A Framework For Validating Wireless Channel Attenuation

Models For Body Sensor Networks

Khade L. Grant1, Philip K. Asare2, John Lach, Ph.D.21. Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284.

2. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904.

Wireless Communication in

Body Sensor Networks❖ Wireless communication plays an important role in Body Sensor Networks (BSNs).

• Body Sensor Networks use wireless communication to send and receive signals that contain medical and environmental data.

• Wireless communication in BSNs provides more flexibility and efficiency than BSNs with wires.

❖ Channel attenuation is a great challenge to wireless communication in BSNs. Figure 1. How wireless communication is used in Body Sensor

Networks

Channel Attenuation ❖ Channel attenuation is the gradual loss

in the intensity of a signal as it propagates along a channel.

❖ During experiments, if attenuation is high, the transmitted signal and its information can be lost.

❖ Various factors contribute to attenuation.

❖ It is important to be able to determine the channel attenuation:

• To add attenuation effects to BSN simulators; making them more realistic.

❖ Wireless channel attenuation models need to be developed to address this challenge.

Figure 2. Attenuation of link or channel. This figure illustrates

attenuation between a transmitting and receiving node (Tallinn

University, n.d.)

Objective

❖ Before attenuation models are built we need a way of validating the future models.

❖ Objective:

• Develop a framework for the validation of attenuation models.

❖ Model validation software determines how accurate future attenuation models are.

❖ Will prevent wasting time with invalid attenuation models.

Materials and Methods❖ Used MATLAB to create the model validation software and

the test data.

❖ Test Data:

• Five sample sets of signals.

‣ Each set contains 10 signals.

Figure 3.

Model Validation Methods

❖ Five signal analysis methods:

• Cross-correlation.

• Root-mean-squared error (RMSE).

• Difference between auto correlations.

• RMSE of auto-correlations.

• Correlation coefficient of the FFTs.

❖ Each analysis method produced a validity number between 0 (worst) and 1 (best).

❖ Took a weighted average of validity numbers to produce the final validity number for validation software.

❖ Calibrations/Predictions:

Data Set Signals

Figure 4. Figure 5.

Figure 6. Figure 7.

Model Validation Methods

❖ Normalizing equation example for RMSE, difference between auto-correlations, and RMSE of auto-correlations tests:

• |(y - c)|/c

‣ Where ‘y’ is the value produced by the analysis between actual and predicted signals.

‣ ‘c’ is the value produced by the analysis between the actual signal and the signal of all zeroes.

❖ Normalized analysis methods to produce value between 0 and 1.

❖ Analyses between actual signal and predicted_1 calibrated to produce validity number of 0. (Since y = c).

❖ Analyses between actual signal and predicted_2 calibrated to produce validity number of 1. (Since y = 0).

Model Validation Methods❖ For cross-correlation and correlation coefficient tests:

• The analyses produced a number between 0 and 1.

• Used as validity number.Figure 8. Figure 9.

Graph of cross-correlation between actual and predicted_2 signal

Graph of cross-correlation between actual and predicted_4 signal

Results

* Weighted average based on relative importance of each validity test. The RMSE test was multiplied by a coefficient of 0.5.Weights can be controlled by user.

Figure 10.

Discussions/Conclusions

❖Preliminary results were consistent with our goals/expectations:

• data_set_1_pred received a validity number of 0.

• data_set_2_pred received a validity number of 1.

• data_set_3_pred received a validity number of 0.5.

• data_set_4_pred received a validity number greater than 0.5.

❖Validation software will serve as the framework for evaluating future attenuation models.

• Can be expanded to evaluate other signal models.

Future Work

❖Explore other validity tests and signals analysis methods.

❖Develop a justification method for determining the weight of the validity tests based on relative importance of each test.

❖Validated models will be used in the programming of Body-Sim (a multi-domain modeling and simulation framework for the research and design of BSNs).• The attenuation models will improve the realism of

Body-Sim.

References

1. Asare, P., Dickerson, R. F., Wu, X., Lach, J., & Stankovic, J. BodySim: A Multi-Domain Modeling and Simulation Framework for Body Sensor Networks Research and Design. ResearchGate. Retrieved July 20, 2014.

2. Smith, D. B., Miniutti, D., Lamahewa, T. A., & Hanlen, L. W. Propagation Models for Body-Area Networks: A Survey and New Outlook. Antennas and Propagation Magazine, IEEE, vol. 55, pages 97-117, October 2013. Retrieved July 20, 2014.

3. Roberts, N. E., Oh, S., & Wentzloff, D. D. Exploiting Channel Periodicity in Body Sensor Networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, pages 4-13, March 2012. Retrieved July 20, 2014.

4. Aoyagi, T., Iswandi, I., Kim, M., Takada, J., Hamaguchi, K., & Kohno, R. Body Motion and Channel Response of Dynamic Body Area Channel. Antennas and Propagation (EUCAP), Proceedings of the 5th European Conference on, pages 3138-3142, 2011. Retrieved July 20, 2014.

5. Tallinn University. (n.d.). Attenuation of link or channel[Chart]. Retrieved from http://www.tlu.ee/~matsak/telecom/cabling/eu_generic_cabling/423_attenuation_insertion_loss.html

6. The MathWorks. Matlab. http://www.mathworks.com/products/.