# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 7 Conclusions  
This paper proposes the design of an indoor WASN with distributed computational capability to face several ambient assisted living challenges. The work has three main focuses in its design: (1) the individual nodes of the network, which are composed of a Raspberry Pi computing unit and a USB microphone; (2) the interconnection of the different nodes among them using a meshed connectivity; and (3) the design of a box to cover and protect the hardware while preventing it from overheating. The final prototype designed satisfies the requirements initially described, and gives the team wide research options to work in indoor environments, with coverage in several different spaces, and with the deployment of machine learning algorithms to detect acoustic events in real-time. The price of the design is affordable and in the criteria of low-cost, what gives it the opportunity of having a large number of devices per home if necessary. The connectivity is solved even in environments with low WiFi coverage, and the microphones used have the required quality to detect events and measure \(L_{eq}\) to draw the sound map, as the use case presented in this work. Furthermore, the microphone study and analysis has been conducted with more precision and detail than most of the solutions proposed in the literature for low-cost WASN, where the acoustic quality of the sensor was barely taken into account, prioritizing its price and assuming a change of the device for a bad performance. Future work directions involve replicating the experiments exposed in this paper in multiple indoor environments with different architectural distributions to validate that the meshed topology is valid for different locations as long as the distances explained in Section 4 are respected. In this line, it is important to work in order to achieve the redundancy needed for the nodes that are farther from the core, i.e., if it only has a connection to one node and this node stops working, this failure will also have an affect on a node that works. However, it is important to make a good selection of the placement of each node and to assure that redundant nodes in terms of sound evaluation--in this example--and connectivity are not used, as it will clearly affect the total cost of the WASN. The more nodes needed to survey an area, the more expensive the WASN will be. To say more, in the case of big spaces and, depending on the needs of sound event detection, these can be places where with one or two nodes per floor are enough to cover the sound topology; if the application was in a factory, for instance. In these situations, the WiFi coverage could not be enough and other type of wireless protocol should be considered for usage (e.g., Zigbee for medium spaces or Long Range Modulation (LoRa) for huge spaces and buildings). The costs of displacing various nodes with WiFi in front of fewer nodes with another type of wireless protocol should also be considered. Or, instead of adding a completely new wireless protocol to all the nodes, using it only in the farthest nodes could also be a solution. On the other hand, authors also want to discuss the algorithm that can be run in each node. The placement of each node must be taken into account when using one algorithm or another, because not all the rooms are used for the same purposes. To illustrate, in an industry, there can be a room with different types of machines, another room with people working and so on. In a closer example, in an ambient assisted living environment, such as the example shown in this work, the sounds coming from the kitchen are clearly different from the sounds coming from the bedroom. Therefore, each node algorithm must be trained with the specific types of sounds that can be found in the node room. Further study should be taken in this direction, as not every algorithm is valid for all types of sound. Some algorithms work better with sounds that are punctual and others with prolonged sounds. Further microphone study is required.