Skip to main content
SpringerLink
Log in

Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

People or friends may encounter with each other offline, when they have a location proximity. With the rapid development of the wireless sensor network, smart city applications can leverage the sensed data of people’s mobility or trajectory to predict their future encounter opportunity and then arrange their offline activities (e.g., meeting, travel) accordingly. This paper studies the encounter prediction problem of mobile users by mining the similarity between their sensed mobile trajectories. We define the similarity of two mobile trajectories both temporally and spatially, and then propose two approaches, namely a probabilistic similarity maximization algorithm and a machine leaning based prediction algorithm, for addressing the encounter prediction problem. Results over a real-world social network dataset show that the proposed recurrent neural network based model can predict the encounter of two users precisely, and it outperforms the probabilistic algorithm and other algorithms, in terms of the precision and F1 score.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://developer.apple.com/documentation/corelocation/cllocationmanager.

References

  1. Costa, C., Anastasiou, C., Chatzimilioudis, G., & Zeinalipour-Yazti, D. (2015). Rayzit: An anonymous and dynamic crowd messaging architecture. In 16th IEEE international conference on mobile data management (Vol. 2, pp. 98–103). https://doi.org/10.1109/MDM.2015.51.

  2. Ellison, N. B., et al. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.

    Article  MathSciNet  Google Scholar 

  3. Ismail, A., & Vigneron, A. (2015). A new trajectory similarity measure for GPS data. In Proceedings of the 6th ACM SIGSPATIAL international workshop on GeoStreaming, IWGS ’15 (pp. 19–22). New York, NY, USA: ACM. https://doi.org/10.1145/2833165.2833173.

  4. James, J. L. (2015). Mobile dating in the digital age: Computer-mediated communication and relationship building on tinder. Ph.D. thesis, Texas State University.

  5. Kishida, K. (2005). Property of average precision and its generalization: An examination of evaluation indicator for information retrieval experiments. Tokyo: National Institute of Informatics Tokyo.

    Google Scholar 

  6. Kwon, J., & Kim, S. (2010). Friend recommendation method using physical and social context. International Journal of Computer Science and Network Security, 10(11), 116–120.

    Google Scholar 

  7. Logesh, R., & Subramaniyaswamy, V. (2017). A reliable point of interest recommendation based on trust relevancy between users. Wireless Personal Communications, 97(2), 2751–2780.

    Article  Google Scholar 

  8. Lv, Q., Qiao, Y., Zhang, Y., Abdesslem, F. B., Lin, W., & Yang, J. (2018). Measuring geospatial properties: Relating online content browsing behaviors to users’ points of interest. Wireless Personal Communications, 101, 1–30.

    Article  Google Scholar 

  9. Ma, S. P., Lee, W. T., & Kuo, C. H. (2013). Location explorer with information services: A mobile application to deliver location-based web services. In IEEE international symposium on next-generation electronics (ISNE) (pp. 283–286). IEEE.

  10. Moricz, M., Dosbayev, Y., & Berlyant, M. (2010). PYMK: Friend recommendation at myspace. In Proceedings of the 2010 ACM SIGMOD international conference on management of data, SIGMOD ’10 (pp. 999–1002). New York, NY, USA: ACM. https://doi.org/10.1145/1807167.1807276.

  11. Rodríguez-Rodríguez, I., González Vidal, A., Ramallo González, A., & Zamora, M. (2018). Commissioning of the controlled and automatized testing facility for human behavior and control (CASITA). Sensors, 18(9), 2829.

    Article  Google Scholar 

  12. Saravanan, P. S., & Balasundaram, S. (2018). Protecting privacy in location-based services through location anonymization using cloaking algorithms based on connected components. Wireless Personal Communications, 102(1), 449–471.

    Article  Google Scholar 

  13. Silva, N. B., Tsang, R., Cavalcanti, G. D., & Tsang, J. (2010). A graph-based friend recommendation system using genetic algorithm. In IEEE congress on evolutionary computation (pp. 1–7). IEEE.

  14. Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings of the 18th international conference on data engineering (pp. 673–684). IEEE.

  15. Wang, G., Wang, B., Wang, T., Nika, A., Zheng, H., & Zhao, B. Y. (2014). Whispers in the dark: Analysis of an anonymous social network. In Proceedings of the 2014 conference on internet measurement conference (pp. 137–150). ACM.

  16. Wang, Z., Liao, J., Cao, Q., Qi, H., & Wang, Z. (2015). Friendbook: A semantic-based friend recommendation system for social networks. IEEE Transactions on Mobile Computing, 14(3), 538–551.

    Article  Google Scholar 

  17. Wu, M., Wang, Z., Sun, H., & Hu, H. (2016). Friend recommendation algorithm for online social networks based on location preference. In 3rd International conference on information science and control engineering (ICISCE) (pp. 379–385). IEEE.

  18. Yi, B. K., Jagadish, H., & Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. In Proceedings of the 14th international conference on data engineering (pp. 201–208). IEEE.

  19. Zhang, Y., Bai, Y., Chen, L., Bian, K., & Li, X. (2016). Influence maximization in messenger-based social networks. In IEEE global communications conference (GLOBECOM) (pp. 1–6). IEEE.

  20. Zhang, Y., Li, Z., Gao, C., Bian, K., Song, L., Dong, S., et al. (2018). Mobile social big data: Wechat moments dataset, network applications, and opportunities. IEEE Network, 32(3), 146–153.

    Article  Google Scholar 

  21. Zheng, Y., Zhang, L., Ma, Z., Xie, X., & Ma, W. Y. (2011). Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB), 5(1), 5.

    Google Scholar 

Download references

Acknowledgements

We would like to thank Ledongli Co. Ltd., for providing the trajectory data used to support the findings of the study under license. The trajectory data used to support the findings of the study were supplied by Ledongli Co. Ltd under license and so cannot be made freely available. Requests for access to these data should be made to Mr. Yuanyuan Zhang. [Contact: zhang.huanzhiyuan@gmail.com] This work was supported by Shenzhen Key Fundamental Research Projects JCYJ20160330095313861 and by NSFC under Grants 61572051, 61632017.

Author information

Authors and Affiliations

  1. Peking University, Beijing, China

    Meng Tong, Yu Tao, Yuanxing Zhang, Kaigui Bian & Wei Yan

  2. Institute of Big Data Technologies, School of SECE, Peking University, Shenzhen, China

    Wei Yan

Authors
  1. Meng Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanxing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaigui Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meng Tong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, M., Tao, Y., Zhang, Y. et al. Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks. Wireless Pers Commun 107, 1933–1949 (2019). https://doi.org/10.1007/s11277-019-06367-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06367-1

Keywords

Search

Navigation