## 2 Related Works
### Literature Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & \begin{tabular}{c} **Environmental** \\ **Factors Used** \\ \end{tabular} & \begin{tabular}{c} **Data Collection** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Machine Learning/** \\ **Analysis Methods** \\ \end{tabular} & \
\begin{tabular}{c} **Contributions and** \\ **Innovations** \\ \end{tabular} &
\begin{tabular}{c} **What is the Added** \\ **Value of Our Paper?** \\ \end{tabular} \\ \hline
[27] & \begin{tabular}{c} Air quality and \\ noise \\ \end{tabular} & \
\begin{tabular}{c} Sensors and \\ AI-based data \\ \end{tabular} & AI &
\begin{tabular}{c} Suggested ways to \\ optimize air quality \\ and noise levels. \\ \end{tabular} & Real-time data analysis  enables more effective  improvement in  environmental conditions. \\ \hline
[28] & \begin{tabular}{c} IAQ, \\ temperature, and \\ acoustics \\ \end{tabular} & \begin{tabular}{c} Surveys and \\ sensors \\ \end{tabular} & - & \
\begin{tabular}{c} Investigated the \\ priorities of library \\ users regarding \\ environmental factors. \\ \end{tabular} &
\begin{tabular}{c} This study offers more \\ flexible and dynamic \\ environmental optimization \\ through user feedback. \\ \end{tabular} \\ \hline
[29] & \begin{tabular}{c} Temperature, \\ light, acoustics, \\ and IAQ \\ \end{tabular} & \begin{tabular}{c} Surveys and \\ observations \\ \end{tabular} & - & \
\begin{tabular}{c} Analyzed the impact \\ of poor environmental \\ conditions on \\ education and \\ learning. \\ \end{tabular} &
\begin{tabular}{c} Continuous monitoring \\ with IoT sensors allows \\ faster and more targeted \\ environmental \\ improvements. \\ \end{tabular} \\ \hline
[30] & \begin{tabular}{c} Temperature, \\ noise, and light \\ \end{tabular} & \begin{tabular}{c} Sensors and \\ observations \\ \end{tabular} & - & \
\begin{tabular}{c} Tested the \\ environmental factors \\ affecting \\ learning efficiency. \\ \end{tabular} &
\begin{tabular}{c} Sensor data and machine \\ learning facilitate rapid \\ adaptation to \\ environmental conditions. \\ \end{tabular} \\ \hline
[31] & \begin{tabular}{c} IAQ and noise \\ \end{tabular} & IoT sensors & - & \
\begin{tabular}{c} Proposed IoT-based \\ solutions for IAQ and \\ noise management. \\ \end{tabular} &
\begin{tabular}{c} Real-time feedback is \\ provided with IoT- and \\ AI-based environmental \\ monitoring systems. \\ \end{tabular} \\ \hline
[32] & \begin{tabular}{c} IAQ and thermal \\ comfort \\ \end{tabular} & \begin{tabular}{c} Surveys and \\ sensors \\ \end{tabular} & - & \
\begin{tabular}{c} Investigated how IoT \\ technologies can be \\ more effectively \\ utilized in library \\ environments. \\ \end{tabular} &
\begin{tabular}{c} In this study, the continuous \\ optimization of \\ environmental data is \\ ensured with IoT and \\ AI integration. \\ \end{tabular} \\ \hline
[33] & \begin{tabular}{c} Thermal comfort \\ \end{tabular} & \begin{tabular}{c} Sensors and AI \\ (Extra Trees classifier) \\ \end{tabular} & \begin{tabular}{c} Machine learning \\ (Extra Trees classifier) \\ \end{tabular} & \
\begin{tabular}{c} Used IoT and machine \\ learning for personal \\ thermal \\ comfort modeling. \\ \end{tabular} &
\begin{tabular}{c} Environmental factors are \\ dynamically optimized \\ according to \\ personal preferences. \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} IAQ \\ \end{tabular} & \begin{tabular}{c} Sensors and \\ microcontrollers \\ \end{tabular} & AI & \
\begin{tabular}{c} Developed an IAQ \\ assessment system. \\ \end{tabular} &
\begin{tabular}{c} Integration of IAQ data with \\ user feedback and \\ environmental factors \\ provides more \\ comprehensive monitoring. \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} IAQ \\ \end{tabular} & \begin{tabular}{c} Biometric data \\ and IoT sensors \\ \end{tabular} & AI & \
\begin{tabular}{c} Conducted IAQ \\ analysis with \\ biometric data. \\ \end{tabular} &
\begin{tabular}{c} This study enables real-time \\ environmental monitoring \\ using a broader sensor \\ network integrated \\ with IoT. \\ \end{tabular} \\ \hline
[36] & \begin{tabular}{c} CO2, PM, NOx, \\ VOCs, and water \\ quality \\ \end{tabular} & IoT sensors & Machine learning & \
\begin{tabular}{c} Developed IoT-based \\ environmental \\ monitoring systems. \\ \end{tabular} &
\begin{tabular}{c} Real-time data analysis and \\ machine learning enable \\ more efficient management \\ of environmental data. \\ \end{tabular} \\ \hline
[37] & \begin{tabular}{c} Environmental \\ factors \\ \end{tabular} & IoT devices & - & \
\begin{tabular}{c} Proposed IoT \\ solutions for library \\ infrastructure \\ and security. \\ \end{tabular} &
\begin{tabular}{c} More detailed data analysis \\ focused on infrastructure \\ management and user \\ satisfaction is achieved \\ with IoT. \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} Environmental \\ factors \\ \end{tabular} & IoT devices & - & \
\begin{tabular}{c} Increased user \\ satisfaction by \\ integrating IoT into \\ library infrastructure. \\ \end{tabular} &
\begin{tabular}{c} By combining IoT-enabled \\ environmental optimization \\ and security solutions, more \\ secure and efficient library \\ environments are provided. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}