无线体域网节点数据压缩节能方法

无线体域网(WBAN)节点通常采用电池供电,能量有限且不易频繁更换.为降低节点能耗,提出了一种数据压缩节能方法,采用稀疏表示分类算法识别正常信号,运用压缩感知(CS)理论进行信号压缩采样,将压缩信号发送至基站并进行重构.对WBAN节点采集的心电图信号进行仿真分析,结果表明:心电图信号经压缩后,具有较好的识别与重构性能,在确保数据传输精度前提下,减少了数据采集量和传输量,有效地降低了WBAN节点能耗.

WBAN node data compression energy-saving method

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 refactoring. The simulation and analysis have been implemented on electrocardiogram(ECG) signal collected by WBAN nodes,the results show that the ECG signal after compression,has good recognition performance and reconstruction performance,and under the premise that ensure the data transmission precision, reduce the amount of data acquisition and transmission,effectively reduce the energy consumption of WBAN nodes.