Self-Sustaining Acoustic Sensor With Programmable Pattern Recognition for Underwater Monitoring

Philipp Mayer, Michele Magno, Luca Benini

Abstract: Minimizing the power consumption of always-on sensors is crucial for extending the lifetime of battery-operated devices that are required to monitor events continuously and for long periods. This paper proposes a novel programmable μW event-driven acoustic detector featuring “always-on” audio pattern recognition. The event-driven detector detects up to eight programmable spectral-temporal features extracted with a low-power single-channel analog circuit and classifies the features by an onboard microcontroller. The event-driven detector is combined with novel microbial fuel cells (MFCs) to achieve self-sustainability in an underwater scenario. Experimental results demonstrate that the power consumption of the detector is only 26.89 μW during always-on mode, achieving up to 59-dB sound pressure level of sensitivity. High detection accuracy of up to 95.89% in recognizing acoustic patterns has been experimentally verified. Accurate measurements with commercial MFCs demonstrate the capability to achieve self-sustainability in always-on monitoring.