Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical ...Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained. A sensor selection method is proposed to improve the localization accuracy as well as to save energy consumption. By selecting only a limited number of sensor nodes based on the model accuracy and geometry structure analysis, localization performance is improved, and energy consumption is reduced. In addition, extensive simulations are presented to demonstrate that the estimation performance with the proposed sensor selection method is better than that without sensor selection.展开更多
基金supported by the National Basic Research Program of China (973 Program) (No. 2010CB731800)the Key Project of National Nature Science Foundation (No. 60934003)the Scientific and Technological Supporting Project of Hebei Province (No. 072435155D)
文摘Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained. A sensor selection method is proposed to improve the localization accuracy as well as to save energy consumption. By selecting only a limited number of sensor nodes based on the model accuracy and geometry structure analysis, localization performance is improved, and energy consumption is reduced. In addition, extensive simulations are presented to demonstrate that the estimation performance with the proposed sensor selection method is better than that without sensor selection.
