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Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory

by Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 96
Year of Publication: 2026
Authors: Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman
10.5120/ijcab8b0de1015a6

Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman . Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory. International Journal of Computer Applications. 187, 96 ( Apr 2026), 15-19. DOI=10.5120/ijcab8b0de1015a6

@article{ 10.5120/ijcab8b0de1015a6,
author = { Yolanda T.L. Pontoan, Feibe L.V. Lalujan, Yuricha M.J. Tumiwang, Marson James Budiman },
title = { Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2026 },
volume = { 187 },
number = { 96 },
month = { Apr },
year = { 2026 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number96/real-time-air-quality-monitoring-based-on-iot-using-the-long-short-term-memory/ },
doi = { 10.5120/ijcab8b0de1015a6 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-04-19T00:40:22.831090+05:30
%A Yolanda T.L. Pontoan
%A Feibe L.V. Lalujan
%A Yuricha M.J. Tumiwang
%A Marson James Budiman
%T Real-Time Air Quality Monitoring based on IoT using the Long Short-Term Memory
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 96
%P 15-19
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Air quality is an important factor that directly impacts human health and the environment, particularly in urban areas with high levels of pollution. This study proposes a real-time air quality prediction system based on the Internet of Things (IoT) using a time-series data approach. The system is designed using an ESP32 microcontroller integrated with a DHT22 sensor for measuring temperature and humidity, as well as an MQ135 sensor for detecting pollutant gases. Environmental data are transmitted in real time to a cloud platform for storage, visualization, and analysis. The prediction method used in this study is the Long Short-Term Memory (LSTM) algorithm, which is effective in modeling temporal patterns in time-series data and generating predictions of future air quality conditions. The system architecture consists of hardware, network communication, and a cloud-based application layer that enables continuous environmental monitoring and remote data access. In addition, a monitoring dashboard is developed to display air quality information and provide early warnings in the event of a decline in air quality. The implementation results indicate that the system is capable of performing real-time air quality monitoring while also providing predictions based on historical data with relatively low cost and high efficiency. The proposed system has the potential to serve as an alternative solution for IoT-based air quality monitoring, supporting rapid decision-making and increasing public awareness of environmental conditions.

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Index Terms

Computer Science
Information Sciences

Keywords

Air quality Internet of Things ESP32 time-series LSTM real-time prediction

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