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This paper presents a novel approach based on the Compressive Sensing (CS) framework to monitor 1-D environmental information using a Wireless Sensor Network (WSN). The proposed method exploits the compressibility of the signal to reduce the number of samples required to recover the sampled signal at the Fusion Center (FC) and so reduce the energy consumption of the sensors. An innovative feature of the authors' approach is a new random sampling scheme that considers the causality of sampling, hardware limitations and the trade-off between the randomization scheme and computational complexity. In addition, a Sampling Rate Indicator (SRI) feedback scheme is proposed to enable the sensor to adjust its sampling rate to maintain an acceptable reconstruction performance while minimizing the energy consumption.
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