Personal and Ubiquitous Computing

, Volume 21, Issue 1, pp 5–15 | Cite as

Redundancy reduction for indoor device-free localization

  • Jinjun Liu
  • Ning AnEmail author
  • Md. Tanbir Hassan
  • Min Peng
  • Zheng Cui
  • Shenghui Zhao
Original Article


To improve localization accuracy, device-free passive localization studies usually deploy a number of sensor nodes in indoor environments, which causes redundant features and produces large data volumes and high deployment costs. This paper proposes the concept of a two-level redundancy and formulates the node reduction problem as a redundancy control problem. With the goal of using fewer nodes while maintaining high localization accuracy, a method is proposed to control the two-level redundancy efficiently and reduce the number of nodes greatly. Experiments are performed in two completely different environments. The proposed method is able to maintain accuracy levels above 90% and can efficiently reduce the total number of nodes by 59.09% in a large room (150 \({\mathrm{m}}^2\)) and by 68.75% in a small room (25 \({\mathrm{m}}^2\)). Furthermore, due to reduced nodes the proposed method can drastically reduce the needed amount of localization data and the hardware costs.


Indoor passive localization Redundancy reduction Node optimization Node reduction Reducing the amount of data 



This work was supported in part by the International S&T Cooperation Program of China (No. 2015DFA11450), the Natural Science Foundation of China (Nos. 71661167004 and 61472057), the ‘111’ Project of the Chinese Ministry of Education and State Administration of Foreign Experts Affairs (No. B14025).


  1. 1.
    Sigg S, Scholz M, Shi S, Ji Y, Beigl M (2014) Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Trans Mobile Comput 13:907–920. doi: 10.1109/TMC.2013.28 CrossRefGoogle Scholar
  2. 2.
    Guo J, Zhang H, Sun Y, Bie R (2014) Square-root unscented kalman filtering-based localization and trackingin the internet of things. Pers Ubiquitous Comput 18:987–996. doi: 10.1007/s00779-013-0713-8 CrossRefGoogle Scholar
  3. 3.
    Han C, Wu K, Wang Y, Ni LM (2014) Wifall: device-free fall detection by wireless networks. In: IEEE conference on computer communications (IEEE INFO-COM 2014), pp 271–279Google Scholar
  4. 4.
    Raja M, Sigg S (2016) Applicability of rf-based methods for emotion recognition: a survey. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), pp 1–6Google Scholar
  5. 5.
    Wan J, OGrady MJ, OHare GMP (2015) Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Pers Ubiquitous Comput 19:287–301. doi: 10.1007/s00779-014-0824-x CrossRefGoogle Scholar
  6. 6.
    Youssef M, Mah M, Agrawala A (2007) Challenges device-free passive localization for wireless environments. In: Proceedings of the 13th annual acm international conference on mobile computing and networking, MOBICOM 07, pp 222–229Google Scholar
  7. 7.
    Seifeldin M, Saeed A, Kosba AE, El-Keyi A, Youssef M (2013) Nuzzer: a large-scale device-free passive localization system for wireless environments. IEEE Trans Mobile Comput 12:1321–1334. doi: 10.1109/TMC.2012.106 CrossRefGoogle Scholar
  8. 8.
    Liu J, An N, Hassan MT, Chen G, Zhang Y (2016) Poster abstract: node deployment mechanism for quick, indoor, and device-free localization. In: 2016 15th ACM/IEEE international conference on information processing in sensor networks, pp 1–2Google Scholar
  9. 9.
    Wilson J, Patwari N (2010) Radio tomographic imaging with wireless networks. IEEE Trans Mobile Computing 9:621–632. doi: 10.1109/TMC.2009.174 CrossRefGoogle Scholar
  10. 10.
    Wilson J, Patwari N (2011) See-through walls: motion tracking using variance-based radio tomography networks. IEEE Trans Mobile Comput. doi: 10.1109/TMC.2010.175 Google Scholar
  11. 11.
    Sabek I, Youssef M, Vasilakos AV (2015) Ace: an accurate and efficient multi-entity device-free wlan localization system. IEEE Trans Mobile Comput 14:261–273. doi: 10.1109/TMC.2014.2320265 CrossRefGoogle Scholar
  12. 12.
    Xu C, Firner B, Moore R, Zhang Y, Trappe W, Howard R, Zhang F, An N (2013) Indoor device-free multi-subject counting and localization using radio signal strength. In: Proceedings of the 2013 ACM/IEEE international conference on information processing in sensor networks (IPSN), pp 79–90Google Scholar
  13. 13.
    Abdel-Nasser H, Samir R, Sabek I, Youssef M (2013) Monophy: mono-stream-based device-free wlan localization via physical layer information. In: 2013 IEEE wireless communications and networking conference (WCNC), pp 4546–4551Google Scholar
  14. 14.
    Liu J, An N, Hassan MT, Xu C, Zhang Y (2017) An efficient node deployment method for indoor passive localization. Int J Ad Hoc Ubiquitous Comput (Accepted)Google Scholar
  15. 15.
    Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. In: Liu H, Motoda H (eds) Feature extraction, construction and selection. Springer, New York, pp 117–136CrossRefGoogle Scholar
  16. 16.
    Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS regression). WIREs Comput Stat 2:97–106. doi: 10.1002/wics.51 CrossRefGoogle Scholar
  17. 17.
    Zhang D, Ma J, Chen Q, Ni L (2007) An rf-based system for tracking transceiver-free objects. In: Proceedings of the fifth annual ieee international conference on pervasive computing and communications, 2007, Per-Com 07, pp 135–144Google Scholar
  18. 18.
    Zhang D, Liu Y, Ni L (2011) A real-time, accurate and scalable system for tracking transceiver-free objects. In Proceedings of the 2011 IEEE international conference on pervasive computing and communications (PerCom), pp 197–204Google Scholar
  19. 19.
    Xu C, Firner B, Zhang Y, Howard R (2015) The case for efficient and robust rf-based device-free localization. IEEE Trans Mobile Comput Early Access PP:1. doi: 10.1109/TMC.2015.2493522 Google Scholar
  20. 20.
    Kosba A, Saeed A, Youssef M (2012) Robust wlan device-free passive motion detection. In: Proceedings of the wireless communications and networking conference (WCNC), pp 3284–3289Google Scholar
  21. 21.
    Saeed A, Kosba AE, Youssef M (2014) Ichnaea: a low-overhead robust wlan device-free passive localization system. IEEE J Sel Top Signal Process 8:5–15. doi: 10.1109/JSTSP.2013.2287480 CrossRefGoogle Scholar
  22. 22.
    Xu C, Firner B, Zhang Y, Howard R, Li J, Lin X (2012) Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In: Proceedings of the 2012 ACM/IEEE 11th international conference on information processing in sensor networks (IPSN), pp 209–220Google Scholar
  23. 23.
    Wilson J, Patwari N (2012) A fade-level skew-laplace signal strength model for device-free localization with wireless networks. IEEE Trans Mobile Comput 11:947–958. doi: 10.1109/TMC.2011.102 CrossRefGoogle Scholar
  24. 24.
    Patwari N, Wilson J (2011) Spatial models for human motion-induced signal strength variance on static links. IEEE Trans Inf Forens Secur 6:791–802. doi: 10.1109/TIFS.2011.2146774 CrossRefGoogle Scholar
  25. 25.
    Kaltiokallio O, Bocca M, Patwari N (2012) Enhancing the accuracy of radio tomographic imaging using channel diversity. In: Proceedings of the 9th international conference on mobile adhoc and sensor systems (MASS), pp 254–262Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Jinjun Liu
    • 1
    • 2
  • Ning An
    • 1
    Email author
  • Md. Tanbir Hassan
    • 1
  • Min Peng
    • 1
  • Zheng Cui
    • 3
  • Shenghui Zhao
    • 2
  1. 1.School of Computer and InformationHefei University of TechnologyHefeiChina
  2. 2.School of Computer and Information EngineeringChuzhou UniversityChuzhouChina
  3. 3.Everjoy Senior HomeHefeiChina

Personalised recommendations