KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

EEC-FM: Energy Efficient Clustering based on Firefly and Midpoint Algorithms in Wireless Sensor Network

  • Daniel, Ravuri (Department of Information Technology, Vignan's Institute of Information Technology Jawaharlal Nehru Technological University-Kakinada) ;
  • Rao, Kuda Nageswara (Department of Computer Science & Systems Engineering Andhra University College of Engineering (A), Andhra University)
  • Received : 2017.10.28
  • Accepted : 2018.04.09
  • Published : 2018.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

Keywords

References

  1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "Wireless Sensor Networks: A Survey," Computer Networks, 393-422, 2002.
  2. Yick, J., Mukherjee, B., Ghosal, D., "Wireless sensor network survey," Comput. Netw., 52, (12), pp. 2292-2330, 2008. https://doi.org/10.1016/j.comnet.2008.04.002
  3. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, "Wireless sensor networks for habitat monitoring," in Proc. of Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), 2002.
  4. D. Steere, A. Baptista, D. McNamee, C. Pu, and J. Walpole, "Research challenges in environmental observation and forecasting systems," in Proc. of Proceedings of the Sixth Annual International Conference on Mobile Computing and Networking (MobiCom), 2000.
  5. Pagano, S., Peirani, S., Valle, M., "Indoor ranging and localisation algorithm based on received signal strength indicator using statistic parameters for wireless sensor networks," IET Wirel. Sens. Syst., 5, (5), pp. 243-249, 2015. https://doi.org/10.1049/iet-wss.2014.0027
  6. Bhatti, S., Xu, J., Memon, M., "Clustering and fault tolerance for target tracking using wireless sensor networks," IET Wirel. Sens. Syst., 1, (2), pp. 66-73, 2011. https://doi.org/10.1049/iet-wss.2010.0085
  7. Li, W., Zhang, W.: 'Sensor selection for improving accuracy of target localisation in wireless visual sensor networks', IET Wirel. Sens. Syst., 2012, 2, (4), pp. 293-301. https://doi.org/10.1049/iet-wss.2012.0033
  8. S. Intille, "Designing a home of the future", IEEE Pervasive Computing, 1(2):76-82, April 2002. https://doi.org/10.1109/MPRV.2002.1012340
  9. C. Kidd et al. "The aware home: A living laboratory for ubiquitous computing research," in Proc. of Proceedings of the Second International Workshop on Cooperative Buildings (CoBuild), 1999.
  10. D. W. Kumar, "Healthcare Monitoring System Using Wireless Sensor Network," Intr. Journal of Advanced Networking and Applications, vol. 4, no. 1, pp. 1497-1500, 2012.
  11. S. Mukherjee, K. Dolui, S. K. Datta, "Patient health management system using e-health monitoring architecture," in Proc. of IEEE International Conference on Advance Computing(IACC), pp. 400-405, 2014.
  12. M. Yamaji, Y. Ishii, T. Shimamura, and S. Yamamoto, "Wireless Sensor Networks for Industrial Automation," in Proc. of 3rd International Conference on Networked Sensing System, 2006.
  13. Vehbi C. Gungor and Gerhard P. Hancke, Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches, IEEE Transactions On Industrial Electronics, VOL. 56, NO. 10, 2009.
  14. Abbasi, A.A., Younis, M., "A survey on clustering algorithms for wireless sensor networks," Comput. Commun., 30, (14), pp. 2826-2841, 2007. https://doi.org/10.1016/j.comcom.2007.05.024
  15. Heinzelman,W.B., Chandrakasan, A.P., Balakrishnan, H., "An application-specific protocol architecture for wireless micro-sensor networks," IEEE Trans. Wirel. Commun., 1, (4), pp. 660-670, 2002. https://doi.org/10.1109/TWC.2002.804190
  16. Ray, A., De, D., "Energy efficient clustering hierarchy protocol for wireless sensor network," in Proc. of IEEE Int. Conf. on Communication and Industrial Application(ICCIA), pp. 1-4, December 2011.
  17. Ray, A., De, D., "Energy efficient cluster head selection in wireless sensor network," in Proc. of IEEE Int. Conf. on Recent Advances in Information Technology (RAIT)-2012, ISM, Dhanbad, Jharkhand, pp. 306-311, March 2012.
  18. Ray, A., De, D., "Energy efficient clustering algorithm for multi-hop green wireless sensor network using gateway node," Adv. Sci. Eng. Med., 5, (11), pp. 1199-1204, 2013. https://doi.org/10.1166/asem.2013.1412
  19. Ray, A., De, D.: 'Level wise initial energy assignment in wireless sensor network for better network lifetime', Proc. Adv. Comput. Netw. Inf., 2014, 2, pp. 67-74.
  20. Guo, P., Jiang, T., Zhang, K., et al., "Clustering algorithm in initialization of multi-hop wireless sensor networks," IEEE Trans. Wirel. Commun., 8, (12), pp. 5713-5717, 2009. https://doi.org/10.1109/TWC.2009.12.080042
  21. Kumar, R., Malik, A., Kumar, B., "NEECP: a novel energy efficient clustering protocol for prolonging lifetime of WSNs," IET Wirel. Sens. Syst., 2016
  22. Cao, F., Liang, J., Jiang, G., "An initialization method for the K-means algorithm using neighbourhood model," Comput. Math, 58, pp. 474-483, Appl, 2009.
  23. Khan Shehroz, S., Amir, A., "Cluster centre initialization algorithm for K-means clustering," Pattern Recognit. Lett., 25, (11), pp. 1293-1302, 2004. https://doi.org/10.1016/j.patrec.2004.04.007
  24. Sasikumar, P., Khara, S., "K-means clustering in wireless sensor networks," in Proc. of IEEE Fourth Int. Conf. of Computational Intelligence and Communication Networks (CICN), pp. 140-144, November 2012.
  25. Hansen, P., Ngai, E., Cheung, B.K., et al., "Analysis of global K-means, an incremental heuristic for minimum sum-of-squares clustering," J. Classif., 22, (2), pp. 287-310, 2005. https://doi.org/10.1007/s00357-005-0018-3
  26. Napoleon, D., Ganga Lakshmi, P., "An enhanced K-means algorithm to improve the efficiency using normal distribution data points," Int. J. Comput. Sci. Eng., 2, (7), pp. 2409-2413, 2010.
  27. Niknam, T., Amiri, B., "An efficient hybrid approach based on PSO, ACO and K-means for cluster analysis," Appl. Soft Comput., 10, (1), pp. 183-197, 2010. https://doi.org/10.1016/j.asoc.2009.07.001
  28. Tong, M., Tang, M., "LEACH-B: an improved LEACH protocol for wireless sensor network," in Proc. of Int. Conf. Wireless Communications Networking and Mobile Computing(WiCOM), pp. 1-4, September 2010.
  29. Khan, A., Tamim, I., Ahmed, E., et al., "Multiple parameter based clustering (MPC): prospective analysis for effective clustering in wireless sensor network (WSN) using K-means algorithm," Wirel. Sens. Netw., 4, (1), pp. 18-24, 2012. https://doi.org/10.4236/wsn.2012.41003
  30. Tan, L., Gong, Y., Chen, G., "A balanced parallel clustering protocol for wireless sensor networks using K-means techniques," in Proc. of IEEE Second Int. Conf. On Sensor Technologies and Applications, pp. 300-305, August 2008.
  31. Park, G.Y., Kim, H., Jeong, H.W., et al., "A novel cluster head selection method based on K-means algorithm for energy efficient wireless sensor network," in Proc. of IEEE 27th Int. Conf. on Advanced Information Networking and Applications Workshops, 2013, pp. 910-915
  32. Periyasamy, S., Khara, S., Thangavelu, S., "Balanced cluster head selection based on modified k-means in a distributed wireless sensor network," Int. J. Distrib. Sens. Netw., 2016, pp. 1-11, Article ID 5040475, 2016.
  33. Anindita Ray, Debashis De, "Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network," IET Wirel. Sens. Syst., pp. 1-11, 2016.
  34. Aggarwal, N., Aggarwal, K.A., "A mid-point based k-mean clustering algorithm for data mining," Int. J. Comput. Sci. Eng., 4, (6), pp. 1174-1180, 2012.
  35. Yang XS, "Multiobjective firefly algorithm for continuous optimization. Engineering with Computers," 29(2),175-184, 2013. https://doi.org/10.1007/s00366-012-0254-1
  36. M. A. Mizher, Saleh H. Al-Sharaeh, Mei Choo Ang, Ayman M. Abdalla, Manal A. Mizher, "Centroid dynamic sink location for clustered wireless mobile sensor networks," Journal of Theoretical and Applied Information Technology, Vol.73 No.3, 2015.