The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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An Analysis of Energy Efficient Cluster Ratio for Hierarchical Wireless Sensor Networks

계층적 센서네트워크에서 에너지 효율성을 위한 최적의 클러스터 비율 분석

  • 김자룡 (경희대학교 컴퓨터공학과 모바일 및 임베디드 시스템 연구실) ;
  • 김대영 ((주)AirPlug) ;
  • 조진성 (경희대학교 컴퓨터공학과)
  • Received : 2013.03.07
  • Accepted : 2013.06.11
  • Published : 2013.06.30
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Abstract

Clustering schemes have been adopted as an efficient solution to prolong network lifetime and improve network scalability. In such clustering schemes cluster ratio is represented by the rate of the number of cluster heads and the number of total nodes, and affects the performance of clustering schemes. In this paper, we mathematically analyze an optimal clustering ratio in wireless sensor networks. We consider a multi-hop to one-hop transmission case and aim to provide the optimal cluster ratio to minimize the system hop-count and maximize packet reception ratio between nodes. We examine its performance through a set of simulations. The simulation results show that the proposed optimal cluster ratio effectively reduce transmission count and enhance energy efficiency in wireless sensor networks.

무선 센서네트워크에서 클러스터링 기법은 네트워크 확장성과 네트워크 수명 연장에 효율적이라고 인정받고 있다. 본 논문에서는 클러스터 기반 센서 네트워크에서 multi-hop to one-hop 전송 환경을 고려하여 에너지 효율성에 최적인 클러스터 비율(cluster ratio, CR)을 분석하는데 초점을 둔다. 본 논문에서는 지정한 클러스터 비율을 통한 시스템 홉 수(hop-count) 최소화와 노드 간 패킷수신율(packet reception ratio, PRR) 최대화 사이의 이해득실(trade-off) 관계를 분석하고 이 두 요소를 종합적으로 고려하여 목표함수를 유도한다. 제안한 목표함수를 통하여 얻은 최적의 클러스터 비율은 네트워크에서 패킷 전송에 드는 비용뿐만 아니라 노드 간 재전송 오버헤드를 줄여줌으로써 에너지 효율성을 향상시킨다. 본 논문에서 제안한 기법은 최소 홉 수 클러스터링 방안과 비교되며 시뮬레이션을 통하여 향상된 에너지 효율성을 검증하였다.

Keywords

References

  1. J. Zheng and A. Jamalipour, Wireless Sensor Networks: A Networking Perspective, John Wiley and Sons, pp. 173-209, 2009.
  2. M. J. Handy, M. Haase, and D. Timmermann, "Low energy adaptive clustering hierarchy with deterministic cluster-head selection," in Proc. 4th Int. Workshop Mobile Wireless Commun. Networks, pp. 368-372, Stockholm, Sweden, Sep. 2002.
  3. O. Younis and S, Fahmy, "HEED: a hybrid, energy efficient distributed clustering approach for ad-hoc sensor networks," IEEE Trans. Mobile Comput., vol. 3, no. 4, pp. 366-379, Oct.-Dec. 2004. https://doi.org/10.1109/TMC.2004.41
  4. S. V. Manisekaran and R. Venkatesan, "An adaptive distributed power efficient clustering algorithm for wireless sensor networks," Amer. J. Sci. Research, vol. 10, pp. 50-63, 2010.
  5. D. Wei, Y. Jin, S. Vural, K. Moessner, and R. Tafazolli, "An energy-efficient clustering solution for wireless sensor networks," IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3973-3983, Nov. 2011. https://doi.org/10.1109/TWC.2011.092011.110717
  6. C. S. Nam, Y. S. Han, and D. R. Shin, "Multi-hop routing-based optimization of the number of cluster-heads in wireless sensor networks," Sensors, vol. 11, no. 3, pp. 2875-2884, Jan. 2011. https://doi.org/10.3390/s110302875
  7. D. Y. Kim, J. S. Cho, and B. S. Jeong, "Practical data transmission in cluster-based sensor networks," KSII Trans. Internet Inform. Syst., vol. 4, no. 3, pp. 224-242, June 2010. https://doi.org/10.3837/tiis.2010.06.002
  8. S. G. Foss and S. A. Zuyev, "On a Voronoi aggregative process with Voronoi clustering," Advances in Appl. Probability. vol. 28, no. 4, pp. 965-981, 1996. https://doi.org/10.2307/1428159
  9. Z. L. Jin and J. Cho, "An analytic model for the optimal number of relay stations in IEEE 802.16j cooperative networks," J. KICS, vol. 36, no. 9, pp. 758-766, Sep. 2011. https://doi.org/10.7840/KICS.2011.36A.9.758
  10. S. Rao, "Estimating the ZigBee transmission - range ISM band," Electron. Design News, pp. 67-72, 2007.
  11. M. Zuniga and B. Krishnamachari, "Analyzing the transitional region in low power wireless link," in Proc. IEEE Commun. Soc. Conf. Sensor Ad Hoc Commun. Networks, pp. 517-526, Santa Clara, U.S.A., Oct. 2004.