On Modeling Air Pollution Detection With Internet of Vehicles

Shakhov, Vladimir; Sokolova, Olga

doi:10.1109/imcom51814.2021.9377350

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Other papers described problems that arise when monitoring the atmosphere of an urban area using mobile devices [22][23][24][25][26]. For example, it is necessary to have a schedule for data collection in order to optimize the power consumption in the network.…”

Section: Related Workmentioning

confidence: 99%

“…In [24], we introduced the following notations to describe the system model. N is the total number of mobile objects (vehicles, buses, unmanned aerial vehicles, and so on), equipped with sensors for air pollution detection.…”

Section: System Modelsmentioning

confidence: 99%

“…ET E a 2 + Na 1 (24) Here ET E is the expected pollution detection time. Thus, using introduced designations, we obtain the following penalty function:…”

Section: System Optimizationmentioning

confidence: 99%

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Optimizing Urban Air Pollution Detection Systems

Shakhov

Materuhin

Sokolova

et al. 2022

Sensors

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Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

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