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Transmission power assignment with postural position inference for on-body wireless communication links

Published:27 August 2010Publication History
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Abstract

This article presents a novel transmission power assignment mechanism for on-body wireless links formed between severely energy-constrained wearable and implanted sensors. The key idea is to develop a measurement-based framework in which the postural position as it pertains to a given wireless link is first inferred based on the measured RF signal strength and packet drops. Then optimal power assignment is done by fitting those measurement results into a model describing the relationship between the assigned power and the resulting signal strength. A closed loop power control mechanism is then added for iterative convergence to the optimal power level as a response to both intra-and-inter posture body movements. This provides a practical paradigm for on-body power assignment, which cannot leverage the existing mechanisms in the literature that rely on localization, which is not realistic for on-body sensors. Extensive experimental results are provided to demonstrate the model building and algorithm performance on a prototype body area network. The proposed mechanism has also been compared with a number of other closed loop mechanisms and an experimental benchmark.

References

  1. Agarwal, S., Krishnamurthy, S. V., Katz, R. H., and Dao, S. K. 2001. Distributed power control in ad hoc wireless networks. In Proceedings of the IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).Google ScholarGoogle Scholar
  2. Bao, S., Zhang, Y., and Shen, L. 2005. Physiological signal based entity authentication for body area sensor networks and mobile healthcare systems. In Proceedings of the 27th IEEE Conference on Engineering in Medicine and Biology. 2455--2458.Google ScholarGoogle Scholar
  3. Chen, K. Y. and Bassett, D. R. 2005. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc 37, 11, S490--S500.Google ScholarGoogle ScholarCross RefCross Ref
  4. Chipcon. http://www.chipcon.com/files/CC1000_Data_Sheet_2_1.pdf.Google ScholarGoogle Scholar
  5. Choi, S., Song, S. J., Sohn, K., Kim, H., Kim, J., Yoo, J., and Yoo, H. J. 2006. A low-power star-topology body area network controller for periodic data monitoring around and inside the human body. In Proceedings of the International Semantic Web Conference (ISWC), 139--140.Google ScholarGoogle Scholar
  6. Grants2. http://grants2.nih.gov/grants/guide/pa-files/PA-07-354.htmlGoogle ScholarGoogle Scholar
  7. Jovanov, E., Milenkovic, A., Otto, C., and De Groen, P. C. 2005a. A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J. Neuro Engin. Rehab. 2, 11, 6.Google ScholarGoogle ScholarCross RefCross Ref
  8. Jovanov, E., Milenković, A., Otto, C., De Groen, P., Johnson, B., Warren, S., and Taibi, G. 2005b. A WBAN system for ambulatory monitoring of physical activity and health status: applications and challenges, In Proc. of the Annual 3810--3.Google ScholarGoogle Scholar
  9. Klee, U., Gehrig, T., and Mcdonough, J. 2005. Kalman filters for time delay of arrival-based source localization. In Proceedings of the International Conference On Spoken Language Processing (INTERSPEECH), 2289--2292.Google ScholarGoogle Scholar
  10. Lee, S.-W. and Mase, K. 2002. Activity and location recognition using wearable sensors. Perv. Comput. 1, 3, 24--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Lim, T. L. and Mohan, G. 2005. Energy aware geographical routing and topology control to improve network lifetime in wireless sensor networks Broadband Networks. In Proceedings of the 2nd International Global Telecommunications Conference. 829--831.Google ScholarGoogle Scholar
  12. Lin, S., Zhang, J., Zhou, G., Gu, L., Stankovic, J. A., and He, T. 2006. ATPC: Adaptive transmission power control for wireless sensor networks. In Proceedings of the 4th International Conference on Embedded Networked Sensor Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Liu, J. and Li, B. 2003. Distributed topology control in wireless sensor networks with asymmetric links. In Proceedings of IEEE Global Telecommunications Conference (GLOBECOM). 1257--1262.Google ScholarGoogle Scholar
  14. Lo, B., Thiemjarus, S., King, R., and Yang, G. 2005. Body sensor network—A wireless sensor platform for pervasive healthcare monitoring. In Proceedings of the 3rd International Conference on Pervasive Computing (PERVASIVE). 77--80.Google ScholarGoogle Scholar
  15. Lopez-Aguilera, E. and Casademont, J. 2006. A transmit power control proposal for IEEE 802.11 cellular networks. In Proceedings of 6th International Workshop on Applications and Services in Wireless Networks. 235--242.Google ScholarGoogle Scholar
  16. Milenkovic, A., Otto, C., and Jovanov, E. 2006. Wireless sensor networks for personal health monitoring: Issues and an implementation. Comp. Comm. (Special Issue: Wireless Sensor Networks: Performance, Reliability, Security, and Beyond), Elsevier. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Moh, M., Culpepper, B., Dung, L., Moh, T.-S., Hamada, T., and Su, C.-F. 2005. On data gathering protocols for in-body biomedical sensor networks. In Proceedings of the 48th IEEE Global Telecommunications Conference (GlobeCom).Google ScholarGoogle Scholar
  18. Nar, P.C. and Cayirci E. 2005. PCSMAC: A power controlled sensor—MAC protocol for wireless sensor networks. In Proceedings of the 2nd European Workshop on Wireless Sensor Networks. 81--92.Google ScholarGoogle Scholar
  19. Norgall, T. 2005. Body area network—a key infrastructure element for patient-centric health services. In Proceeding of the Joint ISO TC215/WG7/IEEE 1073 Meeting.Google ScholarGoogle Scholar
  20. Otto, C., Milenkovic, A., Sanders, C., and Jovanov, E. 2006. System architecture of a wireless body area sensor network for ubiquitous health monitoring. J. Mobile Multimedia, 1, 4, 307--326. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Qiao, D., Choi, S., Jain, A., and Shin, K. G. 2003. Adaptive transmit power control in IEEE 802.11a wireless LANs. In Proceedings of IEEE Vehiculer Technology Conference (VTC).Google ScholarGoogle Scholar
  22. Quwaider, M. and Biswas, S. 2008a. Body posture identification using hidden markov model with wearable sensor networks. In Proceedings of ACM Conference On Body Area Networks (BodyNets). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Quwaider, M. and Biswas, S. 2008b. Physical context detection using multi-modal sensing using wearable wireless networks. J. Comm. Softw. Sys., Special Issue on Medical Applications for Wireless Sensor Networks, 4, 3, 191--202.Google ScholarGoogle Scholar
  24. Rintamaki, M. 2002. Power control in CDMA cellular communication, systems. In Wiley Encyclopedia of Telecommunications, J. G. Proakis, Ed., Wiley, New York.Google ScholarGoogle Scholar
  25. Rintamaki, M., Koivo, H., and Hartimo, I. 2004. Adaptive closed-loop power control algorithms for CDMA cellular communication systems. IEEE Trans. Vehic Tech., 53, 6.Google ScholarGoogle ScholarCross RefCross Ref
  26. Saghaei, H., and Neyestanak, A. 2007. Variable step closed-loop power control in cellular wireless CDMA systems under multipath fading. Comm. Comput. Signal Proc.Google ScholarGoogle Scholar
  27. Xbow. http://www.xbow.comGoogle ScholarGoogle Scholar
  28. Xiao, S., Sivaraman, V., and Burdett, A. 2008. Adapting radio transmit power in wireless body area sensor networks. In Proceedings of ACM Conference on Body Area Networks (BodyNets'08). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Xing, G., Lu, C., and Pless, R. 2007. Localized and configurable topology control in lossy wireless sensor networks. In Proceedings of the International Conference on Computer Communications and Networks (ICCCN). 75--80.Google ScholarGoogle Scholar
  30. Zhou, G., He, T., Krishnamurthy, S., and Stankovic, J. A. 2004. Impact of radio irregularity on wireless sensor networks. In Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services. Google ScholarGoogle ScholarDigital LibraryDigital Library

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