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Internet of Things for noise mapping in smart cities: state of the art and future directions

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journal contribution
posted on 2021-08-03, 13:51 authored by Ye Liu, Xiaoyuan Ma, Lei Shu, Qing Yang, Eve ZhangEve Zhang, Zhiqiang Huo, Zhangbing Zhou
Noise pollution has been an issue since ancient times. Recently, this problem has been exacerbated due to rapid population growth and urbanization. Noise mapping is a strategic action plan that visualizes the long-term and real-time noise pollution of our cities, industrial sites, and other regions of interest. This article first discusses the working principle of general model-based noise mapping and the lessons learned. Then, in-depth descriptions of the technical challenges and design issues of noise mapping using mobile crowdsensing and acoustic sensor networks are presented. Finally, we provide our insights for future research directions regarding artificial intelligence assisted noise prediction, constructive interference for multimedia transmission, and simultaneous noise sensing and sound energy harvesting as well as inaudible sound attacks and defense.

Funding

National Natural Science Foundation of China (No. 61902188)

Fundamental Research Funds for the Central Universities (No. KJQN202047)

China Postdoctoral Science Foundation (No. 2019M651713)

Science and Technology Planning Project of Guangdong Province (No. 2017A050506057)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Network

Volume

34

Issue

4

Pages

112 - 118

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-06-05

Copyright date

2020

ISSN

0890-8044

eISSN

1558-156X

Language

  • en

Depositor

Dr Eve Zhang. Deposit date: 29 July 2021