To ensure public safety internet of things and convolutional neural network algorithm for a surveillance system enabled with 5G - priya, Chandrasekar; Kumuthapriya, Kesavan; Sagayamary, Savarimuthu; Livingston, L. M. Merlin; Venkatesan, Marimuthu (Institute of Advanced Engineering and Science, 2025)
Public safety and security are top priorities in the constantly urbanizing society and research develops and implements a smart surveillance system using fifth generation (5G) of wireless communication technology and internet of things (IoT) technologies to improve public safety. It developed a comprehensive and responsive monitoring solution using machine learning methods, especially convolutional neural networks (CNNs). IoT devices, including high-definition cameras, environmental sensors, and drones, are carefully deployed in urban centers, transit hubs, and essential infrastructure. These devices provide data to a central processing unit through the 5G network and CNNs analyze incoming data in real-time. The CNNs are taught to recognize objects, anomalies, faces, and license plates. These tasks help the system identify risks, odd activities, and intriguing people and warn authorities of real-time irregularities and security issues, simplifying emergency responses. Predictive analytics analyzes previous data to forecast security issues, enabling preventative steps and data are protected by strict privacy protections. According to this analysis, 5G-enabled IoT surveillance systems and machine learning may improve public safety, situational awareness, and emergency response times and approach ensures that security advancements respect privacy and integrity.