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Foerster, T., Jirka, S., Stasch, C., Pross, B., Everding, T., Broring, A. and Jürrens, E.H. (2010) Integrating Human Observations and Sensor Observations—The Example of a Noise Mapping Community. Proceedings Workshop Future Internet Symposium, Berlin, 20-22 September2010, 4 p.
http://www.arne-broering.de/FIS_DE_final.pdf

has been cited by the following article:

• TITLE: Processing and Unification of Environmental Noise Data from Road Traffic with Spatial Dimension Collected through Mobile Phones

  AUTHORS: Petr Duda

  KEYWORDS: Measurement Uncertainty, Environmental Noise, Mobile Phone, Citizen Science, Noise Mapping

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.4 No.13, December 27, 2016

  ABSTRACT: Noise measurement using mobile phones is now developed very well. While there are some good applications for the measurement of noise from road traffic, thus on processing of measured data is only paid a very little attention. The data, however, are burdened by specific errors and for further work with them it is necessary to adjust and determine their uncertainty. One of the biggest problems is inaccuracy in position versus the noise source and the shortest length of measurement that can be regarded as representative. Imprecision in terms of location can be determined by calculating the variance of possible distance from the noise source, which for measurement of traffic noise requires a map-matching data points both transverse to the street (sidewalk) network and in the longwise direction. During typical urban measurements, this error can even reach 7 - 10 dB. Three basic types of algorithms for the calculation of uncertainty and positional correction based on the type of input and output data (raster, vector, vector-oriented) were tested. Uncertainty in the variability of the measurement data is necessary to determine from the number of passing vehicles per time unit. The presented solutions are implemented in the Mobile Noise system.