This document presents a framework for validating wireless channel attenuation models for body sensor networks. The framework uses MATLAB to analyze test signals and predicted attenuation models using various analysis methods. It produces a validity number between 0 and 1 to indicate how accurately a predicted model matches actual signals. The validation software was tested on sample data sets and produced results consistent with expectations, providing a way to evaluate future attenuation models for realism before they are used in body sensor network simulations.

1. A Framework For Validating
Wireless Channel Attenuation
Models For Body Sensor
Networks
Khade L. Grant1
, Philip K. Asare2
, John Lach,
Ph.D.2
1. Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284.
2. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904.

2. 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

3. 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.)

4. 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.

5. 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.

6. ModelValidation 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:

7. Data Set Signals
Figure 4. Figure 5.
Figure 6. Figure 7.

8. ModelValidation 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).

9. ModelValidation 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

10. 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.

11. 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.

12. 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.

13. 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 SelectedTopics 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/.