<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In order to reduce the location estimation error in Wireless Sensor Network(WSN). A localization algorithm is proposed combining adaptive estimation, PI-learning and spring-relaxation techniques for wireless sensor networks in this paper. Our proposed method takes the advantages of the spring-relaxation technique, thus it inherits its simplicity. The overall accuracy of the location estimations is improved by introducing adaptive estimation and PI-learning. Moreover, it requires only a few beacons with known locations to compute the location estimates of all sensors. Simulation examples demonstrate the overall accuracy of the proposed method.</p></abstract>

In order to reduce the location estimation error in Wireless Sensor Network(WSN). A localization algorithm is proposed combining adaptive estimation, PI-learning and spring-relaxation techniques for wireless sensor networks in this paper. Our proposed method takes the advantages of the spring-relaxation technique, thus it inherits its simplicity. The overall accuracy of the location estimations is improved by introducing adaptive estimation and PI-learning. Moreover, it requires only a few beacons with known locations to compute the location estimates of all sensors. Simulation examples demonstrate the overall accuracy of the proposed method.

Adaptive Estimation and Pi Learning Springrelaxation Technique for Location Estimation In Wireless Sensor Networks

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In order to reduce the location estimation error in Wireless Sensor Network(WSN). A localization algorithm is proposed combining adaptive estimation,...

Adaptive Estimation and Pi Learning Springrelaxation Technique for Location Estimation In Wireless Sensor Networks

Pubblicato online: 20 feb 2013

Pagine: 317 - 322

Ricevuto: 14 ott 2012

Accettato: 06 gen 2013

DOI: https://doi.org/10.21307/ijssis-2017-542

© 2013 Li Haiyan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.