Optimization of Cloud Resources for Air Pollution Monitoring Devices

Kaur, Pankaj Deep; Singh, Parampreet

doi:10.1145/2979779.2979807

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…A lot of practice has also taken place on the matter. But this particular paper [10] points out one issue in the ongoing practices of using cloud computing to monitor air pollution. That is managing the cloud resources.…”

Section: Related Workmentioning

confidence: 98%

“…That is managing the cloud resources. This paper [10] addresses that the algorithms which have been used so far take a lot of unnecessary resources thus overload the hosts. Which results in costly maintenance.…”

Section: Related Workmentioning

confidence: 99%

“…Which results in costly maintenance. So, this paper [10] comes out with a solution about how the resources can be managed properly get the best out of them. This paper [10] comes out with an algorithm called "OTCR" to address this problem.…”

Section: Related Workmentioning

confidence: 99%

“…So, this paper [10] comes out with a solution about how the resources can be managed properly get the best out of them. This paper [10] comes out with an algorithm called "OTCR" to address this problem. This algorithm will sort out the resources and distribute them to hosts according to their capability their current situation.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm would work similarly in case of any host being underloaded with resources. This paper [10] would help the usage of Cloud Computing in monitoring air pollution by sharpening it by making the best usage of the resources. Though one drawback of the paper [10] is it doesn't mention how to drop out the unnecessary resources.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Approach to Reduce Air Pollution Through Machine Learning Based PM2.5 Prediction

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et al. 2022

2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)

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AI (Artificial Intelligence) is one of the most fascinating subjects for computer scientists in this century. It is a technology that helps a machine to do tasks by itself which needs human intelligence. This technology reduces human error and increases the productivity of any task by a greater number of times. Nowadays AI is being used by many devices and also it has a limitless possibility to work with. As this technology grows faster scientists are combining it with various other technologies to be perfect. Cloud computing and IoT (Internet of Things) can be used in a framework for an AI to be more intelligent and give access to more data. For the past few years, only a small group of people around the world are thinking about the environment that they are living in where the environment must be the priority to think about. Air pollution is one of the causes which people are gradually becoming sick. Polluted air causes different lung diseases for which people also need to bear medical expenses. When technology is in the hands of every person, it is a must to make them aware of what they are inhaling and how they can make the world a better place. So, the answer to the question of what we are doing is that this research proposes a novel approach for reducing air pollution by making people more aware of their day-to-day life by detecting the environmental air condition with the help of machine learning by using PM2.5 prediction and computational cloud computing. As a result, we will introduce a framework that allows one to know about the air that one is inhaling and how to counter the air pollution by providing a computational output with the help of PM2.5 prediction, Computational Cloud Computing, Networking, and IoT with necessary actions with the help of some known methods such as Logistic Regression Analytics, Artificial Neural Networks, Decision Tree and Long short-term memory neural networks.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Approach to Reduce Air Pollution Through Machine Learning Based PM2.5 Prediction

Monsaif

Alif

Amarth

et al. 2022

2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)

View full text Add to dashboard Cite

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Smart Platforms of Air Quality Monitoring: A Logical Literature Exploration

Rahi

Sood

Bajaj

2020

Futuristic Trends in Networks and Computing Technologies

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The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

Gupta

Eden

2022

Designing Interactive Systems Conference

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The Air Quality Index (AQI) is a set of numerical, color-coded metrics used to communicate air pollution levels. AQI data is available through a variety of mediums such as mobile apps, websites and public displays. However, the information communicated through these may not be easily interpretable to everyone. Especially when scientifc data and associated representations are used to convey information to communities whose shared knowledge and practices are signifcantly diferent from Western scientifc contexts. We discuss fndings from a qualitative study conducted in Delhi, India to understand how residents across both low-and high-resourced communities, assess their personal risk around air pollution and the safety measures they take to mitigate that risk. We reveal incompatibilities between the air-quality information displayed in digital platforms and whether that information is interpretable for people with diferent cultural sensitivities. We conclude with design implications for improving the interpretability and relevance of air quality interfaces. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI; Field studies.

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Optimization of Cloud Resources for Air Pollution Monitoring Devices

Cited by 3 publications

References 5 publications

A Novel Approach to Reduce Air Pollution Through Machine Learning Based PM2.5 Prediction

A Novel Approach to Reduce Air Pollution Through Machine Learning Based PM2.5 Prediction

Smart Platforms of Air Quality Monitoring: A Logical Literature Exploration

The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

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