Environmental Noise Monitoring Using Distributed IoT Sensor Nodes

Common issues associated with wireless sensor networks for analysing environmental noise include high power usage, high cost, and limited scalability. In this paper, we present a novel approach to develop an autonomous system for collecting environmental noise information. We designed a system to be highly scalable, easy to use, low-cost, and low-powered to encourage its widespread adoption. Power usage is kept low by investigating the use of Bluetooth Low Energy communication protocol for streaming environmental noise. Beyond merely detecting noise levels, the proposed system can classify the source of the measured noise. Mel-frequency cepstrum coefficients are used to produce features of the noise and model Gaussian mixture models to classify the source of the noise. Information from the system is sent to a cloud storage service in real-time which enables visualization of noise levels, noise sources and battery level of the sensors in our web application. The system was able to reliably collect information over a 4-week field test. It has an average classification accuracy of 72% when subjected to four common environmental noise sources.