IoT-Enabled CNN Biometrics for Enhanced Security and Verification for Smart Transportation Systems

Integrating biometrics with Internet of Things (IoT) technology offers a potential method to strengthen smart transportation system security and verification processes. In this research, we investigate the possibility of using convolutional neural network (CNN) biometrics enabled by the IoT for more secure authentication and identification of people using transportation services. With the help of IoT sensors and devices spread across transportation networks, it is possible to gather biometric data in real time, allowing for constant tracking and analysis. The foundation of biometric authentication is the CNN model, a deep learning architecture that extracts complex information from biometric inputs. Transportation systems may enhance security while avoiding false positives and negatives by using multi-modal authentication solutions that fuse data supplied by the IoT with biometric analysis based on CNN. Furthermore, biometric authentication's incorporation into smart transportation systems allows for a frictionless user experience and optimizes operations. It explains how the IoT and CNN may work together to improve smart transportation security and verification paradigms, leading to more trustworthy and secure mobility environments.