Saturday , June 23 2018

Determination of the Reliability Interval of Outdoor Channel Model in 802.15.4/ZigBee Networks

Héctor KASCHEL
Departamento de Ingeniería Eléctrica, Universidad de Santiago de Chile
Av. Ecuador 3519, Estación Central, Santiago, Chile

Gustavo QUEZADA
Departamento de Ingeniería Eléctrica, Universidad de Santiago de Chile
Av. Ecuador 3519, Estación Central, Santiago, Chile

Ricardo VEGA
Departamento de Ingeniería Química, Universidad de Santiago de Chile
Av. Ecuador 3519, Estación Central, Santiago, Chile

Luis SÁNCHEZ
Departamento de Matemáticas y Ciencias de la Computación, Universidad de Santiago de Chile
Av. Ecuador 3519, Estación Central, Santiago, Chile

José MARDONES
Departamento de Ingeniería Eléctrica, Universidad de Santiago de Chile
Av. Ecuador 3519, Estación Central, Santiago, Chile

Abstract: Within the framework of the implementation of a wireless network of nodes under the IEEE802.15.4/Zigbee standard, the values obtained from experimental measurements of power as a function of distance between nodes in free space are analyzed. A propagation loss model is proposed and a statistical analysis is made to determine the reliability of the prediction of the distance between nodes as a function of the power measured at the receiver. This analysis confirms that the predictions obtained from the proposed model and from widely used models, are not appropriate for determining, with a high degree of certainty, the distance between transmitter and receiver due to the variability of the data.

Keywords: Wireless Sensor Network, Prediction methods, Outdoor, Zigbee, Channel models, Propagation.

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CITE THIS PAPER AS:
Héctor KASCHEL, Gustavo QUEZADA, Ricardo VEGA, Luis SÁNCHEZ, José MARDONES, Determination of the Reliability Interval of Outdoor Channel Model in 802.15.4/ZigBee Networks, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (2), pp. 187-192, 2011.

1. Introduction

In communications systems such as wireless sensors based on RF (Radio Frequency), the propagation of the waves undergoes fading due to distance and to refraction, diffraction, dispersion, and the Doppler effect if there is motion between the emitter and the receiver [1]. Fading causes a decrease of the signal’s intensity at the receiver, and that can be a source of error because the receivers need a minimum signal intensity to perform an adequate demodulation [2]. There are models that allow the prediction of the intensity of the signal received in a wireless communication between the transmitter and the receiver considering the effects mentioned above [1].

This paper reports on experimental measurements with COTS (Comercial Off-The-Shelf) IEEE802.15.4 devices [2], specifically Xbee series I, for point-to-point communications with LOS (Line Of Sight). The obtained values are analyzed and from these a fitting function is proposed that represents power as a function of distance. Then a statistical analysis is applied to the proposed model, determining from that analysis its pertinence for predicting the values of the distance between nodes from the power of the receiver.

The importance of this analysis lies in the fact that the prediction of the signal’s intensity in the receiver can be useful to determine the distance between two devices and their relative position [3], [4], [5], [6], [7]. From this information it is possible to establish the power required to make the connection with the following node, and it is a useful parameter for selecting routes in the case of networks with multihops [8], [9], [10], [11], [12] similar to generate self-similar traffic in computer network [13].

The arrangement of the paper is the following: Section II presents models of path loss. Section III presents experimental values obtained for distance loss. Section IV presents the fitting of the trend curves for the experimental values. Section V presents the analysis of the models found. Finally, Section VI presents the conclusions of the work.

REFERENCES

  1. RAPPAPORT, T. S., Wireless Communications – Principles and Practice. Prentice Hall, Upper Saddle River, NJ, 2002.
  2. HOLGER, K., A. WILLIG, Protocols and Architectures for Wireless Sensor Network, John Wiley and Sons, 2005. HOOD, B. N., P. BAROOAH, Estimating DoA from Radio-Frequency RSSI Measurements Using an Actuated Reflector, IEEE Sensors Journal, vol. 11, Feb. 2011.
  3. LEE, K., B. KIM, H. LEE, Y. SHIN, Improving Localization Accuracy Using Signal Attenuation due to Neighbour Signal Interference in Wireless Sensor Networks. 13th International Conference on Advanced Communication Technology (ICACT), 13-16 Feb. 2011.
  4. LAU, E.-E.-L., B.-G. LEE, S.-C. LEE, W.-Y. CHUNG, Enhanced RSSI-based High Accuracy Real-time User Location Tracking System for Indoor and Outdoor Environments. International Journal on Smart Sensing and Intelligent Systems, vol. 1, no. 2, June 2008.
  5. LI, J., H.-P. LIU, A New Weighted Centroid Localization Algorithm in Coal Mine Wireless Sensor Networks, 3rd International Conference on Computer Research and Development (ICCRD), 11-13 March 2011.
  6. CASEY, P. R., K. E. TEPE, N. KAR, Design and Implementation of a Testbed for IEEE 802.15.4 (Zigbee) Performance Measurements. EURASIP Journal on Wireless Communications and Networking Volume 2010, ID 103406, 2010.
  7. BARSOCCHI, P., S. LENZI, S. CHESSA, member IEEE, G. GIUNTA, Pisa Research Area, Via G. Moruzzi. Virtual Calibration for RSSI-based Indoor Localization with IEEE 802.15.4. ICC’09. IEEE International Conference on Communications, 2009.
  8. PRADIP, D., Y. LIU, S. K. DAS, Energy-Efficient Reprogramming of a Swarm of Mobile Sensors. IEEE Transactions on Mobile Computing, May 2010.
  9. QUEZADA, G., J. MARDONES, A. SOTO, Applied Research Line to Industrial Wireless Sensor Networks. Valdivia, Chile. August 2008. INGELECTRA 2008.
  10. KASCHEL, H., G. QUEZADA, A. SOTO, J. MARDONES, Requirements to Industrial Wireless Sensor Networks under Cooperative Diversity Operation. XVIII Congreso Asociación Chilena de Control Automático. Santiago Chile 10-12, December 2009, pp. 1-6.
  11. VURAL, S., E. EKICI, On Multihop Distances in Wireless Sensor Networks with Random Node Locations, IEEE Transactions on Mobile Computing, vol. 9, no. 4, April 2010.
  12. MILL?N, G., H. KASCHEL, G. LEFRANC, A Simple Model for the Generation of LRD Self-similar Traffic Using Piecewise Affine Chaotic One-dimensional Maps, in SIC, vol. 19, No. 1, 2010, pp. 67-78.
  13. User manual Fractus Reach Xtend? Bluetooth?, 802.11b/g WLAN Chip Antenna. Abril 2008.
  14. http://www.scilab.org/
  15. RUKPAKAVONG, W., I. PHILLIPS, G. LIN, Neighbour Discovery for Transmit Power Adjustment in IEEE 802.15.4 Using RSSI. 4th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Feb. 2011.
  16. http://www.statgraphics.com/
  17. http://curveexpert.webhop.net/
  18. MONTGOMERY, D. C., RUNGER G. C., Applied Statistics and Probability for Engineers, 3rd. Ed., John Wiley & Sons, USA, 2003.

https://doi.org/10.24846/v20i2y201111