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WSCISOM: wireless sensor data cluster identification through a hybrid SOM/MLP/RBF architecture

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Abstract

Networks of wireless sensors are very popular devices for monitoring and collecting information about phenomena in many aspects of life. While very versatile and widely applicable, there are few key issues related to the operation of wireless sensors as well as the processing of information collected by them. In this paper, we focus on wireless sensor network (WSN) organization and protocols, energy consumption as related to information exchange and calculations, and making sense and applying the concluded decisions by the WSN. In addition to the clustering technique—we are utilizing modified self-organizing map (SOM)—we propose a hybrid multilayer perceptron (MLP) and radial basis functions (RBF) neural network to analyze and classify the possible routes taken by devices activating our WSN. The results demonstrate that the SOM modifications made with energy savings in mind perform very well and provide a quality input for the MLP/RBF classifier. The final goal of determining all possible areas of activity within an input space of interest is successfully achieved as demonstrated by the experiments.

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References

  1. Shareef, A., Zhu, Y., Musavi, M.: Localization using neural networks in wireless sensor networks. In: Proceedings of the 1st International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, pp. 1–7 (2007)

  2. Kohonnen, T.: Self-organizing maps. Springer, New York (2001)

    Book  Google Scholar 

  3. Akkaya, K., Younis, M.: A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 3, 325–349 (2005)

    Article  Google Scholar 

  4. Yick, J.: Wireless sensor network survey. Comput. Netw. 52, 2292–2330 (2008)

    Article  Google Scholar 

  5. Al-Karaki, J., Kamal, A.: Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 11, 6–28 (2004)

    Article  Google Scholar 

  6. Mao, Y., Wang, F., Qiu, L., Lam, S.S., Smith, J.M.: S4: small state and small stretch routing protocol for large wireless sensor networks. In Proceedings of the 4th USENIX Conference on Networked Systems Design and Implementation, p. 8 (2007)

  7. Barbancho, J., Leon, C., Molina, F., Barbancho, A.: A new QoS routing algorithm based on self-organizing maps for wireless sensor networks. Telecommun. Syst. 36, 73–83 (2007)

    Article  Google Scholar 

  8. Kusy, B., Lee, H.J., Wicke, M., Milosavljevic, N., Guibas, L.: Predictive QoS routing to mobile sinks in wireless sensor networks. In Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, pp. 109–120 (2009)

  9. Buhmann, M.D.: Radial Basis Functions, 1st edn. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  10. Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.P.: Machine Learning in Wireless Sensor Networks: Algorithms. Strategies Appl. IEEE Commun. Surv. Tutor. 16(4), 1996–2018 (2014)

    Article  Google Scholar 

  11. Yu, L., Wang, N., Meng, X.: Real-time forest fire detection with wireless sensor networks. In: International Conference on Wireless Communications, Networking and Mobile Computing, vol. 2, pp. 1214–1217 (2005)

  12. Gu, D., Hu, H.: Spatial Gaussian process regression with mobile sensor networks. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1279–1290 (2012)

    Article  Google Scholar 

  13. Paladina, L., Paone, M., Iellamo, G., Puliafito, A.: Self organizing maps for distributed localization in wireless sensor networks. In: 12th IEEE Symposium on Computers and Communications ISCC, pp. 1113–1118 (2007)

  14. Giorgetti, G., Gupta, S.K.S., Manes, G.: Wireless localization using self-organizing maps. In: 6th International Symposium on Information Processing in Sensor Networks, pp. 293–302 (2007)

  15. Li, S., Kong, X., Lowe, D.: Dynamic Path determination of mobile beacons employing reinforcement learning for wireless sensor localization. In: 26th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 760–765 (2012)

  16. Laoudias, C., Kemppi, P., Panayiotou, C.G.: Localization using radial basis function networks and signal strength fingerprints in WLAN. In: IEEE Global Telecommunications Conference, pp. 1–6 (2009)

  17. Von Pless, G., Al Karim, T., Reznik, L.: Modified time-based multilayer perceptron for sensor networks and image processing applications. In: IEEE International Joint Conference on Neural Networks, pp. 2201–2206 (2005)

  18. Kulakov, A., Davcev, D., Trajkovski, G.: Implementing artificial neural-networks in wireless sensor networks. In: IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication. 18–19 April 2005, pp. 94–97. IEEE, Princeton, NJ (2005). doi:10.1109/SARNOF.2005.1426520

  19. Catterall, E., Van Laerhoven, K., Strohbach, M.: Self organization in ad hoc sensor networks: an empirical study. In: Proceedings of Alife VIII: the 8th International Conference on the Simulation and Synthesis of Living Systems, pp. 260—264. MIT Press, USA (2002)

  20. Muruganathan, S.D., Ma, D.C.F., Bhasin, R.I., Fapojuwo, A.: A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Commun. Mag. 43(3), S8 (2005)

    Article  Google Scholar 

  21. Subramanian, L., Katz, R.H.: An architecture for building self-configurable systems, In: First Annual Workshop on Mobile and Ad Hoc Networking and Computing, pp. 63–73 (2000)

  22. Petrovic, D., Shah, R.C., Ramchandran, K., Rabaey, J.: Data funneling: routing with aggregation and compression for wireless sensor networks. In: IEEE International Workshop on Sensor Network Protocols and Applications, pp. 156–162 (2003)

  23. Abbasi, A.A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Comput. Commun. 30(14–15), 2826–2841 (2007)

    Article  Google Scholar 

  24. Xu, N.: A survey of sensor network applications. IEEE Commun. Mag. 40(8), 102–114. doi:10.1109/MCOM.2002.1024422

  25. Puccinelli, D., Haenggi, M.: Wireless sensor networks: applications and challenges of ubiquitous sensing. IEEE Circuits Syst. Mag. 5(3), 19–31 (2005)

    Article  Google Scholar 

  26. Wagner, B., Timmermann, D.: Adaptive clustering for device free user positioning utilizing passive RFID, UbiComp’13, pp. 499–507 (2013)

  27. Rahman, M.S., Park, Y., Kim, K.D.: Localization of Wireless Sensor Network using artificial neural network, 9th International Symposium on Communications and Information Technology. ISCIT 2009, 639–642 (2009)

  28. Chagas, S.H., Martins, J.B., de Oliveira, L.L.: An approach to localization scheme of wireless sensor networks based on artificial neural networks and genetic algorithms. In: IEEE 10th International New Circuits and Systems Conference (NEWCAS), pp. 137–140 (2012)

  29. Abdelhadi, M., Anan, M.: A three-dimensional localization algorithm for wireless sensor networks using artificial neural networks.In: IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 1–5 (2012)

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Massachusetts Dartmouth, Dartmouth, MA, 02747, USA

    Jacob Olson & Howard Michel

  2. Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA, 02747, USA

    Iren Valova

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  1. Jacob Olson
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  2. Iren Valova
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  3. Howard Michel
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Correspondence to Iren Valova.

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Olson, J., Valova, I. & Michel, H. WSCISOM: wireless sensor data cluster identification through a hybrid SOM/MLP/RBF architecture. Prog Artif Intell 5, 233–250 (2016). https://doi.org/10.1007/s13748-016-0099-8

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  • DOI: https://doi.org/10.1007/s13748-016-0099-8

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