Research On The Localization Technology In Wireless Sensor Network Under Complex Conditions

Wireless sensor network (WSN) is becoming a new approach of information collection. It is widelyapplied in the military, the medical treatment, the environmental monitoring and so on. Since the datacollection, object tracking and mesh topology managing are significant only when the positions ofcorresponding sensor nodes have been identified, therefore, as one of the core technologies, the nodeslocating plays a pivotal role in the WSN application.This paper studies the node positioning technology under complex conditions. RSSI values for avariety of complex conditions often subject to interference, the proposed screening strategy based onRSSI values Kalman filter. On the other hand, the RSSI value of the signal attenuation model and theactual distance, the signal attenuation is proposed theoretical equation of linear regression method toobtain an accurate combination of environmental parameters, modified RSSI ranging model.Experimental results show that this method can effectively suppress the influence of interference onthe ranging results.Some positioning occasions, the number of anchor nodes over three, its performance will be goodor bad, you should try to select the better performance of the anchor node localization, otherwise poorperformance by adding an anchor node localization will cause positioning errors increases. Therefore,this paper proposes a positioning strategy based on linear regression analysis. We can evaluate themodel and make the localization strategies correspondingly, utilizing the RSQ and residual standarddeviation. The experimental results show that the modified range model and new positioning strategy,makes the node localization accuracy has improved significantly.In order to solve the sparse anchor nodes under the conditions of the traditional static nodelocalization methods are often unable to achieve the mobile node localization problem, a mobile nodelocalization algorithm based on RSSI ranging and MCL predict. When the mobile node enters theanchor node sparse area by the least squares curve fitting RSSI ranging and prediction of the currentsampling time of each area, the final sample area within the narrow their intersection, reducing thesample area while improving the sampling sample success rate and positioning accuracy. Finally, theproposed RSSI linear regression analysis and prediction based on a combination of a mobile nodelocalization method to achieve a mobile anchor node node localization in case of different densities.Experiments show that the positioning method effect is good.