The problem of wireless sensor network data clustering analysis is researched.In order to solve the shortcomings that traditional k-means is secsitive for the initial clustering center and easily fall into local optimal solution

a distributed immune genetic algorithm k-means based on sensor network is proposed.In the algorithm

cluster center has been as chromosomes

and through the genetic algorithm to optimize the initial cluster centers of traditional k-means clustering algorithm

meanwhile the selecting operation of immune algorithm is introduced into the genetic evolution of chromosome

causing the concentration and fitness of chromosome will impact whether the chromosome is selected in evolutionary

which can realize chromosome population diversity maintaining mechanism and self regulating function

and the searching achieves the global optimum

so the algorithm can solve the prematurity problem of k-means algorithm better.The experimental results show that

the algorithm has been improved data clustering effect

and made the clustering can converge to the global optimal clustering rapidly

and the clustering has higher accuracy.