# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
###### Abstract  
This work presents an on-field noise analysis during the class breaks in Greek school units (a high school and a senior high school) based on the design and deployment of low-cost IoT sensor nodes and IoT platforms. The course breaks form 20% of a regular school day, during which intense mobility and high noise levels usually evolve. Indoor noise levels, along with environmental conditions, have been measured through a wireless network that comprises IoT nodes that integrate humidity, temperature, and acoustic level sensors. PM\({}_{10}\) and PM\({}_{2.5}\) values have also been acquired through data sensors located nearby the school complex. School buildings that have been recently renovated for minimizing their energy footprint and CO\({}_{2}\) emissions have been selected in comparison with similar works in academia. The data are collected, shipped, and stored into a time-series database in cloud facilities where an IoT platform has been developed for processing and analysis purposes. The findings show that low-cost sensors can efficiently monitor noise levels after proper adjustments. Additionally, the statistical evaluation of the received sensor measurements has indicated that ubiquitous high noise levels during the course breaks potentially affect teachers' leisure time, despite the thermal isolation of the facilities. Within this context, we prove that the proposed IoT Sensor Network could form a tool to essentially monitor school infrastructures and thus to prompt for improvements regarding the building facilities. Several guides to further mitigate noise and achieve high-quality levels in learning institutes are also described. s 2020 2020 23 May 2025 2020 230 May 2025 2020 2  
youngsters to properly function [1; 2]. Consequently, WHO has clearly designated that the noise levels inside classes should not exceed 35 dBA (even lower levels for children with hearing impairments), while in extra-gathering spaces, noise levels should be maintained below 55 dBA [3]. Taking into account the aforementioned points, this work studies the effect of noise in learning environments, i.e., schools, based on the utilization and installation of wireless IoT sensor nodes and open-source IoT platforms. During the last years, several attempts have been made to assess the noise levels and effects in educational buildings. To this end, IoT-based systems and devices have been installed in school units around the world. Specifically, researchers in [4] used hand-held sound level meters and calibrators to acquire outdoor and indoor noise level measurements in 15 school buildings in Greece. They noted that roadsides and schoolyards are affected by noise produced during breaks and sports activities. This was even more noticeable in units that faced roads or schoolyards without facilitating double glazing. Similarly, authors in [5] utilized sound level meters to conclude that schools residing in socioeconomically vulnerable areas in Greece are attributed with high levels of noise, which significantly exceeded the recommended WHO limits. These noise levels were introduced by both the internal and external environments of schools. The work, conducted in [6], deployed an IoT infrastructure inside 5 school complexes to monitor the noise sources. They presented comparable results, however, focusing only on the performance of pupils during class activities. Smart schools that integrated both AI technology and intelligent sensors have shown that these collaborations can capture human body movements, emotions, sounds, and other signals to value the performance of teachers and pupils during class lessons [7]. In addition, teachers can be able to react accordingly, based on the noise level measurements from IoT frameworks, as has been demonstrated in [8; 9]. Also, research approaches in [10; 11; 12] resulted in similar outcomes regarding noise levels and how these potentially affect the performance in educational institutes.