Boundary Localization On Wireless Sensor Network

Wireless Sensor Network is the emerging network communication application. Itsapplication can be involved in all areas of human life and social life. Wireless Sensor Networklocation technology as one of its main technique, has a very important role in applications.Recent years, the location technology research at home and abroad has been a large number ofreliable and comprehensive algorithm. But the network boundary node location researchstarted relatively late, algorithm is still not mature enough. The location of the networkboundary nodes is special, and is influenced by actual complex environment. Compared withother node location is more difficult, low accuracy, positioning coordinates deviation is big, itis difficult to practical application. Therefore, to solve boundary location problem is improvedthe original algorithm, effective boundary localization algorithm is put forward.In order to locate the boundaries of Wireless Sensor Network nodes accurately, reduce thepositioning error, this paper presents a wireless sensor network node boundary refinementcycle weighted RSSI (Received Signal Strength Indicator) location algorithm. It is divided intotwo stages, ranging phase and positioning stages, ranging phase for RSSI ranging technology,location positioning method for least squares estimation. The algorithm is based on RSSIranging and comprehensive localization of the least squares estimation algorithm and weightedcentroid algorithm refinement cycle. Firstly, by introducing a threshold RSSI ranging methodselected for positioning the anchor node, and then locate the initial location in least squaresestimation, the last border point coordinates obtained final weighted centroid measurementcycle refinement.Through simulation experiments, positioning accuracy curve analysis, and compared withthe classical RSSI positioning method, derived improved method can reduce the maximumboundary node localization errors, more accurately obtain the location coordinates of theboundary nodes, improve the boundary localization accuracy.