Air Pollution Detection System using Edge Computing

Biondi, Katalina; Al‐Masri, Eyhab; Baiocchi, Orlando; Jeyaraman, Suganya; Pospísil, E; Boyer, Graham; Souza, Cleonilson Protásio de

doi:10.1109/ceap.2019.8883458

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…Methods range from container-based virtualization and federated compressed learning to advanced machine learning algorithms tailored for edge devices (e.g., [53,54,62,63]). These methodologies have shown promising applications in various domains, including general environmental monitoring, smart cities, indoor environments, and even niche sectors like the construction industry (e.g., [54,[64][65][66]). Notably, these edge computing solutions offer substantial benefits, such as real-time data processing, high-resolution analysis, reduced power consumption, and enhanced data security (e.g., [53,54,62,67]).…”

Section: Edge Computing Techniques For Air Quality Monitoring and Pre...mentioning

confidence: 99%

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Abimannan,

El-Alfy,

Hussain

et al. 2023

Sustainability

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Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

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Section: Edge Computing Techniques For Air Quality Monitoring and Pre...mentioning

confidence: 99%

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Abimannan,

El-Alfy,

Hussain

et al. 2023

Sustainability

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“…The testing process will be executed according to test cases. Smoke can affect human health in terms of breathing [9]. When we inhale too much smoke, we will have difficulty in breathing.…”

Section: Fig 3 -System Architecture Designmentioning

confidence: 99%

An Internet of Things-based Air Pollution Detection System

Saari¹,

Kamaludin²,

Safa³

et al. 2022

JSCDM

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The Internet of Things (IoT)-based air pollution detection system using gas sensors is a system that is developed to detect air pollution using IoT technology. Air pollution is a condition where there is a mixture of harmful chemicals in the air we inhale that can cause damage to humans and the surroundings. The issue of polluted air nowadays has made people become more aware of the air quality they inhale. The system has been developed using MQ2 and MQ9 gas sensors to detect the air pollution level. The system involved two features in helping users read the air pollution level and notify the reading through a mobile app. Users also get notified of the reading through a pop-up notification. The mobile application development uses Android Studio, while Google Firebase is used as the database for the system. This IoT-based air pollution detection system is successfully developed and notified users about the air pollution reading to be more aware of their surroundings.

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“…Sinha et al [11] studied the use of edge devices in smart home solutions by monitoring the total power consumption of domestic appliances and allowed the user to set up the limits for power consumption for every appliance. Katalina et al [12] proposed an edge-computing based smart environment solution which monitors the real-time air quality and classifies quantitative interpretation of air quality as fresh air, low pollution, medium pollution, high pollution and very high pollution based on by processing the data acquired and using a metric. This solution uploads a new data entry into the cloud if there is any deviation observed from the known classifications.…”

Section: Related Workmentioning

confidence: 99%

A Smart IoT System for Remote Refrigeration Monitoring

Padmaja¹,

Patro³

et al. 2021

Preprint

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The realm of refrigeration industry has marked its significance for storing food and beverages. Refrigeration warehouses are extensively used to store various combinations of large fresh food stock at favourable temperatures to maintain its quality. Any deviations from these favourable temperatures pose negative impacts on the cold stock. Hence, it is essential to minimize the margin of these potential risks that can incur huge loss to the cold-storage industries. On-site evaluation can be reliable to address these risks, but it is time consuming and an inefficient process as human intervention can be prone to manual errors. The paper, therefore, proposes a methodology to build a smart IoT system to remotely monitor and evaluate the refrigeration warehouses seamlessly at one’s fingertips using a mobile or laptop through dashboards. Furthermore, this paper addresses the difficulties in managing and analysing the large sensory data which are the result of seamless monitoring by unleashing the power of edge computing and cloud computing.

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Air Pollution Detection System using Edge Computing

Cited by 9 publications

References 10 publications

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

An Internet of Things-based Air Pollution Detection System

A Smart IoT System for Remote Refrigeration Monitoring

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