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A Survivability Clustering Algorithm for Ad Hoc Network Based on a Small-World Model

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Abstract

In Ad hoc network, nodes have the characteristics of limited energy, self-organizing and multi-hop. For the purpose of improving the survivability of Ad hoc network effectively, this paper proposes a new algorithm named EMDWCA (Based on Energy, Mobility and Degrees of the nodes on-demand Weighted Clustering Algorithm). The LEACH algorithm is used to cluster the Ad hoc network in the first election, but the EMDWCA is used in the second election. By considering the appearances, disappearances, and communication link failures of the mobile nodes, this algorithm constructs the topology of Ad hoc network based on a small-world network model. To make sure that nodes can still communicate in the following election cycles, it improves the stability of the network topology and overall network invulnerability. The network is analyzed and simulation experiments are performed in order to compare the performance of this new clustering algorithm with the weighted clustering algorithm (WCA) in terms of the correctness, effectiveness, and invulnerability of the networks. The final result proves that the proposed algorithm provides better performance than the original WCA algorithm.

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References

  1. Qing, D. (2013). A new self-adapt clustering algorithm for Ad hoc. Information & Communications, 9, 8–79.

    Google Scholar 

  2. Anastasi, G., Conti, M., & Di Francesco, M. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7, 537–568.

    Article  Google Scholar 

  3. Wu, G., Wang, S., Wang, B., et al. (2012). A novel range-free localization based on regulated neighborhood distance for wireless Ad hoc and sensor networks. Computer Networks, 56, 3581–3593.

    Article  Google Scholar 

  4. Weifeng, C., & Yuping, L. (2010). An improved clustering algorithm for Ad hoc network. Software Guid, 10, 66–68.

    Google Scholar 

  5. Chen, A., Zhang, L., Xia, X., et al. (2012). Study on energy-heterogeneous clustering algorithm in wireless sensor network. Information System and Network, 1(42), 7–10.

    Google Scholar 

  6. Zhou, Y., Xia, C., Wang, H., & Qi, J. (2009). Research on survivability of mobile Ad-hoc network. Journal of Software Engineering & Applications, 2, 50–54.

    Article  Google Scholar 

  7. Fei, X., & Wen-ye, W. (2010). On the survivability of wireless Ad hoc networks with node misbehaviors and failures. IEEE Transactions on Dependable and Secure Computing, 7(2), 284–299.

    Google Scholar 

  8. Azni, A. H., Ahmad, R., & Noh, Z. (2013). Survivability modeling and analysis of mobile Ad hoc network with correlated node behavior. Procedia Engineering, 53, 435–440.

    Article  Google Scholar 

  9. Mahmoud, M., & Shen, X. (2011). An integrated stimulation and punishment mechanism for thwarting packet dropping attack in multihop wireless networks. IEEE Transaction on Vehicular Technology, 60(8), 3947–3962.

    Article  Google Scholar 

  10. Uster, H., & Lin, H. (2011). Integrated topology control and routing in wireless sensor networks for prolonged network lifetime. Ad Hoc Networks, 9, 835–851.

    Article  Google Scholar 

  11. Guo, S. (2012). A clustering algorithm based on weight value for Ad hoc network. Network and Communication, 2, 41–43.

    Google Scholar 

  12. Yimei, K., et al. (2012). A low-power hierarchical wireless sensor network topology control algorithm. Automation Journal, 4(4), 543–549.

    Google Scholar 

  13. Han, G., Chao, J., Zhang, C., Shu, L., & Li, Q. (2014). The impacts of mobility models on DV-hop based localization in mobile wireless sensor networks. Journal of Network and Computer Applications, 42(6), 70–79.

    Article  Google Scholar 

  14. Konak, A., Buchert, G. E., & Juro, J. (2013). A flocking-based approach to maintain connectivity in mobile wireless Ad hoc networks. Applied Soft Computing, 13, 1284–1291.

    Article  Google Scholar 

  15. Liu, A., Ren, J., Li, X., Chen, Z., & Shen, X. (2012). Design principles and improvement of cost function based energy ware routing algorithms for wireless sensor networks. Computer Networks, 56, 19511967.

    Google Scholar 

  16. Han, G., Jiang, J., Shen, W., Shu, L., & Rodrigues, J. J. P. C. (2013). IDSEP: A novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks. IET Information Security, 7(2), 97–105.

    Article  Google Scholar 

  17. Ren, F., Zhang, J., He, T., Lin, C., & Das, S. K. (2011). EBRP: Energy balanced routing protocol for data gathering in wireless sensor networks. IEEE Transaction on Parallel and Distributed System, 22(12), 2391–2405.

    Article  Google Scholar 

  18. Hui, Z., Biao, H., & Qing, D. (2014). A stable and load balanced clustering algorithm for Ad hoc. Information & Communications, 1, 28–29.

    Google Scholar 

  19. Mistra, S., & Thomasinous, P. D. (2010). A simple, least-time and energy efficient routing protocol with one level data aggregation for wireless sensor networks. System and Software, 83, 852860.

    Google Scholar 

  20. Yang, S., Dai, F., Cardei, M. et al. (2005). On multiple point coverage in wireless sensor. In IEEE conference on mobile Ad hoc and sensor systems. Washington, DC, USA: IEEE, pp. 757–764.

  21. Chengfa, L., Guihai, C., Mao, Y., & Jie, W. (2007). An uneven cluster-based routing protocol for wireless sensor networks. Chinese Journal of Computers, 30(1), 27–36.

    Google Scholar 

  22. Wang, Z., Wang, Z., Chen, H., et al. (2013). HierTrack: An energy-efficient cluster-based target tracking system for wireless sensor networks. Journal of Zhejiang University-Science, 14(6), 27–36.

    Google Scholar 

  23. Demigha, O., Hidouci, W. K., & Ahmed, T. (2012). On energy efficiency in collaborative target tracking in wireless sensor network: A review. IEEE Communications Surveys & Tutorials, 99, 1–13.

    Google Scholar 

  24. Xia, S., Haijun, W., & Hongbin, C. (2011). A lower power consumption clustering protocol based on the multi-weight for WSNs. Computer Measurement & Control, 19(9), 2329–2331.

    Google Scholar 

  25. Zhang, Y., Song, R., Chen, Z., et al. (2011). Research on topology control algorithm of mobile sensor networks based on cluster head selection. Chinese Journal of Sensors and Actuators, 11, 1602–1606.

    Google Scholar 

  26. Zhang, D., Zhu, Y., Zhao, C., et al. (2012). A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things. Computers and Mathematics with Applications, 64, 1044–1055.

    Article  Google Scholar 

  27. Liqiang, L., Xiyi, Z., & Ge, Z. (2010). An on-demand weighted clustering algorithm in wireless sensor networks. Computer Applications and Software, 9, 85–87.

    Google Scholar 

  28. Shouhong, Z., Cunhua, Z., & Mingmei, S. (2010). An adaptive distributed weighted clustering algorithm for mobile Ad hoc networks. Journal of Suzhou University of Science and Technology (Natural Science), 6, 43–47.

    Google Scholar 

  29. Yuqing, M., & Xiaoyu, L. I. (2014). Adaptive security weighted clustering algorithm of Ad Hoc network. Computer Engineering and Design, 35(4), 3346–3350.

    Google Scholar 

  30. Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. An efficient distributed trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. DOI:10.1109/TPDS.2014.2320505

  31. Xu, X., & Liang, W. (2011). Placing optimal number of sinks in sensor networks for network lifetime maximization. In Proceedings of IEEE ICC ’11, June, pp. 1–6.

  32. Jiayan, W., Li, C., & Kai, M. (2007). Optimal neighboring nodes of small-world wireless sensor networks. Electronic Measurement Technology, 30(4), 202–205.

  33. Maojia, G. (2012). Small world network model for the wireless sensor networks. Network and Communication, 31(20), 57–59.

    Google Scholar 

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Acknowledgments

This paper is sponsored by Qing Lan Project, the New Century Program for Excellent Talents of the Ministry of Education of China, Liaoning province innovation group Project (LT2011005), and the Shenyang Ligong University Computer Science and Technology Key Discipline Open Foundation (2012, 2013).

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Correspondence to Guangjie Han.

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Zhang, W., Han, G., Feng, Y. et al. A Survivability Clustering Algorithm for Ad Hoc Network Based on a Small-World Model. Wireless Pers Commun 84, 1835–1854 (2015). https://doi.org/10.1007/s11277-015-2518-8

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  • DOI: https://doi.org/10.1007/s11277-015-2518-8

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