In order to reduce wireless sensor networks（ WSNs） algorithm complexity when there are lots of anchor nodes

and improve positioning precision

propose a weighted centroid localization algorithm based on K-means clustering point density（ KCPD-WCLA）. First

group a large number of anchor nodes which are randomly deployed in space

and use trilateral measurement positioning method to obtain many results close to real value in twodimensional（ 2D） plane; then

K-means clustering algorithm is introduced to WSNs localization problem

take K clustering point density into consideration that position results can be achieved by using the weighted centroid localization algorithm（ WCLA）. Theoretical analysis and simulation results show that computational complexity is significantly reduced

positioning precision is improved significantly compared with multilateral localization algorithm（ MLA） and WCLA.