## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.3 Dynamic Resource Allocation  
The dynamic resource allocation engine (illustrated in Figure 4) is the module which orchestrates the optimal distribution of campus resources in response to changing demands and conditions in real time. Figure 3: Asset tracking and lifecycle management. Rather than relying on static timetables or ad hoc managerial updates, the system centralises input from IoT sensors, occupancy data, and institutional constraints to decide where and how to allocate needed capacity. By dynamically matching supply (such as available classrooms, bandwidth, or HVAC output) with actual demand (the number of people using a room, or the network load at a given hour), it reduces waste while also improving user experience. In a typical scenario, occupancy monitors embedded in lecture areas and study rooms provide near-instant feedback on how many seats are filled, which in turn prompts the allocation engine to lower lighting or temperature settings if a space empties earlier than scheduled. Real-time space-use measurement tools have proven valuable in accurately capturing utilisation patterns, thus further enhancing campus space management and resource allocation efficiency [53]. Conversely, if a previously unoccupied lab suddenly hosts an unplanned session, the system can proactively adjust environmental controls, ensuring comfort without waiting for manual intervention. When these capabilities are coupled with next-generation 5G connectivity, the institution gains a strong backbone for fast data transfer, an especially important factor when responding to last-minute classroom reassignments or a surge in network usage, which occurs during large online examinations [8]. Real-time resource allocation also extends to utilities, such as power distribution. For example, a building experiencing low foot traffic in the late afternoon can have its heating or air-conditioning settings scaled down, while another building, unexpectedly busy with an event, can receive extra HVAC output. This principle of selective energy modulation aligns with broader concepts of smart campus optimisation, in which AI-driven policies prioritise sustainability and cost savings. The approach stands in contrast to rigid scheduling systems, which may continue to heat or cool entire floors based on outdated assumptions of usage. Empirical evidence suggests that flexible load management can yield significant energy reductions, while satisfying user comfort levels [54]. Beyond physical infrastructure, the framework can dynamically allocate digital resources. If a large online workshop is set to begin, the system might automatically grant additional server capacity or allocate higher network bandwidth to that application, then decommission these resources when the event concludes. This form of elastic scaling is a concept borrowed from cloud computing environments and has begun making inroads in educational institutions aiming to support remote or hybrid learning. Such an approach can prevent outages during peak traffic, particularly when thousands of students simultaneously access video content or online exams [31; 55]. To determine the best distribution, the allocation engine relies on optimisation and ML algorithms designed to handle multiple objectives. For instance, it might seek to minimise  
Figure 4: Dynamic resource allocation engine. energy consumption, while maintaining comfort constraints for occupied spaces, or to balance classroom sizes so that no single facility becomes overcrowded. Some configurations involve reinforcement learning: the system iteratively refines its policies based on feedback, learning what adjustments best reduce costs or maintain user satisfaction. Over time, the engine grows more adept at anticipating fluctuations, including class cancellations, event overruns, or sudden resource spikes [39]. Still, the engine operates under a governance layer which respects departmental preferences and administrative approvals.