Robust Localization with Crowd Sensors: A Data Cleansing Approach

Article
  • 66 Downloads

Abstract

In this paper, the source localization problem with crowd of anchor nodes is investigated, under the circumstances that abnormal data could be sporadically and randomly produced for the reason of either accidental equipment failures or random malicious behaviors. To cope with the problem that abnormal data brings, we formulate a generalized modeling of abnormal data in localization problem, which involves the impacts of both unexpected equipment failures and malicious data falsifications. The corresponding Cramer-Rao lower bound (CRLB) of the specific localization problem is derived. For the localization enhancement, we propose a data cleansing-based robust localization algorithm which exploits the low occupancy of channel band by sources and the sparsity of abnormal data. The data cleansing approach achieves both the new sensing data matrix that cleansed out abnormal data component and the estimated abnormal data matrix, which are respectively used for the correct source detection process and the position estimation process of the final source localization. The root mean squared error (RMSE) is derived to assess the performance of the proposed robust localization algorithm. Computer simulations show that the proposed data cleansing-based robust localization algorithm can effectively eliminate the impairment of the abnormal data and hence improve the localization performance evidently.

Keywords

Source localization Robust localization Maximum likelihood Data cleansing Cramer-Rao lower bound(CRLB) 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61501510 and Grant No. 61631020), Natural Science Foundation of Jiangsu Province (Grant No. BK20150717), China Postdoctoral Science Foundation Funded Project, and Jiangsu Planned Projects for Postdoctoral Research Funds.

References

  1. 1.
    Haque IT, Assi C (2015) Profiling-based indoor localization schemes. IEEE Systems J 9(1):76–85CrossRefGoogle Scholar
  2. 2.
    Dall'Anese E, Kim S-J, Giannakis GB (2011) Channel gain map tracking via distributed kriging. IEEE Trans Veh Technol 60(3):1205–1211CrossRefGoogle Scholar
  3. 3.
    Hong J, Ohtsuki T (2015) Signal eigenvector-based device-free passive localization using array sensor. IEEE Trans Veh Technol 64(4):1354–1363CrossRefGoogle Scholar
  4. 4.
    Teng J, Zhang B, Zhu J, Li X, Xuan D, Zheng YF (2014) EV-Loc: integrating electronic and visual signals for accurate localization. IEEE/ACM Trans Netw 22(4):1285–1296CrossRefGoogle Scholar
  5. 5.
    Sheng X, Hu Y-H (2005) Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Trans Signal Process 53(1):44–53MathSciNetCrossRefGoogle Scholar
  6. 6.
    Wang B, Wu G, Wang S, Yang LT (2014) Localization based on adaptive regulated neighborhood distance for wireless sensor networks with a general radio propagation model. IEEE Sensors J 14(11):3754–3762CrossRefGoogle Scholar
  7. 7.
    Saeed N, Nam H (2015) Robust multidimensional scaling for cognitive radio network localization. IEEE Trans Veh Technol 64(9):4056–4062CrossRefGoogle Scholar
  8. 8.
    Salman N, Ghogho M, Kemp A (2012) On the joint estimation of the RSS-based location and path loss exponent. IEEE Commun Lett 1(1):34–37CrossRefGoogle Scholar
  9. 9.
    So HC, Lin L (2011) Linear least squares approach for accurate received signal strength based source localization. IEEE Trans Signal Process 59(8):4035–4040MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wang G, Yang K (2011) A new approach to sensor node localization using RSS measurements in wireless sensor networks. IEEE Trans Wirel Commun 10(5):1389–1395CrossRefGoogle Scholar
  11. 11.
    Gholami MR, Vaghefi RM, Strom EG (2013) RSS-based sensor localization in the presence of unknown channel parameters. IEEE Trans Signal Process 61(15):3752–3759MathSciNetCrossRefGoogle Scholar
  12. 12.
    Coluccia A, Ricciato F (2014) RSS-based localization via bayesian ranging and iterative least squares positioning. IEEE Commun Lett 18(5):873–876CrossRefGoogle Scholar
  13. 13.
    Shen H, Ding Z, Dasgupta S, Zhao C (2014) Multiple source localization in wireless sensor networks based on time of arrival measurement. IEEE Trans Signal Process 62(8):1938–1949MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jean O, Weiss AJ (2014) Passive localization and synchronization using arbitrary signals. IEEE Trans Signal Process 62(8):2143–2150MathSciNetCrossRefGoogle Scholar
  15. 15.
    Yuan W, Wu N, Etzlinger B, Wang H, Kuang J (2016) Cooperative joint localization and clock synchronization based on gaussian message passing in asynchronous wireless networks. IEEE Trans Veh Technol 65(9):7258–7273CrossRefGoogle Scholar
  16. 16.
    Lohrasbipeydeh H, Gulliver TA, Amindavar H (2014) Blind received signal strength difference based source localization with system parameter errors. IEEE Trans Signal Process 62(17):4516–4531MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huang B, Xie L, Yang Z (2015) TDOA-based source localization with distance-dependent noises. IEEE Trans Wirel Commun 14(1):468–480CrossRefGoogle Scholar
  18. 18.
    L. Lu, and H.-C. Wu (2012) Novel energy-based localization technique for multiple sources. In Proc. of IEEE ICCGoogle Scholar
  19. 19.
    Lu L, Zhang H, Wu H-C (2014) Novel energy-based localization technique for multiple sources. IEEE Systems J 8(1):142–150CrossRefGoogle Scholar
  20. 20.
    Masazade E, Niu R, Varshney PK, Keskinoz M (2010) Energy aware iterative source localization for wireless sensor networks. IEEE Trans Signal Process 58(9):4824–4835MathSciNetCrossRefGoogle Scholar
  21. 21.
    Niu R, Varshney PK (2006) Target location estimation in sensor networks with quantized data. IEEE Trans Signal Process 54(12):4519–4528CrossRefGoogle Scholar
  22. 22.
    Zhang S, Gao S, Wang G, Li Y (2015) Robust NLOS error mitigation method for TOA-based localization via second-order cone relaxation. IEEE Commun Lett 19(12):2210–2213CrossRefGoogle Scholar
  23. 23.
    Wang G, Chen H, Li Y, Ansari N (2014) NLOS error mitigation for TOA-based localization via convex relaxation. IEEE Trans Wirel Commun 13(8):4119–4131CrossRefGoogle Scholar
  24. 24.
    Wang G, So AM-C, Li Y (2016) Robust convex approximation methods for TDOA-based localization under NLOS conditions. IEEE Trans Signal Process 63(13):3281–3296MathSciNetCrossRefGoogle Scholar
  25. 25.
    Liang C, Wen F (2016) Received signal strength-based robust cooperative localization with dynamic path loss model. IEEE Sensors J 16(5):1265–1270CrossRefGoogle Scholar
  26. 26.
    Shirazi GN, Shenouda MB, Lampe L (2014) Second order cone programming for sensor network localization with anchor position uncertainty. IEEE Trans Wirel Commun 13(2):749–763CrossRefGoogle Scholar
  27. 27.
    Ma Z, Ho KC (2014) A study on the effects of sensor position error and the placement of calibration emitter for source localization. IEEE Trans Wirel Commun 13(10):5440–5452CrossRefGoogle Scholar
  28. 28.
    Karbasi A, Oh S (2013) Robust localization from incomplete local information. IEEE/ACM Trans Netw 21(4):1131–1144CrossRefGoogle Scholar
  29. 29.
    Mourad F, Snoussi H, Abdallah F, Richard C (2011) A robust localization algorithm for mobile sensors using belief functions. IEEE Trans Veh Technol 60(4):1799–1811CrossRefGoogle Scholar
  30. 30.
    Misra S (2009) G. (Larry) Xue, and S. Bhardwaj, secure and robust localization in a wireless ad hoc environment. IEEE Trans Veh Technol 58(3):1480–1489CrossRefGoogle Scholar
  31. 31.
    Wu N, Xiong Y, Wang H, Kuang J (2015) A performance limit of TOA-based location-aware wireless networks with ranging outliers. IEEE Commun Lett 19(8):1414–1417CrossRefGoogle Scholar
  32. 32.
    Ding G, Wang J, Wu Q, Zhang L, Zou Y, Yao Y-D, Chen Y (2014) Robust spectrum sensing with crowd sensors. IEEE Trans Commun 62(9):3129–3143CrossRefGoogle Scholar
  33. 33.
    Vaghefi RM, Gholami MR, Buehrer RM, Strom EG (2013) Cooperative received signal strength-based sensor localization with unknown transmit powers. IEEE Trans Signal Process 61(6):1389–1403MathSciNetCrossRefGoogle Scholar
  34. 34.
    Tomic S, Beko M, Dinis R (2015) RSS-based localization in wireless sensor networks using convex relaxation-noncooperative and cooperative schemes. IEEE Trans Veh Technol 64(5):2037–2050CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Communications EngineeringPLA University of Science and TechnologyNanjingChina
  2. 2.College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations