Skip to main content
SpringerLink
Log in

A localization and tracking scheme for target gangs based on big data of Wi-Fi locations

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The modeling and analysis of target gangs’ usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs’ usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs’ activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201701/P020170123364672657408.pdf

  2. http://www.199it.com/archives/555094.html

  3. https://www.ipass.com/mobile-network/

  4. Isson, J.P., Harriott, J.S.: People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent. Wiley, New York (2016)

    Book  Google Scholar 

  5. Li, J., Zhang, Y., Chen, X., et al.: Secure attribute-based data sharing for resource-limited users in cloud computing. Comput. Sec. 72, 1–12 (2018)

    Article  Google Scholar 

  6. https://wigle.net/

  7. Stergiou, C., Psannis, K.E., Kim, B.G., et al.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)

    Article  Google Scholar 

  8. Wang, Y., Liu, Q., Hou, H., et al.: Big data driven outlier detection for soybean straw near infrared spectroscopy. J. Comput. Sci. (2017). https://doi.org/10.1016/j.jocs.2017.06.008

  9. Alsmirat, M.A., Jararweh, Y., Obaidat, I., et al.: Internet of surveillance: a cloud supported large-scale wireless surveillance system. J. Supercomput. 73(3), 973–992 (2017)

    Article  Google Scholar 

  10. Gupta, B.B., Gupta, S., Chaudhary, P.: Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. Int. J. Cloud Appl. Comput. (IJCAC) 7(1), 1–31 (2017)

    Google Scholar 

  11. LaMarca, A., Chawathe, Y., Consolvo, S., et al.: Place lab: device positioning using radio beacons in the wild. In: Proceedings of International Conference on Pervasive Computing, pp. 116–133 (2005)

  12. Emery, M., Denko, M.K.: IEEE 802.11 WLAN based real-time location tracking in indoor and outdoor environments. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, pp. 1062–1065 (2007)

  13. Vinh, N.K., Long, T.Q., Viet, N.A., et al.: Efficient tracking of industrial equipment using a Wi-Fi based localization system. In: Proceedings of International Conference on Soft Computing and Pattern Recognition, pp. 129–133 (2013)

  14. Xu, Z., Sandrasegaran, K., Kong, X., et al.: Pedestrain monitoring system using Wi-Fi technology and RSSI based localization. Int. J. Wirel. Mobile Netw. 5(4), 17–34 (2013)

    Article  Google Scholar 

  15. Kim, M., Kotz, D., Kim, S.: Extracting a mobility model from real user traces. In: Proceedings of the 25th IEEE International Conference on Computer Communications, pp. 1–13 (2006)

  16. Sevtsuk, A., Huang, S., Calabrese, F., et al.: Mapping the MIT Campus in Real Time Using Wi-Fi. Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City (2009)

  17. Prentow, T.S., Ruiz-Ruiz, A.J., Blunck, H., et al.: Spatio-temporal facility utilization analysis from exhaustive Wi-Fi monitoring. Pervasive Mob. Comput. 16, 305–316 (2015)

    Article  Google Scholar 

  18. Cunche, M.: I know your MAC address: targeted tracking of individual using Wi-Fi. J. Comput. Virol. Hacking Tech. 10(4), 219–227 (2014)

    Article  Google Scholar 

  19. Musa, A.B.M., Eriksson, J.: Tracking unmodified smartphones using Wi-Fi monitors. In: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, pp. 281–294 (2012)

  20. Vu, L., Nahrstedt, K., Retika, S., et al.: Joint bluetooth/Wi-Fi scanning framework for characterizing and leveraging people movement in university campus. In: Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, pp. 257–265 (2010)

  21. Farshad, A., Marina, M.K., Garcia, F.: Urban Wi-Fi characterization via mobile Crowdsensing. In: Proceedings of the IEEE Conference on Network Operations and Management Symposium, pp. 1–9 (2014)

  22. Fukuzaki, Y., Mochizuki, M., Murao, K., et al.: A pedestrian flow analysis system using Wi-Fi packet sensors to a real environment. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 721–730 (2014)

  23. Barbera, M.V., Epasto, A., Mei, A., et al.: Signals from the crowd: uncovering social relationships through smartphone probes. In: Proceedings of the 2013 ACM Conference on Internet measurement, pp. 265–276 (2013)

  24. Chon, Y., Kim, S., Lee, S., et al.: Sensing Wi-Fi packets in the air: practicality and implications in urban mobility monitoring. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 189–200 (2014)

  25. Fu, X., Zhang, N., Pingley, A., et al.: The digital Marauder’s map: a Wi-Fi forensic positioning tool. IEEE Trans. Mob. Comput. 11(3), 377–389 (2012)

    Article  Google Scholar 

  26. Wilkinson, G.: Digital terrestrial tracking: the future of surveillance. In: DEFCON 22 (2014)

  27. Qin, W., Zhang, J., Li, B., et al.: Discovering human presence activities with smartphones using nonintrusive Wi-Fi sniffer sensors: the big data prospective. Int. J. Distrib. Sens. Netw. 9(12), 927–940 (2013)

    Article  Google Scholar 

  28. O’Connor, B.: CreepyDOL: Cheap. Distributed Stalking. Technical Paper by Malice Afterthought, Inc (2013)

  29. Demir, L., Cunche, M., Lauradoux, C.: Analysing the privacy policies of Wi-Fi trackers. In: Proceedings of the ACM Workshop on Physical Analytics, pp. 39–44 (2014)

  30. Scheuner, J., Mazlami, G., Schoni, D., et al.: Probr—a generic and passive WiFi tracking system. In: Proceedings of IEEE Conference on Local Computer Networks, pp. 495–502 (2016)

  31. Li, P., Li, J., Huang, Z., et al.: Privacy-preserving outsourced classification in cloud computing. Clust. Comput. https://doi.org/10.1007/s10586-017-0849-9 (2017)

  32. Li, P., Li, J., Huang, Z., et al.: Multi-key privacy-preserving deep learning in cloud computing. Future Gener. Comput. Syst. 74, 76–85 (2017)

    Article  Google Scholar 

  33. Greenstein, B., Gummadi, R., Pang, J., et al.: Can Ferris Bueller still have his day off? Protecting privacy in the wireless era. In: Proceedings of HotOS (2007)

  34. Gupta, B.B., Agrawal, D.P., Yamaguchi, S.: Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. Springer, Berlin (2016)

  35. Cunche, M., Kaafar, M.A., Boreli, R.: Linking wireless devices using information contained in Wi-Fi probe requests. Pervasive Mob. Comput. 11, 56–69 (2014)

    Article  Google Scholar 

  36. Huang, Z., Liu, S., Mao, X,. et al.: Insight of the protection for data security under selective opening attacks. Inf. Sci. https://doi.org/10.1016/j.ins.2017.05.031 (2017)

  37. Chernyshev, M., Valli, C., Hannay, P.: On 802.11 Access point locatability and named entity recognition in service set identifiers. IEEE Trans. Inf. Forensics Sec. 11(3), 584–593 (2016)

    Article  Google Scholar 

  38. http://www.skyhookwireless.com/

  39. Li, J., Chen, X., Li, M., et al.: Secure deduplication with efficient and reliable convergent key management. IEEE Trans. Parallel Distrib. Syst. 25(6), 1615–1625 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

The National Key R&D Program of China (Nos. 2016YFB0801303, 2016QY01W0105), the National Natural Science Foundation of China (Nos. U1636219, 61379151, 61401512, 61572052) and the Key Technologies R&D Program of Henan Province (No. 162102210032) support the work presented in this paper.

Author information

Authors and Affiliations

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China

    Fan Zhao, Wenqi Shi, Ziru Peng & Xiangyang Luo

  2. Zhengzhou Science and Technology Institute, Zhengzhou, China

    Fan Zhao, Wenqi Shi, Ziru Peng & Xiangyang Luo

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Yong Gan

Authors
  1. Fan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenqi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ziru Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangyang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyang Luo.

Ethics declarations

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, F., Shi, W., Gan, Y. et al. A localization and tracking scheme for target gangs based on big data of Wi-Fi locations. Cluster Comput 22 (Suppl 1), 1679–1690 (2019). https://doi.org/10.1007/s10586-018-1737-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1737-7

Keywords

Search

Navigation