Distributed RSS-Based Localization in Wireless Sensor Networks with Asynchronous Node Communication

  • Slavisa Tomic
  • Marko Beko
  • Rui Dinis
  • Miroslava Raspopovic
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 423)


In this paper we address the node localization problem in large-scale wireless sensor networks (WSNs) by using the received signal strength (RSS) measurements. According to the conventional path loss model, we first pose the maximum likelihood (ML) problem. The ML-based solutions are of particular importance due to their asymptotically optimal performance (for large enough data records). However, the ML problem is highly non-linear and non-convex, which makes the search for the globally optimal solution difficult. To overcome the non-linearity and the non-convexity of the objective function, we propose an efficient second-order cone programming (SOCP) relaxation, which solves the node localization problem in a completely distributed manner. We investigate both synchronous and asynchronous node communication cases. Computer simulations show that the proposed approach works well in various scenarios, and efficiently solves the localization problem. Moreover, simulation results show that the performance of the proposed approach does not deteriorate when synchronous node communication is not feasible.


Wireless localization wireless sensor network (WSN) received signal strength (RSS) second-order cone programming problem (SOCP) cooperative localization distributed localization 


  1. 1.
    Destino, G.: Positioning in Wireless Networks: Noncooperative and Cooperative Algorithms. Thesis of Giuseppe Destino at University of Oulu, Finland (2012)Google Scholar
  2. 2.
    Ouyang, R.W., Wong, A.K.S., Lea, C.T.: Received Signal Strength-based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative schemes. IEEE Trans. Veh. Technol. 59(3), 1307–1318 (2010)CrossRefGoogle Scholar
  3. 3.
    Wang, G., Yang, K.: A New Approach to Sensor Node Localization Using RSS Measurements in Wireless Sensor Networks. IEEE Trans. Wireless Commun. 10(5), 1389–1395 (2011)CrossRefGoogle Scholar
  4. 4.
    Wang, G., Chen, H., Li, Y., Jin, M.: On Received-Signal-Strength Based Localization with Unknown Transmit Power and Path Loss Exponent. IEEE Wireless Commun. Letters (2012)Google Scholar
  5. 5.
    Tomic, S., Beko, M., Dinis, R., Lipovac, V.: RSS-based Localization in Wireless Sensor Networks using SOCP Relaxation. In: IEEE SPAWC (2013)Google Scholar
  6. 6.
    Vaghefi, R.M., Gholami, M.R., Buehrer, R.M., Strom, E.G.: Cooperative Received Signal Strength-Based Sensor Localization With Unknown Transmit Powers. IEEE Trans. Signal. Process. 61(6), 1389–1403 (2013)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Biswas, P., Ye, Y.: Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization. In: IPSN 2004, Berkeley, California, USA (2004)Google Scholar
  8. 8.
    Patwari, N.: Location Estimation in Sensor Networks. Thesis of Neal Patwari at University of Michigan, Michigan, USA (2005)Google Scholar
  9. 9.
    Sundhar Ram, S., Nedic, A., Veeravalli, V.V.: Distributed Subgradient Projection Algorithm for Convex Optimization. In: IEEE ICASSP (2009)Google Scholar
  10. 10.
    Tomic, S., Beko, M., Dinis, R., Raspopovic, M.: Distributed RSS-basedLocalization in Wireless Sensor Networks Using Convex Relaxation. Accepted for publication in ICNC 2014, CNC Workshop, Honolulu, Hawaii, USA (2014)Google Scholar
  11. 11.
    Cota-Ruiz, J., Rosiles, J.G., Rivas-Perea, P., Sifuentes, E.: A Distributed Localization Algorithm for Wireless Sensor Networks Based on the Solution of Spatially-Constrained Local Problems. IEEE Sensors Journal 13(6), 2181–2191 (2013)CrossRefGoogle Scholar
  12. 12.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2004)CrossRefzbMATHGoogle Scholar
  13. 13.
    Jiang, J.A., Zheng, X.Y., Chen, Y.F., Wang, C.H., Chen, P.T., Chuang, C.L., Chen, C.P.: A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism. IEEE Sensors Journal (2013)Google Scholar
  14. 14.
    Rappaport, T.S.: Wireless Communications: Principles and Practice. Prentice-Hall (1996)Google Scholar
  15. 15.
    Sichitiu, M.L., Ramadurai, V.: Localization of wireless sensor networks with a mobile beacon. In: Proc. IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (2004)Google Scholar
  16. 16.
    Srirangarajan, S., Tewfik, A.H., Luo, Z.Q.: Distributed Sensor Network Localization Using SOCP Relaxation. IEEE Trans. Wireless Commun. 7(12), 4886–4894 (2008)CrossRefGoogle Scholar
  17. 17.
    Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 1.21 (2010),
  18. 18.
    Pólik, I., Terlaky, T.: Interior Point Methods for Nonlinear Optimization. In: Di Pillo, G., Schoen, F. (eds.) Nonlinear Optimization, 1st edn., vol. 4. Springer (2010)Google Scholar
  19. 19.
    Sturm, J.F.: Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optim. Meth. Softw., 1–5 (2008)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Slavisa Tomic
    • 4
  • Marko Beko
    • 1
    • 3
  • Rui Dinis
    • 2
    • 5
  • Miroslava Raspopovic
    • 6
  1. 1.Universidade Lusófona de Humanidades e TecnologiasLisbonPortugal
  2. 2.DEE/FCT/UNLCaparicaPortugal
  3. 3.UNINOVA – Campus FCT/UNLCaparicaPortugal
  4. 4.Institute for Systems and Robotics / ISTLisbonPortugal
  5. 5.Instituto de TelecomunicaçõesLisbonPortugal
  6. 6.Faculty of Information TechnologyBelgrade Metropolitan UniversitySerbia

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