Tunable Transmission Power to Improve 2D RSSI Based Localization Algorithm

D. Polese, L. Pazzini, A. Minotti, L. Maiolo, A. Pecora

Abstract

Radio frequency wireless technology is surely one of the most used technologies in indoor localization. RF-signals have been utilized in several ways to estimate the distances among the anchor nodes and the mobile nodes and, probably the methods based on the measure of the Received Signal Strength (RSS) are the most explored ones. RSS depends on the transmission medium and environment and this affects also the distance measurement performances. To mitigate the external influences, transmission parameters, as for example the transmission channel and transmission power, can be tuned. To this purpose, in this work the influence of the power transmission on the localization algorithm performance is investigated. In particular a method to select the power transmission that allows the best localization performance is presented. The results show that the localization performance depend on the transmission power. Moreover, a method to establish the best power transmission for the specific environment is presented and tested.

References

  1. Fu, S., Hou, Z. G., & Yang, G. (2009, March). An indoor navigation system for autonomous mobile robot using wireless sensor network. In Networking, Sensing and Control, 2009. ICNSC'09. International Conference on (pp. 227-232). IEEE.
  2. Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002, September). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (pp. 88-97). ACM.
  3. Vicentini, F., Ruggeri, M., Dariz, L., Pecora, A., Maiolo, L., Polese, D., Pazzini, L., Molinari Tosatti, L. (2014, June). Wireless sensor networks and safe protocols for user tracking in human-robot cooperative workspaces. In Industrial Electronics (ISIE), 2014 IEEE 23rd International Symposium on (pp. 1274-1279). IEEE.
  4. García-Hernández, C. F., Ibarguengoytia-Gonzalez, P. H., García-Hernández, J., & Pérez-Díaz, J. A. (2007). Wireless sensor networks and applications: a survey. IJCSNS International Journal of Computer Science and Network Security, 7(3), 264-273.
  5. Randell, C., & Muller, H. (2001, January). Low cost indoor positioning system. In Ubicomp 2001: Ubiquitous Computing (pp. 42-48). Springer Berlin Heidelberg.
  6. IEEE 802.11™-2012 PDF format IEEE Standard for Information technology--Telecommunications and information exchange between systems Local and metropolitan area networks--Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications.
  7. IEEE 802.15.4f™-2012 IEEE Standard for Local and metropolitan area networks-- Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs) Amendment 2: Active Radio Frequency Identification (RFID) System Physical Layer (PHY).
  8. Polese, D., Pazzini, L., Minotti, A., Maiolo, L., Pecora, A. (2014). Compensation of the Antenna Polarization Misalignment in the RSSI Estimation. In SENSORNETS (pp. 263-267).
  9. Wu, K., Xiao, J., Yi, Y., Gao, M., Ni, L. M. (2012, March). Fila: Fine-grained indoor localization. In INFOCOM, 2012 Proceedings IEEE (pp. 2210-2218). IEEE.
  10. Jeske, Daniel R., and Ashwin Sampath. "Signal-tointerference-plus-noise ratio estimation for wireless communication systems: Methods and analysis." Naval Research Logistics (NRL) 51.5 (2004): 720- 740.
  11. Shin, Soo Young, Hong Seong Park, and Wook Hyun Kwon. "Mutual interference analysis of IEEE 802.15. 4 and IEEE 802.11 b." Computer Networks 51.12 (2007): 3338-3353.
  12. D'Amico, A., & Di Natale, C. (2001). A contribution on some basic definitions of sensors properties. IEEE Sensors Journal, 1(3), 183-190.
  13. Zanca, G., Zorzi, F., Zanella, A., & Zorzi, M. (2008, April). Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. In Proceedings of the workshop on Realworld wireless sensor networks (pp. 1-5). ACM.
  14. Zolertia Z1 http://www.zolertia.com/products/z1 CC2420 (2013) 2.4 GHz IEEE 802.15.4 / ZigBee - ready RF Transceiver, SWRS041c datasheet, http://www.ti.com/general/docs/lit/getliterature.tsp?ge nericPartNumber=cc2420&fileType=pdf.
  15. Hastie, T. Tibshirani, R. Friedman J., (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition.
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Paper Citation


in Harvard Style

Polese D., Pazzini L., Minotti A., Maiolo L. and Pecora A. (2015). Tunable Transmission Power to Improve 2D RSSI Based Localization Algorithm . In Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-086-4, pages 151-156. DOI: 10.5220/0005330801510156


in Bibtex Style

@conference{sensornets15,
author={D. Polese and L. Pazzini and A. Minotti and L. Maiolo and A. Pecora},
title={Tunable Transmission Power to Improve 2D RSSI Based Localization Algorithm},
booktitle={Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2015},
pages={151-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005330801510156},
isbn={978-989-758-086-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Tunable Transmission Power to Improve 2D RSSI Based Localization Algorithm
SN - 978-989-758-086-4
AU - Polese D.
AU - Pazzini L.
AU - Minotti A.
AU - Maiolo L.
AU - Pecora A.
PY - 2015
SP - 151
EP - 156
DO - 10.5220/0005330801510156