Journal
WIRELESS NETWORKS
Volume 25, Issue 5, Pages 2351-2364Publisher
SPRINGER
DOI: 10.1007/s11276-018-1663-x
Keywords
Noise pollution; Crowd sensing; Noise calibration; Tensor completion; Noise map
Categories
Funding
- 973 Program [2014CB340303]
- 863 Program [2015AA015303]
- NSFC [61472254, 61170238, 61420106010]
- STCSM [14511107500, 15DZ1100305]
- Research Grant for Young Faculty in Shenzhen Polytechnic [601522K30015]
- SZSTI [JCYJ20160407160609492]
- Singapore NRF (CREATE E2S2)
- Program for New Century Excellent Talents in University of China
- Program for Changjiang Young Scholars in University of China
- Program for Shanghai Top Young Talents
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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 hybrid approach including a neighborhood-based noise level estimation method and a semi-supervised tensor completion algorithm for inferring noise levels for locations without measurements by smartphone users. This approach leverages a variety of urban data sources, such as Point of Interests, road networks, and check-in data. We also provide a noise prediction method for forecasting the noise levels in the next few hours. We implemented the system and developed an APP for smartphone users. We conducted experiments and field study. The experimental results show that the proposed approach is superior in inferring noise levels merely with sparse measurements from smartphone users. And the prediction approach also outperforms other baseline methods.
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