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Correlating mobility with social encounters: distributed localization in sparse mobile networks

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Abstract

Most existing connectivity-based localization algorithms require high node density which is unavailable in many large-scale sparse mobile networks. By analyzing large datasets of real user traces from Dartmouth and MIT, we observe that user mobility exhibits high spatiotemporal regularity and, more importantly, that user mobility is strongly correlated with the user’s social encounters (including so called Familiar Strangers). Motivated by these important observations, we propose a distributed localization scheme called SOMA that is particularly suitable for sparse mobile networks. To exploit the correlation between mobility and social encounters, we formulate the localization process as an optimization problem with the objective of maximizing the probability of visiting a sequence of locations when the user witnesses the given set of social encounters at different time. Employing the Hidden Markov Model, we design an efficient algorithm based on dynamic programming for solving the optimization problem. SOMA is fully distributed, in which each user only makes use of the connectivity information with other users. Since different users may have varying levels of mobility regularity, one critical challenge with SOMA is that a user with weak mobility regularity may result in poor localization accuracy. We introduce the concept of mobility irregularity to distinguish users. Then, one optimization is made to SOMA that allows a user with weak mobility regularity to leverage the locations from the users encounters. Experimental results based on large-scale real traces demonstrate that SOMA achieves much smaller localization error than many state-of-the-art localization schemes, but requires it minimal running time.

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References

  1. Bardram, J. E., Mihailidis, A., & Wan, D. (2007). Pervasive computing in healthcare (Vol. 1). Boca Raton: CRC Press.

    Google Scholar 

  2. Rao, A., & Ratnasamy, S, Papadimitriou, C., Shenker, S., & Stoica, I. (2003). Geographic routing without location information. In Proceedings of the ACM MobiCom (MobiCom ‘03) (pp. 96–108).

  3. Li, M., & Liu, Yu. (2009). Underground coal mine monitoring with wireless sensor networks. ACM Transactions on Sensor Networks, 5(2), 10:1–10:29.

    Article  Google Scholar 

  4. Mo, L., He, Y., Liu, Y., Zhao, J., Tang, S.-J., Li, X.-Y., et al. (2009). Canopy closure estimates with greenorbs: sustainable sensing in the forest. In Proceedings of the ACM SenSys (SenSys ‘09) (pp. 99–112). ACM, New York, NY, USA.

  5. Yang, Z., Li, Z., & Liu, Y. (2007). Sea depth measurement with restricted floating sensors. In IEEE international real-time systems symposium (pp. 469–478).

  6. Li, M., & Liu, Y. (2010). Iso-map: Energy-efficient contour mapping in wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering, 22(5), 699–710.

    Article  MATH  Google Scholar 

  7. Li, M., Liu, Y., & Chen, L. (2008). Nonthreshold-based event detection for 3D environment monitoring in sensor networks. IEEE Transactions on Knowledge and Data Engineering, 20(12), 1699–1711.

    Article  Google Scholar 

  8. Mobile Sensing group at Dartmouth. http://sensorlab.cs.dartmouth.edu.

  9. Chintalapudi, K., Iyer, A. P., & Padmanabhan, V. N. (2010). Indoor localization without the pain. In Proceedings of the ACM MobiCom (MobiCom ‘10) (pp. 173–184). ACM, New York, NY, USA.

  10. Chipara, O., Hackmann, G., Lu, C., Smart, W. D., & Roman, G.-C. (2010). Practical modeling and prediction of radio coverage of indoor sensor networks. In Proceedings of the ACM/IEEE IPSN (IPSN ‘10) (pp. 339–349). ACM, New York, NY, USA.

  11. Kim, M., Kotz, D., & Kim, S. (2006). Extracting a mobility model from real user traces. In Proceedings of the IEEE INFOCOM (Vol. 6, pp. 1–13).

  12. Ni, L. M., Liu, Y., Lau, Y. C., & Patil, A. P. (2004). LANDMARC: Indoor location sensing using active RFID. Wireless Networks, 10(6), 701–710.

    Article  Google Scholar 

  13. Niculescu, D., & Nath, B. (2004). Vor base stations for indoor 802.11 positioning. In Proceedings of the ACM MobiCom (MobiCom ‘04) (pp. 58–69). ACM, New York, NY, USA.

  14. Nasipuri, A., & Li, K. (2002). A directionality based location discovery scheme for wireless sensor networks. In Proceedings of the ACM WSNA (WSNA ‘02) (pp. 105–111). ACM, New York, NY, USA.

  15. Priyantha, N. B., Miu, A. K.L., Balakrishnan, H., & Teller, S. (2001). The cricket compass for context-aware mobile applications. In Proceedings of the ACM MobiCom (MobiCom ‘01) (pp. 1–14).

  16. Priyantha, N. B., Chakraborty, A., & Balakrishnan, H. (2000). The cricket location-support system. In Proceedings of the ACM MobiCom (MobiCom ‘00) (pp. 32–43).

  17. Savvides, A., Han, C.-C., & Strivastava, M. B. (2001). Dynamic fine-grained localization in ad hoc networks of sensors. In Proceedings of the ACM MobiCom (MobiCom ‘01) pp. (166–179).

  18. Whitehouse, K., & Culler, D. (2002). Calibration as parameter estimation in sensor networks. In Proceedings of the ACM WSNA (WSNA ‘02) (pp. 59–67).

  19. Jin, M., Xia, S., Wu, H., & Gu, X. (2011). Scalable and fully distributed localization with mere connectivity. In Proceedings of the IEEE INFOCOM (pp. 3164–3172).

  20. Li, L., & Kunz, T. (2007). Localization applying an efficient neural network mapping. In Proceedings of the 1st international conference on autonomic computing and communication systems (Autonomics ‘07).

  21. Shang, Y., Ruml, W., Zhang, Y., Fromherz, M. P. J. (2003). Localization from mere connectivity. In Proceedings of the ACM MobiHoc (MobiHoc ‘03) (pp. 201–212).

  22. Rallapalli, S., Qiu, L., Zhang, Y., & Chen, Y.-C. (2010). Exploiting temporal stability and low-rank structure for localization in mobile networks. In Proceedings of the ACM MobiCom (MobiCom ‘10) (pp. 161–172).

  23. Baggio, A., & Langendoen, K. (2008). Monte Carlo localization for mobile wireless sensor networks. Ad Hoc Networks, 6(5), 718–733.

    Article  Google Scholar 

  24. Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274–15278.

    Article  Google Scholar 

  25. Paulos, E., & Goodman, E. (2004). The familiar stranger: Anxiety, comfort, and play in public places. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 223–230). ACM.

  26. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.

    Article  Google Scholar 

  27. Seidel, S. Y., & Rappaport, T. S. (1992). 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE Transactions on Antennas and Propagation, 40(2), 207–217.

    Article  Google Scholar 

  28. Doherty, L., Pister, K. S. J., El Ghaoui, L. (2001). Convex position estimation in wireless sensor networks. In Proceedings of the IEEE INFOCOM (Vol. 3, pp. 1655–1663).

  29. Galstyan, A., Krishnamachari, B., Lerman, K., & Pattem, S. (2004). Distributed online localization in sensor networks using a moving target. In Proceedings of the IPSN 2004 (pp. 61–70).

  30. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the ACM MobiCom (MobiCom ‘03) (pp. 81–95). ACM, New York, NY, USA.

  31. Shang, Y., & Ruml, W. (2004). Improved mds-based localization. In Proceedings of the IEEE INFOCOM (Vol. 4, pp. 2640–2651).

  32. Vivekanandan, V., & Wong, V. W. S. (2006). Ordinal MDS-based localisation for wireless sensor networks. International Journal of Sensor Networks, 1(3), 169–178.

    Article  Google Scholar 

  33. Chow, B., & Luo, F. (2003). Combinatorial ricci flows on surfaces. Journal of Differential Geometry, 63(1), 97–129.

    MATH  MathSciNet  Google Scholar 

  34. Jin, M., Kim, J., & Luo, F. (2008). Discrete surface Ricci flow. IEEE Transactions on Visualization and Computer Graphics, 14(5), 1030–1043.

    Article  Google Scholar 

  35. Sarkar, R., Yin, X., Gao, J. Luo, F., & Gu, X.D. (2009). Greedy routing with guaranteed delivery using ricci flows. In International conference on information processing in sensor networks (pp. 121–132).

  36. Sheu, J.-P., & Wei-Kai, H. (2010). Distributed localization scheme for mobile sensor networks. IEEE Transactions on Mobile Computing, 9(4), 516–526.

    Article  Google Scholar 

  37. Hamers, L., Hemeryck, Y., Herweyers, G., Janssen, M., Keters, H., Rousseau, R., et al. (1989). Similarity measures in scientometric research: The Jaccard index versus Salton’s cosine formula. Information Processing and Management, 25(3), 315–318.

    Article  Google Scholar 

  38. Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of the ACM MobiCom (MobiCom ‘04) (pp. 45–57). ACM, New York, NY, USA.

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Acknowledgments

This research is supported by the 973 Program (2014CB340303), NSFC (Nos. 61170238, 60903190, 91118008 and 61021004), National 863 Program (2013AA01A601), SJTU SMC Project (201120), Singapore NRF (CREATE E2S2), and MSRA funding for Urban Computing and for Star Track program. This work is also supported by the Program for Changjiang Scholars and Innovative Research Team in University (IRT1158, PCSIRT), China.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yanmin Zhu, Ruobing Jiang, Junbo Zhao & Lionel M. Ni

  2. Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai, China

    Yanmin Zhu, Ruobing Jiang & Junbo Zhao

  3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lionel M. Ni

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  1. Yanmin Zhu
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Correspondence to Yanmin Zhu.

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Zhu, Y., Jiang, R., Zhao, J. et al. Correlating mobility with social encounters: distributed localization in sparse mobile networks. Wireless Netw 21, 201–215 (2015). https://doi.org/10.1007/s11276-014-0778-y

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