## 1 Introduction
In recent years, there has been a rapid evolution of WASN, and many works have been developed. To date, several authors have designed and deployed WASN for different purposes such as noise monitoring [11] or sound identification as road traffic, horns, and people [12]. For instance, in [13] the production and analysis of a real-life environmental audio database in two urban and suburban scenarios corresponding to the pilot areas of the DYNAMAP project was presented. The WASN of the DYNAMAP project is based on low cost acoustic sensor but it is connected to digital recorder for data saving. Hence, unlike our research, audio samples cannot be sent to a central server using wireless communications, and neither audio processing can not be carried out at node. Audio recordings have been categorized as road traffic noise, background city noise, and anomalous noise events. However, it was carried out offline with Audicity and Matlab software [14]. In [15], a distributed noise measurement system based on IoT technology was developed. The sensor node is based on a Raspberry Pi with an electret omnidirectional microphone and a sound card in order to record the audio. The data from WASN was interpolated for obtaining a spatial noise level in a small-sized city. However, the system was designed to measure, represent and predict urban noise levels, and not for audio processing and classification. In [16], the design of low cost wireless sensor node for environmental noise measurement is described. The sensor node platform is built on ATmega128L with 4 kB RAM, and its internal 10-bit ADC can operate a peak sampling rate of 33 kHz. However, according the microcontroller specifications, the maximum sampling rate for 10-bit resolution is 9.8 kHz and not 33 kHz. In addition, only the effective sound pressure is sent, and an audio processing is not carried out. Nevertheless, the WASN paradigm presents several challenges, ranging from those derived from the design and development of the wireless sensor network, such as energy harvesting and low cost hardware development and maintenance, to some specific challenges derived from the automation of the data collection and subsequent signal processing, such as to detect anomalous noise events [13]. In addition, the sensor of a WASN designed for AAL systems should process the environmental sounds to rapidly infer hazardous situations instead to send the full audio record to a server for a centralized processing. Thus, processing data at the node can ensure shorter response time and better reliability. In this context, the use of devices with an increasing storage and computation capacity coins a new term: Edge or Fog Computing. This model extends Cloud computing and services to the edge of the network reducing network latency and offloading computation [17], as well as to avoid bottlenecks at remote server due to the throughput and volume of data to be collected and processed. Edge Computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy [18]. This concept covers computational to be performed at the edge of the network and to exchange data from or to cloud IoT services. In [19], the design and deployment of a WASN at home, inspired by the Mobile Edge Computing paradigm [20] able to gather the data of all acoustic sensing nodes deployed to infer the audio events of interest in an AAL context is described. It follows a distributed implementation, where all digital signal processing is carried out in a concentrator offloading the sensor nodes and avoiding the use of the server to remotely process the data. This concentrator is based on a GPU embedded platform. As has been discussed, many research using low cost sensors in a WASN have been developed. Nevertheless, those works have been designed to measure only noise levels and not for sound identification.