## 2 Materials and Methods
### Objectives  
The core objectives guiding the design and implementation of the proposed smart educational management framework are summarised in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Focus Area/ Reference** & **Contribution** \\ \hline Smart Campus Conceptualisation [13] & \begin{tabular}{c} Presents a comprehensive review and \\ conceptual framework for smart campuses as \\ testbeds for digital transformation. \\ \end{tabular} \\ \hline IoT Integration in Campus Environments [17] & \begin{tabular}{c} Illustrates real-world challenges and solutions \\ for deploying IoT systems in smart campuses. \\ \end{tabular} \\ \hline Framework and Service Provision [6] & \begin{tabular}{c} Defines and frames the smart campus concept \\ by outlining the key technologies and \\ services required. \\ \end{tabular} \\ \hline Conceptual Modelling [12] & \begin{tabular}{c} Proposes a generic model for smart campus \\ initiatives and priorities interventions based \\ on institutional needs. \\ \end{tabular} \\ \hline Educational Management Transformation [7] & \begin{tabular}{c} Analyses the impact of IoT and AI on \\ educational administration and \\ informatisation management. \\ \end{tabular} \\ \hline Scheduling Optimisation [21] & \begin{tabular}{c} Reviews meta-heuristic approaches to solve the \\ NP-hard university course \\ timetabling problem. \\ \end{tabular} \\ \hline AI in Administrative Decision-Making [19] & \begin{tabular}{c} Provides a framework for symbiotic \\ collaboration between AI and educational \\ leaders in decision-making. \\ \end{tabular} \\ \hline Resource Management in IoT Networks [22] & \
\begin{tabular}{c} Surveys ML and deep learning techniques for \\ efficient resource management in large-scale \\ IoT environments. \\ \end{tabular} \\ \hline Data Governance [23] &
\begin{tabular}{c} Discusses the construction of a comprehensive \\ data governance platform for smart campuses. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key research informing the proposed framework. \begin{table}
\begin{tabular}{l} \hline \hline
**General Foundations of the Framework Design** \\ \hline F1: continuous, near-real-time data acquisition via heterogeneous sensors, \\ \hline F2: AI-based analytics for both automated and semi-automated decision support, transforming \\ sensor data into operational decisions, \\ \hline F3: cross-domain integration across maintenance, resource allocation, and timetabling, \\ \hline F4: privacy safeguards incorporating encryption, identity management, and role-based access. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: General foundations. 5G/IoT Convergence in Educational Settings  
The synergy of 5G and IoT is important in achieving a hyper-connected campus. Fifth-generation networks offer high reliability, substantial device capacity, and low-latency communication, critical for large-scale deployments of campus sensors and devices [24]. Thousands of interconnected nodes--ranging from temperature monitors and motion detectors to smart door locks and energy meters--can thus operate in near real time. The Ultra-Reliable Low-Latency Communication (URLLC) feature of 5G, often targeting latencies as low as 1 ms, is especially useful in time-sensitive applications, such as emergency notifications and adaptive learning systems. The ability to scale via massive Machine-Type Communication (mMTC) allows seamless operation of diverse IoT modules for environmental monitoring, security alerts, and occupancy tracking. In this architecture, the perception layer comprises devices connected through 5G small cells or complementary protocols like Wi-Fi 6 and Low-Power Wide-Area Network (LPWAN), ensuring continuous coverage across campus facilities. Additionally, integrating IoT with cloud and edge computing technologies addresses major architectural challenges, providing solutions, which optimise data management, security, and personalised services in smart campuses [25].