A Wireless Sensor Network Localization Method Based on Dynamic Path Loss Exponent

2012 ◽  
Vol 433-440 ◽  
pp. 4530-4535
Author(s):  
Gang Zhu Qiao ◽  
Jian Chao Zeng

The path loss exponent shows the effect of space environment on the RF signals in wireless communication model. In most RSSI based location method the path loss exponent is assigned a fixed empirical value which can not reflect the actual environmental impact of the wireless signal, which lead to low position accuracy and considerable positioning error. Aiming at some complex and rapidly changing environment a path loss exponent dynamic acquired algorithm is proposed, which can calculate the actual path loss exponent with the distance and the RSSI value information between adjacent beacon nodes. On basis of the path loss exponent dynamic acquired algorithm a path loss exponent dynamic acquired based localization algorithm is proposed which can estimate the blind node position with the actual path loss exponent, and can improve the adaptability to the environment of the RSSI location algorithm. The simulation shows that the positioning accuracy of proposed method is significantly improved and the effect of proposed method is more precise than the common RSSI method under the same environment.

2011 ◽  
Vol 128-129 ◽  
pp. 1325-1328
Author(s):  
Zheng Zhang ◽  
Min Jiang ◽  
Guo Cheng Wan ◽  
Xiao Wei Liu

Node position technology is an internal demand of wireless sensor network, however, in most project account; it’s hard to obtain the nodes' exact position because of uncontrollability in the network deployment process. For this purpose, an improved self-correcting location algorithm which based on RSSI multilateral ranging was proposed in this paper. Other than the primary multilateral localization algorithm, a correcting node which adjacent to the blind node was imported into the pending location region. The main function is to reduce the influence caused by unilateral ranging error in the calculation of multilateral positioning and improves the positioning precision. Detailedly introduced the principle of self-correcting location algorithm, moreover, in order to check the improved algorithm’s location performance, a back testing was done via CC2430/CC2431 wireless nodes. Test results show that improved algorithm enhances the system’s accuracy and stability.


2015 ◽  
Vol 738-739 ◽  
pp. 339-343 ◽  
Author(s):  
Xuan Liang ◽  
Xv Feng ◽  
Lei Yao

As the geographical location of sensors is of great importance in the applications of a wireless sensor network, localization attracts significant research interest. Among the localization methods, received signal strength indicator (RSSI) localization algorithm is widely studied due to its low-cost. However, the radio signal transmission is affected by the factors of channel attenuation and noise, etc. Therefore, the actual location is impossible to estimate. To alleviate the effect of the RSSI measure errors, we proposed a novel error modified method for RSSI location algorithm based on neural network, short for RSSI-NN. First, we use the fitting parameters of the monitoring area to determine the parameters of RSSI model. Second, the distance between anchor node i and the unknown node Liis calculated. Then, the error modified algorithm based on neural network is proposed. Finally, we can obtain the modified value of the output layer data. Simulations demonstrate that RSSI-NN achieves lower localization error compared with conventional RSSI-based localization algorithm.


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