Fuzzy Centroid Localization Scheme for Unbalanced Deployments of Wireless Sensor Networks

Authors

  • Songyut Permpol Applied Network Technology (ANT) Department of Computer Science, Faculty of Science, Khon Kaen University
  • Chudapa Thammasakorn Applied Network Technology (ANT) Department of Computer Science, Faculty of Science, Khon Kaen University
  • Kanokmon Rujirakul Applied Network Technology (ANT) Department of Computer Science, Faculty of Science, Khon Kaen University
  • Chakchai So-In Applied Network Technology (ANT) Department of Computer Science, Faculty of Science, Khon Kaen University

Keywords:

Centroid, Clustering, Deployment, Fuzzy Logic, K-Means, Non-uniform, Unbalance, Wireless Sensor Networks,

Abstract

This paper proposes a novel methodology to mitigate the effect of unbalanced known nodes’ positions for location approximation in wireless sensor networks. In a practical deployment, some nodes may not properly be in uniform places, and perhaps, due to unequal power consumption of large-scale networks while performing sensing, computing, and transmitting tasks. Kmeans clustering is applied to select a representative of the known nodes where their positions are close together, and each of which will be then fed into fuzzy logic systems to determine a proper weight to finally use in the actual location determination process with weighted Centroid. The effectiveness of our methodology is evaluated via a large scale simulation with regard to node density, coverage, and topology, against a traditional Centroid, its fuzzy systems, and DV-Hop.

References

Atzori, L., Iera, A., Morabito, G.: The Internet of things: a survey. Comput. Netw. vol. 54, pp. 2787--2805 (2010).

Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. vol. 29, pp. 1645--1660 (2013).

Lindroos, V., Tilli, M., Lehto, A., Motooka, T.: Handbook of Silicon Based MEMS Materials and Technologies. William Andrew, Burlington, MA, USA (2010).

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. vol. 40, pp. 102--114 (2002).

Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Comput. Netw. vol. 52, pp. 2292--2330 (2008).

Han, G., Xu, H., Duong, T. Q., Jiang, J., Hara, T.: Localization algorithms of Wireless Sensor Networks: a survey. Telecommun. Syst. vol. 52, pp. 2419--2436 (2013).

Cheng, L., Wu, C., Zhang, Y., Wu, H., Li, M., Maple, C.: A survey of localization in Wireless Sensor Network. Int. J. Distrib. Sensor Netw. vol. 2012, pp. 1--12 (2012).

Alrajeh, N.A., Bashir, M., Shams, B.: Localization techniques in wireless sensor networks. Int. J. Distrib. Sensor Netw. vol. 2013, pp. 1--9 (2013).

Bulusu, N., Heidemann, J., Estrin, D.: GPS-less low-cost outdoor localization for very small devices. IEEE Pers. Commun. vol. 7, no. 5, pp. 28--34 (2000).

He, T., Huang, C., Blum, B. M., Stankovic, J. A., Abdelzaher, T.: RangeFree localization schemes for large scale sensor networks. In: Int. Conf. on Mobile Comput. Netw. pp. 81--95. ACM, CA, USA (2003).

Niculescu, D., Nath, B.: DV based positioning in ad hoc networks. Telecommun. Syst. vol. 22, pp. 267--280 (2003).

Nagpal, R.: Organizing a global coordinate system from Local information on an amorphous computer. In: Proc. A.I. Memo MIT Artificial Intell. Lab. (1999).

Yun, S., Lee, J., Chung, W., Kim, E.: Centroid localization method in wireless sensor networks using TSK fuzzy modeling. In: Proc. Int. Symp. on Advanced Intell. Syst. pp. 971--974, (2005).

Jang, H., Topal, E.: A review of soft computing technology applications in several mining problems. Applied Soft Comput. vol. 22, pp. 638--651 (2014).

Yun, S., Lee, J., Chung, W., Kim, E., Kim, S.: A soft computing approach to localization in wireless sensor networks. Expert Syst. with Appl. vol. 36, no. 4, pp. 7552--7561 (2009).

Kumar, V., Kumar, A., Soni, S.: A combined Mamdani-Sugeno fuzzy approach for localization in wireless sensor networks. In: Proc. Int. Cnf. & Workshop on Emerging Trends in Technol. pp. 798--803. ACM, NY, USA (2011).

Zadeh, L. A.: Fuzzy algorithms. Info. and Control. vol. 12, no. 2, pp. 94--102 (1968).

Larios, D. F., Barbancho, J., Molina, F. J., León, C.: LIS: localization based on an intelligent distributed fuzzy system applied to a WSN. Ad Hoc Netw. vol. 10, no. 3, pp. 604--622 (2012).

Shang, Y., Ruml, W., Zhang, Y., Fromherz, M. P. J.: Localization from mere Connectivity. In: Proc. 4th ACM Int. Symp. on Mobile Ad Hoc Netw. & Comp. pp. 201--212. ACM, MD, USA (2003).

Tran, D.A., Nguyen, T.: Localization in wireless sensor networks based on support vector machines. IEEE Trans. Parallel and Distrib. Syst. vol. 19, pp. 981--994 (2008).

Lloyd, S.: Least Squares Quantization in PCM. IEEE Trans. on Info. Theory. vol. 28, pp. 129--137 (1982).

So-In, C., Permpol, S., Rujirakul, K.: Soft computing-based localizations in wireless sensor networks. Pervasive and Mobile Comput. (in press) (2015).

Gu, S., Yue, Y., Maple, C., Wu, C.: Fuzzy logic based localization in Wireless Sensor Networks for disaster environments. In: Proc. 18th Int. Conf. on Automation & Comput. pp. 1--5. IEEE, CA, USA (2012).

Lee, H., Chung, K., Jhang, K.: A study of Wireless Sensor Network Routing protocols for maintenance access hatch condition surveillance. J. Info. Process. Syst. vol. 9, pp. 237--246 (2013).

Pantazis, N. A., Nikolidakis, S. A., Vergados, D. D.: Energy-efficient routing protocols in wireless sensor networks. IEEE Commun. Survey & Tutorials. vol. 15, pp. 551--591 (2013).

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Published

2016-09-01

How to Cite

Permpol, S., Thammasakorn, C., Rujirakul, K., & So-In, C. (2016). Fuzzy Centroid Localization Scheme for Unbalanced Deployments of Wireless Sensor Networks. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(6), 55–59. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1245

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