Abstract
In wireless sensor nodes with a tight power budget, minimizing both the amount of transmitted data and the complexity of the algorithms used for data compression are fundamental in achieving long battery life-time. Compressed Sensing (CS) has been proposed to process incoming samples and produce a smaller amount of data sufficient to reconstruct the original signal. We show that the architecture implementing parallel projection-based CS can be reused to realize a linear estimator able to minimize the transmitted data when the primary interest is the acquisition of a scalar feature of the signal rather than its entire profile. Further, we increase the energy-efficiency of the architecture by puncturing the sample stream which allows the duty-cycle of both the analog front-end and the analog-to-digital converter to be reduced. We found that conventional CS acquisition can be made more energy-efficient as it tolerates a certain amount of random puncturing, and also that more substantial power savings can be achieved when estimation is the target and undersampling is optimized by a suitable algorithm. In the latter case, the power consumption of all circuit blocks in the signal chain can be reduced by more than one order of magnitude with respect to the standard solution that samples and transmits raw data for off-board processing. The effectiveness of optimized undersampling is demonstrated in two case studies; first, the estimation of the amplitude of an electrical signal, and second, the estimation of the maximum solar radiation measured by a real-world sensor.