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Strengthening barrier-coverage of static sensor network with mobile sensor nodes

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Abstract

A wireless sensor network (WSN) provides a barrier-coverage over an area of interest if no intruder can enter the area without being detected by the WSN. Recently, barrier-coverage model has received lots of attentions. In reality, sensor nodes are subject to fail to detect objects within its sensing range due to many reasons, and thus such a barrier of sensors may have temporal loopholes. In case of the WSN for border surveillance applications, it is reasonable to assume that the intruders are smart enough to identify such loopholes of the barrier to penetrate. Once a loophole is found, the other intruders have a good chance to use it continuously until the known path turns out to be insecure due to the increased security. In this paper, we investigate the potential of mobile sensor nodes such as unmanned aerial vehicles and human patrols to fortify the barrier-coverage quality of a WSN of cheap and static sensor nodes. For this purpose, we first use a single variable first-order grey model, GM(1,1), based on the intruder detection history from the sensor nodes to determine which parts of the barrier is more vulnerable. Then, we relocate the available mobile sensor nodes to the identified vulnerable parts of the barrier in a timely manner, and prove this relocation strategy is optimal. Throughout the simulations, we evaluate the effectiveness of our algorithm.

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References

  1. Xu, B., Kim, D., Li, D., Lee, J., Jiang, H., & Tokuta, A. O. (2014). Fortifying Barrier-coverage of wireless sensor network with mobile sensor nodes. In Proceedings of the 9th international conference on wireless algorithms, systems, and applications (WASA 2014), 23–25 June 2014, Harbin, China.

  2. Cheng, S., Li, J., & Cai, Z. (2013). \(O(\epsilon )\)-Approximation to physical world by sensor networks. In Proceedings of the 32nd annual IEEE international conference on computer communications (IEEE INFOCOM).

  3. Cai, Z., Ji, S., & Li, J. (2009). Data caching-based query processing in multi-sink wireless sensor networks. International Journal of Sensor Networks (IJSN), 11(2), 109–125.

    Article  Google Scholar 

  4. Li, J., Cheng, S., Gao, H., & Cai, Z. (2014). Approximate physical world reconstruction algorithms in sensor networks. In IEEE Transactions on Parallel and Distributed Systems (TPDS).

  5. Cheng, X., Du, D.-Z., Wang, L., & Xu, B. (2008). Relay sensor placement in wireless sensor networks. Wireless Networks, 14(3), 347–355.

    Article  Google Scholar 

  6. Ding, M., Chen, D., Xing, K., & Cheng, X. (2005). Localized fault-tolerant event boundary detection in sensor networks. In Proceedings of the 24th annual joint conference of the IEEE computer and communications societies (INFOCOM).

  7. Vu, C. T., Cai, Z., & Li, Y. (2009). Distributed energy-efficient algorithms for coverage problem in adjustable sensing ranges wireless sensor networks. Discrete Mathematics, Algorithms and Applications (DMAA), 1(3), 299–317.

    Article  MATH  MathSciNet  Google Scholar 

  8. Gage, D. (1992). Command control for many-robot systems. In Proceedings of the 19th annual AUVS Technical Symposium (AUVS).

  9. Kapitanova, K., Hoque, E., Li, J., Alessandrelli, D., Stankovic, J. A., Son, S. H., & Whitehouse, K. ( 2011). Repair assessment of sensor node failures for activity detection. In Proceedings of the 2nd international workshop on networks of cooperating objects (CONET).

  10. Mao, M., & Chirwa, E. C. (2006). Application of grey model GM(1,1) to vehicle fatality risk estimation. Technological Forecasting and Social Change, 73(5), 588–605.

    Article  Google Scholar 

  11. Fang, X., & Fang, J. (2009). Human motion tracking based on adaptive template matching and GM(1,1). In Proceedings of 2009 international workshop on intelligent systems and applications (ISA).

  12. Xiao, L., Peng, X., Wang, Z., Xu, B., & Hong, P. (2009). Research on traffic monitoring network and its traffic flow forecast and congestion control model based on wireless sensor networks. In Proceedings of the 3rd international conference on measuring technology and mechatronics automation (ICMTMA).

  13. Soni, S. K., Chand, N., & Singh, D. P. (2012). Reducing the data transmission in WSNs using time series prediction model. In Proceedings of IEEE international conference on signal processing, computing and control (ISPCC).

  14. Zhanfeng, Z. (2008). Forecast model of logistics on central plains area based on gray system theory. Industrial Technology Economy, 27(3), 73–76.

    Google Scholar 

  15. Cardei, M., Thai, M. T., Li, Y., & Wu, W. (2005). Energy-efficient target coverage in wireless sensor networks. In Proceedings of the 24th annual joint conference of the IEEE computer and communications societies (INFOCOM).

  16. Zhou, Z., Das, S., & Gupta, H. (2004). Connected k-coverage problem in sensor networks. In Proceedings of the 13th annual conference of the IEEE international conference on computer communications and networks(ICCCN).

  17. Chen, A., Lai, T., & Xuan, D. (2008). Measuring and guaranteeing quality of barrier-coverage in wireless sensor networks. In Proceedings of the 9th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).

  18. Li, J., Chen, J., & Lai, T. H. (2012). Energy-efficient intrusion detection with a barrier of probabilistic sensors. In Proceedings of the 31th annual joint conference of the IEEE computer and communications societies (INFOCOM).

  19. Chen, A., Kumar, S., & Lai, T. H. (2007). Designing localized algorithms for barrier coverage. In Proceedings of the 13th ACM annual international conference on mobile computing and networking (Mobicom).

  20. Kumar, S., Lai, T., & Arora, A. (2005). Barrier coverage with wireless sensors. In Proceedings of the 11th ACM annual international conference on mobile computing and networking (Mobicom).

  21. Saipulla, A., Liu, B., Xing, G., Fu, X., & Wang, J. (2010). Barrier coverage with sensors of limited mobility. In Proceedings of the 11th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (pp. 201–210).

  22. Keung, G. Y., Li, B., & Zhang, Q. (2012). The intrusion detection in mobile sensor network. IEEE/ACM Transactions on Networking (TON), 20(4), 1152–1161.

    Article  Google Scholar 

  23. He, S., Chen, J., Li, X., Shen, X., & Sun, Y. (2012). Cost-effective barrier coverage by mobile sensor networks. In Proceedings of the 31th annual joint conference of the IEEE computer and communications societies (INFOCOM).

  24. Zhao, M., Mason, L., & Wang, W. (2008). Empirical study on human mobility for mobile wireless networks. In Proceedings of IEEE military communications conference (MILCOM).

  25. Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc Sensor Wireless Network, 1(1–2), 89–124.

    Google Scholar 

  26. Cormen, T. H., Leiserson, C. E., Rives, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed.). Cambridge: MIT Press and McGraw-Hill.

    MATH  Google Scholar 

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Acknowledgments

This paper was supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China 10XNJ032.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, People’s Republic of China

    Biaofei Xu & Deying Li

  2. Department of Computer Science, California State University, Los Angeles, CA, 90032, USA

    Yuqing Zhu

  3. Department of Mathematics and Physics, North Carolina Central University, Durham, NC, 27707, USA

    Donghyun Kim & Alade O. Tokuta

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, People’s Republic of China

    Huaipan Jiang

Authors
  1. Biaofei Xu
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  2. Yuqing Zhu
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  3. Donghyun Kim
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  4. Deying Li
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  5. Huaipan Jiang
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  6. Alade O. Tokuta
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Corresponding author

Correspondence to Deying Li.

Additional information

The preliminary version of this paper has been appeared in the Proceedings of the 9th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2014) [1].

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Xu, B., Zhu, Y., Kim, D. et al. Strengthening barrier-coverage of static sensor network with mobile sensor nodes. Wireless Netw 22, 1–10 (2016). https://doi.org/10.1007/s11276-015-0946-8

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  • DOI: https://doi.org/10.1007/s11276-015-0946-8

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