Noise mapping based on participative
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Ear-Phone also implements in-situ calibration which performs simple calibration that can be carried out by general public. Upon context discovery, Ear-Phone automatically decides whether to sense or not. We develop classifiers to accurately determine the phone sensing context. Smartphone participative measurements are alternatively being developed, offering the high temporal and spatial granularities recommended by the EU directive. To address this problem, Ear-Phone leverages context-aware sensing. Nowadays, this assessment is addressed based on simulated noise maps, which however present some limitations due to the simplification of noise generation and propagation phenomena. A major challenge of using smart phones as sensors is that even at the same location, the sensor reading may vary depending on the phone orientation and user context (for example, whether the user is carrying the phone in a bag or holding it in her hand). Ear-Phone, implemented on Nokia N95, N97 and HP iPAQ, HTC One mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. You can load and display a GeoJSON file by calling the loadGeoJSON() method of the data object. In this paper, we present the design, implementation and performance evaluation of an end-to-end, context-aware, noise mapping system called Ear-Phone.Įar-Phone investigates the use of different interpolation and regularization methods to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Every map has a map.data object, which acts as a data layer for arbitrary geospatial data, including GeoJSON. Smart phone based urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (for months or even years), as they rely on population and traffic models rather than on real data. A final meeting in May 2011 presented the outcomes of both phases of the experiment and the resulting noise maps, including a discussion round, ample time for questioning, and a survey polling volunteers on their experience with NoiseTube and participatory noise mapping. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. 'Participatory Noise Mapping: Harnessing the Potential of Smartphones Through the Development of a Dedicated Citizen-Science Platform.' Proceedings of the ASME 2017 International Mechanical Engineering Congress and Exposition. A noise map facilitates the monitoring of environmental noise pollution in urban areas.