Based On The Indoor Mobile Wsn Node Positioning Technology Research

As one of the sensor network (WSN) support technology,node positioningtechnology in the next generation of network applications has importanttheoretical significance and practical value through position information data ofthe objective world,.Positioning technology applications sustained attentionbased on the radio map for indoor, which take advantage of the training data set inthe offline map library and matching algorithms in real-time positioning toimprove positioning accuracy.It reduce positioning costs, at the same time layingthe foundation for indoor mobile node localization.This paper studies wireless sensor network node RSSI location algorithmbased the radio map, by recording RSSI and collecting the training set.It takesinto account a variety of factors, including signal under different environmentalstructure,space fading effects,time fading effects and so on.And it focus on theindoor wall-baffle fading effects.Establishing offline radio map and real-timepositioning stage to take advantage of the improved algorithm estimates thelocation of indoor mobile node positioning,This paper propose an algorithmbased on the Radio-map by combining with the trilateral positioning algorithmfor steping forward to improve indoor wireless sensor network node positioningaccuracy. The main tasks are as follows: frist, it proposed wall and obstructionsare the most impact on the indoor environment model complexity and accuracyfactor.According to the degree of signal attenuation, the wall is divided into a small number of different classes to construct the propagation model. Second,proposing a practical radio-map combined the trilateral positioning algorithm.The key of the algorithm is based on the positioning of the sampling of the indoorenvironment as well as a small number of nodes.The offline database collectionof RSS values and position matching on real-time positioning stage. Third,In theoffice of the experimental experience assessment and classic on the number ofradio propagation models and KNN algorithm, this positioning algorithm hassmaller positioning errors, and improve the positioning accuracy.