## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.2 Asset Tracking and Lifecycle Management  
Figure 3 provides an overview of the system for monitoring campus assets, such as computers, lab equipment, vehicles, and managing them throughout their lifecycle. By combining real-time IoT tracking technologies with AI-driven analytics, the framework can reduce losses, ensure optimal utilisation, and support proactive decision-making. IoT and ML-driven management systems have been effectively applied to student-related data, significantly improving data handling efficiency, predictive capabilities, and privacy management in university settings [50].  
Real-Time Asset Monitoring. The system may use a mix of tagging technologies to track different categories of items. For instance, RFID tags work well for indoor objects like projectors or desktop computers, while GPS trackers are installed on outdoor assets, such as maintenance vehicles or library books. Strategic placement of RFID gateways or handheld scanners ensures that every ingress or egress is recorded, maintaining a constantly updated \"digital twin\" of the entire inventory [5; 51]. This live registry offers instant insight into an asset's location, status, and recent movement history, e.g., James Madison University (JMU), which tracks roughly 4000 audiovisual devices distributed across 50 buildings [52]. JMU significantly reduced manual audit times and achieved faster identification of misplacements or unauthorised relocations. Inspired by this success, the present framework automatically detects if an asset leaves its assigned room without a proper request and raises alerts to relevant staff, thereby deterring theft and improving operational efficiency.  
Lifecycle Analytics. Beyond positional data, the system ingests usage metrics, such as the frequency of projector utilisation or lab equipment uptime, to identify patterns of overuse or underuse. An AI module correlates this information with repair logs, anticipating when a particular device might approach the end of its functional life.Underutilised equipment may be prime for reassignment between departments, whereas overburdened assets might warrant additional units to avert bottlenecks. For example, if a certain model of a 3D printer consistently fails after 800 operating hours, the platform forecasts a service interval for that device before disruption occurs.  
Automated Inventory and Security. Because every asset is continuously monitored, annual audits and compliance checks transform into straightforward database queries. The framework can generate real-time reports, which detail asset locations and condition levels. In parallel, security protocols trigger alerts if an expensive microscope or specialised server exits its designated zone unexpectedly, reinforcing loss prevention.  
Lifecycle Decision Support. Leveraging the accumulated historical data, the system's analytics module estimates each asset's optimal replacement schedule or potential for upgrades. It can also align these recommendations with campus budget cycles, enabling strategic procurement planning, rather than hazard replacements. By consolidating asset tracking, performance analytics, and predictive modelling, the framework enables universities to extend resource lifetimes, forecast replacement costs, and ensure that mission-critical devices remain operational, ultimately elevating the standard of educational services and facility management [11].