WSN Clustering Routing Algorithm Based on Improved K-means and Entropy Weight Method
FANG Wangsheng
WANG Xu
Abstract：Aiming at the problems of limited energy and unbalanced load of the wireless sensor network,a WSN clustering routing algorithm based on improved K-means and entropy weight method(IKEW)is proposed.During the clustering phase,this al-gorithm improves the K-means algorithm using density-based methods and maximum-minimum distance,and adopts a reassign-ment scheme to balance the number of nodes in each cluster.During the cluster head selection phase,the entropy weight method is used to calculate the weight of each node index,making the selection of cluster heads more reasonable.During the data transmission phase,a communication consumption function is constructed based on the cluster head's remaining energy and the data's transmis-sion distance to select relay nodes.Simulation results show that the proposed algorithm can effectively balance network energy con-sumption and prolong the network lifetime.
Keywords：wireless sensor networkK-meansnode redistributionentropy weight methodload balancing
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 623-627,683 )