research-article

Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement

Authors:

Yuan ChenAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 18, Pages 1 - 18

https://doi.org/10.1145/3191750

Published: 26 March 2018 Publication History

Abstract

Urban air quality information, e.g., PM2.5 concentration, is of great importance to both the government and society. Recently, there is a growing interest in developing low-cost sensors, installed on moving vehicles, for fine-grained air quality measurement. However, low-cost mobile sensors typically suffer from low accuracy and thus need careful calibration to preserve a high measurement quality. In this paper, we propose a two-phase data calibration method consisting of a linear part and a nonlinear part. We use MLS (multiple least square) to train the linear part, and use RF (random forest) to train the nonlinear part. We propose an automatic feature selection algorithm based on AIC (Akaike information criterion) for the linear model, which helps avoid overfitting due to the inclusion of inappropriate features. We evaluate our method extensively. Results show that our method outperforms existing approaches, achieving an overall accuracy improvement of 16.4% in terms of PM2.5 levels compared with state-of-the-art approach.

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Cited By

Narayana MRachavarapu KJalihal DNagendra S(2024)Sens-BERT: A BERT-Based Approach for Enabling Transferability and Re-Calibration of Calibration Models for Low-Cost Sensors Under Reference Measurements ScarcityIEEE Sensors Journal10.1109/JSEN.2024.336296224:7(11362-11373)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JSEN.2024.3362962
Liu XConcas FMotlagh NZaidan MFung PVarjonen SNiemi JTimonen HHussein TPetäjä TKulmala MNurmi PTarkoma S(2024)Estimating Black Carbon Levels With Proxy Variables and Low-Cost SensorsIEEE Internet of Things Journal10.1109/JIOT.2024.336197711:10(17577-17588)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3361977
Li ZMan FChen XXu SDang FZhang XChen X(2024)QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile CrowdsensingIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621374(1811-1820)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621374
Show More Cited By

Index Terms

Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Mobile devices

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 August 2017

Published in IMWUT Volume 2, Issue 1

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Funding Sources

Fundamental Research Funds for the Central Universities
National Science Foundation of China
Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang Provincial Key Research and Development Program

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Cited By

Narayana MRachavarapu KJalihal DNagendra S(2024)Sens-BERT: A BERT-Based Approach for Enabling Transferability and Re-Calibration of Calibration Models for Low-Cost Sensors Under Reference Measurements ScarcityIEEE Sensors Journal10.1109/JSEN.2024.336296224:7(11362-11373)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JSEN.2024.3362962
Liu XConcas FMotlagh NZaidan MFung PVarjonen SNiemi JTimonen HHussein TPetäjä TKulmala MNurmi PTarkoma S(2024)Estimating Black Carbon Levels With Proxy Variables and Low-Cost SensorsIEEE Internet of Things Journal10.1109/JIOT.2024.336197711:10(17577-17588)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3361977
Li ZMan FChen XXu SDang FZhang XChen X(2024)QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile CrowdsensingIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621374(1811-1820)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621374
Cavaliere ABrilli LAndreini BCarotenuto FGioli BGiordano TStefanelli MVagnoli CZaldei AGualtieri G(2023) Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO 2 and O 3 sensors Atmospheric Measurement Techniques10.5194/amt-16-4723-202316:20(4723-4740)Online publication date: 20-Oct-2023
https://doi.org/10.5194/amt-16-4723-2023
Vajs IDrajic DCica Z(2023)Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality MonitoringSensors10.3390/s2305281523:5(2815)Online publication date: 4-Mar-2023
https://doi.org/10.3390/s23052815
Ahmed MRony RHadi MHossain EAhmed N(2023)A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic PerformanceProceedings of the ACM on Human-Computer Interaction10.1145/36042407:MHCI(1-28)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604240
Gao HFeng JXiao YZhang BWang W(2023)A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd SensingIEEE Transactions on Mobile Computing10.1109/TMC.2022.314787122:7(3790-3804)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3147871
Narayana MJalihal DNagendra S(2023)EEATC: A Novel Calibration Approach for Low-Cost SensorsIEEE Sensors Journal10.1109/JSEN.2023.330436623:19(23500-23511)Online publication date: 1-Oct-2023
https://doi.org/10.1109/JSEN.2023.3304366
Li GWu ZLiu NLiu XWang YZhang L(2023)A Variational Bayesian Blind Calibration Approach for Air Quality Sensor DeploymentsIEEE Sensors Journal10.1109/JSEN.2022.321200923:7(7129-7141)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JSEN.2022.3212009
Shoushtari HWillemsen TSternberg H(2023)Supervised Learning Regression for Sensor Calibration2023 DGON Inertial Sensors and Systems (ISS)10.1109/ISS58390.2023.10361922(1-20)Online publication date: 24-Oct-2023
https://doi.org/10.1109/ISS58390.2023.10361922
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