## 3 Results
### Smart Institution Proposed Framework in a Typical Scenario  
Morning: As the day begins, the dynamic scheduling module checks real-time data on class enrolments, faculty availability, and room occupancy. When an instructor calls in sick at 7:00 a.m., the system's AI-driven scheduler automatically searches for a substitute or an optimal rescheduling slot. It evaluates constraints (room capacity, student schedules, instructor qualifications) and promptly revises the timetable. Students and staff receive instant notifications via a campus app, avoiding confusion and idle time. According to Algorithm 1, the adaptive rescheduling optimisation process can be described as iterating over affected classes, finding feasible time-room alternatives, and selecting the option that minimises disruption cost (e.g., minimising the number of student schedule conflicts). Mid-Day: IoT sensors embedded in projectors and HVAC systems report anomalous vibrations and temperatures. An AI-based predictive model detects a likely projector bulbfailure in Room 210 within the next hour. Immediately, the system triggers a maintenance ticket and flags the real-time resource allocation module. Before the scheduled 2:00 p.m. lecture in Room 210, the system proactively reallocates that class to a nearby equipped room and informs the instructor and students. The maintenance staff, guided by the system's alert, replace the projector bulb at 1:30 p.m., avoiding downtime during scheduled classes. This illustrates how predictive maintenance and scheduling modules interact: the former forecasts issues, and the latter dynamically adjusts schedules to prevent disruptions. In parallel, energy sensors report low occupancy in certain study areas, prompting the system to dim lighting and moderate HVAC output to save energy, then restoring normal settings as students return, thereby optimising utilities without human intervention. Afternoon: In the library, the asset tracking module is actively helping students and staff. A student needs a portable VR headset for a design class; she uses the campus app to locate the nearest available headset. The asset tracking system, using RFID and wireless networks, pinpoints a free device in Lab A and guides her there with turn-by-turn directions. Meanwhile, a faculty member schedules an impromptu study session after an exam. The dynamic scheduling module cross-checks room availabilities and reserves a suitable space, while the asset tracker ensures a digital projector is in that room and functioning. The integration between scheduling and asset tracking guarantees resources (rooms, equipment) are matched to needs in real-time, eliminating the common scenario of double-booked rooms or missing equipment. Students arriving at the session automatically check in via their smartphones, updating occupancy data for that space. These occupancy data feed back into the resource allocation module, which continuously analyses utilisation patterns. Evening Disruption: At 5:00 p.m., if an unforeseen incident occurs, e.g., a minor water leak in a building, IoT flood sensors trigger an alert after detection to the maintenance module, which classifies it as a moderate issue requiring attention, but not an immediate evacuation. The incident response system (part of the maintenance module) correlates this with the schedule and finds two evening classes and a club meeting planned in that building. In seconds, the dynamic scheduler relocates those sessions to alternate venues, avoiding cancellation. Attendees are alerted to the new locations via push notifications on their mobile phones. The wireless 5G network infrastructure enables all modules to share this information instantly. Maintenance crews, directed by the predictive maintenance system's workflow, contain the leak within an hour.