Using Adaptive Sampling and DWT Lifting Scheme for Efficient Data Reduction in Wireless Body Sensor Networks

In the recent years, many researches have been done on Wireless Body Sensor Networks, consisting of wearable devices that provide personalized healthcare through continuous monitoring of the patients' health condition. One of the major difficulties in WBSNs is the power consumption due to wireless transmission of sensed data. Data reduction can be considered a direct way to reduce the power consumption due to data transmission. However, most of the data reduction techniques suffer when the variation of the collected samples is high, or when the data are noisy. In this paper, we propose to enhance a data reduction scheme based on an adaptive sampling technique using dynamically adapted risk level by combining it with the Discrete Wavelet Transform lifting scheme for noise filtering. To assess our approach, we have run different series of simulation on real sensor data. The results show that combining the lifting scheme method with adaptive sampling increased the data reduction percentage by up to 50%.