## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.1 AI for Predictive Maintenance  
Figure 2 illustrates how the proposed framework conceptually employs predictive analytics to maintain critical campus assets, ranging from audiovisual equipment and HVAC systems to IT servers, by leveraging IoT sensor data and ML models. This proactive approach aims to preempt failures, minimise downtime, and extend the lifecycle of high-value infrastructure. Prescriptive maintenance methodologies, such as those proposed in [46], could further enhance this system by enabling autonomous decision-making, anomaly detection, and optimised scheduling for the physical infrastructure of campus buildings. Continuous Condition Monitoring. Campus assets--ranging from HVAC units to network servers--are equipped with IoT sensors which capture operational metrics, such as vibration intensity, temperature fluctuations, and resource utilisation [7]. High-frequency data undergo preliminary screening at the edge computing layer, which efficiently filters routine readings from potential anomalies. Meanwhile, historical data accumulate in cloud repositories, supporting broader trend analysis and comparative diagnostics. Data Analysis and Anomaly Detection. Incoming signals feed into advanced AI models, including time-series forecasting, anomaly detection, and ML methods capable of learning complex, nonlinear relationships in asset behaviour [9; 47]. Concretely, the framework streams the multivariate sensor vector \(\mathbf{x}_{t}\) via 5 G to an LSTM-based time-series forecaster which outputs the probability \(P(\text{failure}\leq h)\) within horizon \(h\)[47]. In parallel, an edge-deployed Isolation Forest flags point anomalies so that bandwidth-light diagnostics remain available during back-haul congestion. If either model exceeds a preset confidence, the maintenance micro-service publishes an MQTT work-order to the CMMS queue. For instance, abrupt spikes in vibration signatures or temperature may indicate imminent component failure. Edge nodes handle rapid assessments (e.g., threshold breaches) to expedite alerts, whereas high-fidelity analytics, such as Remaining Useful Life estimation, are performed in the cloud using computationally intensive algorithms. Figure 2: Predictive maintenance module workflow. Maintenance Decision and Scheduling. When anomalies surpass a predetermined confidence level, the system automatically generates a maintenance request, coupled with an AI-derived timeline, estimating how soon the issue could escalate. To minimise disruptions, the module consults campus timetables and resource availability, optimising for factors, such as technician schedules and room occupancy [17]. This process ensures that, for example, repairs on a failing projector are slotted into unoccupied time blocks or carried out during off-peak hours. Action and Feedback. Approved maintenance orders are dispatched to relevant personnel or integrated into the institution's computerised maintenance management system. Once repairs are completed, sensor data verify the asset's return to acceptable operational thresholds. These post-intervention metrics are then fed back into the AI models, allowing continuous refinement of detection and prediction parameters [48]. Many institutions still fix equipment only after breakdowns, leading to class disruptions or safety risks. The proposed approach aligns with trends in smart building management, where predictive analytics reduce downtime by scheduling fixes in advance. For example, the University of Southern California has deployed AI-driven predictive maintenance to keep facilities running smoothly, prevent equipment breakdowns, focusing on energy efficiency and sustainability [49]. Broad Applicability. Although often associated with mechanical or electrical equipment (e.g., motors and air conditioning units), this predictive protocol also extends to network devices and software systems. For example, analysing router throughput patterns can reveal early signs of degradation, prompting upgrades or reboots before a critical outage occurs.