Title :
RSS-Based Node Localization in the Existence of Moving Obstructions
Author :
Zhou, Yun ; Zhao, Guang ; Yang, Bo ; Men, Aidong ; Chen, Qingchao
Author_Institution :
Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
In the context of wireless sensor networks, a node´s location must be known for its data to be meaningful in many cases. Received signal strength (RSS)-based localization has been widely used because of low complexity and easy deployment. This paper proposes a novel method to localize nodes in the presence of randomly moving obstructions. We introduce background learning to reduce interferences caused by moving obstructions such as people or other objects. Based on our experimental results, each link of data is modeled as a mixture of Gaussians (MoG) and its parameters are updated by background learning. In this way, we can reduce the interferences of moving obstructions from obtained RSS measurements. Then we use least-square (LS) cooperative localization algorithm to implement node localization and the experimental results show good performance.
Keywords :
Gaussian distribution; interference suppression; learning (artificial intelligence); least squares approximations; sensor placement; wireless sensor networks; MoG; RSS-based node localization; background learning; interference reduction; least square cooperative localization algorithm; mixture of Gaussians; moving obstructions; node location; received signal strength; wireless sensor networks; Accuracy; Computational modeling; Convergence; Data models; Estimation; Wireless communication; Wireless sensor networks;
Conference_Titel :
Vehicular Technology Conference (VTC Fall), 2012 IEEE
Conference_Location :
Quebec City, QC
Print_ISBN :
978-1-4673-1880-8
Electronic_ISBN :
1090-3038
DOI :
10.1109/VTCFall.2012.6398970
