Toward energy-proportional compressive sensors

The vision of the Internet of things (IoT) entails connecting all possible objects to the Internet for remote monitoring and actuation. Its realization requires sensing devices that are highly optimized in terms of performance, size, cost, and energy-efficiency. Compressive sensing (CS) is a signal acquisition method that fuses sampling and compression in a single procedure, and thereby potentially reduces the energy cost of acquisition and transmission of information with respect to the conventional combination of Nyquist-rate sampling and digital data compression. The focus of this thesis is the quantitative assessment of the quality/compression trade-off, the hardware complexity and power consumption of CS-based signal acquisition systems for typical low-rate IoT sensor applications, such as biomedical and environmental monitoring. The investigations are conducted employing both mathematical models as well as measurements from prototype mixed-signal system-on-chips (SoC). An 8-channel compressive biomedical signal acquisition SoC in 130 nm CMOS, and a compressive current sensing SoC in 160 nm CMOS have been developed, were fabricated and measured for this purpose.