Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring

On assessing the accuracy of air pollution models exploiting a strategic sensors deployment

This paper presents a preliminary experiment done to identify potential problems and issues in setting up a testbed for air pollution measurement and modeling. Our final testbed, part of a joint research activity between the University of Bologna and the Macao Polytechnic Institute, will be composed of three lines of the air pollution sensors Canarin II and it will be used to produce spatio-temporal open data to test third-party air pollution models. Here, we present a preliminary experiment based on a single line of sensors, showing interesting insights into the actual open challenge of air pollution modeling techniques validation, taking into account the effects of air pollutant emissions sources, meteorology, atmospheric concentrations and urban vegetation