Abstract
Wireless Body Area Network (WBAN) node is usually powered by batteries, which is energy limited and not easy to change frequently. A data compression energy-saving method is proposed to reduce the energy consumption of WBAN node, adopting Sparse Representation Classification (SRC) algorithm to identify the normal signal, using Compressed Sensing (CS) theory for signal compression sampling, and the compressed signal is sent to the base station for reconstruction. When the physiological signals collected by WBAN nodes are in the normal range, need not to transmit normal signals, and make nodes convert into a sleep state to prolong the sleep time of nodes and reduce the amount of transmitting data, thus the node energy consumption can be reduced. The simulation and analysis have been implemented on Electrocardiogram (ECG) signal, the results shows that the ECG signal after compression, has good recognition performance and reconstruction performance, and reduces the amount of data acquisition and transmission, effectively reduce the energy consumption of WBAN nodes.
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