A case study on monitoring and geolocation of noise in urban environments using the internet of things

This paper presents the development of a system to monitor and georeference noise in urban environments using the Internet-offerings (IoT). the system intends to help control agencies and citizens to monitor noise using smart devices and services available on the Cloud for data sharing. the system includes a mobile application that periodically captures the microphone's audio signal during a configurable time window, obtains the mobile's global position after each measurement using the built-in GPS, and assigns a timestamp from the operating system. then, a Fast Fourier Transform is applied to recorded audio and the power spectrum in decibels is extracted. the resulting vector is sampled at specific frequencies to create a vector of audio features that can be used to assess noise pollution completely offline. If an Internet connection is available, a telemetry message is assembled and sent to an IoT Hub in Microsoft. Azure Cloud containing a unique ID, a position stamp, a time stamp, and all audio features. the message is transferred to a Stream Analytics service, and from there it is sent to a Cloud SQL Database for permanent storage. the historical information collected and shared by different users can be examined online by any individual through a customized report developed in Microsoft Power BI.