WSN data fusion algorithm based on IDBO optimized k-means and support weighted

Abstract

Wireless sensor networks suffer from uneven energy consumption among nodes and limited energy resources. When data is transmitted to the sink node, data fusion technology can be employed to reduce the amount of data transmission, thereby extending the network’s lifespan. Designing efficient transmission paths and fusion methods is a challenging task. To address this issue, a data fusion algorithm based on the improved dung beetle optimization algorithm (IDBO) is proposed. This algorithm optimizes k-means clustering and adopts a weighted support mechanism (IDKSW). Firstly, the dung beetle optimization algorithm is improved to enhance its convergence. Then, the improved dung beetle optimization algorithm is used to optimize k-means to achieve network clustering. Finally, nodes send reliable data to the cluster head, which calculates the support, assigns weights, performs data fusion, and sends the processed data to the base station. Through simulation experiments and comparisons with other algorithms, the IDKSW algorithm increases the node survival rate by approximately 50%, 26.3%, 17.6%, and 9.0%, the remaining energy of the network by approximately 37%, 25.7%, 16.1%, and 12.5%, and the data reception volume by approximately 34.7%, 13.5%, 7.2%, and 3.0%. The IDKSW algorithm can effectively reduce network energy consumption and extend the network’s lifespan.