Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

Abstract—This work introduces the use of compressed sensing (CS) algorithms for data compression in wireless sensors to ad-dress the energy and telemetry bandwidth constraints common to wireless sensor nodes. Circuit models of both analog and dig-ital implementations of the CS system are presented that enable analysis of the power/performance costs associated with the design space for any potential CS application, including analog-to-infor-mation converters (AIC). Results of the analysis show that a digital implementation is significantly more energy-efficient for the wire-less sensor space where signals require high gain and medium to high resolutions. The resulting circuit architecture is implemented in a 90 nm CMOS process. Measured power results correlate well with the circuit models, and the test system demonstrates contin-uous, on-the-fly data processing, resulting in more than an order of magnitude compression for electroencephalography (EEG) signals while consuming only 1.9 W at 0.6 V for sub-20 kS/s sampling rates. The design and measurement of the proposed architecture is presented in the context of medical sensors, however the tools and insights are generally applicable to any sparse data acquisition. Index Terms—Biomedical electronics, circuit analysis, com-pressed sensing, electroencephalography, encoding, low power electronics, sensors, wireless sensor networks. I