Mission-critical services (MCX) comprise a standardized suite of capabilities including Mission-Critical Push-to-Talk (MCPTT), MCVideo, and MCData, designed to meet stringent requirements for availability, reliability, latency, security, and Quality of Service (QoS). These services are essential for public safety, emergency response, and other critical infrastructure domains, where communication performance directly affects operational effectiveness. Integration into 4G and 5G mobile networks, supported by targeted standardization efforts, has extended broadband capabilities to mission-critical environments. 5G networks provide the technical foundations for MCX through ultra-low latency (below 1 ms), high availability (99.999%), broadband throughput over 100 Mbps per user, deterministic QoS via network slicing, massive device connectivity (over one million devices per square kilometer), and seamless Non-Terrestrial Network (NTN) integration. Technical enablers such as Proximity Services (ProSe), network slicing, and Ultra-Reliable Low-Latency Communications (URLLC) are fundamental to delivering these capabilities. This paper reviews MCX architectures, service frameworks, and protocols, relating MCPTT, MCData, and MCVideo to the key performance requirements defined in ITU-T M.2377-2. It also examines the frozen features of 3GPP Release 19, including enhancements to MC services, NTN integration, Reduced Capability device support, sub-meter positioning, extended network slicing for Public Protection and Disaster Relief (PPDR), and strengthened security mechanisms. Finally, the study addresses challenges such as standard maturity, interoperability, and deterministic QoS, identifying research priorities toward 6G readiness. By consolidating advances from standards bodies, research initiatives, and deployments, this work serves as a technical reference for scalable, secure, and standards-compliant MCX solutions in current and future networks.

The IoT market has grown significantly in recent years, and it is estimated that it will continue to do so. For this reason, the need to identify new solutions to ensure security is vital for the future development in this field. Inadequate sources of entropy are one of the factors that negatively influence security. In this study, inspired by NIST’s latest entropy estimation recommendations, we proposed a methodology for analyzing and validating a sensor-based entropy source, highlighted by an innovative experiment design. Moreover, the proposed solution is analyzed in terms of resistance to multiple types of attacks. Following an analysis of the influence of sensor characteristics and settings on the entropy rate, we obtain a maximum entropy value of 0.63 per bit, and a throughput of 3.12 Kb/s, even when no motion is applied on the sensors. Our results show that a stable and resistant entropy source can be built based on the data obtained from the sensors. Our assessment of the proposed entropy source also achieves a higher complexity than previous studies, in terms of the variety of approached situations and the types of the performed experiments.

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient.