On Context Awareness and Analysis of Various Classification Algorithms

  • Umang Nanda
  • Shrey Rajput
  • Himanshu Agrawal
  • Antriksh Goel
  • Mohit Gurnani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


Internet of Things (IoT) is currently connecting 9 billion devices and is expected to grow by three times in next 5 years, and hence will connect over 27 billion devices. IoT is touching every walk of human life such as health care, smart utilities, smart grid, smart homes, and smart spaces. To make things or object smart, IoT middleware makes use of appropriate intelligent mechanisms. Context-aware solutions are addressing the challenges of IoT middleware, hence becoming an important building block. We provide an analytical study of various algorithms for classification. We consider three algorithms and test the performances of each on small dataset as well as on larger dataset with 1969 instances. Performance evaluation is done using Mean Square Error and Absolute Mean Square Error.


Context awareness Classification Internet of things 


  1. 1.
    Abowd, G.D., et al.: Towards a better understanding of context and context-awareness. In: Proceedings of 1st International symposium on Handheld and Ubiquitous Computing, pp. 304–307 (2012)Google Scholar
  2. 2.
    Schilit, B., Theimer, M.: Disseminating active map information to mobile hosts. IEEE Netw. 8(5), 22–32 (1994)Google Scholar
  3. 3.
    Sanchez, L., et al.: A generic context management framework for personal networking environments. In: Mobile and Ubiquitous Systems—Workshops, 3rd Annual International Conference on, pp. 1–8 (2006)Google Scholar
  4. 4.
    Dey, A.: Towards a better understanding of context and context-awareness. In: 1st International Symposium on Handheld and Ubiquitous Computing (1999)Google Scholar
  5. 5.
    Ranganathan, C.: An infrastructure for context-awareness based on first order logic. Pers. Ubiquit. Comput. (2003)Google Scholar
  6. 6.
    Xu, He, Li: Internet of things in industries—a survey. IEEE Trans. Industr. Inf. (2014)Google Scholar
  7. 7.
    Beynon, C.: The dempster–shafer theory of evidence: an alternative approach to multicriteria decision modelling. Omega 37–50 (2000)Google Scholar
  8. 8.
    Charalampopoulos, A.: A comparable study employing weka clustering/classification algorithms for web page classification. In: 15th Panhellenic Conference, pp. 235–239 (2011)Google Scholar
  9. 9.
    Narwal, M.: Comparison of the various clustering and classification algorithms of WEKA tools. Int. J. Adv. Res. Comput. Sci. Software Eng. (2013)Google Scholar
  10. 10.
    Holmes, Donkin, Witten: WEKA: A machine learning workbench. In: Second Australian and New Zealand Conference, pp. 357–361 (1994)Google Scholar
  11. 11.
    The University of Waikato:
  12. 12.

Copyright information

© Springer India 2016

Authors and Affiliations

  • Umang Nanda
    • 1
  • Shrey Rajput
    • 1
  • Himanshu Agrawal
    • 1
  • Antriksh Goel
    • 1
  • Mohit Gurnani
    • 1
  1. 1.Computer Science and Engineering DepartmentSymbiosis Institute of TechnologyPuneIndia

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