# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
## 2 Methods
### Architecture of the IoT Infrastructure  
The IoT wireless network was installed in two school units in Larissa (a high school and a senior high school). The aim was to control, evaluate, and monitor real-time environmental variables relevant to the functioning of schools and the activity of pupils, teachers, and other employees. The deployed IoT infrastructure collects data for temperature in degrees Celsius (\({}^{\circ}\)C), relative humidity (%), noise-environment volume in decibels (dBs), and PM\({}_{10}\) and PM\({}_{2.5}\) microparticles in \(\upmu\)g/m\({}^{3}\). The first three variables (temperature, humidity, and noise) are captured by the installed sensors. The PM\({}_{10}\) and PM\({}_{2.5}\) are retrieved via http REST API access from a Waqi International Network (World Air Quality Index Platform) from the IoT sensor located 1400 m away from the school complex and is the closest station [17]. The selected school buildings did not provide proper datacom infrastructure. Specifically, the high school had deployed a Wi-Fi network with Internet access, while the senior high school did not support any telecom utility. Therefore, in order to transmit the sensor data from both buildings to the cloud platform where the dashboards and the time-series database have been installed and configured via the Internet, we had to construct a low-cost telecom infrastructure. This resulted in the implementation of 3 different IoT sensor nodes. The first one has been used to both acquire sensor data and send it via the Wi-Fi IEEE802.11n network and the MQTT protocol to the cloud platform, which was deployed to the high school. The second IoT sensor node utilized a LoRaWAN protocol to collect sensor data and transmit it over a peer-to-peer LoRa link at a distance of 300 m to the third type of node, i.e., the IoT sensor node gateway. The second IoT node has been installed inside the senior high school, and since no obstacles in the direct link are present apart from the building's walls, the SF7 spreading factor has been employed at the EU-RF frequency of 868 MHz configured at 125 KHz bandwidth with a coding rate equal to 4/5, a 55-byte-long buffer size, 5 dBm transmit output power, and a preamble length equal to 8 bytes. No reception discrepancies or data degradation were present. The third IoT sensor node was deployed to the high school and acted as a network gateway to receive data over the LoRa peer-to-peer link, processed transform it accordingly, and send it over the Wi-Fi connection to the Internet (cloud platform) via the MQTT protocol. The sensor node, being attached to the sensors, could also acquire environmental data from the high school building and send it to the cloud platform. Therefore, taking advantage of the fact that a Wi-Fi network was available in the high school, the core deployment of the Wi-Fi IoT sensor nodes took place there. Specifically, the measuring stations were installed on each floor (ground floor and 1 st floor) of both school buildings (buildings 1 and 2), and through the MQTT TCP protocol, the collected data were transmitted to the IoT platform. On the other hand, we incorporated a LoRa-based IoT sensor node in the senior high school (building 3, ground floor). Therefore, a LoRa peer-to-peer network between high school and senior high school was implemented and established. This means that one of the Wi-Fi IoT sensor nodes utilized in the high school not only operated as a sensor node but also functioned as a gateway to translate the LoRa packets to MQTT data packets and eventually transmit them over Wi-Fi to the IoT platform. Consequently, all measurements from the sensors of the 5 total installed nodes were sent via MQTT protocol to the Node-Red platform [18], which has been installed in cloud infrastructure. Furthermore, the InfluxDB time series database [19] was used to store the sensor data, while dashboards created in the Grafana visualization platform [20]were utilized to monitor and analyze the received data.