Skip to main content
Log in

A Method for Estimating Angular Separation in Mobile Wireless Sensor Networks

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Resource-constrained mobile sensors require periodic position measurements for navigation around the sensing region. Such information is often obtained using GPS or onboard sensors such as optical encoders. However, GPS is not reliable in all environments, and odometry accrues error over time. Although several localization techniques exist for wireless sensor networks, they are typically time consuming, resource intensive, and/or require expensive hardware, all of which are undesirable for lightweight mobile devices. In this paper, we describe a technique for determining spatial relationships that is suitable for resource-constrained mobile sensors. Angular separation between multiple pairs of stationary sensor nodes is derived using wheel encoder data in conjunction with the measured Doppler shift of an RF interference signal. Our experimental results demonstrate that using this technique, a robot is able to determine the angular separation between four pairs of sensors in a 45 × 35 m sensing region with an average error of 0.28 rad. in 0.68 s.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Altun, K., Koku, A.: Evaluation of egocentric navigation methods. In: Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication (ROMAN), pp. 396–401 (2005)

  2. Amundson, I., Koutsoukos, X.: A survey on localization for mobile wireless sensor networks. In: Fuller, R., Koutsoukos, X. (eds.) Proceedings of the 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT). LNCS, vol. 5801, pp. 235–254. Springer (2009)

  3. Amundson, I., Koutsoukos, X., Sallai, J.: Mobile sensor localization and navigation using RF doppler shifts. In: Proceedings of the 1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT), pp. 97–102. ACM, San Francisco, CA, USA (2008)

    Chapter  Google Scholar 

  4. Amundson, I., Koutsoukos, X., Sallai, J., Ledeczi, A.: Mobile sensor navigation using rapid RF-based angle of arrival localization. In: Proceedings of the 17th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 316–325. Chicago, IL, USA (2011)

  5. Amundson, I., Kushwaha, M., Koutsoukos, X.: On the feasibility of determining angular separation in mobile wireless sensor networks. In: Fuller, R., Koutsoukos, X. (eds.) Proceedings of the 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT). LNCS, vol. 5801, pp. 115–127. Springer (2009)

  6. Amundson, I., Sallai, J., Koutsoukos, X., Ledeczi, A.: Radio interferometric angle of arrival estimation. In: Sá Silva, J., et al. (eds.) Proceedings of the 7th European Conference on Wireless Sensor Networks (EWSN). LNCS, vol. 5970, pp. 1–16. Springer, Coimbra, Portugal (2010)

    Chapter  Google Scholar 

  7. Bekris, K.E., Argyros, A.A., Kavraki, L.E.: Angle-based methods for mobile robot navigation: reaching the entire plane. In: Proceedings of the International Conference on Robotics and Automation (ICRA), pp. 2373–2378. New Orleans, LA (2004)

  8. Betke, M., Gurvits, L.: Mobile robot localization using landmarks. IEEE Trans. Robot. Autom. 13(2), 251–263 (1997)

    Article  Google Scholar 

  9. Chen, J., Yao, K., Hudson, R.: Source localization and beamforming. IEEE Sig. Proc. Mag. 19(2), 30–39 (2002)

    Article  Google Scholar 

  10. Dantu, K., Rahimi, M., Shah, H., Babel, S., Dhariwal, A., Sukhatme, G.S.: Robomote: enabling mobility in sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN), p. 55 (2005)

  11. Dutta, P., Grimmer, M., Arora, A., Bibyk, S., Culler, D.: Design of a wireless sensor network platform for detecting rare, random, and ephemeral events. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN/SPOTS) (2005)

  12. Esteves, J., Carvalho, A., Couto, C.: Generalized geometric triangulation algorithm for mobile robot absolute self-localization. In: Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE), pp. 346–351 (2003)

  13. Friedman, J., Charbiwala, Z., Schmid, T., Cho, Y., Srivastava, M.: Angle-of-arrival assisted radio interferometry (ARI) target localization. In: Proceedings of the Military Communications Conference (MILCOM), pp. 1–7 (2008)

  14. Friedman, J., Lee, D.C., Tsigkogiannis, I., Wong, S., Chao, D., Levin, D., Kaisera, W.J., Srivastava, M.B.: Ragobot: a new platform for wireless mobile sensor networks. In: Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS). LNCS, vol. 3560, p. 412. Springer (2005)

  15. Gay, D., Levis, P., von Behren, R., Welsh, M., Brewer, E., Culler, D.: The nesC language: a holistic approach to networked embedded systems. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), pp. 1–11 (2003)

  16. Brown, Jr., H.B., Vandeweghe, J.M., Bererton, C.A., Khosla, P.K.: Millibot trains for enhanced mobility. IEEE/ASME Trans. Mechatronics 7(4), 452–461 (2002)

    Article  Google Scholar 

  17. Hill, J., Culler, D.: Mica: a wireless platform for deeply embedded networks. IEEE Micro 22(6), 12–24 (2002)

    Article  Google Scholar 

  18. Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., Pister, K.: System architecture directions for networked sensors. In: Proceedings of the Ninth International Conference on Achitectural Support for Programming Languages and Operating Systems (ASPLOS-IX), pp. 93–104 (2000)

  19. Hong, J., Tan, X., Pinette, B., Weiss, R., Riseman, E.: Image-based homing. Cont. Syst. Mag. IEEE 12(1), 38–45 (1992)

    Article  Google Scholar 

  20. Kay, S.M.: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory. Prentice Hall (1993)

  21. Koku, A., Sekmen, A., Wilkes, D.: A novel approach for robot homing. In: Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003, vol. 2, pp. 1477–1482 (2003)

  22. Kusy, B., Amundson, I., Sallai, J., Volgyesi, P., Ledeczi, A., Koutsoukos, X.: RF doppler shift-based mobile sensor tracking and navigation. ACM Trans. Sensor Netw. 7(1), 1–32 (2011)

    Article  Google Scholar 

  23. Kusý, B., Lédeczi, A., Koutsoukos, X.: Tracking mobile nodes using RF doppler shifts. In: Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys), pp. 29–42. Sydney, Australia (2007)

  24. Kusý, B., Sallai, J., Balogh, G., Lédeczi, A., Protopopescu, V., Tolliver, J., DeNap, F., Parang, M.: Radio interferometric tracking of mobile wireless nodes. In: Proceedings of the 5th International Conference on Mobile Systems, Applications and Services (MobiSys), pp. 139–151 (2007)

  25. Ladd, A.M., Bekris, K.E., Rudys, A., Marceau, G., Kavraki, L.E., Wallach, D.S.: Robotics-based location sensing using wireless ethernet. In: Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 227–238 (2002)

  26. Loizou, S., Kumar, V.: Biologically inspired bearing-only navigation and tracking. In: 46th IEEE Conference on Decision and Control, pp. 1386–1391 (2007)

  27. Maróti, M., Kusý, B., Balogh, G., Völgyesi, P., Nádas, A., Molnár, K., Dóra, S., Lédeczi, A.: Radio interferometric geolocation. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys), pp. 1–12 (2005)

  28. McGillem, C., Rappaport, T.: A beacon navigation method for autonomous vehicles. IEEE Trans. Veh. Technol. 38(3), 132–139 (1989)

    Article  Google Scholar 

  29. McMickell, M.B., Goodwine, B., Montestruque, L.A.: MICAbot: a robotic platform for large-scale distributed robotics. In: Proceedings of International Conference on Robotics and Automation (ICRA), pp. 1600–1605 (2003)

  30. MobileRobots: Pioneer P3-Dx. http://www.mobilerobots.com/ResearchRobots/PioneerP3DX.aspx (2012). Accessed 3 Oct 2012

  31. Niculescu, D., Nath, B.: Ad hoc positioning system (APS) using AOA. In: Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), pp. 1734–1743 (2003)

  32. Purohit, A., Sun, Z., Salas, M., Zhang, P.: SensorFly: Controlled-mobile sensing platform for indoor emergency response applications. In: Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN) (2011)

  33. Shah, R., Roy, S., Jain, S., Brunette, W.: Data MULEs: modeling a three-tier architecture for sparse sensor networks. In: Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and Applications, pp. 30–41 (2003)

  34. Texas Instruments: CC1000: Single chip very low power RF transceiver. http://focus.ti.com/docs/prod/print/cc1000.html (2007). Accessed 3 Oct 2012

  35. Wang, G., Cao, G., Porta, T., Zhang, W.: Sensor relocation in mobile sensor networks. In: Proceedings of the IEEE International Conference on Computer Communication (INFOCOM), pp. 2302–2312 (2005)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Isaac Amundson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Amundson, I., Kushwaha, M. & Koutsoukos, X. A Method for Estimating Angular Separation in Mobile Wireless Sensor Networks. J Intell Robot Syst 71, 273–286 (2013). https://doi.org/10.1007/s10846-012-9788-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-012-9788-0

Keywords

Navigation