NoiseSense: A Crowd Sensing System for Urban Noise Mapping Service

Abstract

Noise pollution poses a serious threat to people living in cities today. To alleviate the negative impact of noise pollution, an urban noise mapping can be helpful. In this paper, we present the design of NoiseSense, a crowd sensing system for housing a real-time urban noise mapping service. A major challenge in building such a system is caused by the sparsity problem of the limited noise measurement data from smartphones. To tackle this challenge, we propose a semi-supervised tensor completion algorithm for inferring noise levels for locations without measurements by smartphone users. This algorithm leverages a variety of urban data sources, such as Point of Interests (PoIs), road networks, and check-in data. We implemented the system and developed an APP for smartphone users. We conducted experiments and field study. The experimental results show that the proposed algorithm is superior in inferring noise levels merely with sparse measurements from smartphone users.