Abstract
In this work, we address the target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy range measurements, extracted from the received signal strength (RSS) information, we formulate the localization problem based on the maximum likelihood (ML) criterion. ML-based solutions are particularly important due to their asymptotically optimal performance, but the localization problem is highly non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP), which can be efficiently solved by interior-point algorithms. Furthermore, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS measurements. This simple procedure may decrease the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy. Moreover, they show that the new approach does not suffer significant degradation in its performance when the number of cooperating nodes is reduced.
Chapter PDF
Similar content being viewed by others
Keywords
References
Patwari, N.: Location Estimation in Sensor Networks. PhD Thesis, University of Michigan, Ann Arbor, MI, USA (2005)
Destino, G.: Positioning in Wireless Networks: Noncooperative and Cooperative Algorithms. PhD Thesis, University of Oulu, Oulu, Finland (2012)
Patwari, N., Ash, J.N., Kyperountas, S., Hero III, A.O., Moses, R.L., Correal, N.S.: Locating the Nodes: Cooperative Localization in Wireless Sensor Networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)
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)
Wang, G., Yang, K.: A New Approach to Sensor Node Localization Using RSS Measurements in Wireless Sensor Networks. IEEE Trans. Wirel. Commun. 10(5), 1389–1395 (2011)
Wang, G., Chen, H., Li, Y., Jin, M.: On Received-Signal-Strength Based Localization with Unknown Transmit Power and Path Loss Exponent. IEEE Wirel. Commun. Lett. 1(5), 536–539 (2012)
Vaghefi, R.M., Gholami, M.R., Buehrer, R.M., Ström, E.G.: Cooperative Received Signal Strength-Based Sensor Localization With Unknown Transmit Powers. IEEE Trans. Signal Process. 61(6), 1389–1403 (2013)
Salman, N., Ghogho, M., Kemp, A.: H: On the Joint Estimation of the RSS-based Location and Path-loss Exponent. IEEE Wireless Commun. Lett. 1(1), 34–37 (2012)
Béjar, B., Zazo, S.: A Practical Approach for Outdoors Distributed Target Localization in Wireless Sensor Networks. EURASIP J. Adv. Signal Process. 1–11 (May 2012)
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 Sens. J. 13(6), 2181–2191 (2013)
Bel, A., Vicario, J.L., Seco-Granados, G.: Localization Algorithm with On-line Path Loss Estimation and Node Selection. Sensors 11(7), 6905–6925 (2011)
Rappaport, T.S.: Wireless Communications: Principles and Practice. Prentice-Hall, Upper Saddle River (1996)
Sichitiu, M. L., Ramadurai, V.: Localization of wireless sensor networks with a mobile beacon. In: Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), Fort Lauderdale, FL, USA, pp. 174–183 pp. 25–27, October 2004
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Grant, M., Boyd, S.: CVX: Matlab Software for Disciplined Convex Programming. Version 1.21 http://cvxr.com/cvx (accessed on April 15, 2010)
Pólik, I., Terlaky, T.: Interior Point Methods for Nonlinear Optimization. In: Di Pillo, G., Schoen, F. (eds.) Nonlinear Optimization, 1st ed. Springer, Heidelberg, Cetraro, Italy (2010)
Sturm, J.F.: Using SeDuMi 1.02, a MATLAB Toolbox for Optimization Over Symmetric Cones. Optim. Meth. Softw. 11, 625–653 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Tomic, S., Beko, M., Dinis, R., Dimic, G., Tuba, M. (2015). Distributed RSS-Based Localization in Wireless Sensor Networks with Node Selection Mechanism. In: Camarinha-Matos, L., Baldissera, T., Di Orio, G., Marques, F. (eds) Technological Innovation for Cloud-Based Engineering Systems. DoCEIS 2015. IFIP Advances in Information and Communication Technology, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-16766-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-16766-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16765-7
Online ISBN: 978-3-319-16766-4
eBook Packages: Computer ScienceComputer Science (R0)