Optimization schemes for wireless sensor network localization
International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 2, pp. 291-302

Voir la notice de l'article provenant de la source Library of Science

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Self-organization and localization capabilities are one of the most important requirements in sensor networks. This paper provides an overview of centralized distance-based algorithms for estimating the positions of nodes in a sensor network. We discuss and compare three approaches: semidefinite programming, simulated annealing and two-phase stochastic optimization-a hybrid scheme that we have proposed. We analyze the properties of all listed methods and report the results of numerical tests. Particular attention is paid to our technique-the two-phase method-that uses a combination of trilateration, and stochastic optimization for performing sensor localization. We describe its performance in the case of centralized and distributed implementations.
Keywords: wireless sensor networks, localization, stochastic optimization, simulated annealing
Mots-clés : sieć bezprzewodowa, sieć sensorowa, lokalizacja, optymalizacja stochastyczna, wyżarzanie symulowane
@article{IJAMCS_2009_19_2_a9,
     author = {Niewiadomska-Szynkiewicz, E. and Marks, M.},
     title = {Optimization schemes for wireless sensor network localization},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {291--302},
     publisher = {mathdoc},
     volume = {19},
     number = {2},
     year = {2009},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_2_a9/}
}
TY  - JOUR
AU  - Niewiadomska-Szynkiewicz, E.
AU  - Marks, M.
TI  - Optimization schemes for wireless sensor network localization
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2009
SP  - 291
EP  - 302
VL  - 19
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_2_a9/
LA  - en
ID  - IJAMCS_2009_19_2_a9
ER  - 
%0 Journal Article
%A Niewiadomska-Szynkiewicz, E.
%A Marks, M.
%T Optimization schemes for wireless sensor network localization
%J International Journal of Applied Mathematics and Computer Science
%D 2009
%P 291-302
%V 19
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_2_a9/
%G en
%F IJAMCS_2009_19_2_a9
Niewiadomska-Szynkiewicz, E.; Marks, M. Optimization schemes for wireless sensor network localization. International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 2, pp. 291-302. http://geodesic.mathdoc.fr/item/IJAMCS_2009_19_2_a9/

[1] Anderson, B. D. O., Mao, G. and Fidan, B. (2007). Wireless sensor network localization techniques, Computer Networks 51(10): 2529-2553.

[2] Biswas, P. and Ye, Y. (2004). Semidefinite programming for ad hoc wireless sensor network localization, IPSN '04: Proceedings of the 3-rd International Symposium on Information Processing in Sensor Networks, Berkeley, CA, USA, ACM Press, New York, NY, pp. 46-54.

[3] Borchers, B. (1999). CSDP, a C library for semidefinite programming, Optimization Methods Software 11(1-4): 613-623.

[4] Boyd, S., Ghaoui, L. E., Feron, E. and Balakrishnan, V. (1994). Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, PA.

[5] de Brito, L. M. P. L. and Peralta, L. M. R. (2007). Collaborative localization in wireless sensor networks, SENSORCOMM 2007: Proceedings of the International Conference on Sensor Technologies and Applications, Valencia, Spain, IEEE Computer Society, pp. 94-100.

[6] Dekkers, A. and Aarts, E. (1991). Global optimization and simulated annealing, Mathematical Programming 50(8): 367-393.

[7] Doherty, L., Pister, K. and Ghaoui, L. E. (2001). Convex postion estimation in wireless sensor networks, INFOCOM 2001: Proceedings of the 20-th Annual Joint Conference of the IEEE Computer and Communications Societies, Anchorage, USA, pp. 1655-1663.

[8] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Studies in Applied Mathematics, Addison-Wesley, Boston, MA.

[9] Hightower, J. and Borriello, G. (2001). Localization systems for ubiquitous computing, Computer 34(8): 57-66.

[10] Hu, L. and Evans, D. (2004). Localization for mobile sensor networks, MobiCom 2004: Proceedings of the 10-th Annual International Conference on Mobile Computing and Networking, Philadelphia, PA, USA, IEEE Computer Society, pp. 45-57.

[11] Ji, X. and Zha, H. (2004). Sensor positioning in wireless ad hoc sensor networks with multidimensional scaling, INFOCOM 2004: Proceedings of the 23-rd Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, China, pp. 2652-2661.

[12] Kannan, A. A., Mao, G. and Vucetic, B. (2005). Simulated annealing based localization in wireless sensor network, LCN '05: Proceedings of the IEEE Conference on Local Computer Networks. 30-th Anniversary, Sydney, Australia, IEEE Computer Society, pp. 513-514.

[13] Kannan, A. A., Mao, G. and Vucetic, B. (2006). Simulated annealing based wireless sensor network localization with flip ambiguity mitigation, Proceedings of the 63-rd IEEE Vehicular Technology Conference, Melbourne, Australia, pp. 1022-1026.

[14] Marks, M. and Niewiadomska-Szynkiewicz, E. (2007). Two-phase stochastic optimization to sensor network localization, SENSORCOMM 2007: Proceedings of the International Conference on Sensor Technologies and Applications, Valencia, Spain, IEEE Computer Society, pp. 134-139.

[15] Niculescu, D. and Nath, B. (2001). Ad hoc positioning system (APS), GLOBECOM: Proceeding of the Global Telecommunications Conference, San Antonio, CA, USA, pp. 2926-2931.

[16] Shang, Y., Ruml,W., Zhang, Y. and Fromherz, M. (2004). Localization from connectivity in sensor networks, IEEE Transactions on Parallel and Distributed Systems 15(11): 961-974.

[17] Sturm, J. F. (1999). Using SeDuMi 1.02, aMATLAB toolbox for optimization over symmetric cones, Optimization Methods Software 11(1-4): 625-653.