Optimization schemes for wireless sensor network localization

• Autorzy: Niewiadomska-Szynkiewicz E. , Marks M.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

wireless sensor networks; localization; stochastic optimization; simulated annealing

PL

sieć bezprzewodowa; sieć sensorowa; lokalizacja; optymalizacja stochastyczna; wyżarzanie symulowane

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2009
• Tom: Vol. 19, no 2
• Strony: 291--302
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Niewiadomska-Szynkiewicz E.

• Research and Academic Computer Network, ul. Wąwozowa 18, 02–796 Warsaw, Poland; Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00–665 Warsaw, Poland

autor

Marks M.

• Research and Academic Computer Network, ul. Wąwozowa 18, 02–796 Warsaw, Poland; Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00–665 Warsaw, Poland

Bibliografia

• [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.