Hibernets: Energy-Efficient Sensor Networks Using Analog Signal Processing

Preprocessing of data before transmission is recommended for many sensor network applications to reduce communication and improve energy efficiency. However, constraints on memory, speed, and energy currently limit the processing capabilities within a sensor network. In this paper, we describe how ultra-low-power analog circuitry can be integrated with sensor nodes to create energy-efficient sensor networks. To demonstrate this concept, we present a custom analog front-end which performs spectral analysis at a fraction of the power used by a digital counterpart. Furthermore, we show that the front-end can be combined with existing sensor nodes to 1) selectively wake up the mote based upon spectral content of the signal, thus increasing battery life without missing interesting events, and to 2) achieve low-power signal analysis using an analog spectral decomposition block, freeing up digital computation resources for higher-level analysis. Experiments in the context of vehicle classification show improved performance for our ASP-interfaced mote over an all-digital implementation.

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