Indoor Air Quality Management Using Raspberry Pi and Graphlab

Acharyya, Somdeep; Hazra, Sayan; Pal, Atanu; Sengupta, Nabarun

doi:10.15623/ijret.2017.0609007

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…With the support of utilizing the capabilities of the Raspberry Pi single-board computer and GraphLab, a machine learning framework was deployed for tracking and analysing indoor air quality [28]. The hardware platform for data gathering and processing was the Raspberry Pi, while the tools and algorithms for data analysis and visualization were provided by GraphLab.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementing raspberry Pi for tracking black carbon with machine learning in climate monitoring

2024

IJATEE

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Section: Literature Reviewmentioning

confidence: 99%

Implementing raspberry Pi for tracking black carbon with machine learning in climate monitoring

2024

IJATEE

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“…The results are messages sent from an account TWITTER associated with ThingSpeak. Acharya, S. et al [7] to monitor the air quality indoor, the researcher proposed Indoor Air Quality Monitoring (IAQM) system by using Raspberry Pi. The sensing data is sent to Raspberry thought Zigbee, a robust, lightweight wireless networking protocol that builds upon the IEEE 802.15.4 standard.…”

Section: Related Workmentioning

confidence: 99%

“…According to a survey of research articles, most researchers have employed Raspberry Pi [6][7][8][9], which has its own set of drawbacks. It can only be used locally, but if there are many IoT devices, these devices must all be connected to the same Internet network.…”

Section: Related Workmentioning

confidence: 99%

Environmental Pollution Monitoring System Based on IoT

2022

IJCCCE

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Air pollution is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. This problem results from the abundance of automobiles, industrial production, and combustion of transportation and electricity generation petroleum products. Therefore, forecasting air pollution is necessary. In this paper, a system is proposed to monitor the level of air pollution by integrating the Internet of Things(IoT) with Wireless Sensor Networks (WSN), where pollution levels are observed in three areas in Baghdad using different types of sensors connected with ESP32 (It is the name of the chip developed by Espressif Systems) to detect Particulate Matter (PM2.5 and PM10), Nitrogen Oxides (NOx), Carbon monoxide (CO) as well as temperature and humidity to monitor indoor and outdoor air quality. Observed results are monitored by ThingSpeak, an open-source IoT platform. Success has been achieved using the ESP32 microcontroller, as the project is low-cost and uncomplicated, and pollutant measurement is accurate compared to the natural proportions of pollutants. Data display is easy and it can be monitored easily. This encourages the improvement of the model and its use in other monitoring systems. Index Terms— Internet of Things (IoT), Wireless Sensor Networks (WSN), ESP32 microcontroller, ThingSpeak, Air Pollution Mentoring, Gas Sensors.

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