Abstract
Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual‐metric distance is employed, and redundant nodes are preliminarily identified by the improved k‐means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.
Highlights
Wireless sensor networks (WSNs) are common in people’s lives and are widely used in various fields [1, 2]
Focusing on the issue mentioned above, this paper explores the sensing data deredundancy problem to decrease energy consumption and extend the lifetime of a WSN
Unconscionable deredundancy can degrade the accuracy of sensing data. Focusing on filling this gap, this paper proposes a multiphase hierarchical clustering similarity deredundancy algorithm to overcome the limitations mentioned above
Summary
Wireless sensor networks (WSNs) are common in people’s lives and are widely used in various fields [1, 2]. Prediction-based schemes require relatively long-term data sensing and processing abilities, which increase the burden of resourcelimited sensors and decrease the lifetime of WSNs. it is necessary to consider both spatial and temporal correlations and location and data similarity clustering to decrease the sensing data transmission and processing. Focusing on the issue mentioned above, this paper explores the sensing data deredundancy problem to decrease energy consumption and extend the lifetime of a WSN. MHCSD considers both spatial and temporal correlations and location and data similarity clustering to overcome the accuracy degradation of sensing data (2) A dual-metric distance is employed in the first stage, and an improved k-means algorithm is proposed to judge the similarity of nodes based on the dualmetric distance in sinks.
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