NoizCrowd: A crowd-based data gathering and management system for noise level data

Abstract

Many systems require access to very large amounts of data to properly function, like systems allowing to visualize or predict meteorological changes in a country over a given period of time, or any other system holding, processing and displaying scientific or sensor data. However, filling out a database with large amounts of valuable data can be a difficult, costly and time-consuming task. In this paper, we present techniques to create large amounts of data by combining crowdsourcing, data generation models, mobile computing, and big data analytics. We have implemented our methods in a system, NoizCrowd, allowing to crowdsource noise levels in a given region and to generate noise models by using state-of-the-art noise propagation models and array data management techniques. The resulting models and data can then be accessed using a visual interface. © 2013 Springer-Verlag.