research-article

Spatio-Temporal Aware Collaborative Mobile Sensing with Online Multi-Hop Calibration

Authors:

Teng Xi,

Wendong Wang,

Edith C.-H. Ngai,

Xiuming LiuAuthors Info & Claims

Mobihoc '18: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

Pages 310 - 311

https://doi.org/10.1145/3209582.3225202

Published: 26 June 2018 Publication History

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Abstract

Real-time accurate air quality data is very important for pollution exposure monitoring and urban planning. However, there are limited high-quality air quality monitoring stations (AQMS) in cities due to their high equipment costs. To provide real-time and accurate data covering large area, this paper proposes a novel scheme that jointly considers online multi-hop calibration and spatio-temporal coverage in route selection for mobile sensors. A novel sensor carrier selection problem (SCSP) is formulated, which aims to maximize the spatio-temporal coverage ratio and guarantee the accuracy of measurements through sensor calibration. An online Bayesian based collaborative calibration (OBCC) scheme is proposed to relax the multi-hop calibration constraint in the SCSP. Based on the OBCC, a multi-hop calibration judgment algorithm (MCJA) is proposed to decide whether the data accuracy of a given set of routes can be guaranteed through collaborative calibration. Furthermore, a heuristic sensor route selection algorithm (SRSA) is then developed to solve the SCSP.

References

[1]

Kaibo Fu, Wei Ren, and Wei Dong. {n. d.}. Multihop Calibration for Mobile Sensing: k-hop Calibratability and Reference Sensor Deployment. In Proceedings of INFOCOM'17, Atlanta, GA, USA, 2017, IEEE.

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[2]

Yi Gao, Wei Dong, Kai Guo, Xue Liu, Yuan Chen, Xiaojin Liu, Jiajun Bu, and Chun Chen. {n. d.}. Mosaic: A Low-Cost Mobile Sensing System for Urban Air Quality Monitoring. In Proceedings of INFOCOM'16, pages 1--9, San Francisco, CA, USA, 2016, IEEE.

Google Scholar

[3]

Zhu Liu. 2015. Steps to China's carbon peak. Nature 522, 7556 (2015), 279.

Google Scholar

[4]

Olga Saukh, David Hasenfratz, and Lothar Thiele. {n. d.}. Reducing multi-hop calibration errors in large-scale mobile sensor networks. In Proceedings of IPSN'15, pages 274--285, Seattle, WA, USA, 2015, ACM.

Digital Library

Google Scholar

[5]

Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. {n. d.}. U-Air: when urban air quality inference meets big data. In Proceedings of SIGKDD'13, pages 1436--1444, Chicago, IL, USA, 2013, ACM.

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Cited By

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Zarrar HDyo V(2023)Drive-by Air Pollution Sensing Systems: Challenges and Future DirectionsIEEE Sensors Journal10.1109/JSEN.2023.330577923:19(23692-23703)Online publication date: 1-Oct-2023
https://doi.org/10.1109/JSEN.2023.3305779
Okorn KHannigan M(2021)Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor AgeAtmosphere10.3390/atmos1205064512:5(645)Online publication date: 19-May-2021
https://doi.org/10.3390/atmos12050645

Index Terms

Spatio-Temporal Aware Collaborative Mobile Sensing with Online Multi-Hop Calibration
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Kernel methods
      2. Learning linear models
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Published In

Mobihoc '18: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 2018

329 pages

ISBN:9781450357708

DOI:10.1145/3209582

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

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Mobihoc '18

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SIGMOBILE

Mobihoc '18: The Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 26 - 29, 2018

CA, Los Angeles, USA

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Overall Acceptance Rate 296 of 1,843 submissions, 16%

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Cited By

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Zarrar HDyo V(2023)Drive-by Air Pollution Sensing Systems: Challenges and Future DirectionsIEEE Sensors Journal10.1109/JSEN.2023.330577923:19(23692-23703)Online publication date: 1-Oct-2023
https://doi.org/10.1109/JSEN.2023.3305779
Okorn KHannigan M(2021)Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor AgeAtmosphere10.3390/atmos1205064512:5(645)Online publication date: 19-May-2021
https://doi.org/10.3390/atmos12050645

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