Challenge of Dynamic Body Area Networks
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Challenge of Dynamic Body Area Networks

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An outline of the dynamics of the radio channel around the human body and the implications this has for designing reliable wearable wireless devices

An outline of the dynamics of the radio channel around the human body and the implications this has for designing reliable wearable wireless devices

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Challenge of Dynamic Body Area Networks Challenge of Dynamic Body Area Networks Presentation Transcript

  • Challenge of a dynamic BAN channel Leif Hanlen with support from A. Boulis, B. Gilbert, V. Chaganti, L. Craven, D. Fang, T. Lamahewa, D. Lewis,D. Miniutti, O. Nagy, D. Rodda, K. Sithamparanathan, D. Smith, Y. Tselishchev, A. Zhang, National ICT Australia, & Australian National University leif.hanlen@nicta.com.au Director eHealth @ NICTA PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au
  • Humans are hard to modelPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 1
  • Why are BANs different? • Whole networks are in motion • Base-station is weak • base-stations are mobile, AND may be in range of other networks vs – some networks stay in range for long periods (family members) – some networks pass in and out of range very quickly (shoppers) – nodes in network A may have stronger signal from network B with thanks: Ohio University – coordination between BANs impossiblePIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 2
  • Interference • Your arm span is approx. 2.5m tip-of-finger to tip-of-finger • How many BANs in 6m (edge length) cube around you? • How much interference? how many networks is he interfering with?[Hanlen et al., 2010b] Hanlen, L. W., Miniutti, D., Smith, D. B., Rodda, D., and Gilbert, B. (2010b). Co-Channel interference in body area networks with indoor measurements at 2.4GHz: Distance-to-interferer is a poor estimate of received interference power. Springer Intl. J. Wireless Inform. Net., 17.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 3
  • Myth busters1. Distance-based path-loss models? (no)2. Dynamics (single- and multi-link), little/no ISI3. Cellular interference models (no!)4. Sleeping is (very) bad for BANsPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 4
  • Warning Distance considered harmful [Friis, 1946] Friis free-space linear: Preceived ∝ D−a · Ptransmit dB: Ploss = a · 20 log10 (Dmetres) +b + σ · N (0, 1) path loss wrt distance modelling noise• α is exponential path loss, for far-field• ‘Noise’ is actually model error – not measurement error[Friis, 1946] Friis, H. T. (1946). A note on a simple transmission formula. Proc. IRE, 34(5):254–256.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 5
  • Co-channel interference 40 −50 Median 30 Max −55 Min 20 Exponent−fit −60 Free−space 10 −65 0 −70 −10 −75 −20 −80 −30 −85 −40 −90 Signal Interference −50 −95 0 2 4 6 8 50 60 70 80 90 100 Subjects moved randomly on grid, we selected one subject as “signal”one as “intererer”: ‘line-of-best fit’ is meaningless: ±20dB errors.[Hanlen et al., 2010b] Hanlen, L. W., Miniutti, D., Smith, D. B., Rodda, D., and Gilbert, B. (2010b). Co-Channel interference in body area networks with indoor measurements at 2.4GHz: Distance-to-interferer is a poor estimate of received interference power. Springer Intl. J. Wireless Inform. Net., 17.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 6
  • Dynamics• How to capture real dynamic channels?• Is frequency/ISI a factor?• Can we use “simple” transceivers?• What do we want to know?[Smith et al., 2008a] Smith, D. B., Hanlen, L. W., Miniutti, D., Zhang, J. A., Rodda, D., and Gilbert, B. (2008a). Statistical characterization of the dynamic narrowband body area channel. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 7
  • CDF − Back to Chest Standing CDF − Left ankle to right hip walking 1 1 0.9 0.9 0.8 0.8 0.7 0.7 Cumulative probability Cumulative probability 0.6 0.6 0.5 0.5 0.4 Measured data 0.4 Normal 0.3 0.3 Measured data Lognormal Normal Gamma 0.2 0.2 Lognormal Gamma 0.1 0.1 0 0 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.2 0.4 0.6 0.8 1 Normalized Received Power Normalized Received Powerstanding walking Some measurements based on the National Instruments approach.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 8
  • 3 Measured Data Normal fit 2.5 Lognormal Nakagami−m Rayleigh 2 Density 1.5 1 0.5 0 0 0.2 0.4 0.6 0.8 1 Normalised Amplitude (0..1) Left-ankle to Right-hip, walking Almost every fit is ”ok” except Rayleigh.[Smith et al., 2010b] Smith, D. B., Hanlen, L. W., Zhang, J. A., Miniutti, D., Rodda, D., and Gilbert, B. (2010b). First and second-order statistical characterizations of the dynamic body-area propagation channel of various bandwidths. Annals of Telecommunications.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 9
  • Inter-symbol Interference? [Islam and Kwak, 2010]• “..environment of WBAN causes a dense multipath...”• “[60GHz] multipath is present...much less deep than [2.4GHz]” [Hall et al., 2010] [Smith et al., 2008a]• “no resolvable multipath..” [Cao et al., 2009]• “need to assess [UWB] multipath”[Islam and Kwak, 2010] Islam, S. M. R. and Kwak, K. S. (2010). A comprehensive study of channel estimation for WBAN-based healthcare systems: Feasibility of using multiband UWB. J Med Syst.[Hall et al., 2010] Hall, P. S., Hao, Y., and Cotton, S. L. (2010). Progress in antennas and propagation for body area networks. In Intl. Symp. Sig., Sys. and Elect.[Smith et al., 2008a] Smith, D. B., Hanlen, L. W., Miniutti, D., Zhang, J. A., Rodda, D., and Gilbert, B. (2008a). Statistical characterization of the dynamic narrowband body area channel. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.[Cao et al., 2009] Cao, H., Leung, V., Chow, C., and Chan, H. (2009). Enabling technologies for wireless body area networks: A survey and outlook. IEEE Commun. Mag., 47(12):84–93.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 10
  • Frequency response from mean tap values 0.1 2360MHz 0 820MHz 427MHz −0.1 Response (dB) −0.2 −0.3 −0.4 −0.5 −0.6 −0.7 4 5 6 7 8 10 10 10 10 10 Frequency [Smith et al., 2009a] Tap values in[Smith et al., 2009a] Smith, D. B., Miniutti, D., Hanlen, L. W., Zhang, J. A., Rodda, D., and Gilbert, B. (2009a). Power delay profiles for dynamic narrowband body area network channels ID: 802.15-09-0187. IEEE submission.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 11
  • How to model the real body area channel? test subject "free" to move as per normal Velcro(TM) sounder on chest 3rd party accelerometer on waist sounder on wrist NICTA open source channel sounder [250kHz @ 2.4GHz] Build transceiver, transmit 200 packets per second, measure RSSI[Hanlen et al., 2010a] Hanlen, L. W., Chaganti, V. G., Gilbert, B., Rodda, D., Lamahewa, T. A., and Smith, D. B. (2010a). Open-source testbed for body area networks: 200 sample/sec, 12 hrs continuous measurement. In IEEE PIMRC.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 12
  • How do we fit the distributions? 2nd Order, Akaike Information Criterion 2K(K + 1) AICc = −2 ln Lθ,data ˆ + 2K + n−K −1 AIC 1st order ˆ• Lθ,data maximum log-likelihood score over parameters θ ˆ• K number of parameters (=1,2 for us)• n number of sample points (=4000 for us)Lower scores imply better fits.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 13
  • Stat fits: what we want• First order stats: gives simple independent sample model for channel. Ensemble amplitude distribution. – Likelihood of having (in)sufficient receive signal strength• Second order stats: level crossing rate, and fade durations – Likelihood of dropping (1 or more) packets – Likelihood of achieving latency requirements – Indication of packet length[Smith et al., 2010b] Smith, D. B., Hanlen, L. W., Zhang, J. A., Miniutti, D., Rodda, D., and Gilbert, B. (2010b). First and second-order statistical characterizations of the dynamic body-area propagation channel of various bandwidths. Annals of Telecommunications.[Chaganti et al., 2010] Chaganti, V. G., Smith, D. B., and Hanlen, L. W. (2010). Second order statistics for many-link body area networks. IEEE Antennas Wireless Propagat. Lett., 9:322–325.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 14
  • Example of body-worn channel Human subject with sensors for 15 hours continuous use Data online @ nicta.com.auPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 15
  • Open source hardware Transceiver, all design files are online @ nicta.com.auPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 16
  • Example of body-worn sleeping channel −75 off−body on−body −80 −85 −90 −95 −100 −105 0 20 40 60 80 100 Time (minutes)PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 17
  • Some of the measurement setup (moved away during exp.) subject researcherPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 18
  • Complexity and Error Error (inaccurate) poor lossy models complexity accuracy trade the ideal the system is model the model complexity (# params)PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 19
  • −4 x 10 6 median 2 mean per link 5 Error E = H − F mean 4 3 stat fit per link 2 agglomerate fit agglomerate hist. 1 mean per link & agglomerate stat per-link hist. 0 0 1 2 3 4 5 6 7 8 Complexity C = log2(P )[Hanlen et al., 2011] Hanlen, L. W., Smith, D. B., and Lamahewa, T. A. (2011). A new look at the body area network channel model. In Europe. Conf. Ant. Prop.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 20
  • PHY simulation1. Generate Weibull random numbers2. Generate Rayleigh random numbers with appropriate Doppler spread [Filho et al., 2007]3. Apply order-statistics (a) {Rp, I} = sort(Rayleigh power) (b) Weibull power = sort(Weibull power) (c) Weibull power(I) = Weibull power Available from NICTA website[Filho et al., 2007] Filho, J., Yacoub, M., and Fraidenraich, G. (2007). A simple accurate method for generating autocorrelated Nakagami-m envelope sequences. IEEE Commun. Lett., 11(3):231–233.[Smith et al., 2008b] Smith, D. B., Miniutti, D., Zhang, J. A., and Hanlen, L. W. (2008b). Matlab code for generating BAN fading profile ID: 802.15-08-0850. IEEE submission.[Smith et al., 2009b] Smith, D. B., Zhang, J. A., Hanlen, L. W., Miniutti, D., Rodda, D., and Gilbert, B. (2009b). A simulator for the dynamic on-body area propagation channel. In IEEE Int. Symp. Antennas & Propagation, Charleston, USA.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 21
  • MAC simulation • Event-based MAC simulation tool, based on Omnet++ • channel gain conditional probability P (at+τ = γ|at = γ ) Available open-source from NICTA • Allows complete sensor network simulation (cross- layer)[Tselishchev et al., 2010] Tselishchev, Y., Boulis, A., and Libman, L. (2010). Experiences and lessons from implementing a wireless sensor network MAC protocol in the Castalia simulator. In IEEE Wireless Commun. Net. Conf., WCNC.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 22
  • Having the right model is key[Boulis et al., 2010] Boulis, A., Tselishchev, Y., Libman, L., Smith, D. B., and Hanlen, L. W. (2010). Impact of wireless channel temporal variation on MAC design for body area networks. ACM Transactions on Embedded Computing, to appear.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 23
  • Conclusion• Models are for using, not for demonstrating mathematical skill• Beware models that match intuition: likely they are wrong!• Good models make good simulators• We built it: so you don’t have toPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 24
  • References[ACIL Tasman, 2009] ACIL Tasman (2009). The ‘apartments for life’ housing, care & support concept for older people: An assessment of economic and budgetary implications of The Benevolent Society’s proposed model for some housing developments for older people in Australia. prepared for The Benevolent Society of Australia.[Bartlett et al., 2010] Bartlett, C., Boehncke, K., Wallace, V., and Johnstone-Burt, A. (2010). Optimising e-health value using an investment model to build a foundation for program success. online, Booz-Allen http://www.booz.com/anzsea/home/40212171/40212709/40213345/eHealth.[Boulis et al., 2010] Boulis, A., Tselishchev, Y., Libman, L., Smith, D. B., and Hanlen, L. W. (2010). Impact of wireless channel temporal variation on MAC design for body area networks. ACM Transactions on Embedded Computing, to appear.[Cao et al., 2009] Cao, H., Leung, V., Chow, C., and Chan, H. (2009). Enabling technologies for wireless body area networks: A survey and outlook. IEEE Commun. Mag., 47(12):84–93.[Chaganti et al., 2011] Chaganti, V. G., Hanlen, L. W., and Lamahewa, T. A. (2011). Semi-Markov modeling for body area networks. In IEEE Intl. Conf. Commun., ICC.[Chaganti et al., 2010] Chaganti, V. G., Smith, D. B., and Hanlen, L. W. (2010). Second order statistics for many-link body area networks. IEEE Antennas Wireless Propagat. Lett., 9:322–325.[Chebbo, 2008] Chebbo, H. (2008). Literature review of energy efficient MAC in WSN/BAN ID:802-15- 08-0331. IEEE submission.[Chen and Viberg, 2009] Chen, M. and Viberg, M. (2009). Long-range channel prediction based on nonstationary parameteric modeling. IEEE Trans. Signal Processing, 57(2):622–634.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 25
  • [Chipcon, 2009] Chipcon (2009). CC2500 low-cost low-power 2.4 GHz RF transceiver (rev. c). Datasheet http://focus.ti.com/docs/prod/folders/print/cc2500.html.[Continua Health Alliance, 2009] Continua Health Alliance (2009). Continua health alliance:the next generation of personal telehealth is here. online.[Cotton and Scanlon, 2009] Cotton, S. and Scanlon, W. (2009). Channel characterization for single- and multiple-antenna wearable systems used for indoor body-to-body communications. IEEE Trans. Antennas Propagat., 57(4):980–990.[Filho et al., 2007] Filho, J., Yacoub, M., and Fraidenraich, G. (2007). A simple accurate method for generating autocorrelated Nakagami-m envelope sequences. IEEE Commun. Lett., 11(3):231–233.[Fox, 2010] Fox, S. (2010). The future of health: Robots, enchanted objects, and networks. online http://e-patients.net/archives/2010/11/ the-future-of-health-robots-enchanted-objects-and-networks.html.[Fraidenraich and Yacoub, 2006] Fraidenraich, G. and Yacoub, M. D. (2006). The α-η -κ and α-κ-µ fading distributions. In IEEE Intl. Symp. Spread Spectrum Techniques and Applications ISSSTA, pages 16–20.[Friis, 1946] Friis, H. T. (1946). A note on a simple transmission formula. Proc. IRE, 34(5):254–256.[Fu, 1975] Fu, J. C. (1975). The rate of convergence of consistent point estimators. Ann. Stat., 3(1):234–240.[G´ron and Villian, 2009] G´ron, E. and Villian, A. (2009). A new insight into Bluetooth piconets e e coexistence. Wireless Commun. Mob. Comput., 9:673–683.[Hall et al., 2010] Hall, P. S., Hao, Y., and Cotton, S. L. (2010). Progress in antennas and propagation for body area networks. In Intl. Symp. Sig., Sys. and Elect.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 26
  • [Hanlen et al., 2010a] Hanlen, L. W., Chaganti, V. G., Gilbert, B., Rodda, D., Lamahewa, T. A., and Smith, D. B. (2010a). Open-source testbed for body area networks: 200 sample/sec, 12 hrs continuous measurement. In IEEE PIMRC.[Hanlen et al., 2010b] Hanlen, L. W., Miniutti, D., Smith, D. B., Rodda, D., and Gilbert, B. (2010b). Co-Channel interference in body area networks with indoor measurements at 2.4GHz: Distance-to- interferer is a poor estimate of received interference power. Springer Intl. J. Wireless Inform. Net., 17.[Hanlen et al., 2011] Hanlen, L. W., Smith, D. B., and Lamahewa, T. A. (2011). A new look at the body area network channel model. In Europe. Conf. Ant. Prop.[Hanlen et al., 2009] Hanlen, L. W., Smith, D. B., Lewis, D., and Zhang, J. A. (2009). Key-sharing via channel randomness in body area networks: Is everyday movement sufficient? In Bodynets.[Hao et al., 2006] Hao, Y., Alomainy, A., Zhao, Y., Parini, C., Nechayev, Y., Hall, P., and Constantinou, C. (2006). Statistical and deterministic modelling of radio propagation channels in wban at 2.45 ghz. In Antennas and Propagation Society International Symposium 2006, IEEE, pages 2169–2172.[Islam and Kwak, 2010] Islam, S. M. R. and Kwak, K. S. (2010). A comprehensive study of channel estimation for WBAN-based healthcare systems: Feasibility of using multiband UWB. J Med Syst.[Jung et al., 2008] Jung, J. W., Kailas, A., Ingram, M. A., and Popovici, E. M. (2008). An evaluation of cooperative transmission considering practical energy models and passive reception. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.[Katayama et al., 2008] Katayama, N., Takizawa, K., Aoyagi, T., Takada, J.-i., Li, H.-B., and Kohno, R. (2008). Channel model on various frequency bands for wearable body area network. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 27
  • [Lamahewa et al., 2010] Lamahewa, T., Hanlen, L., Miniutti, D., Smith, D., Rodda, D., and Gilbert, B. (2010). BAN sleeping channel: Implications for relays, IEEE 802.15-10-0306-00-0006. IEEE submission.[Miniutti et al., 2008] Miniutti, D., Hanlen, L. W., Smith, D. B., Zhang, J. A., Lewis, D., Rodda, D., and Gilbert, B. (2008). Narrowband channel characterization for body area networks ID: 802.15.08.0421. IEEE submission.[Moulton et al., 2010] Moulton, B., Hanlen, L. W., Chen, J., Croucher, G., Mahendran, L., and Varis, A. (2010). Body-area-network transmission power control using variable adaptive feedback periodicity. In Aust. Commun. Theory Workshop AusCTW, pages 139–144.[Nagaoka et al., 2004] Nagaoka, T., Watanabe, S., Sakurai, K., Kunieda, E., Watanabe, S., Taki, M., and Yamanaka, Y. (2004). Development of realistic high-resolution whole-body voxel models of Japanese adult males and females of average height and weight, and application of models to radio-frequency electromagnetic-field dosimetry. Physics in Medicine and Biology, 49:1.[Price Waterhouse Coopers, 2010] Price Waterhouse Coopers (2010). Healthleaders media breakthoughs: The impact of personalized medicine today. online http://www.healthleadersmedia.com/ breakthroughs/250079/The-Impact-of-Personalized-Medicine-Today.[Smith et al., 2010a] Smith, D., Miniutti, D., Hanlen, L. W., Rodda, D., and Gilbert, B. (2010a). Dynamic narrowband body area communications: Link-margin based performance analysis and second-order temporal statistics. In IEEE Wireless Commun. Net. Conf., WCNC.[Smith, 2008] Smith, D. B. (2008). Electromagnetic characterisation through and around human body by simulation using SEMCAD X. Technical Report CRL-3282 http://www.nicta.com.au/research/ research_publications/show?id=1504, NICTA.[Smith et al., 2011a] Smith, D. B., Hanlen, L. W., Miniutti, D., and Zhang, J. A. (2011a). PowerPIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 28
  • delay profiles for narrowband body area networks: Flat fading is reasonable. submitted 2010, see [Smith et al., 2009a].[Smith et al., 2008a] Smith, D. B., Hanlen, L. W., Miniutti, D., Zhang, J. A., Rodda, D., and Gilbert, B. (2008a). Statistical characterization of the dynamic narrowband body area channel. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.[Smith et al., 2010b] Smith, D. B., Hanlen, L. W., Zhang, J. A., Miniutti, D., Rodda, D., and Gilbert, B. (2010b). First and second-order statistical characterizations of the dynamic body-area propagation channel of various bandwidths. Annals of Telecommunications.[Smith et al., 2011b] Smith, D. B., Lamahewa, T. A., Hanlen, L. W., and Miniutti, D. (2011b). Simple prediction-based power control for the on-body area communications channel. In IEEE Intl. Conf. Commun., ICC.[Smith et al., 2009a] Smith, D. B., Miniutti, D., Hanlen, L. W., Zhang, J. A., Rodda, D., and Gilbert, B. (2009a). Power delay profiles for dynamic narrowband body area network channels ID: 802.15-09-0187. IEEE submission.[Smith et al., 2008b] Smith, D. B., Miniutti, D., Zhang, J. A., and Hanlen, L. W. (2008b). Matlab code for generating BAN fading profile ID: 802.15-08-0850. IEEE submission.[Smith et al., 2009b] Smith, D. B., Zhang, J. A., Hanlen, L. W., Miniutti, D., Rodda, D., and Gilbert, B. (2009b). A simulator for the dynamic on-body area propagation channel. In IEEE Int. Symp. Antennas & Propagation, Charleston, USA.[Stuart et al., 2008] Stuart, E., Moh, M., and Moh, T.-S. (2008). Privacy and security in biomedical applications of wireless sensor networks. In Intl. Symp. App. Sci. Bio-Med. Comm. Tech., Aalborg, Denmark.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 29
  • [Takizawa et al., 2008a] Takizawa, K., Aoyagi, T., Takada, J.-i., Katayama, N., Yekeh, K., Takehiko, Y., and Kohno, K. (2008a). Channel models for wireless body area networks. In Proc. 30th Annual Inter. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS), pages 1549–1552, Vancouver, BC.[Takizawa et al., 2008b] Takizawa, K., Yazdandoost, K. Y., Aoyagi, T., Katayama, N., Takada, J.-i., Kobayashi, T., Li, H.-b., and Kohno, R. (2008b). Preliminary channel models for wearable WBAN, ID: 802.15-08-0155. IEEE submission.[Tegart, 2010] Tegart, W. G. M. G. (2010). Smart technology for health longevity: Report of a study by the Australian Academy of Technological Sciences and Engineering. Australian Academy of Technological Sciences and Engineering.[Timo et al., 2010] Timo, R. C., Blackmore, K. L., and Hanlen, L. W. (2010). Word-valued sources: An ergodic theorem, an AEP, and the conservation of entropy. IEEE Trans. Inform. Theory, 56(7):3139–3148.[Tselishchev et al., 2010] Tselishchev, Y., Boulis, A., and Libman, L. (2010). Experiences and lessons from implementing a wireless sensor network MAC protocol in the Castalia simulator. In IEEE Wireless Commun. Net. Conf., WCNC.[Ullah et al., 2010] Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., Saleem, S., Rahman, Z., and Kwak, K. S. (2010). A comprehensive survey of wireless body area networks : On phy, mac, and network layers solutions. J Med Syst.[Yazdandoost and Sayrafian-Pour, 2008] Yazdandoost, K. Y. and Sayrafian-Pour, K. (2008). Channel model for body area network (BAN) ID:-802.15-08-0033. IEEE submission,.[Zhang et al., 2009] Zhang, J. A., Smith, D. B., Hanlen, L. W., Miniutti, D., Rodda, D., and Gilbert, B.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 30
  • (2009). Stability of narrowband dynamic body area channel. IEEE Antennas Wireless Propagat. Lett., 8:53–56.PIMRC: challenge of modeling dynamic BANs c 2011 leif.hanlen@nicta.com.au 31