Rajagopal, Vishal;
(2025)
IOT Indoor Air Quality Networks for Smart Homes.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Utilising predictive modelling and innovative data collection methods can yield a comprehensive understanding, thus guiding the enhancement of indoor air quality (IAQ). The central goal of this doctoral study is to construct a customised intelligent IoT system that integrates diverse air quality sensing techniques and data from smart home automation systems. By implementing neural network-based methodologies, the research showcases the system's adeptness in accurately forecasting forthcoming air quality conditions. These projections can facilitate proactive adjustments to household elements, including ventilation, to enhance air quality. The data collection framework encompasses a wireless sensor node equipped with various strategically positioned sensors within households, complemented by the capacity to gather data from existing building and home automation systems. Initially employing the Long Short-Term Memory Neural Network (LSTM), the study examines the relationships among air quality factors through univariate and multivariate LSTM analyses. Preliminary findings underscore the effectiveness of the wireless sensor modules in capturing crucial and dependable data for neural network training. The neural network employs this data to construct a dynamic predictive model for anticipated air quality, assuming a continuous influx of real-time air quality data into the system. Furthermore, this study explores a novel variant of LSTM that integrates a shared hidden state. The primary objective is to facilitate the examination of interconnected prediction data sourced from various locations to identify potential correlations between indoor air quality levels across different sites. The study seeks to explore how these correlations can enhance predictions related to indoor air quality. In the future, the research will broaden the scope of IAQ data integration by incorporating data from existing building automation systems into the LSTM model. The objective is to identify correlations between controllable aspects of building automation systems and indoor air quality, thus paving the way for further advancements in this domain.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | IOT Indoor Air Quality Networks for Smart Homes |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10207148 |
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