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Ghost: Voronoi-based tracking in sparse wireless networks using virtual nodes

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Abstract

Conventional tracking techniques for wireless networks locate a target by using at least three non-collinear tracker nodes. However, having such a high density of trackers over the monitored area is not always possible. This paper presents Ghost, a new tracking method based on Voronoi tessellations able to track a target by using less than three tracker nodes in wireless networks. In Ghost, different locations of the target create different Voronoi diagrams of the monitored area by placing virtual nodes around tracker nodes. These diagrams are used to estimate the current location of the target by intersecting the previous and current Voronoi diagrams. The target’s route is constructed by systematically searching the most likely estimated target’s locations over time. Simulation results validate that the proposed method has better tracking accuracy compared with existing proposals. Moreover, our approach is not tied to a specific technology, thus it can be applied in different platforms (e.g., WLAN and WSN).

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References

  1. Kumar, N. (2006). A weighted center of mass based trilateration approach for locating wireless devices in indoor environment. In Proceedings of the forth ACM international workshop on mobility management & wireless access, Terromolinos, Spain, October 2. Malaga: ACM

  2. Subramanian, A. P., Deshpande, P., Gaojgao, J., & Das, S. R. (2008). Drive-by localization of roadside WiFi networks. In Proceedings of INFOCOM, Apr 2008 (pp. 718–725).

  3. Bhuiyan, M. Z. A., Wang, G., & Wu, J. (2009). Target tracking with monitor and backup sensors in wireless sensor networks. In Proceedings of international conference on computer communications and networks (pp. 1–6).

  4. Zhong, Z., & He, T. (2007). MSP: Multi-sequence positioning of wireless sensor nodes. In Proceedings of international conference on embedded networked sensor systems, Sydney, Australia, November 6–9, 2007 (pp. 15–28).

  5. Chan, T. M. (2006). Point location in O(log n) time, Voronoi diagrams in O(n log n) time, and other transdichotomous results in computational geometry. In Proceedings of foundations of computer science, Oct 2006 (pp. 333–344).

  6. Moravek, P., Komosny, D., Simek, M., Jelinek, M., Girbau, D., & Lazaro, A. (2011). Investigation of radio channel uncertainty in distance estimation in wireless sensor networks. Telecommunication Systems, 52, 1–10.

    Google Scholar 

  7. Haji Talib, A. Z. B., Chen, M., & Townsend, P. (1996). Three major extensions to Kirkpatrick’s point location algorithm. In Proceedings of computer graphics international, Jun 1996 (pp. 112–121).

  8. Wang, X., Wang, Z., & O’Dea, B. (2003). A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. IEEE Transactions on Vehicular Technology, 52(1), 112–116.

    Article  Google Scholar 

  9. Yamasaki, R., Ogino, A., Tamaki, T., Uta, T., Matsuzawa, N., & Kato, T. (2005). TDOA location system for IEEE 802.11b WLAN. In Proceedings of IEEE wireless communications and networking conference, Mar 2005 (Vol. 4, pp. 2338–2343).

  10. Gustafsson, F., & Gunnarsson, F. (2003). Positioning using time-difference of arrival measurements. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing, Apr 2003.

  11. Niculescu, D., & Nath, B. (2003). Ad hoc positioning system (APS) using AOA. In Proceedings of conference of the IEEE computer and communications, Mar 2003 (Vol. 3, pp. 1734–1743). New York: IEEE Societies.

  12. Elnahrawy, E., Xiaoyan, L., & Martin, R. P. (2004). The limits of localization using signal strength: A comparative study. In Proceedings of first annual IEEE communications society conference on sensor and Ad Hoc communications and networks, Oct 2004 (pp. 406–414).

  13. Kitasuka, T., Hisazumi, K., Nakanishi, T., & Fukuda, A. (2005). Positioning technique of wireless LAN terminals using RSSI between terminals. In Proceedings of the international conference on pervasive systems and computing, Las Vegas, Nevada, June 27–30, 2005 (pp. 47–53).

  14. Stoyanova, T., Kerasiotis, F., Prayati, A. S., & Papadopoulos, G. D. (2009). Evaluation of impact factors on RSS accuracy for localization and tracking applications in sensor networks. Telecommunication Systems, 42(3–4), 235–248.

    Article  Google Scholar 

  15. Ma, D., Er, M. J., Wang, B., & Lim, H. B. (2009). Range-free localization based on hop-count quantization in wireless sensor networks. Telecommunication Systems, 50, 199–213.

  16. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes in large-scale sensor networks. In Proceedings of Mobi-Com.

  17. Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2011). Localization algorithms of wireless sensor networks: A survey. Telecommunication Systems, 52, 2419–2436.

  18. Guvenc, I., Abdallah, C. T., Jordan, R., & Dedeoglu, O. (2003). Enhancements to RSS based indoor tracking systems using Kalman filters. In Proceedings of GSPx and international signal processing conference, 2003.

  19. Souza, E. L., Nakamura, E. F., & de Oliveira, H. A. B. F. (2009). On the performance of target tracking algorithms using actual localization systems for wireless sensor networks. In Proceedings of international symposium o modeling analysis and simulation of wireless and mobile systems, Tenerife, Canary Islands, Spain, October 26–19, 2009 (pp. 418–423).

  20. Chao-Lin, C., & Kai-Ten, F. (2005). Hybrid location estimation and tracking system for mobile devices. In Proceedings of the IEEE 61st vehicular technology conference (Vol. 4, pp. 2648–2652).

  21. Liao, L., Fox, D., Hightower, J., Kautz, H., & Schulz, D. (2003). Voronoi tracking: Location estimation using sparse and noisy sensor data. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems.

  22. Bhuiyan, M. Z. A., Wang, G., & Wu, J. (2009). Polygon-based tracking framework in surveillance wireless sensor networks. In Proceedings of Parallel and Distributed Systems (pp. 174–181).

  23. Po-Hsuan, T., Kai-Ten, F., Yu-Chiun, L., & Chao-Lin, C. (2009). Wireless location tracking algorithms for environments with insufficient signal sources. IEEE Transactions on Mobile Computing, 8(12), 1676–1689.

    Article  Google Scholar 

  24. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.

    Article  Google Scholar 

  25. Niculescu, D., & Nath, B. (2003). DV based positioning in ad hoc networks. Telecommunication Systems, 22, 267–280.

    Article  Google Scholar 

  26. Alsalih, W., Islam, K., Nunez-Rodriguez, Y., & Xiao, H. (2008). Distributed Voronoi diagram computation in wireless sensor networks. In Proceedings of annual ACM symposium on parallel algorithms and architectures, Jun 2008.

  27. Yang, C., Shi, P., Zao, W., Wang, L., Meng, X., & Wang, J. (2006). New intersection algorithm of convex polygons based on Voronoi diagrams. In Proceedings of international symposium on Voronoi diagrams in science and engineering, Jul 2006 (pp. 224–231).

  28. Yershova, A., & LaValle, S. M. (2007). Improving motion-planning algorithms by efficient nearest-neighbor searching. IEEE Transactions on Robotics, 23(1), 151–157.

    Article  Google Scholar 

  29. Hong-Bo, L., Zhan-Shan, L., Yang, A., & Hui-Ying, D. (2009). On research of optimization strategy for dynamic backtracking. In Proceedings of international conference on machine learning and cybernetics, Jul 2009 (Vol. 1, pp. 266–271).

  30. Dechter, R. (1990). Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition. SciencieDirect, Artificial Intelligence, 41, 273–312.

    Article  Google Scholar 

  31. Benedetto, F., Giunta, G., Toscano, A., & Vegni, L. (2007). Dynamic LOS/NLOS statistical discrimination of wireless mobile channels. In Proceedings of IEEE vehicular technology conference, Apr 2007 (pp. 3071–3075).

  32. Navidi, W., & Camp, T. (2003). Stationary distributions for the random waypoint mobility model. IEEE Transactions on Mobile Computing, 3(1), 99–108.

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by research funds from CONACyT (105117/105279), DGAPA-PAPIIT (IN108910/ IN114813) and PAPIME PE 103807.

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Authors and Affiliations

  1. Department of Computer Sciences, National Autonomous University of Mexico, 04510, Mexico City, Mexico

    Francisco Garcia & Marco A. Gonzalez

  2. Department of Telecommunications Engineering, National Autonomous University of Mexico, 04510, Mexico City, Mexico

    Javier Gomez & Victor Rangel

  3. Department of Electrical Engineering, Metropolitan Autonomous University, Iztapalapa, 09340, Mexico City, Mexico

    Miguel Lopez-Guerrero

Authors
  1. Francisco Garcia
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  2. Javier Gomez
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  3. Marco A. Gonzalez
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  4. Miguel Lopez-Guerrero
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  5. Victor Rangel
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Correspondence to Francisco Garcia.

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Garcia, F., Gomez, J., Gonzalez, M.A. et al. Ghost: Voronoi-based tracking in sparse wireless networks using virtual nodes. Telecommun Syst 61, 387–401 (2016). https://doi.org/10.1007/s11235-015-0046-1

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  • DOI: https://doi.org/10.1007/s11235-015-0046-1

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