A Configurable Energy-Efficient Compressed Sensing Architecture with Its Application on Body Sensor Networks

The past decades have witnessed a rapid surge in new sensing and monitoring devices for well-being and healthcare. One key representative in this field is body sensor networks (BSNs). However, with advances in sensing technologies and embedded systems, wireless communication has gradually become one of the dominant energy-consuming sectors in BSN applications. Recently, compressed sensing (CS) has attracted increasing attention in solving this problem due to its enabled sub-Nyquest sampling rate. In this paper, we investigate the quantization effect in CS architecture and argue that the quantization configuration is a critical factor of the energy efficiency for the entire CS architecture. To this end, we present a novel configurable quantized compressed sensing (QCS) architecture, in which the sampling rate and quantization are jointly explored for better energy efficiency. Furthermore, to combat the computational complexity of the configuration procedure, we propose a rapid configuration algorithm, called RapQCS. According to the experiments involving several categories of real biosignals, the proposed configurable QCS architecture can gain more than 66% performance-energy tradeoff than the fixed QCS architecture. Moreover, our proposed RapQCS algorithm can achieve over 150× speedup on average, while decreasing the reconstructed signal fidelity by only 2.32%.