## 2 Wireless Acoustic Sensor Networks for Wildlife Monitoring  
Wireless Acoustic Sensor Networks (WASN) are used to collect sounds at frequent intervals over large areas for the intensive sampling of real-time data and to achieve a rapid reaction time. The use of networks equips researchers with the capacity to sample distances or rates that would not be possible otherwise [24]. To detect animal activity, a human manually recording the emitted sounds can only be in one place at a time, and the presence of a human can alter the soundscape. Wireless acoustic sensors can replace this role, with the added advantage of being able of being positioned in locations which are inaccessible to humans. If they are finely synchronized; the amount and distribution of individuals can be determined. The acoustic data obtained at fixed locations over time provides knowledge about ecosystem cycles and the data obtained from different locations can be compared. Their main limitations are the environmental noise and the energy consumption on sensors, requiring monitoring methods with low energy consumption [25].  
As far as this team knows, the first WASN implemented for wildlife monitoring was deployed back in 2003 [26], working with the final goal of identifying and localizing a specific type of bird call. Later on, [27], the authors describe how embedded devices distributed in wildlife can run a real-time acoustic source detection and localization. The most common use in this type of applications is to count endangered species in critical environments, in order to extract information to help their conservation [28]. In other projects, a WASN are deployed to record bird-song audio samples to study their fingerprints [29]. In other more recent works, the focus is the obtaining of continuous data of the birds under study in their natural environment [25].  
WASNs have multiple applications especially in urban environments, such as surveillance in urban areas and noise monitoring, rather than the aforementioned wildlife monitoring [30]. The deployment of WASNs in the cities is becoming increasingly common, and further data can be collected in addition to the audible sounds, provided that the nodes are suitably designed. In the literature, several WASNs have been designed and deployed to monitor urban sounds. Several projects deal with the noise monitoring issue, as the IDEA project in Belgium [31], or the RUMEUR network in France [32], in which the authors focused on aircraft noise, or the Barcelona noise monitoring network [33], in which data are collected and worked via a Sentilo platform [34]. More recently, the Sounds of New York Project (SONYC) deployed 56 low-cost sensors across the city to conduct a multilabel classification of urban sound sources real-time [35], despite the fact that they do not conduct the signal processing and machine learning on-site. Finally, the DYNAMAP project [36] has completed the deployment and testing of two pilots, one in a suburban area of Rome and another in an urban area of Milan which conduct a binary classification between road traffic noise and any other anomalous noise events at each node [37].  
The development of these recent projects has shown that the paradigm of WASN in the city has multiple challenges, from the most basic technical issues [38; 39], to the automatizing of the data collection and signal processing [35; 37]. The additional requirement of having ultrasound capabilities into the monitoring networks increases the difficulty of the task, both in terms of sampling frequency and computational capabilities of each of the WASN nodes if the processing is conducted locally. Achieving a complete and exhaustive dataset of background noise and bat call examples is a challenging task since the amount of available resources is limited, e.g., processing and storage capabilities and data collection.