Proceedings ArticleDOI
Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor Hub
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TLDR
It is demonstrated that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information.Abstract:
Air pollution monitoring systems with energy-intensive sensors cannot afford to sample frequently in order to maximize time between successive recharges. In this paper, we propose an energy-efficient machine learning based sensor duty-cycling method for a sensor hub receiving data from the air-pollution sensors. In particular, we demonstrate that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information. Support Vector Regression is used to predict the missing samples during the period sensor is turned off.read more
Citations
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The rise of low-cost sensing for managing air pollution in cities
Prashant Kumar,Lidia Morawska,Claudio Martani,George Biskos,George Biskos,George Biskos,Marina Neophytou,Silvana Di Sabatino,Margaret Bell,Leslie Norford,Rex Britter +10 more
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Posted Content
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Francesco Concas,Julien Mineraud,Eemil Lagerspetz,Samu Varjonen,Xiaoli Liu,Kai Puolamäki,Petteri Nurmi,Sasu Tarkoma +7 more
TL;DR: The rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques is surveyed and open research challenges are identified and present directions for future research.
Journal ArticleDOI
Green Sensing and Communication: A Step Towards Sustainable IoT Systems
TL;DR: This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications and presents a few case studies that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality.
Journal ArticleDOI
Transit Pollution Exposure Monitoring using Low-Cost Wearable Sensors
Naser Hossein Motlagh,Martha A. Zaidan,Martha A. Zaidan,Pak Lun Fung,Eemil Lagerspetz,Kasimir Aula,Samu Varjonen,Matti Siekkinen,Andrew Rebeiro-Hargrave,Tuukka Petäjä,Tuukka Petäjä,Yutaka Matsumi,Markku Kulmala,Markku Kulmala,Markku Kulmala,Tareq Hussein,Tareq Hussein,Petteri Nurmi,Sasu Tarkoma +18 more
TL;DR: In this paper, the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters is analyzed by using extensive experiments carried out in the Helsinki metropolitan region, and they demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors.
Proceedings ArticleDOI
Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements
Naser Hossein Motlagh,Martha A. Zaidan,Pak Lun Fung,Xinyang Li,Yutaka Matsumi,Tuukka Petäjä,Markku Kulmala,Sasu Tarkoma,Tareq Hussein +8 more
TL;DR: In this paper, the authors present an air quality sensing process needed for low-cost sensors which are planned for long-term use, including design and production, laboratory tests, field tests, deployment, and maintenance.
References
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Proceedings ArticleDOI
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Journal ArticleDOI
Forecasting of air quality in Delhi using principal component regression technique
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TL;DR: In this article, the authors used principal component regression (PCR) to forecast short-term daily air quality index (AQI) through previous day's AQI and meteorological variables.