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1

College of Computer Science, Zhejiang University, Hangzhou, 310027, China

2

Hangzhou Normal University, Hangzhou, 310036, China





*

Author to whom correspondence should be addressed.



Abstract Localization is one of the most important subjects in Wireless Sensor Networks WSNs. To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization MBL, Adapting MBL A-MBL, and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization SA-MBL approach to achieve more flexibility and achieve almost the same performance with A-MBL. View Full-Text

Keywords: wireless sensor networks WSNs; localization; particle filter; self-adapting mobile beacon-assisted localization SA-MBL wireless sensor networks WSNs; localization; particle filter; self-adapting mobile beacon-assisted localization SA-MBL





Autor: Guodong Teng 1,2, Kougen Zheng 1,* and Wei Dong 1

Fuente: http://mdpi.com/



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