Leveraging data fusion to improve barrier coverage in wireless sensor networks

doi:10.1016/S1005-8885(13)60004-7

Acta Metallurgica Sinica(English letters) ›› 2013, Vol. 20 ›› Issue (1): 26-36.doi: 10.1016/S1005-8885(13)60004-7

• Networks • Previous Articles Next Articles

Leveraging data fusion to improve barrier coverage in wireless sensor networks

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China 2. Graduate School of the Chinese Academy of Sciences, Beijing 100190, China

Received:2012-08-02 Revised:2012-09-22 Online:2013-02-28 Published:2013-02-28
Contact: Zhaoliang Zhang E-mail:zhangzhaoliang@yahoo.com
Supported by:
This work was supported by the National Basic Research Program of China (2011CB302803), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA060307000), and the National Natural Science Foundation of China (61003293), and the IoT Development Project of MIIT and MoF under title ‘Research & Development of IoT Application Middleware and Its Industrialization’

Abstract

Abstract:

Intruder detection and border surveillance are amongst the most promising applications of wireless sensor networks. Barrier coverage formulates these problems as constructing barriers in a long-thin region to detect intruders that cross the region. Existing studies on this topic are not only based on simplistic binary sensing model but also neglect the collaboration employed in many systems. In this paper, we propose a solution which exploits the collaboration of sensors to improve the performance of barrier coverage under probabilistic sensing model. First, the network width requirement, the sensor density and the number of barriers are derived under data fusion model when sensors are randomly distributed. Then, we present an efficient algorithm to construct barriers with a small number of sensors. The theoretical comparison shows that our solution can greatly improve barrier coverage via collaboration of sensors. We also conduct extensive simulations to demonstrate the effectiveness of our solution.

Key words:

wireless sensor networks, coverage, deployment, data fusion, percolation theory

CLC Number:

TP393

ZHANG Zhao-liang, LI Dong, HUANG Ting-pei, CUI Li. Leveraging data fusion to improve barrier coverage in wireless sensor networks[J]. Acta Metallurgica Sinica(English letters), 2013, 20(1): 26-36.

References

1. Kumar S, Lai T H, Arora A. Barrier coverage with wireless sensors. Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom’05), Aug 28-Sep 2, 2005, Gologne, Germany. New York, NY, USA: ACM, 2005: 284-298

2. Balister P, Bollobas B, Sarkar A, et al. Reliable density estimates for coverage and connectivity in thin strips of finite length. Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom’07), Sep 9-14, 2007, Montreal, Canada. New York, NY, USA: ACM, 2007: 75-86

3. Liu B, Dousse O, Wang J, et al. Strong barrier coverage of wireless sensor networks. Proceedings of the 13th International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’08), May 27-30, 2008, Hong Kong, China. New York, NY, USA: ACM, 2008: 411-420

4. He S, Chen J, Li X, et al. Cost-effective barrier coverage by mobile sensor networks. Proceedings of the 31st Annual IEEE International Conference on Computer Communications (INFOCOM’12), Mar 25-30, 2012, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2012: 819-827

5. Ma H, Yang M, Li D, et al. Minimum camera barrier coverage in wireless camera sensor networks. Proceedings of the 31st Annual IEEE International Conference on Computer Communications (INFOCOM’12), Mar 25-30, 2012, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2012: 217-225

6. Wang Y, Cao G. Barrier coverage in camera sensor networks. Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’11), May 16-19, 2011, Paris, France. New York, NY, USA: ACM, 2011: 10p

7. Yang G, Qiao D. Multi-round sensor deployment for guaranteed barrier coverage. Proceedings of the 29st Annual IEEE International Conference on Computer Communications (INFOCOM’10), Mar 15-19, 2010, San Diego, CA, USA. Piscataway, NJ, USA: IEEE, 2010: 9p

8. Chen A, Li Z, Lai T H, et al. One-way barrier coverage with wireless sensors. Proceedings of the 30th Annual Joint Conference of the IEEE Computer and Communications (INFOCOM’11), Apr 10-15, 2011, Shanghai, China. Piscataway, NJ, USA: IEEE, 2011: 626-630

9. Hwang J, He T, Kim Y. Exploring in-situ sensing irregularity in wireless sensor networks. Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys’07), Nov 6-9, 2007, Sydney, Australia. New York, NY, USA: ACM, 2007: 289-303

10. Luo W, Hu X. Efficient and secure data aggregation protocol for wireless sensor networks. Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition, 2009, 21(1): 110-114(in Chinese)

11. Wang Y, Tan R, Xing G, et al. Accuracy-aware aquatic diffusion process profiling using robotic sensor networks. Proceedings of the 11th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN’12), Apr 16-19, 2012, Beijing, China. Los Alamitos, CA, USA: IEEE Computer Society, IEEE, 2012: 281-292

12. Li D, Zhu Y, Cui L, et al. Hotness-aware sensor networks. Proceedings of the 28th IEEE International Conference on Distributed Computing Systems (ICDCS’08), Jun 17-20, 2008, Beijing, China. Piscataway, NJ, USA: IEEE, 2008: 793-800

13. Wang R, Zhang L, Sun R, et al. EasiTia: a pervasive traffic information acquisition system based on wireless sensor networks. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 615-621

14. Xing G, Tan R, Liu B, et al. Data fusion improves the coverage of wireless sensor networks. Proceedings of the 11th ACM Annual International Conference on Mobile Computing and Networking (MobiCom’09), Sep 20-25, 2009, Beijing, China. New York, NY, USA: ACM, 2009: 157-168

15. Xing G, Chang X, Lu C, et al. Efficient coverage maintenance based on probabilistic distributed detection. IEEE Transactions on Mobile Computing, 2010, 9(9): 1346-1360

16. Yang G, Qiao D. Barrier information coverage with wireless sensors. Proceedings of the 28th Annual IEEE International Conference on Computer Communications (INFOCOM’09), Apr 19-25, 2009, Rio de Janeiro, Brazil. Piscataway, NJ, USA: IEEE, 2009: 918-926

17. Hall P. Introduction to the theory of coverage processes. New York, NY, USA: John Wiley & Sons, 1988

18. Tan R, Xing G, Wang J, et al. Exploiting reactive mobility for collaborative target detection in wireless sensor networks. IEEE Transactions on Mobile Computing, 2010, 9(3): 317-332

19. Varshney P. Distributed detection and data fusion. New York, NY, USA: Springer, 1997

20. Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to algorithm. Cambridge, MA, USA: MIT Press, 2001

21. Franceschetti M, Dousse O, Tse D N C, et al. Closing the gap in the capacity of wireless networks via percolation theory. IEEE Transactions on Information Theory, 2007, 53(3): 1009-1018

22. Grimmett G. Percolation.2nd edition. New York, NY, USA: Springer, 1999

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

[1]	. Trusted GPSR protocol without reputation faking in VANET [J]. Acta Metallurgica Sinica(English letters), 2015, 22(5): 22-31.
[2]	Wen-Xiao SHI Dan WU Yin-Long XU Ji-Hong WANG. Routing metric of interference-aware link quality: an improved ETX in wireless mesh networks [J]. Acta Metallurgica Sinica(English letters), 2014, 21(5): 61-67.
[3]	. Autonomic discovery of subgoals in hierarchical reinforcement learning [J]. Acta Metallurgica Sinica(English letters), 2014, 21(5): 94-104.
[4]	. Host load prediction in cloud based on classification methods [J]. Acta Metallurgica Sinica(English letters), 2014, 21(4): 40-46.
[5]	. Cloud download system optimizing by job and notification scheduling [J]. Acta Metallurgica Sinica(English letters), 2014, 21(4): 47-53.
[6]	WANG Ruo-yu , LIU Zhen, ZHANG Ling. Method of data cleaning for network traffic classification [J]. Acta Metallurgica Sinica(English letters), 2014, 21(3): 35-45.
[7]	. MapReduce optimization algorithm based on machine learning in heterogeneous cloud environment [J]. Acta Metallurgica Sinica(English letters), 2013, 20(6): 77-87.
[8]	LI Wen-ji, ZHENG Kang-feng, ZHANG Dong-mei, YE-Qing, YANG Yi-xian. Efficient identity-based signature scheme with batch authentication for delay tolerant mobile sensor network [J]. Acta Metallurgica Sinica(English letters), 2013, 20(4): 80-86.
[9]	FU Xiong , ZHU Xin-xin, HAN Jing-yu, WANG Ru-chuan. QoS-aware replica placement for data intensive applications [J]. Acta Metallurgica Sinica(English letters), 2013, 20(3): 43-47.
[10]	YAO Yu-kun , WANG Guan, REN Zhi, LI Peng-xiang, CHEN Yong-chao. Efficient distributed address assignment algorithm based on topology maintenance in ZigBee networks [J]. Acta Metallurgica Sinica(English letters), 2013, 20(3): 53-59.
[11]	SHA Chao, WANG Ru-chuan. Energy-efficient node deployment strategy forwireless sensor networks [J]. Acta Metallurgica Sinica(English letters), 2013, 20(1): 54-57.
[12]	YAO Yu-kun, WEN Ya-di, REN Zhi, LIU Zhi-hu. High efficient multipacket decoding approach for network coding in wireless networks [J]. Acta Metallurgica Sinica(English letters), 2013, 20(1): 95-100.
[13]	. Further results on local stability of LRC-RED algorithm in Internet [J]. Acta Metallurgica Sinica(English letters), 2012, 19(5): 99-103.
[14]	. Stability analysis in an AVQ model of Internet congestion control algorithm [J]. Acta Metallurgica Sinica(English letters), 2012, 19(4): 22-28.
[15]	Ji-Xiang WU. Enhanced calculation of necessary QoS for user satisfaction with a QoS mapping matrix [J]. Acta Metallurgica Sinica(English letters), 2012, 19(4): 29-33.

Leveraging data fusion to improve barrier coverage in wireless sensor networks

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments