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VDTS: A Voronoi Diagram Based Tracking Schemes in Wireless Sensor Networks

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Book cover Wireless Sensor Networks (CWSN 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 984))

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Abstract

Sleeping scheduling has been widely employed in target tracking due to its energy conservation. However, the randomness of target’s trajectory makes it difficult to implement with accuracy and real time guarantee. We propose VDTS, a novel, simple and efficient tracking technique. VDTS first constructs a Voronoi based network model, then makes nodes in the Voronoi polygon that the target is in work and others sleep. The target is hence detected by nodes closest to it. VDTS further presents a weighted centroid based algorithm to locate the target with the chosen nodes and reduce the influence of data noise on localization accuracy. We have implemented VDTS, and our extensive simulation show the excellent performs of our schemes.

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References

  1. Zheng, K., et al.: Energy efficient localization and tracking of mobile devices in wireless sensor networks. IEEE Trans. Veh. Technol. 66(3), 2714–2726 (2017)

    Article  Google Scholar 

  2. Ahmadi, H., Viani, F., Bouallegue, R.: An accurate prediction method for moving target localization and tracking in wireless sensor networks. Ad Hoc Netw. 70, 14–22 (2018)

    Article  Google Scholar 

  3. Martin, E., Vinyals, O., Friedland, G., Bajcsy, R.: Precise indoor localization using smart phones. In: Proceedings of the 18th ACM International Conference on Multimedia (MM 2010), NewYork, NY, October 2010, pp. 787–790 (2010)

    Google Scholar 

  4. Yiu, S., et al.: Wireless RSSI fingerprinting localization. Signal Process. 131, 235–244 (2017)

    Article  Google Scholar 

  5. Xing, G., Tan, R., Liu, B., Wang, J., Jia, X., Yi, C.-W.: Data fusion improves the coverage of wireless sensor networks. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, MobiCom 2009. ACM, New York, pp. 157–168 (2009)

    Google Scholar 

  6. Bhuiyan, M.Z.A., Wang, G., Vasilakos, A.V.: Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Trans. Comput. 64(7), 1968–1982 (2015)

    Article  MathSciNet  Google Scholar 

  7. Vasuhi, S., Vaidehi, V.: Target tracking using interactive multiple model for wireless sensor network. Inf. Fusion 27, 41–53 (2016)

    Article  Google Scholar 

  8. Yu, Y.: Distributed target tracking in wireless sensor networks with data association uncertainty. IEEE Commun. Lett. 21(6), 1281–1284 (2017)

    Article  Google Scholar 

  9. Wang, J., et al.: Weighted centroid localization algorithm: theoretical analysis and distributed implementation. IEEE Trans. Wirel. Commun. 10(10), 3403–3413 (2011)

    Article  Google Scholar 

  10. Pivato, P., Palopoli, L., Petri, D.: Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Trans. Instrum. Meas. 60(10), 3451–3460 (2011)

    Article  Google Scholar 

  11. Wang, T., et al.: Following targets for mobile tracking in wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 12(4), 31 (2016)

    Google Scholar 

  12. Atia, G.K., Veeravalli, V.V., Fuemmeler, J.A.: Sensor scheduling for energy-efficient target tracking in sensor networks. IEEE Trans. Signal Process. 59(10), 4923–4937 (2011)

    Article  MathSciNet  Google Scholar 

  13. Lersteau, C., Rossi, A., Sevaux, M.: Robust scheduling of wireless sensor networks for target tracking under uncertainty. Eur. J. Oper. Res. 252(2), 407–417 (2016)

    Article  MathSciNet  Google Scholar 

  14. Han, G., et al.: A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Commun. Surv. Tutor. 18(3), 2220–2243 (2016)

    Article  Google Scholar 

  15. Rezazadeh, J., et al.: Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks. IEEE Sens. J. 14(9), 3052–3064 (2014)

    Article  Google Scholar 

  16. Wang, G., Cao, G., La Porta, T.F.: Movement assisted sensor deployment. IEEE Trans. Mob. Comput. 5(6), 640–652 (2006)

    Article  Google Scholar 

  17. Ren, Q., Li, J., Liu, H.: Energy efficient tracking in uncertain sensor networks. Ad Hoc Netw. 81, 45–55 (2018)

    Article  Google Scholar 

  18. Mizmizi, M., Reggiani, L.: Binary fingerprinting-based indoor positioning systems. 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE (2017)

    Google Scholar 

  19. Lasla, N., et al.: An effective area-based localization algorithm for wireless networks. IEEE Trans. Comput. 64(8), 2103–2118 (2015)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Harbin, China

    Qianqian Ren, Jinbao Li & Yong Liu

  2. School of Computer Science and Technology, Heilongjiang University, Harbin, China

    Qianqian Ren, Jinbao Li & Yong Liu

Authors
  1. Qianqian Ren
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  2. Jinbao Li
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  3. Yong Liu
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Corresponding authors

Correspondence to Qianqian Ren , Jinbao Li or Yong Liu .

Editor information

Editors and Affiliations

  1. Kunming University, Kunming, China

    Shikai Shen

  2. Kunming University, Kunming, China

    Kaiguo Qian

  3. Kunming University, Kunming, China

    Shaojun Yu

  4. Yunnan Nationalities University, Kunming, China

    Wu Wang

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© 2019 Springer Nature Singapore Pte Ltd.

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Ren, Q., Li, J., Liu, Y. (2019). VDTS: A Voronoi Diagram Based Tracking Schemes in Wireless Sensor Networks. In: Shen, S., Qian, K., Yu, S., Wang, W. (eds) Wireless Sensor Networks. CWSN 2018. Communications in Computer and Information Science, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-13-6834-9_3

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  • DOI: https://doi.org/10.1007/978-981-13-6834-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6833-2

  • Online ISBN: 978-981-13-6834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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