Indoor Location Estimation in Sensor Networks using AI Algorithm

József Dániel Dombi

2012

Abstract

To determine the indoor location of a person or object, we can use a suitable wireless network. There are different kinds of wireless networks available for this. Independent of the type of the network, using RSSI it is possible to find the position of the moving person close by. Here, we present Wireless Sensor Network and apply it in a real environment. We will mainly concentrate on locating a person using standard artificial intelligence methods. In our system we define nodes (the fingerprint), and supervised learning algorithms that should predict these nodes. In addition, we test whether we can get nice results if we change the granularity of the nodes. Real simulation demonstrates that this system can supply the current position of the moving person with good accuracy.

References

  1. Agrawala, A. K. and Shankar, A. U. (2003). WLAN location determination via clustering and probability distributions. In PerCom.
  2. Bahl, P. and Padmanabhan, V. N. (2000). RADAR: An inbuilding RF-based user location and tracking system. In INFOCOM.
  3. Battiti, R., Villani, A., Villani, R., and Nhat, T. L. (2002). Neural network models for intelligent networks: Deriving the location from signal patterns. In Citeseer.
  4. Enge, P. and Misra, P. (1999). Special issue on gps: The global positioning system. In Proceedings of the of the IEEE.
  5. Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wallach, D. S., and Kavraki, L. E. (2004). Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking.
  6. Harter, A., Hopper, A., Steggles, P., Ward, A., and Webster, P. (1999). The anatomy of a context-aware application. In Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom-99).
  7. Hopper, A., Harter, A., and Blackie, T. (1993). The active badge system. In INTERCHI'93 Conference on Human Factors in Computing Systems.
  8. Ladd, A. M., Bekris, K. E., Rudys, A., Kavraki, L. E., and Wallach, D. S. (2005). Robotics-based location sensing using wireless ethernet. Wireless Networks.
  9. Priyantha, N. B., Chakraborty, A., and Balakrishnan, H. (2000). The cricket location-support system. In MOBICOM.
  10. Quinlan, R. (1986). Induction of decision trees. Machine Learning, 1.
  11. RTLS (2012). Rtls. http://www.rtls.eu.
  12. Ubisense (2012). Ubisense. http://www.ubisense.net.
  13. Versus (2012). Versus. http://www.versustech.com.
  14. Ward, A. and Jones, A. (1997). A new location technique for the active office. In IEEE Personal Communications.
  15. Yim, J. (2008). Introducing a decision tree-based indoor positioning technique. Expert Syst. Appl, 34(2).
  16. ZigBee (2012). Zigbee. http://www.zigbee.org/Specificati ons.aspx.
Download


Paper Citation


in Harvard Style

Dombi J. (2012). Indoor Location Estimation in Sensor Networks using AI Algorithm . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 349-352. DOI: 10.5220/0004098303490352


in Bibtex Style

@conference{iceis12,
author={József Dániel Dombi},
title={Indoor Location Estimation in Sensor Networks using AI Algorithm},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={349-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004098303490352},
isbn={978-989-8565-10-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Indoor Location Estimation in Sensor Networks using AI Algorithm
SN - 978-989-8565-10-5
AU - Dombi J.
PY - 2012
SP - 349
EP - 352
DO - 10.5220/0004098303490352