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C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on prior Knowledge

Published: 24 January 2023 Publication History

Abstract

CO2 concentration data with high resolution in large venues is highly required during indoor sport events for in-time environment adjustment to guarantee the athlete performances and audience experience. However, the limited battery energy of the wireless sensors cannot support high data resolution and long time coverage simultaneously. Besides, there also lacks effective embedded methods to clean anomaly data caused by the human and environmental factors probably occurring in large venues. Thus, in this paper, we propose C-RIDGE, a low-power sensing system for high resolution CO2 data collection in large venues. Based on prior knowledge, firstly, an adaptive sampling rate adjustment policy is developed for lower energy consumption to extend the time coverage of data. Secondly, CO2 physical property (CPP) aided data cleaning algorithm is designed to improve data quality as well, using Pearson Correlation Coefficient (PCC) and standard deviation with sliding windows. C-RIDGE has been deployed in one venue during a world-class event. The experiments and collected data have shown the system power consumption can be reduced by 36.1%, with measurement error less than 10.2%. The outliers and anomaly trends can also be detected and calibrated effectively via CPP algorithm. The dataset is available at https://doi.org/10.5281/zenodo.7160830.

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cover image ACM Conferences
SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
November 2022
1280 pages
ISBN:9781450398862
DOI:10.1145/3560905
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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Publication History

Published: 24 January 2023

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Author Tags

  1. CO2 sensing
  2. data analysis
  3. data collection
  4. low power system

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  • Research-article

Funding Sources

  • Key Laboratory of Precision Opto-mechatronics Technology, Ministry of Education (Beihang University)
  • National Key R&D Program of China

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SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;
Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

View all
  • (2024)BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area NetworkACM Transactions on Sensor Networks10.1145/364128020:3(1-23)Online publication date: 23-Feb-2024
  • (2024)Physics-informed Neural ODE for Post-disaster Mobility RecoveryProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672027(1587-1598)Online publication date: 25-Aug-2024
  • (2023)CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised LearningAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612917(743-748)Online publication date: 8-Oct-2023

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