Detection of malfunctions in sensor networks

Alavi, Maryam; Williams, David E.; Salmond, Jennifer; Kaipio, Jari P.

doi:10.1002/env.2206

Cited by 19 publications

(5 citation statements)

References 33 publications

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“…Routine, regular calibration, such as is applied to the reference network, is too expensive to be practical. In other work, 17 we have explored the use of correlations across the network to generate alarms, with the network being "trained" on data obtained when the instruments are newly deployed, but this method was of only limited use in the present study because of the relatively short period of deployment. In the absence of a present complete solution to the problem of data validation without regular calibration, we have used several criteria to accept valid data from the low-cost instruments.…”

Section: ■ Background and Methodsmentioning

confidence: 99%

High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia

Bart¹,

Williams

Ainslie

et al. 2014

Environ. Sci. Technol.

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A cost-efficient technology for accurate surface ozone monitoring using gas-sensitive semiconducting oxide (GSS) technology, solar power, and automated cell-phone communications was deployed and validated in a 50 sensor test-bed in the Lower Fraser Valley of British Columbia, over 3 months from May-September 2012. Before field deployment, the entire set of instruments was colocated with reference instruments for at least 48 h, comparing hourly averaged data. The standard error of estimate over a typical range 0-50 ppb for the set was 3 ± 2 ppb. Long-term accuracy was assessed over several months by colocation of a subset of ten instruments each at a different reference site. The differences (GSS-reference) of hourly average ozone concentration were normally distributed with mean -1 ppb and standard deviation 6 ppb (6000 measurement pairs). Instrument failures in the field were detected using network correlations and consistency checks on the raw sensor resistance data. Comparisons with modeled spatial O3 fields demonstrate the enhanced monitoring capability of a network that was a hybrid of low-cost and reference instruments, in which GSS sensors are used both to increase station density within a network as well as to extend monitoring into remote areas. This ambitious deployment exposed a number of challenges and lessons, including the logistical effort required to deploy and maintain sites over a summer period, and deficiencies in cell phone communications and battery life. Instrument failures at remote sites suggested that redundancy should be built into the network (especially at critical sites) as well as the possible addition of a "sleep-mode" for GSS monitors. At the network design phase, a more objective approach to optimize interstation distances, and the "information" content of the network is recommended. This study has demonstrated the utility and affordability of the GSS technology for a variety of applications, and the effectiveness of this technology as a means substantially and economically to extend the coverage of an air quality monitoring network. Low-cost, neighborhood-scale networks that produce reliable data can be envisaged.

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Section: ■ Background and Methodsmentioning

confidence: 99%

High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia

Bart¹,

Williams

Ainslie

et al. 2014

Environ. Sci. Technol.

Self Cite

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“…Although the 65 reference monitoring stations provide a good overview of air quality in the country, they are generally located away from emission sources and are unable to provide observations at high spatial resolutions. Therefore, low-cost air quality sensors for air quality measurements are deployed by local authorities and researchers globally to fill in the research gap (Alavi-Shoshtari et al, 2013;Austin et al, 2015;Alhasa et al, 2018). One potential solution for air pollution monitoring in urban areas is the use of 'Low-cost Air Quality Sensors' or LAQS.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Movement Control Order (MCO) during Pandemic COVID-19 on Local Air Quality in an Urban Area of Klang Valley, Malaysia

Nadzir

Ooi

Alhasa

et al. 2020

Aerosol Air Qual. Res.

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The world is currently going through the COVID-19 pandemic which has caused hundreds of thousands of deaths in just a few months. Considering the need for lockdown measures, most countries, including Malaysia, have implemented 'Movement Control Orders' (MCOs) as a prevention step to reduce the deadly spread of this disease. Local and worldwide media have reported the immediate improvement of air quality due to this event. Nevertheless, data on the effects of MCOs on air quality at local scales are still sparse. Here, we investigate changes in air quality during the MCO at an urban area using the air sensor network AiRBOXSense which measures monoxide (CO) and particulate matter (PM 2.5 and PM 10). In this study, air pollutant data during normal days were compared with MCO days using a reference analyser and AiRBOXSense. The results showed that the levels of the measured pollutants dropped by ~20 to 60% during the MCO days at most locations. However, CO in Kota Damansara (KD) dropped to 48.7%, but PM 2.5 and PM 10 increased up to 60% and 9.7% respectively during MCO days. Local burning activities in the residential area of KD are believed to be the main cause of the increased PM levels. This study has proven that air pollutant levels have significantly fallen due to the MCO. This air quality level information showed that the reduction of air pollutants can be achieved if traffic and industry emissions are strictly controlled.

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“…Consequently, we were able to demonstrate remote correction of low-cost sensors that are deployed in dense networks (Alavi-Shoshtari et al, 2013;Miskell et al, 2016;Miskell et al, 2018, Miskell et al 2019. The management framework was tested using hierarchical networks, consisting of well-maintained regulatory-grade instruments and low-cost O3 sensors deployed around the Lower Fraser Valley (LFV) in Canada (Miskell et al, 2018) and Southern California (Miskell et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical network design for nitrogen dioxide measurement in urban environments

Weissert

Miles²,

Miskell

et al. 2020

Atmospheric Environment

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We present a management and data correction framework for low-cost electrochemical sensors for nitrogen dioxide (NO2) deployed within a hierarchical network of low-cost and regulatorygrade instruments. The framework is founded on the idea that it is possible in a suitably configured network to identify a source of reliable 'proxy' data for each sensor site that has a similar probability distribution of measurement values over a suitable time period. Previous work successfully applied these ideas to a sensor system with a simple linear 2-parameter (slope and offset) response, with parameters estimated by moment matching site and proxy data distributions. However, applying these ideas to electrochemical sensors for NO2 presents significant additional difficulties for which we demonstrate solutions. The three NO2 sensor response parameters (offset, ozone (O3) response slope, and NO2 response slope) are known to vary significantly as a consequence of ambient humidity and temperature variations. Here we demonstrate that these response parameters can be estimated by minimising the Kullback-Leibler divergence between sensor-estimated and proxy NO2 distributions over a 3-day b Present address: Trustpower, 108 Durham St, Tauranga, New Zealand 1 window. We then estimate an additional offset term by using co-location data. This offset term is dependent on climate and spatially correlated and can thus be projected across the network.Co-location data also estimates the time-, space-and concentration-dependent error distribution between sensors and regulatory-grade instruments. Robust O3 measurements are obtained using a semiconducting oxide-based instrument, previously described. We show how the parameter variations can be used to indicate both sensor failure and failure of the proxy assumption. With these procedures, we demonstrate measurement at nine different locations across two regions of Southern California over seven months with average root mean square error ± 7.2 ppb (range over locations 4 -11 ppb) without calibration other than the remote proxy comparison. We apply the procedures to a network of 56 sensors distributed across the Inland Empire and Los Angeles County regions. The results show large variations in NO2 concentration taking place on short time-and distance scales across the region. These spatiotemporal NO2 variations were not captured by the more sparsely distributed regulatory network of air monitoring stations demonstrating the need for reliable data from dense networks of monitors to supplement the existing regulatory networks.

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Detection of malfunctions in sensor networks

Cited by 19 publications

References 33 publications

High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia

High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia

The Impact of Movement Control Order (MCO) during Pandemic COVID-19 on Local Air Quality in an Urban Area of Klang Valley, Malaysia

Hierarchical network design for nitrogen dioxide measurement in urban environments

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