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Multi-criteria anomaly detection in urban noise sensor networks

Samuel Dauwe (UGent) , Damiano Oldoni (UGent) , Bernard De Baets (UGent) , Timothy Van Renterghem (UGent) , Dick Botteldooren (UGent) and Bart Dhoedt (UGent)
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Abstract
The growing concern of citizens about the quality of their living environment and the emergence of low-cost microphones and data acquisition systems triggered the deployment of numerous noise monitoring networks spread over large geograPhical areas. Due to the local character of noise pollution in an urban environment, a dense measurement network is needed in order to accurately assess the spatial and temporal variations. The use of consumer grade microphones in this context appears to be very cost-efficient compared to the use of measurement microphones. However, the lower reliability of these sensing units requires a strong quality control of the measured data. To automatically validate sensor (microphone) data, prior to their use in further processing, a multi-criteria measurement quality assessment model for detecting anomalies such as microphone breakdowns, drifts and critical outliers was developed. Each of the criteria results in a quality score between 0 and 1. An ordered weighted average (OWA) operator combines these individual scores into a global quality score. The model is validated on datasets acquired from a real-world, extensive noise monitoring network consisting of more than 50 microphones. Over a period of more than a year, the proposed approach successfully detected several microphone faults and anomalies.
Keywords
AUDITORY ATTENTION, IBCN, HEALTH

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MLA
Dauwe, Samuel, Damiano Oldoni, Bernard De Baets, et al. “Multi-criteria Anomaly Detection in Urban Noise Sensor Networks.” ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS 16.10 (2014): 2249–2258. Print.
APA
Dauwe, S., Oldoni, D., De Baets, B., Van Renterghem, T., Botteldooren, D., & Dhoedt, B. (2014). Multi-criteria anomaly detection in urban noise sensor networks. ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 16(10), 2249–2258.
Chicago author-date
Dauwe, Samuel, Damiano Oldoni, Bernard De Baets, Timothy Van Renterghem, Dick Botteldooren, and Bart Dhoedt. 2014. “Multi-criteria Anomaly Detection in Urban Noise Sensor Networks.” Environmental Science-processes & Impacts 16 (10): 2249–2258.
Chicago author-date (all authors)
Dauwe, Samuel, Damiano Oldoni, Bernard De Baets, Timothy Van Renterghem, Dick Botteldooren, and Bart Dhoedt. 2014. “Multi-criteria Anomaly Detection in Urban Noise Sensor Networks.” Environmental Science-processes & Impacts 16 (10): 2249–2258.
Vancouver
1.
Dauwe S, Oldoni D, De Baets B, Van Renterghem T, Botteldooren D, Dhoedt B. Multi-criteria anomaly detection in urban noise sensor networks. ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS. 2014;16(10):2249–58.
IEEE
[1]
S. Dauwe, D. Oldoni, B. De Baets, T. Van Renterghem, D. Botteldooren, and B. Dhoedt, “Multi-criteria anomaly detection in urban noise sensor networks,” ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, vol. 16, no. 10, pp. 2249–2258, 2014.
@article{5782463,
  abstract     = {The growing concern of citizens about the quality of their living environment and the emergence of low-cost microphones and data acquisition systems triggered the deployment of numerous noise monitoring networks spread over large geograPhical areas. Due to the local character of noise pollution in an urban environment, a dense measurement network is needed in order to accurately assess the spatial and temporal variations. The use of consumer grade microphones in this context appears to be very cost-efficient compared to the use of measurement microphones. However, the lower reliability of these sensing units requires a strong quality control of the measured data. To automatically validate sensor (microphone) data, prior to their use in further processing, a multi-criteria measurement quality assessment model for detecting anomalies such as microphone breakdowns, drifts and critical outliers was developed. Each of the criteria results in a quality score between 0 and 1. An ordered weighted average (OWA) operator combines these individual scores into a global quality score. The model is validated on datasets acquired from a real-world, extensive noise monitoring network consisting of more than 50 microphones. Over a period of more than a year, the proposed approach successfully detected several microphone faults and anomalies.},
  author       = {Dauwe, Samuel and Oldoni, Damiano and De Baets, Bernard and Van Renterghem, Timothy and Botteldooren, Dick and Dhoedt, Bart},
  issn         = {2050-7887},
  journal      = {ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS},
  keywords     = {AUDITORY ATTENTION,IBCN,HEALTH},
  language     = {eng},
  number       = {10},
  pages        = {2249--2258},
  title        = {Multi-criteria anomaly detection in urban noise sensor networks},
  url          = {http://dx.doi.org/10.1039/c4em00273c},
  volume       = {16},
  year         = {2014},
}

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