A multi-objective localization algorithm based on K-means clustering is proposed in binary wireless sensor
networks with false alarm. The K-means clustering-improved subtract on negative add on positive(KMC-ISNAP) algorithm
is applied to localize the multiple objectives where the distance between nodes and objectives is unknown, and influencing
factors are used to reduce the influence of fuzzy nodes on localization errors. The simulation results show that the
K-means clustering method is able to divide the alarmed sensors into parts accurately when multiple objectives are
randomly distributed, and the proposed KMC-ISNAP has higher estimation accuracy and better fault tolerance than centroid
estimator(CE) algorithm and subtract on negative add on positive(SANP) algorithm.