# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 1 Introduction  
Humans are everyday more used to coexisting with technological devices, even in private environments such as homes. When a home is provided with a set of sensors that enable the automation and monitoring of different day-to-day actions, it can be categorized as a smart home, with a special focus--but not exclusively--on ambient assisted living applications [1]. Nonetheless, the same sensors can be applied in other environments such as private homes, hospitals, universities, students' residences or nursing homes for different purposes. However, and especially for specific segments of the population, privacy becomes a key issue when accepting those devices [2]. Actually, for a technological system to be accepted, it should be perceived by the users as non-intrusive and non-obtrusive, terms defined in [3]. Mainly, a system is perceived or not as intrusive or obtrusive depending on eight different dimensions: physical dimensions, usability dimensions, privacy dimensions, function dimensions, human interaction dimensions, self-concept dimensions, routine dimensions and sustainability dimensions. In summary, the devices should (i) be of reduced dimensions and be integrated with the architectural aesthetic, (ii) be easy to use, (iii) not reveal more information than the user is willing to share, (iv) not affect the personalrelationships of the people using them or make them feel treated differently, (v) not make the people feel anxious about not being able to afford the device in the future. Most of the users of smart devices believe that their personal raw data gathered by sensors should not be shared with third parties such as ISPs (Internet Services Providers) [4], especially when thinking of sensitive groups, such as old people, children or teenagers. Furthermore, the study in [5] shows the most recent trends in the technical challenges to face attacks and prevent vulnerabilities in networks, especially from an industrial point of view. Based on those requirements and recommendations, and in this regard, the edge computing paradigm has emerged as a powerful tool to perform the signal processing in the sensing nodes to avoid unnecessary transmissions of sensitive raw data. Edge computing refers to moving the computation load and/or storage of data of a system closer to the source in which the data is generated [6]. In this sense, this computation is performed in edge devices, which are composed of computing units connected to sensors that gather data. Besides other advantages, such as faster response times or bandwidth cost savings (achieved by sending processed data instead of raw data), data safety and privacy are also key issues to be taken into account when deciding to use an edge-computing architecture in front of a cloud-based system. From a communication point of view, the edge nodes may use wired (such as Ethernet) or wireless (such as WiFi or 2G, 3G, 4G or 5G cellular networks) communication protocols depending on the intrinsic characteristics of the scenario in which they would be deployed and the desired features of the network [7]. Another parameter to consider is the amount of data that has to be sent in order to integrate the information required in the central node; this volume of information changes depending on the application, the size of the network and the final goal of the evaluation of the acoustic data collected and processed. These features also involve the desired speed, the distance between nodes, the number of supported devices, the covering area of near technologies (e.g., cellular antennas) and the budget constraints, among others, in order to design the best possible network to satisfy the requirements of the problem under study. Following this paradigm, this paper presents a meshed Wireless Acoustic Sensor Network (WASN), which consists of a network composed of microphones (i.e., the sensors of the edge devices) and computing units spatially distributed over an indoor environment, capable of monitoring noise levels and detecting certain predefined acoustic events.