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KABLOSUZ HETEROJEN ALGILAYICI AĞLAR IÇIN BULANIK MANTIK TABANLI BIR KÜME BAŞI SEÇME ALGORITMASI

Year 2015, Volume: 7 Issue: 2, 48 - 59, 01.06.2015

Abstract

Kümeleme, algılayıcı ağın yaşam süresini arttırmak için enerji tüketimini azaltan önemli bir tekniktir. Homojen kümeleme teknikleri tüm algılayıcı düğümlerin enerji miktarlarının ve özelliklerinin aynı olduğunu varsaymaktadır. Bu yüzden, geliştirilmiş küme başı seçme algoritmaları algılayıcı düğüm heterojenliğini dikkate almamaktadır. Bu çalışmada, heterojen algılayıcı ağlar için yeni bir bulanık mantık tabanlı küme başı seçme algoritması amaçlanmıştır. Veri oranı, enerji seviyesi ve mesafe parametreleri geliştirilen bulanık mantık biriminin girişleri olarak belirlenmiştir. Amaçlanan algoritma, küme başı değişim sayısı ve enerji tüketim oranı ölçütleri ile literatürdeki benzer bir bulanık mantık tabanlı algoritma ile karşılaştırılmıştır. Geliştirilen algoritma, sadece küme başı değişim sayısını azaltmakla kalmayıp enerji tüketim oranını da düğümlere göre %5 ile %75 arasında azaltmaktadır.

References

  • Akyildiz I. F., Su W., Sankarasubramaniam, Y, Cayirci, E. (2002). Wireless sensor networks: a survey. Comput Netw. 38, 393-422.
  • Sohraby K., Minoli D., Znati T. (2007) Wireless sensor networks: Technology, Protocols and Applications. 1st Ed., John Wiley and Son, USA.
  • Fang Y., Ma X., Jiang M. (2011). A relay-based clustering algorithm for heterogeneous energy wireless sensor networks. Computer Science and Automation Engineering (CSAE),
  • IEEE International Conference on, 10-12 June 2011; vol.4, pp.715-718.
  • Heinzelman W.R., Chandrakasan A., Balakrishnan H. (2000). Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Annual
  • Hawaii International Conference on System Sciences, 4-7 Jan. 2000; vol.2, pp. 10.
  • Abusaimeh H., Yang S.H. (2012). Energy-aware optimization of the number of clusters and cluster-heads in WSN, 2012 International Conference on Innovations in Information
  • Technology (IIT), 18-20 March 2012; pp.178-183.
  • Zhang C., Hou E., Ansari N. (2008). Node Clustering, in Wireless Sensor Networks: A
  • Networking Perspective (eds J. Zheng and A. Jamalipour), John Wiley & Sons, Inc., Hoboken, NJ, USA. Tuah N., Ismail M., Jumari K. (2012). An Energy-Efficient Node-Clustering Algorithm in
  • Heterogeneous Wireless Sensor Networks: A Survey. Journal of Applied Sciences. 12, 1332
  • Younis O., Fahmy S., (2004). HEED: A hybrid, energy efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mobile Comput. 3, 366-379.
  • Kumar P., Ylianttila M., Gurtov A., Lee S.G., Lee H.J. (2014). An Efficient and Adaptive
  • Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications. Sensors, 14(2): 2732–2755.
  • OPNET Modeler (2014). Optimum network simulation and engineering tool, available from http:// http://www.riverbed.com/products/performance-management-control/opnet.html
  • Ben Alla S., Ezzati A., Mohsen A. (2012). Gateway and cluster head election using FL in heterogeneous wireless sensor networks. 2012 International Conference on Multimedia
  • Computing and Systems (ICMCS); 10-12 May 2012, pp.761-766.
  • Chatterjee M., Das S.K., Turgut D. (2002). Wca: a weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing. 5, 193-204.
  • Banerjee S., Khuller S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. Proc. of IEEE INFOCOM; 2001, pp. 1028-1037.
  • Chen W.P., How J.C., Sha L. (2004). Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans. on Mobile Computing. 3, 258-271.
  • Gupta I., Riordanand D., Sampalli S. (2005). Cluster-head Election using FL for Wireless
  • Sensor Networks. Communication Networks and Services Rearch Conference; May 2005; pp.255-260.
  • Ran H.Z. (2010). Improving on LEACH protocol of wireless sensor networks using FL.
  • Journal of Information & Computational Science. 7, 767-775. Kumar D., Aseri T.C., Patel R.B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications. 32, 662- 667.
  • Kumar D, Aseri T.C., Patel R.B. (2009). Analysis On deployment cost and network performance for heterogeneous wireless sensor networks. International Journal of Computer science & Information Technology (IJCSIT); November 2009; 1: 109-120.
  • Duan C., Fan H. (2007). A distributed energy balance clustering protocol for heterogeneous wireless sensor networks. International Conference on Wireless Communications,
  • Networking and Mobile Computing; 21-25, Sept. 2007. 2469-2473.
  • Qing L., Zhu Q., Wang M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications. 29, 2230
  • Katiyar V., Chand N., Soni S. (2011). A survey on clustering algorithms for heterogeneous wireless sensor networks. Int. J. Advanced Networking and Applications. 02, 745-754.
  • Onel T., Ersoy C., Cayırcı E., Parr G. (2004). A multicriteria handoff decision scheme for the next generation tactical communications systems. Computer Networks. 46, 695-708.
  • Sun J.Z. (2007). A review of vertical handoff algorithms for cross-domain mobility. in:
  • Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2007), Sept. 21–25, 2007, pp. 3156-3159.
  • Ceken C., Arslan H., (2009). An adaptive fl based vertical handoff decision algorithm for wireless heterogeneous networks. In: Wireless and Microwave Technology (WAMI)
  • Conference (WAMICON 2009), 2009, pp. 1-9.
  • Wang L.X. Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.

A FUZZY LOGIC BASED CLUSTER HEAD SELECTION ALGORITHM FOR WIRELESS HETEROGENEOUS SENSOR NETWORKS

Year 2015, Volume: 7 Issue: 2, 48 - 59, 01.06.2015

Abstract

Clustering is an important technique utilized to prolong the lifetime of a sensor network by reducing energy consumption. Homogeneous clustering techniques assume that all the sensor nodes are equipped with the same amount of energy and have the same capabilities. So, the developed cluster head selection algorithms cannot take sensor node heterogeneity into account. In this paper we propose a new fuzzy logic (FL) based cluster head selection algorithm for heterogeneous wireless sensor networks (HWSNs). The parameters, i.e., data rate, energy level and distance are considered as inputs of the developed FL unit. The proposed algorithm is compared with a FL based counterpart in the literature in terms of number of cluster head variations and energy consumption rate. The results show that the proposed FL based cluster head selection algorithm not only decreases cluster head variation dramatically but also reduces energy comsumption of heterogeneous sensor nodes between 5% and 75%.

References

  • Akyildiz I. F., Su W., Sankarasubramaniam, Y, Cayirci, E. (2002). Wireless sensor networks: a survey. Comput Netw. 38, 393-422.
  • Sohraby K., Minoli D., Znati T. (2007) Wireless sensor networks: Technology, Protocols and Applications. 1st Ed., John Wiley and Son, USA.
  • Fang Y., Ma X., Jiang M. (2011). A relay-based clustering algorithm for heterogeneous energy wireless sensor networks. Computer Science and Automation Engineering (CSAE),
  • IEEE International Conference on, 10-12 June 2011; vol.4, pp.715-718.
  • Heinzelman W.R., Chandrakasan A., Balakrishnan H. (2000). Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Annual
  • Hawaii International Conference on System Sciences, 4-7 Jan. 2000; vol.2, pp. 10.
  • Abusaimeh H., Yang S.H. (2012). Energy-aware optimization of the number of clusters and cluster-heads in WSN, 2012 International Conference on Innovations in Information
  • Technology (IIT), 18-20 March 2012; pp.178-183.
  • Zhang C., Hou E., Ansari N. (2008). Node Clustering, in Wireless Sensor Networks: A
  • Networking Perspective (eds J. Zheng and A. Jamalipour), John Wiley & Sons, Inc., Hoboken, NJ, USA. Tuah N., Ismail M., Jumari K. (2012). An Energy-Efficient Node-Clustering Algorithm in
  • Heterogeneous Wireless Sensor Networks: A Survey. Journal of Applied Sciences. 12, 1332
  • Younis O., Fahmy S., (2004). HEED: A hybrid, energy efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mobile Comput. 3, 366-379.
  • Kumar P., Ylianttila M., Gurtov A., Lee S.G., Lee H.J. (2014). An Efficient and Adaptive
  • Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications. Sensors, 14(2): 2732–2755.
  • OPNET Modeler (2014). Optimum network simulation and engineering tool, available from http:// http://www.riverbed.com/products/performance-management-control/opnet.html
  • Ben Alla S., Ezzati A., Mohsen A. (2012). Gateway and cluster head election using FL in heterogeneous wireless sensor networks. 2012 International Conference on Multimedia
  • Computing and Systems (ICMCS); 10-12 May 2012, pp.761-766.
  • Chatterjee M., Das S.K., Turgut D. (2002). Wca: a weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing. 5, 193-204.
  • Banerjee S., Khuller S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. Proc. of IEEE INFOCOM; 2001, pp. 1028-1037.
  • Chen W.P., How J.C., Sha L. (2004). Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans. on Mobile Computing. 3, 258-271.
  • Gupta I., Riordanand D., Sampalli S. (2005). Cluster-head Election using FL for Wireless
  • Sensor Networks. Communication Networks and Services Rearch Conference; May 2005; pp.255-260.
  • Ran H.Z. (2010). Improving on LEACH protocol of wireless sensor networks using FL.
  • Journal of Information & Computational Science. 7, 767-775. Kumar D., Aseri T.C., Patel R.B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications. 32, 662- 667.
  • Kumar D, Aseri T.C., Patel R.B. (2009). Analysis On deployment cost and network performance for heterogeneous wireless sensor networks. International Journal of Computer science & Information Technology (IJCSIT); November 2009; 1: 109-120.
  • Duan C., Fan H. (2007). A distributed energy balance clustering protocol for heterogeneous wireless sensor networks. International Conference on Wireless Communications,
  • Networking and Mobile Computing; 21-25, Sept. 2007. 2469-2473.
  • Qing L., Zhu Q., Wang M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications. 29, 2230
  • Katiyar V., Chand N., Soni S. (2011). A survey on clustering algorithms for heterogeneous wireless sensor networks. Int. J. Advanced Networking and Applications. 02, 745-754.
  • Onel T., Ersoy C., Cayırcı E., Parr G. (2004). A multicriteria handoff decision scheme for the next generation tactical communications systems. Computer Networks. 46, 695-708.
  • Sun J.Z. (2007). A review of vertical handoff algorithms for cross-domain mobility. in:
  • Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2007), Sept. 21–25, 2007, pp. 3156-3159.
  • Ceken C., Arslan H., (2009). An adaptive fl based vertical handoff decision algorithm for wireless heterogeneous networks. In: Wireless and Microwave Technology (WAMI)
  • Conference (WAMICON 2009), 2009, pp. 1-9.
  • Wang L.X. Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.
There are 35 citations in total.

Details

Other ID JA46EZ52VV
Journal Section Articles
Authors

Ali Çalhan This is me

Publication Date June 1, 2015
Published in Issue Year 2015 Volume: 7 Issue: 2

Cite

IEEE A. Çalhan, “KABLOSUZ HETEROJEN ALGILAYICI AĞLAR IÇIN BULANIK MANTIK TABANLI BIR KÜME BAŞI SEÇME ALGORITMASI”, IJTS, vol. 7, no. 2, pp. 48–59, 2015.

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