This paper introduces an innovative data compression methodology in an adaptive way guaranteeing signal interpretation quality and energy efficiency in wireless body area sensor network. The approach is based on the discrete cosine transform. Under different channel conditions, the resulting wavelet coefficients are thresholded adaptively to match a user-specified percentage of root-mean-square difference. The nonzero coefficients of the thresholded vector are quantized adaptively by the linear quantizer of the lowest possible resolution. Signal interpretation quality and energy consuming are influenced by threshold Th, quantization length Q, and modulation method M which can be taken as a discrete optimization problem. A specific case study is designed to achieve better energy efficiency and guaranteed signal interpretation quality than typical efforts. The simulation results show that the proposed compression scheme can achieve considerable gains for ECG signals in wireless body area sensor networks.
['Kun Hua', 'Honggang Wang', 'Wei Wang', 'Shaoen Wu']
Adaptive Data Compression in Wireless Body Sensor Networks