.
    Akter, S.S., Holder, L.B.: Activity recognition using graphical features. In: Proceedings of 13th International Conference on Machine Learning and Applications (ICMLA), pp. 165–170 (2014)Google Scholar
  17. 17.
    Twomey, N., Diethe, T., Flach, P.: Unsupervised Learning of Sensor Topologies for Improving Activity Recognition in Smart Environments. Neurocomputing-2017 234, 93–106 (2017)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologyGauhati UniversityGuwahatiIndia

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