Distributed compressive sampling algorithm based on linear regression
B Zhang · Journal of Chongqing University of Posts and Telecommunications · 2014
In order to reduce the data traffic in wireless sensor networks( WSNs) and maximize the network lifetime,we proposed a distributed sampling method through joint linear regression and compressed sensing( CS). Firstly,a dynamic clustering algorithm based on inter-signal correlation was proposed. The proposed algorithm can divide nodes that sample data are extremely significant linear correlation into the same cluster. Then,a distributed compressive sampling algorithm was proposed. Using linear regression and CS,the proposed algorithm can reconstruct the data of nodes with high accuracy by significantly reducing the sampling rate. Our results based on real temperature data sets indicate that a reduction in sensor sampling by up to 71% can be achieved compared to periodically sampling.