Optimized K-means routing protocol with black-winged kite algorithm for sustainable 5G/6G sensor networks

Abstract

Wireless sensor networks (WSNs) face critical challenges due to energy-constrained nodes, affecting their longevity, reliability, and efficiency. To improve energy effectiveness in WSNs for fifth-generation and sixth-generation (5G/6G) networks, various clustering techniques have been developed. These techniques aim to optimize energy use, ensuring better system performance. Moreover, to overcome these complications, this article proposes a K-means online-learning routing protocol optimized with the black-winged kite optimization algorithm for sustainable communication (KORP-BWKOA-SC-WSN). Initially, the input data is collected from the sink node. This data is fed to a binarized simplicial convolutional neural network for cluster formation, in which the network nodes are clustered. Next, the formed cluster is used for cluster head selection by using the hiking optimization algorithm for better data transmission. Finally, the K-means online learning routing protocol is implemented to improve node coordination and energy efficiency. The black-winged kite optimization approach is employed to enhance the system performance. The proposed KORP-BWKOA-SC-WSN achieves throughput improvements of 21.51%, 12.38%, and 21.51%, respectively, and energy consumption reductions of 15.85%, 23.37%, and 22.04% compared to existing methods The performance of the proposed technique is evaluated and is found to attain higher throughput and high network lifetime when compared with other existing methods.