A Sparse Low Energy Adaptive Clustering Hierarchy Method for Wireless Sensor Networks
Yi Xu, Guiling Sun, Tianyu Geng · 2018
Reducing energy consumption and prolonging lifetime are the main goals of protocol and data collection methods in wireless sensor networks. As for Low Energy Adaptive Clustering Hierarchy method, cluster head nodes are randomly selected in a round which is similar to the sampling method of matrix completion theory. Combining the data sensing with data compression, we propose a Sparse Low Energy Adaptive Clustering Hierarchy method based on matrix completion. Through setting multiple thresholds, the selection process of sensing nodes and cluster heads can be completed at the same time, and after that, only a portion of nodes are active, which greatly reduce the total energy consumption. Simulations for synthetic data and real-world data demonstrate that our method can prolong the lifetime while maintaining the accuracy of reconstructed data.