Edge Sensing-Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Due to the defects caused by limited energy, storage capacity, and computing ability, increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease additional power consumption extend lifetime WSN, multistage hierarchical clustering deredundancy algorithm is proposed. In first stage, dual-metric distance employed, redundant nodes are preliminarily identified improved k -means obtain clusters similar nodes. Then, Gaussian hybrid classification presented implement similarity for edge second stage. third clustered randomly weighted deduplicate spatial correlation data. Detailed experimental results show that, compared with existing schemes, proposed can achieve better performance terms ratio, energy consumption, network lifetime.