LSTM-based IoT-enabled CO2 steady-state forecasting for indoor air quality monitoring

Yingbo Zhu, Shahriar Abdullah Al-Ahmed, Muhammad Zeeshan Shakir*, Joanna Isabelle Olszewska

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%.
Original languageEnglish
Article number107
Number of pages12
Issue number1
Early online date27 Dec 2022
Publication statusPublished - 1 Jan 2023


  • IoT
  • LSTM
  • AI
  • deep learning
  • CO2
  • IAQ monitoring
  • smart living


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