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The Tradeoff between Energy Efficiency and User State Estimation Accuracy in Mobile Sensing

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 35)

Abstract

People-centric sensing and user state recognition can provide rich contextual information for various mobile applications and services. However, continuously capturing this contextual information on mobile devices drains device battery very quickly. In this paper, we study the tradeoff between device energy consumption and user state recognition accuracy from a novel perspective. We assume the user state evolves as a hidden discrete time Markov chain (DTMC) and an embedded sensor on mobile device discovers user state by performing a sensing observation. We investigate a stationary deterministic sensor sampling policy which assigns different sensor duty cycles based on different user states, and propose two state estimation mechanisms providing the best “guess” of user state sequence when observations are missing. We analyze the effect of varying sensor duty cycles on (a) device energy consumption and (b) user state estimation error, and visualize the tradeoff between the two numerically for a two-state setting.

Keywords

  • mobile sensing
  • energy efficiency
  • user state estimation accuracy
  • tradeoff

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  • DOI: 10.1007/978-3-642-12607-9_4
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References

  1. Annavaram, M., Medvidovic, N., Mitra, U., Narayanan, S., Spruijt-Metz, D., Sukhatme, G., Meng, Z., Qiu, S., Kumar, R., Thatte, G.: Multimodal sensing for pediatric obesity applications. In: UrbanSense 2008 Workshop at SenSys, Raleigh, NC, USA (November 2008)

    Google Scholar 

  2. Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: IEEE International Symposium on Wearable Computers (2002)

    Google Scholar 

  3. Bahl, L.R., Jelinek, F., Mercer, R.L.: A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (1983)

    Google Scholar 

  4. Bhattacharya, A., Das, S.K.: Lezi-update: An information-theoretic approach to track mobile users in PCS networks. In: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking (1999)

    Google Scholar 

  5. Biswas, S., Quwaider, M.: Body posture identification using hidden markov model with wearable sensor networks. In: BodyNets Workshop, Tempe, AZ, USA (March 2008)

    Google Scholar 

  6. Krause, A., Ihmig, M., Rankin, E., Gupta, S., Leong, D., Siewiorek, D.P., Smailagic, A., Deisher, M., Sengupta, U.: Trading off prediction accuracy and power consumption for context-aware wearable computing. In: IEEE International Symposium on Wearable Computers (2005)

    Google Scholar 

  7. Lester, J., Choudhury, T., Borriello, G., Consolvo, S., Landay, J., Everitt, K., Smith, I.: Sensing and modeling activities to support physical fitness. In: Proceedings of UbiComp, Tokyo, Japan (2005)

    Google Scholar 

  8. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Proceedings of the IEEE (1989)

    Google Scholar 

  9. Shih, E., Bahl, P., Sinclair, M.J.: Wake on wireless: an event driven energy saving strategy for battery operated devices. In: Proceedings of MobiCom, Atlanta, Georgia, USA (2002)

    Google Scholar 

  10. Twitter, http://www.twitter.com

  11. Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory (1967)

    Google Scholar 

  12. Wang, Y., Lin, J., Annavaram, M., Jacobson, Q.A., Hong, J., krishnamachari, B., Sadeh, N.: A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of MobiSys, Krakow, Poland (June 2009)

    Google Scholar 

  13. Yu, S., Kobayashi, H.: A hidden semi-markov model with missing data and multiple observation sequences for mobility tracking. In: Signal Processing (2003)

    Google Scholar 

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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Wang, Y., Krishnamachari, B., Zhao, Q., Annavaram, M. (2010). The Tradeoff between Energy Efficiency and User State Estimation Accuracy in Mobile Sensing. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_4

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  • DOI: https://doi.org/10.1007/978-3-642-12607-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12606-2

  • Online ISBN: 978-3-642-12607-9

  • eBook Packages: Computer ScienceComputer Science (R0)