Abstract Biometric technologies are fast becoming a requirement in security systems today, providing solutions where traditional means alone would not be adequate. This paper proposes FootprintNet, a Siamese network that utilizes pre-trained convolutional neural networks, specifically EfficientNet, MobileNet, and ShuffleNet, to improve the robustness and accuracy of footprint recognition. By learning the ability to identify fine distinctions between images of footprints, FootprintNet offers great biometric identification potential. Detailed analysis of the Biometric 220 × 6 Human Footprint dataset shows a true positive rate over 99% under various thresholds and a precision rate of 100% during training. Most importantly, this system is also applicable to newborn and infant identification, making it especially significant in medical settings, including hospitals and birthing clinics. Furthermore, the model sizes range from 7.8 MB (ShuffleNet) to 24.5 MB (EfficientNet), which makes FootprintNet deployable on low-computational-power devices—a highly desirable trait for mobile or high-security applications.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth‐generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction‐aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria‐Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Computational grid provides virtual powerful computer for solving any large‐scale scientific, commercial, and engineering problems by aggregating and sharing heterogeneous resources. However, designing an effective resource allocation method is a complicated task. To undertake the task, several economic‐based resource management models have been studied. Bargaining (or negotiation) is one of the most used and effective models even though it has several main defects such as high communication demand and the risk of losing a deal. Instead of repeating negotiation for every generated grid job (GJ), the novel contribution of threshold‐based negotiation framework is to determine whether a new negotiation is needed or not, according to the current market conditions which depend on supply of resource providers and demand of consumers. In the proposed framework, agents request for renewing negotiation, provided that the amount of change in market conditions exceeds pre‐defined threshold. Therefore, communication overhead and the risk of losing the deal are minimised by avoiding unnecessary negotiations.