Knowledge-based multi-criteria optimization to support indoor positioning

  • Alessandra Mileo
  • Torsten Schaub
  • Davide Merico
  • Roberto Bisiani
Article

Abstract

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting.

Keywords

Knowledge representation Answer Set Programming Wireless Sensor Networks Localization Tracking 

Mathematics Subject Classifications (2010)

68T27 68T30 68T37 68N17 94A99 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press (2003)MATHCrossRefGoogle Scholar
  2. 2.
    Broxton, M., Lifton, J., Paradiso, J.A.: Localization on the pushpin computing sensor network using spectral graph drawing and mesh relaxation. SIGMOBILE MC2R 10, 1–12 (2006)CrossRefGoogle Scholar
  3. 3.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: Gringo 2.0 user’s manual. http://potassco.sourceforge.net (2010)
  4. 4.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: Engineering an incremental ASP solver. In: Garcia de la Banda, M., Pontelli, E. (eds.) Proceedings of the Twenty-fourth International Conference on Logic Programming (ICLP’08). LNCS, vol. 5366, pp. 190–205. Springer (2008)Google Scholar
  5. 5.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: a conflict-driven answer set solver. In: Ninth International Conference on Logic Programming and Nonmonotonic Reasoning, pp. 260–265. Springer (2007)Google Scholar
  6. 6.
    Gebser, M., Schaub, T., Thiele, S.: Gringo: a new grounder for answer set programming. In: LPNMR, pp. 266–271 (2007)Google Scholar
  7. 7.
    Hedetniemi, S.M., Hedetniemi, S.T., Liestman, A.L.: A survey of gossiping and broadcasting in communication networks. NETWORKS 18, 319–349 (1988)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME 82, 35–45 (1960)Google Scholar
  9. 9.
    Merico, D.: Tracking with high-density, large-scale wireless sensor networks. Ph.D. thesis, University of Milano-Bicocca, Dottorato di ricerca in INFORMATICA, p. 22 (2010-02-03). http://hdl.handle.net/10281/7785
  10. 10.
    Nakamura, E.F., Loureiro, A.A.F., Frery, A.C.: Information fusion for wireless sensor networks: methods, models, and classifications. ACM Comput. Surv. 39(3), 9 (2007)CrossRefGoogle Scholar
  11. 11.
    North, M.J., Macal, C.M.: Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press, Inc., New York (2007)Google Scholar
  12. 12.
    Patwari, N., Ash, J., Kyperountas, S., Hero A.O., I., Moses, R., Correal, N.: Locating the nodes: cooperative localization in Wireless Sensor Networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)CrossRefGoogle Scholar
  13. 13.
    Perkins, C.E., Royer, E.M.: Ad-hoc on-demand distance vector routing. In: Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications, WMCSA ’99, pp. 90–100. IEEE Computer Society, Washington (1999). http://portal.acm.org/citation.cfm?id=520551.837511 CrossRefGoogle Scholar
  14. 14.
    Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House (2004)Google Scholar
  15. 15.
    Shen, Y., Win, M.: Fundamental limits of wideband localization; part i: a general framework. IEEE Trans. Inf. Theory 56(10), 4956–4980 (2010). doi:10.1109/TIT.2010.2060110 MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shen, Y., Wymeersch, H., Win, M.: Fundamental limits of wideband localization; part ii: Cooperative networks. IEEE Trans. Inf. Theory 56(10), 4981–5000 (2010). doi:10.1109/TIT.2010.2059720 MathSciNetCrossRefGoogle Scholar
  17. 17.
    Syrjänen, T.: Lparse 1.0 user’s manual. http://www.tcs.hut.fi/Software/smodels/lparse.ps.gz (2011)
  18. 18.
    Thrun, S., Fox, D., Burgard, W., Dallaert, F.: Robust monte carlo localization for mobile robots. Artif. Intell. 128(1–2), 99–141 (2001)MATHCrossRefGoogle Scholar
  19. 19.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
  20. 20.
    Tseng, Y.C., Kuo, S.P., Lee, H.W., Huang, C.F.: Location tracking in a wireless sensor network by mobile agents and its data fusion strategies. IPSN 2634, 554–554 (2003)Google Scholar
  21. 21.
    Verdone, R., Dardari, D., Mazzini, G., Conti, A.: Wireless Sensor and Actuator Networks: Technologies, Analysis and Design. Academic (2008)Google Scholar
  22. 22.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38, 1–45 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Alessandra Mileo
    • 1
  • Torsten Schaub
    • 2
  • Davide Merico
    • 3
  • Roberto Bisiani
    • 3
  1. 1.Digital Enterprise Research InstituteNational University of Ireland, Galway (NUIG)GalwayIreland
  2. 2.Institut für InformatikUniversität PotsdamPotsdamGermany
  3. 3.NOMADIS Research Lab, Department of Informatics, Systems and CommunicationUniversity of Milan-BicoccaMilanItaly

Personalised recommendations