Optimizing Intelligent Camera Surveillance in Smart Buildings: An SPN-based Edge-Fog Analysis - Araújo, José Miqueias; Lopes, Lucas Silva; Lima, Luiz Nelson; Barbosa, Vandirleya; Sabino, Arthur; Feitosa, Leonel; Delicato, Flávia C.; Nguyen, Tuan Anh; Silva, Francisco Airton (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2025)
Surveillance cameras play a pivotal role in modern security strategies, yet real-time video analysis demands substantial computational capabilities. This research employs Stochastic Petri Net (SPN) models to refine the planning and optimization of video surveillance infrastructures within intelligent buildings, leveraging Edge and Fog computing paradigms. By systematically evaluating key performance metrics such as mean response time, throughput, resource utilization, and drop probability, the proposed models inform more judicious resource allocation and scaling decisions. Our findings indicate that increasing Fog layer processing cores to 10 reduces the drop probability to around 35% at an arrival rate of 47.37 msg/ms, and maintains a mean response time below 10 ms at moderate arrival rates (up to approximately 29 msg/ms). These insights facilitate the design of more efficient, reliable, and scalable surveillance solutions, ensuring prompt incident responses and optimized resource usage within smart building environments.