• Proposed an optimization-driven resource orchestration algorithm that enhances admission control and ensures Quality of Service (QoS) in SDN-based network slicing. • Introduced a Partial Admission Control (PAC) strategy, dynamically allocating network resources based on priority and real-time availability, addressing the limitations of rigid binary admission models. Efficient resource orchestration is essential for ensuring high Quality of Service (QoS) and reliability in Software-defined Networks (SDNs). This paper introduces an optimization-based algorithm that integrates Lagrangian Relaxation (LR) and Queueing Theory to enhance admission control and priority scheduling in SDNs. The proposed approach overcomes the limitations of traditional binary admission control methods by enabling Partial Admission Control (PAC), which allows more flexible resource allocation. The system’s performance is significantly improved through the use of non-preemptive and preemptive priority scheduling, while LR techniques effectively manage complex network conditions. Specifically, the proposed Bisection-Search (B-S) heuristic leverages the Lagrangian multipliers generated during the optimization process to intelligently guide resource allocation, consistently producing high-quality feasible solutions ( Z primal ). These solutions are validated against the theoretical bound ( Z LR ) provided by the LR method, demonstrating a provably small duality gap. The proposed algorithm is evaluated through extensive simulations across diverse network scales, traffic loads, and delay constraints, demonstrating substantial improvements in network performance and service differentiation. These results provide a comprehensive analysis of the performance envelope of the proposed framework, highlighting the trade-offs between solution quality, computational complexity, and network scale. The study offers an adaptive and mathematically grounded solution, demonstrating its effectiveness in complex, high-contention networking environments.

Biometric recognition plays an increasingly pivotal role in cybersecurity, where the CIA triad, Confidentiality, Integrity, and Availability, forms the cornerstone of information security, with authentication as a critical yet challenging component. This paper presents the Biometric Multi-modal Authentication System using Geometric Programming (BMMA-GPT), tailored for deployment in Fast IDentity Online (FIDO/FIDO2)-enabled environments and Zero Trust Architectures (ZTA). The system employs a dual-threshold mechanism integrated with Defense-in-Depth (DiD) strategies to simultaneously enhance accuracy, efficiency, and security. The underlying optimization problem is formulated as a mathematical programming task and reformulated into a Geometric Programming (GP) model to efficiently compute optimal biometric permutations and verification thresholds under constrained estimation errors. BMMA-GPT enables the flexible integration of multiple biometric modalities, allowing dynamic adjustments to meet both individual user profiles and organizational security requirements. It achieves a high Area Under Curve (AUC) of approximately 0.99 while maintaining authentication latency under 1.5 seconds. This design supports Chief Information Security Officers (CISOs) in configuring tailored authentication processes with minimal computational cost, enhancing resilience against spoofing attacks and ensuring seamless user experience. By aligning biometric verification with DiD principles and GP-based optimization, the proposed framework offers a scalable and robust solution for identity authentication in complex digital ecosystems.

This paper tackles key challenges in Software-Defined Networking (SDN) by proposing a novel approach for optimizing resource allocation and dynamic priority assignment using OpenFlow’s priority field. The proposed Lagrangian relaxation (LR)-based algorithms significantly reduces network delay, achieving performance management with dynamic priority levels while demonstrating adaptability and efficiency in a sliced network. The algorithms’ effectiveness were validated through computational experiments, highlighting the strong potential for QoS management across diverse industries. Compared to the Same Priority baseline, the proposed methods: RPA, AP–1, and AP–2, exhibited notable performance improvements, particularly under strict delay constraints. For future applications, the study recommends expanding the algorithm to handle larger networks, integrating it with artificial intelligence technologies for proactive resource optimization. Additionally, the proposed methods lay a solid foundation for addressing the unique demands of 6G networks, particularly in areas such as base station mobility (Low-Earth Orbit, LEO), ultra-low latency, and multi-path transmission strategies.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.

Coexistence and interference management issues for machine-to-machine (M2M) and human-to-human (H2H) communications are crucial for the Internet of Things (IoT). This article considers beamforming and power allocation for M2M/H2H coexistence networks adopting the dynamic time division duplex (TDD) spectrum sharing scheme and energy harvesting (EH). The design objective is total system power minimization with device Quality-of-Service (QoS) constraints as well as EH constraints. Since the dynamic TDD introduces new types of interference, i.e., uplink/downlink cross-interference, the considered problem is a challenging nonconvex coupled problem. We first consider a simplified problem without the EH considerations. We propose a novel low-complexity algorithm based on uplink-downlink duality (UDD) and alternating optimization (AO) to tackle this problem. Then, we propose a second-order cone programming (SOCP) relaxation-based AO low-complexity algorithm to deal with the general problem. In the simulation, we study the performance of the QoS, the number of antennas, the number of users, and the power splitting ratio. Finally, the performance of the proposed algorithms have low-complexity than the classical convex optimization method.

A distributed coordination function is a serial fundamental media access control (MAC) mechanism of the IEEE 802.11 wireless network standards for accessing a medium and reducing the probability of collisions. Before sending data, each sender must check whether the medium is available. However, asymmetric detection and collision-avoidance mechanisms create transmission starvation problems. Hence, this paper addresses starvation problems to improve the quality of service in multihop wireless networks by adapting the transmission power range and carrier-sense threshold to alleviate starvation problems and to improve system performance. The problem is modelled as a mathematical formulation, and a Lagrangian relaxation (LR) approach is applied to obtain the approximation solution. The performance of the proposed algorithm is verified by applying it to several simulated starvation-avoidance routing problems. The results show that the proposed algorithm can near-optimally solve these problems, with an average lower bound-upper bound gap of 12.4%.

Wi-Fi wireless communication has become a basic service in public areas. However, the quality of service (QoS) might be inadequate in some areas because of the following influential factors: 1) the limited number of non-overlapping channels, 2) the interference caused by unplanned deployment and mobility, 3) the rapidly changing distribution of customers, and 4) access point (AP) failures. These factors result in non-uniform network traffic distribution and unavailability of service in some areas. Hence, this study focused on addressing the problems of AP failure and interference. We propose a self-healing algorithm to provide seamless and reliable service despite AP failures. The proposed water ripple algorithm maximizes the minimum delay difference, which is defined as the difference between transmission delay and tolerable delay, to satisfy requirements of multiple users simultaneously. Variable transmission power ranges (which involve cell breathing method types) and association management are adjusted to minimize the number of out-of-service mobile devices (MDs). Thus, fair allocation of resources to users and adequate QoS are satisfied.

Wireless Sensor Networks (WSNs), which are connected by sensor nodes with wireless communication techniques, are used to collect, process, and transmit the various types of environmental data, such as temperature, fire and smoke detectors, CO gas, humidity, pressure, and video-based data. The network lifetime is limited due to sensors are equipped with batteries. The measurement uncertainties and detection faults lead higher power consumption by retransmissions due to event detection error. These two factors influence with each other. In this study, there are two indicators are introduced for detection errors, false positive (FP) and false negative (FN) to evaluate a detection-based sensor network. For operation perspectives, three models are addressed to near-optimize the network lifetime and detection errors. Linear-and non-linear mathematical planning problems are formulated as (1) to minimize event FP rate subject to FN constraints, (2) to minimize the network energy consumption subject to detection accuracy requirements, turning sensor ON/OFF decisions, the required number of voting nodes, and given FP/FN constraints, and (3) to extend from the previous model as a planning problem for various types of sensor nodes deployed with diverse FP/FN characteristics and costs. An integer programming (IP) tool, LINGO, is adopted to solve the problems with computational experiments on several situations. The experimental results show that the objectives can be achieved, lots of useful hints or configurations can be applied for system implementations to improve system performance efficiently and effectively.

Wi-Fi wireless communication has become a basic service in public areas. But the quality is not stable due to the factors that are influenced by 1) a limited number of channels results in access interference, 2) various transmission ranges, carrier sensing and hidden terminal starvation problems, and 3) the barriers reduce the quality of transmissions. This work aims to explore and analyze the factors to show the level of performance effect on various transmission ranges of access points. Accordingly, this study designs several simulation cases to evaluate whether a small cell size can provide high performance co-existing with a large cell size by controlling the effect of the sensing ranges, transmission ranges, traffic types, data rates, and packet sizes. Network Simulation 3 (NS3) tool is used to implement the simulation cases and compare the results. We discussed our findings on these factors that affect the levels of starvation caused by the various signal ranges.

Abstract Multicast Broadcast Service (MBS) applications can efficiently reduce the usage of network resources, still providing mobile users with real‐time high‐quality content. MBS capabilities are usually implemented by using a single frequency network; moreover, new features, such as connection identifier for broadcast/multicast messages and other MBS‐enabled descriptors, are added to cope with already existent entities and services. With the intention to optimize performances and verify the on‐field feasibility, we propose an MBS approach to Orthogonal Frequency Division Multiple Access systems based on superposition coding (SPC). Because MBS features have a large impact in the architectural design of the network protocols, an integrated framework is mandatory to speed up the system simulation, verification, and redesign steps. This paper shows the design of an experimental testbed for performance evaluation of SPC‐enabled physical (PHY) and medium access control (MAC) layers over Mobile WiMAX systems. In addition, it proposes some architectural modifications of the WiMAX protocols, by exploiting its core network capabilities. The experimental results obtained from the testbed confirm that augmented throughput capabilities can be achieved by SPC‐enabled PHY/MAC layers. However, to fully exploit the additional available throughput, an integrated framework must be adopted to evaluate the protocol modifications for the MBS‐enabled entities. Copyright © 2014 John Wiley & Sons, Ltd.