Compressive Sensing for Efficiently Collecting Wildlife Sounds with Wireless Sensor Networks

Wildlife sounds provide relevant information for non-intrusive environmental monitoring when Wireless Sensor Networks (WSNs) are used. Thus, collecting such audio data, while maximizing the network lifetime, is a key challenge for WSNs. In this work, we propose a methodology that applies Compressive Sensing (CS) aiming at collecting as little data as possible to allow the signal reconstruction, so that the reconstructed signal is still representative. The key issue is to determine a sparse base that best represents the audio information used for identifying the target species. As a proof-of- concept, we focus on anuran (frogs and toads) calls, but the methodology can be applied for other animal families and species. The reason for that choice is that long-term anuran monitoring has been used by biologists as an early indicator for ecological stress. By using real wild anuran calls, we show that 98% classification rate can be achieved by using as little as 10% of the original data. We also use simulation to evaluate the impact of our solution on the network performance (energy consumption, delivery rate, and network delay).