Node-to-node field calibration of wireless distributed air pollution sensor network

Kizel, Fadi; Etzion, Yael; Shafran-Nathan, Rakefet; Levy, Ilan; Fishbain, Barak; Bartoňová, Alena; Broday, David M.

doi:10.1016/j.envpol.2017.09.042

Cited by 73 publications

(41 citation statements)

References 28 publications

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“…Monitoring networks with online analytics and visualization were more common in recent studies where some degree of human safety or health was related to the sensed parameters. Examples include geo-hazards (Finazzi and Fassò, 2017), flooding (Jones et al, 2015;Acosta-Coll et al, 2018;Bartos et al, 2018) or air quality (Schneider et al, 2017;Kizel et al, 2018). A further type of monitoring network with analytics and visualization was associated with agriculture (Kubicek et al, 2013) and in several studies this was advanced toward automated control of nutrient addition/irrigation to optimize resource use and yield (López et al, 2015;Srbinovska et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias toward studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualizations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focused on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts.

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Section: Resultsmentioning

confidence: 99%

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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“…Chain co-location: co-locate sensor with reference site; move to buddy with test site for a period; move original test site sensor to buddy at next test site etc eventually back to reference station Sensor parameter variation is slow with respect to time scale for the sensor progression around the sites. 71 Hierarchical network, of a number of well-maintained reference stations together with a much larger number of low-cost sensors. For each sensor site, identify a 'proxy' that has a similar probability distribution of measurement values, evaluated over a suitable period.…”

Section: Acknowledgementmentioning

confidence: 99%

Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

Williams

2019

ACS Sens.

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Plausibility of data from networks of low-cost measurement devices is a growing and important contentious issue. Informal networks of low-cost devices have particularly come to prominence for air quality monitoring. The contentious point is the believability of data without regular on-site calibration since that is a specialist task and the costs very quickly become very much larger than the cost of installation in the first place. This article suggests that approaches to the problem that involve appropriate use of independent information have the potential to resolve the contention. Ideas are illustrated particularly with reference to low-cost sensor networks for air quality measurement.

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“…However, it would still rely on the deployment of the sensor nodes. In [14], authors utilize nodeto-node calibration, in which at a time one sensor in each chain is directly calibrated against the reference measurements and the rest are calibrated sequentially while they are deployed and occur in pairs. This deployment reduces the load on sensor relocations and ensures simultaneous calibration and data collection.…”

Section: Wireless Network Based Techniquesmentioning

confidence: 99%

VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting

Madaan

Dua

Mukherjee

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking effective pollution control measures, and accurate air pollution forecasting has become an important task. In this paper, we forecast fine-grained ambient air quality information for 5 prominent locations in Delhi based on the historical and realtime ambient air quality and meteorological data reported by Central Pollution Control board. We present VayuAnukulani system, a novel end-to-end solution to predict air quality for next 24 hours by estimating the concentration and level of different air pollutants including nitrogen dioxide (N O2), particulate matter (P M2.5 and P M10) for Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models. The accuracy of the proposed adaptive system is ∼ 15 − 20% better than the same offline trained model. We compare the proposed methodology on several competing baselines, and show that the network outperforms conventional methods by ∼ 3 − 5%.

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Node-to-node field calibration of wireless distributed air pollution sensor network

Cited by 73 publications

References 28 publications

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting

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