Design and Simulation of an IoT-Based Library Noise Monitoring System Using ESP32 and ThingSpeak

Abstract

This research details the creation and simulation of a noise monitoring system for libraries that is based on the Internet of Things (IoT), making use of an ESP32 microcontroller, a KY-037 sound detection device, the ThingSpeak cloud service, and a Mamdani fuzzy inference system. The main aim of this study is to constantly keep track of how loud it is and sort the levels into different noise conditions described in words, so that library supervisors can assess the acoustic setting and any possible disruptions in the areas where people read. In the designed system, the ESP32 acts as an IoT point that takes analog signals from the KY-037 sensor, changes these signals into decibel (dB) values, and sends the data to a safe ThingSpeak channel using a way of sending data that depends on events, which happens when noise levels go over certain set limits. Old noise data is taken out in CSV format and handled in MATLAB, where a Mamdani fuzzy model with three types of input (quiet, moderate, and noisy) and three levels of output (low, medium, and high) is put in place to make a noise index that goes from 0 to 100. The simulation results show that the ESP32–KY-037 setup sends noise data to ThingSpeak in a trustworthy manner without any breaks in communication, and the fuzzy output tracks the general pattern of the sensor data, giving a smoother way to sort noise. The suggested system can tell the difference between calm and potentially disruptive conditions, which backs up the idea of a smart library space with abilities for remote monitoring. This research shows that it is doable to put together ESP32, ThingSpeak, and fuzzy logic for noise monitoring that adjusts itself, and it lays the groundwork for future work that includes more sensor points and warning systems that work in real time.