On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks

Wireless Body Sensor Networks open up tremendous important applications such as consistent monitoring of a patient's vital signs. One of the main challenges that a Wireless Body Sensor Network faces is the transmission of the collected vital signs measurements. Data transmission is considered to be the greatest consumer of energy in a sensor node. Multiple data compression techniques have been proposed in the literature to reduce the size of the collected data. Thus, the transmission energy consumption. In this paper, we compare the performance of three resource-aware data compression techniques that are proposed in the literature and showed good results: Lightweight Temporal Compression, Differential Pulse Code Modulation and Discrete Wavelet Transform lifting scheme. Then, we propose to adapt the lossy Lightweight Temporal Compression algorithm and combine it with the lossless Differential Pulse Code Modulation algorithm in order to achieve a higher level of compression and reduce the data reconstruction error rate. To evaluate our approach, multiple series of simulation has been done on vital signs data. The results showed that our proposed compression scheme achieved a reduction by up to 95%, and reduced the transmission energy consumption by up to 5 times.