## 2 Materials and Methods
### AI-Driven Decision Support  
At the core of the framework, AI-driven decision support transforms raw sensor outputs into actionable insights. Dedicated modules target specific management needs, such as maintenance scheduling, optimal asset usage, resource allocation, and timetable construction, employing modern techniques in ML, optimisation, and knowledge-based systems. By focusing AI on large-scale, high-complexity tasks, the system uncovers patterns or forecasts, which human operators might overlook [22]. A prime example is predictive analytics for upkeeping facilities: models can process years of usage data to forecast imminent HVAC failures or identify power consumption anomalies. Additionally, advanced optimisation algorithms handle scheduling tasks, which include numerous constraints, like a classroom's attention monitoring, outperforming conventional manual or human-centered approaches [27]. University timetabling, in particular, has seen tangible benefits from meta-heuristic and AI-based techniques, enabling swift resolution of room conflicts and instructor overlaps [21]. To support administrators, the framework provides an explainable dashboard, ensuring data-informed suggestions remain transparent and adjustable. This maintains a balance between algorithmic assistance and human oversight, echoing the recommendation that AI serve as a \"smart assistant\" rather than an absolute decision-maker [19]. For instance, if a scheduling algorithm proposes relocating a class due to a projector's predicted breakdown, relevant staff can review the rationale and either approve or reject the change. Institutional guidelines, including faculty workload policies and programme-specific constraints, are encoded in a knowledge base, guaranteeing that AI-driven recommendations adhere to local rules and ethical considerations. Deployed in this manner, AI bolsters the consistency, efficiency, and adaptability of campus resource management [28]. Security and Privacy Integration  
Security and privacy considerations underpin every aspect of the proposed framework, given the critical infrastructure and sensitive data involved. The intersection of IoT and AI introduces broader attack surfaces: connected devices may be susceptible to hacking, while AI algorithms can be manipulated with malicious inputs. To mitigate these vulnerabilities, the framework adopts a defense-in-depth strategy, as showcased in Section 2.3.5. All IoT endpoints and edge gateways undergo strict authentication (e.g., certificate-based device identities and role-based user access) to prevent unauthorised entry. End-to-end encryption is enforced from each sensor at the device layer up through the network and into the cloud, ensuring that data travel securely and remain confidential even if intermediaries are untrusted [29]. In practice, this means that both the 5G wireless link and any application-layer channels are encrypted with robust cryptographic protocols (for example, WPA3 and VPN tunnels for campus wireless networks). Processing is often offloaded to secure edge computing nodes on campus, which perform initial data filtering and analytics locally. This localised processing not only reduces latency but also keeps raw data near its source, minimising exposure of sensitive information beyond the campus boundary [30]. By bringing computation closer to where data are generated, the framework limits what must be sent over wide-area networks and enables additional security controls at the edge, thereby further reducing the risk of data leakage or tampering during transmission. An AI-enhanced intrusion detection system (IDS) further fortifies security by detecting anomalous traffic or behaviour. Traditional signature-based tools often prove inadequate against novel or evolving threats in IoT ecosystems, so the IDS may employ deep learning and other support technologies to identify suspicious patterns in network logs and devicetelemetry [18; 31; 32].