Feed Forward Neural Network-Based Sensor Node Localization in Internet of Things

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Internet of Things and wireless sensor networks have attracted worldwide researchers because of their various applications in different fields. Specifically, in IoT, knowledge of location is a very critical issue which deals with identifying the position of deployed node in the sensor network. It is extremely advantageous to propose scalable, cost efficient, and proficient localization procedure for IoT. This paper provides a sensor positioning algorithm named centroid algorithm which is a range-free position identifying scheme. The centroid of the polygon is used to compute the coordinates of estimate position to get better node location precision and further neural networks such as feed forward have been implemented to improve the accuracy. A comparison of centroid algorithm and feed forward neural network-based localization has been done and found that the neural network promises better results for higher localization accuracy.

Keywords

Centroid algorithm Global positioning system Neural networks Internet of Things Wireless sensor networks 

References

  1. 1.
    Ian F. Akyildiz, Weilian Su, Yogesh Sankarasubramaniam and Erdal Cayirci, “A Survey on Sensor Networks,” IEEE Communications Magazine, Vol. 40, No. 8, pp. 102–114, 2002.Google Scholar
  2. 2.
    Carlos F. García-Hernández, Pablo H. Ibargüengoytia-González, Joaquín García and Hernández, “Wireless Sensor Networks and Applications: a Survey,” International Journal of Computer Science and Network Security (IJCSNS), Vol. 7, No. 3, pp. 264–273, 2007.Google Scholar
  3. 3.
    Y. Shang, W. Rumi, Y. Zhang, and M. Fromherz, “Localization from connectivity in sensor networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 15, No. 11, pp. 961–974, 2004.Google Scholar
  4. 4.
    Shweta Singh, R. Shakya and Y. Singh, “Localization Techniques in Wireless Sensor Networks,” International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 6, No. 1, pp. 844–850, 2015.Google Scholar
  5. 5.
    P. K. Singh, B. Tripathi and N. P. Singh, “Node Localization in Wireless Sensor Networks,” International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2, No. 6, pp. 2568–2572, 2011.Google Scholar
  6. 6.
    Nabil Ali Alrajeh, Maryam Bashir and Bilal Shams, “Localization Techniques in Wireless Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 304628, 9 pages, 2013.Google Scholar
  7. 7.
    N. Patwari, A. Hero, M. Perkins, N. Correal, and R. O’Dea, “Relative location estimation in wireless sensor networks,” IEEE Transactions on Signal Processing, Vol. 51, No. 8, pp. 2137–2148, 2003.Google Scholar
  8. 8.
    N. Bulusu, J. Heidemann, and D. Estri n, “GPS-less low-cost outdoor localization for very small devices,” Personal Communications, IEEE, Vol. 7, No. 5, pp. 28–34, 2000.Google Scholar
  9. 9.
    Chen, Chien-Sheng, “Artificial neural network for location estimation in wireless communication systems,” Sensors, Vol. 12, No. 3, pp. 2798–2817, 2012.Google Scholar
  10. 10.
    Ali Shareef, Yifeng Zhu, and Mohamad Musavi, “Localization using neural networks in wireless sensor networks,” Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ACM, 2008.Google Scholar
  11. 11.
    J. Blumenthal, R. Grossmann, F. Golatowski, and D. Timmermann, “Weighted Centroid Localization in Zigbee-based Sensor Networks,” Intelligent Signal Processing, IEEE, pp. 1–6, 2007.Google Scholar
  12. 12.
    C. Alippi, A. Mottarella, and G. Vanini. “A RF map-based localization algorithm for indoor environments,” IEEE International Symposium on Circuits and Systems, (ISCAS), pp. 652–655, 2005.Google Scholar
  13. 13.
    G.-A. Lusilao Zodi, Gerhard P. Hancke, and Antoine B. Bagula, “Enhanced Centroid Localization of Wireless Sensor Nodes using Linear and Neighbor Weighting Mechanisms,” Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, ACM, 2015.Google Scholar
  14. 14.
    Liu, Yu, Xiao Yi, and You He, “A novel centroid localization for wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 2012, Article ID 829253, 8 pages, 2012.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of CSECollege of Engineering and Technology, Mody University of Science and TechnologyLakshmangarhIndia

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