Advertisement

A Comparative Study of Classification Techniques for Managing IoT Devices of Common Specifications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10537)

Abstract

As information technology and telecommunication systems continue to grow in size and complexity, especially with the Internet of Things (IoT) domain that is being hailed as the next industrial revolution, emerging technologies have to anticipate this dramatic increase of heterogeneous connected devices. This paper proposes a solution that can be used to manage this huge number of devices, by classifying them and predicting their device’s type, based on their specifications. Four (4) classification algorithms are being applied on a dataset containing the specifications of known devices (in terms of known device type), which is being used for predicting the unknown devices’ types. These algorithms are analyzed using the WEKA data mining tool and a comparative study is undertaken to find the classifier that performs the best analysis on the dataset obtained, using a set of predefined performance metrics to compare the results of each classifier.

Keywords

IoT devices Heterogeneous devices Device specification Device type Classification Classification algorithms 

Notes

Acknowledgements

The CrowdHEALTH project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727560. Athanasios Kiourtis would also like to acknowledge the financial support from the “Foundation for Education and European Culture (IPEP)”.

References

  1. 1.
    Oweis, N.E., Aracenay, C., George, W., Oweis, M., Soori, H., Snasel, V.: Internet of Things: overview, sources, applications and challenges. In: Second International Afro-European Conference for Industrial Advancement, AECIA, pp. 57–67 (2016)Google Scholar
  2. 2.
  3. 3.
    Cisco: Cisco visual networking index: global mobile data traffic forecast update. White Paper (2015)Google Scholar
  4. 4.
    Pires, F. et al.: A platform for integrating physical devices in the Internet of Things. In: Embedded and Ubiquitous Computing, EUC, pp. 23–241. IEEE (2014)Google Scholar
  5. 5.
    Pham, C., Lim, Y., Tan, Y.: Management architecture for heterogeneous IoT devices in home network. In: Consumer Electronics, pp. 1–5. IEEE (2016)Google Scholar
  6. 6.
    Han, J., Cai, Y., Cercone, N.: Concept-based data classification in relational databases. In: 1991 AAAI Workshop Knowledge Discovery in Databases, pp. 77–94 (1991)Google Scholar
  7. 7.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)Google Scholar
  8. 8.
    Giusti, A., Ritter, G., Vichi, M. (eds.): Classification and Data Mining. Springer Science & Business Media, Heidelberg (2012)Google Scholar
  9. 9.
    Gorade, S.M., Deo, A., Purohit, P.: A Study of Some Data Mining Classification Techniques (2017)Google Scholar
  10. 10.
    Jabbar, M.A., Chandrab, P.: Heart disease prediction system using associative classification and genetic algorithm. In: International Conference on Emerging Trends in Electrical, Electronics and Communication Technologies, ICECIT (2012)Google Scholar
  11. 11.
    Akinola, S.O., Oyabugbe, O.J.: Accuracies and training times of data mining classification algorithms: an empirical comparative study. J. Softw. Eng. Appl. 8, 470–477 (2015)CrossRefGoogle Scholar
  12. 12.
    Majali, J., Niranjan, R., Phatak, V.: Data mining techniques for diagnosis and prognosis of cancer. Int. J. Adv. Res. Comput. Commun. Eng. 4(3), 613–616 (2015)CrossRefGoogle Scholar
  13. 13.
    Salvithal, N.N.: Appraisal management system using data mining. Int. J. Comput. Appl. 135(12), 45–50 (2016). ISSN: 0975-8887Google Scholar
  14. 14.
    Sharma, T., Sharma, A., Mansotra, V.: Performance analysis of data mining classification techniques on public health care data. Int. J. Innov. Res. Comput. Commun. Eng. 4(6), 155–169 (2016)Google Scholar
  15. 15.
    Li, Y., Shen, T., Sun, X., Pan, X., Mao, B.: Detection, classification and characterization of android malware using API data dependency. In: Thuraisingham, B., Wang, X., Yegneswaran, V. (eds.) SecureComm 2015. LNICSSITE, vol. 164, pp. 23–40. Springer, Cham (2015). doi: 10.1007/978-3-319-28865-9_2 CrossRefGoogle Scholar
  16. 16.
    Arora, D., Li, K.F., Loffler, A.: Big data analytics for classification of network enabled devices. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA. IEEE (2016)Google Scholar
  17. 17.
    Kim, H., et al.: Collaborative classification for daily activity recognition with a smartwatch. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC. IEEE (2016)Google Scholar
  18. 18.
    Introduction to k-nearest neighbors: simplified. https://www.analyticsvidhya.com/blog/2014/10/introduction-k-neighbours-algorithm-clustering/. Accessed 13 July 2017
  19. 19.
    A Detailed Introduction to K-Nearest Neighbor (KNN) Algorithm. https://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/. Accessed 13 July 2017
  20. 20.
    K Nearest Neighbors – Classification. http://www.saedsayad.com/k_nearest_neighbors.htm. Accessed 13 July 2017
  21. 21.
    Kłopotek, M.A.: Very large Bayesian multinets for text classification. Futur. Gener. Comput. Syst. 21, 1068–1082 (2015)CrossRefGoogle Scholar
  22. 22.
    Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22. IBM (2001)Google Scholar
  23. 23.
  24. 24.
    Naive Bayesian. http://www.saedsayad.com/naive_bayesian.htm. Accessed 13 July 2017
  25. 25.
    Support Vector Machine - Classification (SVM). http://www.saedsayad.com/support_vector_machine.htm. Accessed 13 July 2017
  26. 26.
    Chau, A.L., Li, X., Yu, W.: Support vector machine classification for large datasets using decision tree and Fisher linear discriminant. Futur. Gener. Comput. Syst. 36, 57–65 (2014)CrossRefGoogle Scholar
  27. 27.
  28. 28.
    Ho, T.K.: A data complexity analysis of comparative advantages of decision forest constructors. Pattern Anal. Appl. 5(2), 102–112 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Zakariah, M.: Classification of large datasets using Random Forest Algorithm in various applications: Survey. Money 4(3), 189–198 (2014)Google Scholar
  30. 30.
    Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: Plug‘n’play IoT devices: an approach for dynamic data acquisition from unknown heterogeneous devices. In: Barolli, L., Terzo, O. (eds.) Complex, Intelligent, and Software Intensive Systems, CISIS 2017. Advances in Intelligent Systems and Computing, vol. 611, pp. 885–895. Springer, Cham (2018)CrossRefGoogle Scholar
  31. 31.
    Weka 3: Data Mining Software in Java. http://www.cs.waikato.ac.nz/ml/weka/, Accessed 13 July 2017
  32. 32.
    Kawade, D.R., Oza, K.S.: SMS spam classification using WEKA. Int. J. Electron. Commun. Comput. Technol. 5 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Digital SystemsUniversity of PiraeusPiraeusGreece

Personalised recommendations