Performance Evaluation of a Camera Surveillance System in Smart Buildings Using Queuing Models

Security is increasingly prioritized, driving the use of camera surveillance in various settings such as companies, schools, and hospitals. Cameras deter crime and enable continuous monitoring. Integrating Edge and Fog Computing into these systems decentralizes data processing, allowing for faster responses to critical events. Challenges in deploying such systems include high costs, complex technology integration, and precise sizing. Costs cover cameras, Edge devices, cabling, and software, while integration requires technical expertise and time. Accurate sizing is essential to prevent resource under- or over-utilization. Analytical modeling helps simulate scenarios and calculate needed resources. This work proposes an M/M/c/K queuing model to assess surveillance system performance in smart buildings, considering data arrival rates and Edge and Fog container capacities. The model allows parameter customization to analyze various scenarios. Results show that increasing the number of containers more significantly improves system performance than increasing the number of cores per container.