## 2 Materials and Methods
### Proposed Framework Architecture  
This conceptual framework consolidates advanced IoT sensing, AI-driven analytics, and secure networking to establish a dynamic and holistic platform for managing campus resources. Figure 1 visualises the multi-layered system, wherein each stratum fulfils a distinct role, collectively enabling real-time decision-making and transparent data governance in educational settings of all levels [37; 38].  
Perception Layer (IoT Devices). At the base is a pervasive IoT infrastructure consisting of sensors, actuators, and edge devices distributed throughout the campus. These components monitor energy usage, occupancy, environment metrics, and security status in real time. Actuators (e.g., thermostats and lighting systems) respond to commands for automated control of physical facilities. This setup supplies the raw data streams critical to generating context-aware insights.  
Network Communication Layer. Data from various sensors pass through a connectivity framework on 5G. Such heterogeneous networking supports scalability and accommodates diverse devices operating under different bandwidth or latency needs [39; 40]. Moreover, a comprehensive architectural and operational model enhances interoperability,resource optimisation, and data governance within smart campus infrastructures [41; 42]. Quality-of-service mechanisms guarantee that vital alerts (e.g., safety notifications) are prioritised over routine status reports.  
Edge/Cloud Computing Layer. Proximal \"edge\" servers execute latency-sensitive tasks, including anomaly detection or resource allocation, thus preserving speed and continuity. More computationally demanding processes, such as data mining or advanced ML training, are offloaded to cloud environments. This hybrid approach reduces the load on local nodes and leverages substantial cloud resources for historical analysis. The integration of Heterogeneous Information Networks (HINs) [43] could further enhance semantic data management and facilitate efficient querying of interconnected data across diverse IoT devices and campus systems.  
AI Analytics and Security Layer. This core tier processes input from the lower layers using ML, deep reinforcement learning, and optimisation algorithms. It addresses scheduling complexities, predictive maintenance, and dynamic resource reallocation. Concurrently, an adaptive security module employs continuous monitoring, intrusion detection, and role-based authorisation to mitigate risks in an environment where connected devices often enlarge the attack surface.  
Application Layer (User Services). Finally, stakeholders, including facility managers, academic staff, and students, access analytics via dashboards or mobile applications. Advanced chatbot interfaces may further streamline queries (e.g., requesting classroom availability), facilitating direct interactions without specialised training [44]. Data-driven recommendations appear with interpretability options, allowing campus officials to override or customise system outputs, when policy considerations demand [45].  
Figure 1: Architecture and data flow loop of the proposed AI wireless framework.