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Cooperative Techniques Supporting Sensor-Based People-Centric Inferencing

  • Nicholas D. Lane
  • Hong Lu
  • Shane B. Eisenman
  • Andrew T. Campbell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5013)

Abstract

People-centric sensor-based applications targeting mobile device users offer enormous potential. However, learning inference models in this setting is hampered by the lack of labeled training data and appropriate feature inputs. Data features that lead to better classification models are not available at all devices due to device heterogeneity. Even for devices that provide superior data features, models require sufficient training data, perhaps manually labeled by users, before they work well. We propose opportunistic feature vector merging, and the social-network-driven sharing of training data and models between users. Model and training data sharing within social circles combine to reduce the user effort and time involved in collecting training data to attain the maximum classification accuracy possible for a given model, while feature vector merging can enable a higher maximum classification accuracy by enabling better performing models even for more resource-constrained devices. We evaluate our proposed techniques with a significant places classifier that infers and tags locations of importance to a user based on data gathered from cell phones.

Keywords

Training Data Feature Vector Cell Phone Social Connection Cooperative Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Acuna, E., Rodriguez, C.: The treatment of missing values and its effect in the classifier accuracy. In: Classification, Clustering and Data Mining Applications, pp. 639–648 (2004)Google Scholar
  2. 2.
    Ashbrook, D., Starner, T.: Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 4.
    Choudhury, T., Pentland, A.: Sensing and modeling human networks using the sociometer. In: ISWC 2003: Proc. of the 7th IEEE Int’l Symp. on Wearable Computersp, Washington, DC, USA, p. 216 (2003)Google Scholar
  5. 5.
    Cox, L.P., Dalton, A., Marupadi, V.: Smokescreen: flexible privacy controls for presence-sharing. In: MobiSys 2007: Proc. of the 5th int’l conf. on Mobile systems, applications and services, pp. 233–245. ACM, New York (2007)Google Scholar
  6. 6.
    Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Personal Ubiquitous Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  8. 8.
    Gruteser, M., Grunwald, D.: Enhancing location privacy in wireless lan through disposable interface identifiers: a quantitative analysis. In: WMASH 2003: Proc. of the 1st ACM Int’l workshop on Wireless mobile applications and services on WLAN hotspots, New York, NY, USA, pp. 46–55 (2003)Google Scholar
  9. 9.
    Hightower, J., Consolvo, S., LaMarca, A., Smith, I., Hughes, J.: Learning and recognizing the places we go. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 159–176. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Hofmann, T., Basilico, J.: Collaborative machine learning. In: From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments, pp. 173–182 (2005)Google Scholar
  11. 11.
    Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting places from traces of locations. SIGMOBILE Mob. Comput. Commun. Rev. 9(3), 58–68 (2005)CrossRefGoogle Scholar
  12. 12.
    Krumm, J., Hinckley, K.: The nearme wireless proximity server. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 283–300. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Liao, L., Fox, D., Kautz, H.: Location-based activity recognition. In: Advances in Neural Information Processing Systems 18, pp. 787–794. MIT Press, Cambridge (2006)Google Scholar
  14. 14.
    Luo, H., Luo, J., Liu, Y., Das, S.K.: Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks. IEEE Trans. on Comp. 55(10), 1286–1299 (2006)CrossRefGoogle Scholar
  15. 15.
    Marmasse, N., Schmandt, C., Spectre, D.: Watchme: Communication and awareness between members of a closely-knit group. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 214–231. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Miluzzo, E., Lane, N.D., Eisenman, S.B., Campbell, A.T.: Cenceme - injecting sensing presence into social networking applications. In: Kortuem, G., Finney, J., Lea, R., Sundramoorthy, V. (eds.) EuroSSC 2007. LNCS, vol. 4793, pp. 1–28. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Patterson, D.J., Liao, L., Fox, D., Kautz, H.A.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Patterson, D.J., Liao, L., Gajos, K., Collier, M., Livic, N., Olson, K., Wang, S., Fox, D., Kautz, H.A.: Opportunity knocks: A system to provide cognitive assistance with transportation services. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 433–450. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: CHI 1995: Proc. of the SIGCHI conf. on Human factors in computing systems, New York, NY, USA, pp. 210–217 (1995)Google Scholar
  20. 20.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  21. 21.
    Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., Terveen, L.: Discovering personally meaningful places: An interactive clustering approach. ACM Trans. Inf. Syst. 25(3), 12 (2007)CrossRefGoogle Scholar
  22. 22.
    Zhu, X.: Semi-Supervised Learning Literature Survey. Tech. Report UW-Madison 1530 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nicholas D. Lane
    • 1
  • Hong Lu
    • 1
  • Shane B. Eisenman
    • 2
  • Andrew T. Campbell
    • 1
  1. 1.Dartmouth CollegeHanoverUSA
  2. 2.Columbia UniversityNew YorkUSA

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