A Low-Power Signal-Dependent Sampling Technique: Analysis, Implementation, and Applications

Abstract

Sensors are among essential building blocks of any Cyber-Physical Systems (CPSs). Acquisition and processing of their sensory data contribute to the power consumption and computation load of the overall CPSs. For data acquisition, the conventional fixed frequency sampling in many such systems is sub-optimal since a sizable number of samples do not contain important information. In this work, we propose a Signal-Dependent Sampling (SDS) method and present its associated circuit implementation. Using the proposed SDS method, the number of retained samples is significantly reduced with little or negligible compromise in the quality of the (reconstructed) signal. The associated error and added noise are analyzed and their boundaries - which can be controlled by the user - are calculated. Our experiments show that the proposed system is able to improve power efficiency of the overall system in various applications. For example, for wireless Electrocardiography (ECG), Photoplethysmography (PPG), and Electroencephalogram (EEG) monitoring systems, the proposed approach can achieve a power saving of 81%, 76%, and 64% respectively. The proof-of-concept prototype system is implemented using TSMC 0.18 μm and has a foot-print and power consumption that compare favorably with those of the state-of-the-art implementations. The method can be used in a variety of applications including wireless sensor networks, mobile and wearable devices, as well as Internet of Things (IoT) nodes.