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A Centralized Balance Clustering Routing Protocol for Wireless Sensor Network

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Abstract

Routing protocol plays a role of great importance in the performance of wireless sensor networks (WSNs). A centralized balance clustering routing protocol based on location is proposed for WSN with random distribution in this paper. In order to keep clustering balanced through the whole lifetime of the network and adapt to the non-uniform distribution of sensor nodes, we design a systemic algorithm for clustering. First, the algorithm determines the cluster number according to condition of the network, and adjusts the hexagonal clustering results to balance the number of nodes of each cluster. Second, it selects cluster heads in each cluster base on the energy and distribution of nodes, and optimizes the clustering results to minimize energy consumption. Finally, it allocates suitable time slots for transmission to avoid collision. Simulation results demonstrate that the proposed protocol can balance the energy consumption and improve the network throughput and lifetime significantly.

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References

  1. Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  2. Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Network, 3(3), 325–349.

    Article  Google Scholar 

  3. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (pp. 3005–3014). Maui.

  4. Li, X. F., Xu, L. Z., & Wang, H. B. (2010). A differential evolution-based routing algorithm for environmental monitoring wireless sensor networks. Sensors, 10(6), 5425–5442.

    Article  Google Scholar 

  5. He, S. J., Dai, Y. Y., Zhou, R. Y., & Zhao, S. T. (2012). A Clustering Routing Protocol for Energy Balance of WSN based on Genetic Clustering Algorithm. IERI Procedia, 2, 788–793.

    Article  Google Scholar 

  6. Yu, J., Qi, Y. Y., Wang, G. H., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. International Journal of Electronics and Communications (AEÜ), 66(1), 54–61.

    Article  Google Scholar 

  7. Heinzelman, W. B., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  8. Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarmand Evolutionary Computation, 1(4), 195–203.

    Article  Google Scholar 

  9. Xiang, M., Shi, W. R., Jiang, C. J., & Zhang, Y. (2010). Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks. International Journal of Electronics and Communications (AEÜ), 64(4), 289–298.

    Article  Google Scholar 

  10. Ferng, H. W., Tendean, R., & Kurniawan, A. (2012). Energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic structure. Wireless Personal Communications, 65(2), 347–367.

    Google Scholar 

  11. Xu, Y., Heidemann, J., & Estrin, D. (2001). Geography-informed Energy Conservation for Ad Hoc Routing. In Proceedings of the Seventh Annual ACM/IEEE International Conference on Mobile Computing and Networking (pp. 70–84). Rome.

  12. Salzmann, J., Behnke, R., & Timmermann, D. (2010). Tessellating Cell Shapes for Geographical Clustering. In 2010 10th IEEE International Conference on Computer and Information Technology (pp. 2891–2896). Bradford.

  13. Guan, X., Wang, Y. X., & Liu, F. (2008). An energy-efficient clustering technique for wireless sensor networks. In International Conference on Networking, Architecture, and Storage (pp. 248–252). Chongqing.

  14. Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117–131.

    Article  Google Scholar 

  15. Long H., Liu Y., Fan X., Dick R. P., & Yang H. (2009). Energy-efficient spatially-adaptive clustering and routing in wireless sensor networks. In Design, Automation Test in Europe Conference Exhibition (pp. 1267–1272). Leuven.

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Acknowledgments

This work was supported by National Science Foundation China under Grant 60972072 and the 111 Project of China (B08038).

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Correspondence to Jian Chen.

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Chen, J., Li, Z. & Kuo, YH. A Centralized Balance Clustering Routing Protocol for Wireless Sensor Network. Wireless Pers Commun 72, 623–634 (2013). https://doi.org/10.1007/s11277-013-1033-z

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