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Energy-efficient information routing in sensor networks for robotic target tracking

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Abstract

Target tracking problems have been studied for both robots and sensor networks. However, existing robotic target tracking algorithms require the tracker to have access to information-rich sensors, and may have difficulty recovering when the target is out of the tracker’s sensing range. In this paper, we present a target tracking algorithm that combines an extremely simple mobile robot with a networked collection of wireless sensor nodes, each of which is equipped with an unreliable, limited-range, boolean sensor for detecting the target. The tracker maintains close proximity to the target using only information sensed by the network, and can effectively recover from temporarily losing track of the target. We present two algorithms that manage message delivery on this network. The first, which is appropriate for memoryless sensor nodes, is based on dynamic adjustments to the time-to-live (TTL) of transmitted messages. The second, for more capable sensor nodes, makes message delivery decisions on-the-fly based on geometric considerations driven by the messages’ content. We present an implementation along with simulation results. The results show that our system achieves both good tracking precision and low energy consumption.

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Notes

  1. See, for example, Chapters 11 and 12 of LaValle’s book [10].

  2. Note that false positive sensing errors can be problematic for target detection and tracker detection updates, because they may make the I-state inaccurate or even empty. See Sect. 7.

  3. We note that the energy consumption C measured in our experiments will be reduced if we selected a tighter bound for ϕ0.

References

  1. Bandyopadhyay, T., Li, Y. P., Ang, M. H., & Hsu, D. (2004). Stealth tracking of an unpredictable target among obstacles. In Proceedings of workshop on the algorithmic foundations of robotics (pp. 43–58).

  2. LaValle, S. M., González-Baños, H. H., Becker, C., & Latombe, J.-C. (1997). Motion strategies for maintaining visibility of a moving target. In Proceedings of the IEEE international conference on robotics and automation (pp. 731–736).

  3. Murrieta, R., Sarmiento, A., Bhattacharya, S., & Hutchinson, S. A. (2004). Maintaining visibility of a moving target at a fixed distance: The case of observer bounded speed. In Proceedings of the IEEE international conference on robotics and automation.

  4. Murrieta-Cid, R., Gonzalez-Banos, H. H., & Tovar, B. (2002). A reactive motion planner to maintain visibility of unpredictable targets. In Proceedings of the IEEE international conference on robotics and automation.

  5. Murrieta-Cid, R., Tovar, B., & Hutchinson, S. (2005). A sampling-based motion planning approach to maintain visibility of unpredictable targets. Autonomous Robots, 19(3), 285–300.

    Article  Google Scholar 

  6. Guibas, L. J., Latombe, J.-C., LaValle, S. M., Lin, D., & Motwani, R. (1999). Visibility-based pursuit-evasion in a polygonal environment. International Journal on Computational Geometry and Applications, 9(5), 471–494.

    Article  MathSciNet  Google Scholar 

  7. Jung, B., & Sukhatme, G. S. (2006). Cooperative multi-robot target tracking. In Proceedings of the international symposium on distributed autonomous robotic systems (pp. 81–90). Minneapolis, Minnesota.

  8. Balluff. Inductive proximity sensors. http://www.balluff.com/.

  9. Fargo Controls. Photoelectric proximity sensors. http://www.fargocontrols.com/sensors/photoelectric.html.

  10. LaValle, S. M. (2006). Planning algorithms. Cambridge University Press, Cambridge. Available at http://planning.cs.uiuc.edu/.

  11. O’Kane, J. M., & Xu, W. (2009). Energy-efficient target tracking with a sensorless robot and a network of unreliable one-bit proximity sensors. In Proceedings of the IEEE international conference on robotics and automation.

  12. O’Kane, J. M., & Xu, W. (2010). Network-assisted target tracking via smart local routing. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems.

  13. O’Kane, J. M. (2008). On the value of ignorance: Balancing tracking and privacy using a two-bit sensor. In Proceedings of the workshop on the algorithmic foundations of robotics.

  14. Guilamo, L., Tovar, B., & LaValle, S. M. (2004). Pursuit-evasion in an unkown environment using gap navigation trees. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems.

  15. Isler, V., Sun, D., & Sastry, S. (2005). Roadmap based pursuit-evasion and collision avoidance. In Proceedings of the robotics: Science and systems.

  16. Vidal, R., Shakernia, O., Kim, H. J., Shim, D. H., & Sastry, S. (2002). Probabilistic pursuit-evasion games: Theory, implementation, and experimental evaluation. IEEE Transactions on Robotics and Automation.

  17. Vieira, M. A., Govindan, R., & Sukhatme, G. S. (2009). Scalable and practical pursuit-evasion with networked robots. Intelligent Service Robotics, 2(4), 247–263.

    Article  Google Scholar 

  18. Chen, P., Oh, S., Manzo, M., Sinopoli, B., Sharp, C., & Whitehouse, K., et al. (2006). Instrumenting wireless sensor networks for real-time surveillance. In Proceedings of IEEE international conference on robotics and automation (pp. 3128–3133).

  19. Simon, G., Maróti, M., Lédeczi, Á., Balogh, G., Kusy, B., & Nádas, A., et al. (2004). Sensor network-based countersniper system. In SenSys ’04: Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 1–12).

  20. He, T., Krishnamurthy, S., Stankovic, J., Abdelzaher, T., Luo, L., & Stoleru, R., et al. (2004). Jonathan Hui, and Bruce Krogh. Energy-efficient surveillance system using wireless sensor networks. In MobiSys ’04: Proceedings of the 2nd international conference on mobile systems, applications, and services (pp. 270–283).

  21. Zhao, F., Shin, J., & Reich, J. (2002). Information-driven dynamic sensor collaboration. IEEE Signal Processing Magazine, 19(2):61–72.

    Article  Google Scholar 

  22. Aslam, J., Butler, Z., Constantin, F., Crespi, V., Cybenko, G., & Rus D. (2003). Tracking a moving object with a binary sensor network. In SenSys ’03: Proceedings of the 1st international conference on embedded networked sensor systems (pp 150–161). ACM: New York, NY

  23. Gui, C. & Mohapatra, P. (2004). Power conservation and quality of surveillance in target tracking sensor networks. In MobiCom ’04: Proceedings of the 10th annual international conference on mobile computing and networking (pp. 129–143).

  24. Shrivastava, N., Mudumbai, R., Madhow, U., Suri, S. (2006). Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms. In Proceedings of the International conference on embedded networked sensor systems (pp. 251–264).

  25. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L., Rubenstein D. (2002). Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with zebranet. In ASPLOS-X: Proceedings of the 10th international conference on Architectural support for programming languages and operating systems, (pp. 96–107).

  26. Chu, C. P., Tsai, H. W., Chen, T. -S. (2007). Mobile object tracking in wireless sensor networks. Computer Communications 30, 1811–1825.

    Article  Google Scholar 

  27. Olfati-Saber, R. (2007). Distributed tracking for mobile sensor networks with information-driven mobility. In American control conference (pp. 4606–4612).

  28. Zou Y., & Chakrabarty K. (2007) Distributed mobility management for target tracking in mobile sensor networks. IEEE Transactions on Mobile Computing, 6(8), 872–887.

    Article  Google Scholar 

  29. Baryshnikov, Y., & Ghrist R. (2008). Target enumeration via integration over planar sensor networks. In Proceedings of robotics: Science and systems.

  30. Singh, J., Madhow, U., Kumar, R., Suri, S., & Cagley, R. (2007). Tracking multiple targets using binary proximity sensors. In Proceedings of the information processing in sensor networks (pp. 529–538).

  31. Shrivastava, N., Mudumbai, R., Madhow, U., & Suri, S. (2006). Target tracking with binary proximity sensors: Fundamental limits, minimal descriptions, and algorithms. In Proceedings of the international conference on embedded networked sensor systems (pp. 251–264).

  32. Woo, A., Tong, T., & Culler, D. (2003). Taming the underlying challenges of reliable multihop routing in sensor networks. In SenSys ’03: Proceedings of the 1st international conference on embedded networked sensor systems (pp. 14–27).

  33. Kusy, B., Lee, H. J., Wicke, M., Milosavljevic, N., & Guibas, L. (2009). Predictive qos routing to mobile sinks in wireless sensor networks. In Proceedings of the 2009 international conference on information processing in sensor networks, IPSN ’09 (pp. 109–120). Washington, DC. IEEE Computer Society.

  34. Luo, H., Ye, F., Cheng, J., Lu, S, & Zhang, L. (2003). Ttdd: A two-tier data dissemination model for large-scale wireless sensor networks. In Proceedings of international conference on mobile computing and networking (MobiCom.

  35. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: a scalable and robust communication paradigm for sensor networks. In Proceedings of the international conference on mobile computing and networking (pp. 56–67).

  36. Sarkar, R., Zhu, X., & Gao, J. (2006). Double rulings for information brokerage in sensor networks. In Proceedings of the conference on mobile computing and networking, MobiCom ’06 (pp. 286–297).

  37. Ratnasamy, S., Karp, B., Yin, L., Yu, F., Estrin, D., Govindan, R., et al. (2002). GHT: a geographic hash table for data-centric storage. In Proceedings of the workshop on Wireless sensor networks and applications (pp. 78–87).

  38. Perkins, C., & Royer, E. (1997) Ad-hoc on-demand distance vector routing. In Proceedings of the 2nd IEEE workshop on mobile computing systems and applications (pp. 90–100).

  39. Clausen, T., & Jacquet, P. (2003). RFC-3626 optimized link state routing protocol (OLSR).

  40. Karp, B. & Kung, H. T. (2000). Gpsr: greedy perimeter stateless routing for wireless networks. In Proceedings of conference on mobile computing and networking, MobiCom ’00 (pp. 243–254).

  41. Li, J., Jannotti, J., De Couto, D. S. J., Karger, D. R., Morris, R. (2000). A scalable location service for geographic ad hoc routing. In Proceedings of conference on mobile computing and networking, MobiCom ’00 (pp. 120–130).

  42. Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). Tag: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Operating System Review 36(SI), 131–146.

    Article  Google Scholar 

  43. Batalin, M., & Sukhatme, G. S. (2002). Sensor coverage using mobile robots and stationary nodes. In SPIE conference on scalability and traffic control in IP networks II (Disaster Recovery Networks) (pp. 269–276).

  44. Batalin, M. & Sukhatme, G. S. (2005). The analysis of an efficient algorithm for robot coverage and exploration based on sensor network deployment. In IEEE international conference on robotics and automation (pp. 3489–3496). Barcelona, Spain.

  45. Dantu, K., & Sukhatme, G. S. (2009). Connectivity vs. control: Using directional and positional cues to stabilize routing in robot networks. In International conference on robot communication and coordination.

  46. Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (1999). Monte Carlo localization for mobile robots. In Proceedings of IEEE international conference on robotics and automation.

  47. Fox, D., Thrun, S., Burgard, W., & Dallaert, F. (2001) Particle filters for mobile robot localization. In A. Doucet, N. de Freitas, N. Gordon (Eds.), Sequential Monte Carlo methods in practice (pp 401–428). Berlin: Springer.

    Google Scholar 

  48. Reina, G., Vargas, A., Nagatani, K., & Yoshida, K. (2007). Adaptive kalman filtering for gps-based mobile robot localization. In Proceedings of the IEEE international workshop on safety, security and rescue robotics.

  49. Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of IEEE INFOCOM’00.

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

    Article  Google Scholar 

  51. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking (pp. 81–95). ACM: New York.

  52. Priyantha, N. B., Chakraborty, A., & Balakrishnan, H. (2000). The cricket location-support system. In MobiCom ’00: Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 32–43).

  53. Greiner, G., & Hormann, K. (1998). Efcient clipping of arbitrary polygons. ACM Transactions on Graphics 17(2), 71–83.

    Article  Google Scholar 

  54. Wein, R. (2006). Exact and efficient construction of planar minkowski sums using the convolution method. In European symposium on algorithms.

  55. Vatti, B. R. (1992). A generic solution to polygon clipping. Communications of the ACM, 35(7), 56–63.

    Article  Google Scholar 

  56. Guibas, L. J., & Hershberger, J. (1989). Optimal shortest path queries in a simple polygon. Journal of Computer and Systems Sciences 39(2), 126–152.

    Article  MathSciNet  MATH  Google Scholar 

  57. Allman, M., Paxson, V., & Stevens, W. (1999). Tcp congestion control.

  58. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press:Cambridge, MA.

    MATH  Google Scholar 

  59. O’Kane, J. M. (2011). Decentralized tracking of indistinguishable targets using low-resolution sensors. In Proceedings of the IEEE international conference on robotics and automation.

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Acknowledgments

O’Kane is supported by NSF (CAREER award IIS-0953503) and DARPA (N10AP20015). Xu is supported by NSF (CAREER award 0845671).

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  1. Department of Computer Science and Engineering, University of South Carolina Columbia, Columbia, SC, 29208, USA

    Jason M. O’Kane & Wenyuan Xu

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  1. Jason M. O’Kane
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  2. Wenyuan Xu
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Correspondence to Wenyuan Xu.

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O’Kane, J.M., Xu, W. Energy-efficient information routing in sensor networks for robotic target tracking. Wireless Netw 18, 713–733 (2012). https://doi.org/10.1007/s11276-012-0471-y

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