## 3 Proposed System Model
### Proposed Fog-Cloud Architecture  
Higher education institutions are facing challenges arising from the increasing number of students, the diversification of educational activities, and technological advancements. In this context, the concept of a smart campus represents the vision of creating an efficient, sustainable, secure, and user-centered learning environment. In alignment with our university's smart campus vision, this project, which initially started with the smart library project and is planned to gradually expand to different campuses and buildings (such as faculties, cafeterias, administrative buildings, etc. ), highlights the need for a next-generation computing infrastructure that involves the collection, processing, and analysis of large amounts of data. While the existing system was initially designed to collect data from a single library, the projected expansion of the project requires data to be gathered from multiple campuses and different types of buildings, with real-time processing of these data. This situation could render a traditional, centralized cloud-based approach insufficient, leading to latency, bandwidth, and scalability issues. To overcome these challenges and establish a scalable, flexible, reliable, and efficient computing infrastructure that will form the foundation of the smart campus ecosystem, a fog-cloud-based architecture, as presented in Figure 1, is proposed. Figure 4 illustrates the data flow, starting from the distributed sensors (Layer 1) across three different campuses (Main Campus, North Campus, South Campus) and various buildings within these campuses (library, faculty buildings, cafeterias, administrative buildings, etc. ), extending through edge devices (Layer 2), the fog layer (Layer 3), data communication (Layer 4, MQTT protocol), and up to the cloud layer (Layer 5). This proposed architecture is based on the principle of processing data close to the source (fog layer), aiming to offer critical advantages such as real-time data analysis across the campus, fast response times, optimized resource utilization, and enhanced data security. The fog-cloud infrastructure optimizes the data flow between IoT devices, the cloud, and the fog, providing low latency and high bandwidth. The fog computing layer typically performs data processing and storage locally, reducing dependence on cloud systems. This layer is commonly used in areas such as industrial automation, smart cities, healthcare, and agriculture, especially to meet high processing power and real-time data analysis requirements. Fog computing also offers additional advantages, such as local data sharing between devices, enhanced security measures, and optimized network management. This not only leads to the more efficient use of cloud resources but also increases the capacity to respond faster to users. Furthermore, fog computing provides local analytics to monitor the lifecycle and performance of IoT devices, ensuring energy savings and system efficiency [83]. The fog-cloud infrastructure consists of three layers: the cloud, fog, and IoT layers. The cloud layer includes cloud servers, the fog layer consists of fog servers, and the IoT layer encompasses IoT devices. The fog computing layer, similar to the cloud layer but operating on a smaller scale, provides data, processing, network, and storage services to end users. Fog nodes can be devices such as smart gateways, routers, or embedded servers. Fog computing resides between the cloud and IoT layers, being closer to IoT devices, which results in low latency and high bandwidth. It is a critical model, especially for dynamic networks like the IoT and VANET. With its processing capacity near the user, it offers a potential solution for delivering services required by IoT and VANET users [84]. In the Conclusions and Discussion Section of this study, the feasibility of this architecture, the advantages it offers, and its impact on the campus ecosystem will be elaborated.