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Urban noise mapping with a crowd sensing system

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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 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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Notes

  1. The “unfold” operation along the kth mode on a tensor \(\varvec{X}\) is defined by \(unfol{d_k}({\varvec{X}} ): = {{\varvec{X}}_{(k)}} \in {R^{{I_k} \times ({{I_1}, \ldots {I_{k - 1,}} \ldots {I_N}})}}\)

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Acknowledgements

This research is supported in part by 973 Program (2014CB340303), 863 Program (No. 2015AA015303), NSFC (Nos. 61472254, 61170238 and 61420106010), STCSM (Grant Nos. 14511107500 and 15DZ1100305), Research Grant for Young Faculty in Shenzhen Polytechnic (No. 601522K30015), SZSTI (No. JCYJ20160407160609492) and Singapore NRF (CREATE E2S2). This work is also supported by the Program for New Century Excellent Talents in University of China, the Program for Changjiang Young Scholars in University of China, and the Program for Shanghai Top Young Talents.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yanan Xu, Yanmin Zhu & Zhaokun Qin

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  1. Yanan Xu
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  2. Yanmin Zhu
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  3. Zhaokun Qin
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Correspondence to Yanmin Zhu.

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Xu, Y., Zhu, Y. & Qin, Z. Urban noise mapping with a crowd sensing system. Wireless Netw 25, 2351–2364 (2019). https://doi.org/10.1007/s11276-018-1663-x

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