Network metadata analysis improves detection accuracy by examining network metadata, flow metadata, and network traffic metadata for behavioral patterns. Teams analyze threat detection metadata, anomaly detection flows, and behavioral analytics network signals to identify suspicious activity. DPI vs. metadata analysis: Which technology suits modern NDR needs? Choosing the right detection technology is crucial for securing and monitoring modern networks. Beyond application identification, our DPI solution aims to provide real-time trafic analysis and threat detection capabilities. This includes identifying anomalies, detecting malicious trafic, and providing actionable insights for network security. This Editorial introduces the Special Issue on “Advanced Measurement, Monitoring, and Sensing Technologies for Smart Urban Mobility” published in Meas… The increasing density of traffic has necessitated the deployment of advanced surveillance systems for effective traffic management. In this study, we present an enhanced vehicle tracking and detection method that leverages improved optical flow. The selected works were critically examined according to their algorithms, methodological practices, dataset characteristics and performance metrics, culminating in a meta-analysis to quantify and fairly compare results. Learn what metadata is, the 6 types, and why it matters for cybersecurity. Discover how attackers exploit cloud metadata and how to protect your organization. This white paper explores these benefits in detail and looks at how integrated surveillance command and control solutions are supporting and harnessing metadata search capabilities to deliver much more than video categorization. This review provides a unified analytical framework bridging low-level and high-level perception tasks, systematically analyzes current limitations and solutions, and presents a structured roadmap for integrating emerging technologies, particularly foundation models, to enhance TSS capabilities. This research paper presents a web app-based ML/DL model that performs traffic surveillance and detection with high accuracy and efficiency. The paper aims to improve the existing traffic system in India by proposing a Deep Learning/Computer.