Modeling and Testing University Campus Networks: A CPS Communication Framework with Cisco Packet Tracer
Keywords:
Cisco Packet Tracer, CPS communication protocols, Cyber-physical systems (CPS), Network simulation, Simulation toolsAbstract
The increasing complexity and scale of campus networks require advanced methods for effective design, analysis, and management. This paper presents a comprehensive approach to modeling and simulation of campus networks using a cyber-physical systems (CPS) framework within Cisco Packet Tracer. The CPS framework integrates computational, networking, and physical elements, enabling real-time interaction and enhanced control of network components. By adopting this methodology, we bridge the gap between physical network infrastructure and its virtual representation, offering a dynamic and interactive environment for simulation and testing.
In this study, a scalable campus network model is designed incorporating core, distribution, and access layers, along with integrated services such as VLANs, routing protocols (OSPF/EIGRP), wireless access, and IoT components. The CPS framework enables the simulation of real-time events, such as device failures, dynamic routing adjustments, and traffic behavior, under varying loads. Cisco Packet Tracer is used as the primary tool due to its extensive support for network protocols, IoT device simulation, and a user-friendly interface.
The model demonstrates the effectiveness of applying CPS principles in visualizing, monitoring, and troubleshooting network scenarios before physical deployment. Performance metrics such as latency, throughput, and packet loss are analyzed under different configurations. The simulation results validate the CPS approach in enhancing decision-making and ensuring robust campus network design.
This work contributes to educational and practical perspectives by providing a reproducible and scalable template for campus network simulation using modern CPS-based tools. Future extensions may involve integration with cloud platforms and AI-driven network automation.
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