Optimization schemes for wireless sensor network localization

Ewa Niewiadomska-Szynkiewicz; Michał Marks

International Journal of Applied Mathematics and Computer Science (2009)

  • Volume: 19, Issue: 2, page 291-302
  • ISSN: 1641-876X

Abstract

top
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.

How to cite

top

Ewa Niewiadomska-Szynkiewicz, and Michał Marks. "Optimization schemes for wireless sensor network localization." International Journal of Applied Mathematics and Computer Science 19.2 (2009): 291-302. <http://eudml.org/doc/207936>.

@article{EwaNiewiadomska2009,
abstract = {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.},
author = {Ewa Niewiadomska-Szynkiewicz, Michał Marks},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {wireless sensor networks; localization; stochastic optimization; simulated annealing},
language = {eng},
number = {2},
pages = {291-302},
title = {Optimization schemes for wireless sensor network localization},
url = {http://eudml.org/doc/207936},
volume = {19},
year = {2009},
}

TY - JOUR
AU - Ewa Niewiadomska-Szynkiewicz
AU - Michał Marks
TI - Optimization schemes for wireless sensor network localization
JO - International Journal of Applied Mathematics and Computer Science
PY - 2009
VL - 19
IS - 2
SP - 291
EP - 302
AB - 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.
LA - eng
KW - wireless sensor networks; localization; stochastic optimization; simulated annealing
UR - http://eudml.org/doc/207936
ER -

References

top
  1. Anderson, B. D. O., Mao, G. and Fidan, B. (2007). Wireless sensor network localization techniques, Computer Networks 51(10): 2529-2553. Zbl1120.68021
  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. Zbl0973.90524
  4. Boyd, S., Ghaoui, L. E., Feron, E. and Balakrishnan, V. (1994). Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, PA. Zbl0816.93004
  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. Zbl0753.90060
  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. Zbl0721.68056
  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 adhoc 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). Twophase 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, a MATLAB toolbox for optimization over symmetric cones, Optimization Methods & Software 11(1-4): 625-653. Zbl0973.90526

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.