Explore the vast range of titles available.

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra’s shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called “Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)”, which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

In this study, we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking (IBN). Previous studies on IBN analyzed the basis of the network intent resolution processes. These processes produce the artifacts required by network intent policy enforcement. Thus, we continued such studies with the inclusion of network intent policy enforcement in the analysis, for which we constructed a model that predicts the accuracy of a multi-domain network intent policy enforcement system. We validated the model by designing a new multi-domain network intent policy enforcement system, and evaluated the accuracy and performance of the new system through experimentation over a large-scale multi-domain platform that involves sites separated by more than ten thousand kilometers. The results show that, on the one hand, the new system improves accuracy by 10% and, on the other hand, that policies obtained from the multi-domain network intents, including the most complex ones, can be enforced in less than 1.75 s in a platform comprising sites located in almost opposite sides of the world. The experiment confirmed that the long distance existing between the sites involved in our experimental multi-domain IBN platform had little impact on the performance of the new system, and that the predictions obtained with the new model are as much as 99% accurate with respect to the behavior observed in the experiment.

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods to solve the problem of simultaneously sparse-view and metal artifact reduction (SVMAR) are plagued by insufficient correction accuracy. To overcome this limitation, we propose a multi-domain deep unrolling network, called Mud-Net, for SVMAR. Specifically, we establish a joint sinogram, image, artifact, and coding domains deep unrolling reconstruction model to recover high-quality CT images from the under-sampled sinograms corrupted by metallic implants. To train this multi-domain network effectively, we embed multi-domain knowledge into the network training process. Comprehensive experiments demonstrate that our method is superior to both existing MAR methods in the full-view MAR task and previous SVCT methods in the SVMAR task.

The present work is inspired by the recent developments in laboratory experiments made on chips, where the culturing of multiple cell species was possible. The model is based on coupled reaction-diffusion-transport equations with chemotaxis and takes into account the interactions among cell populations and the possibility of drug administration for drug testing effects. Our effort is devoted to the development of a simulation tool that is able to reproduce the chemotactic movement and the interactions between different cell species (immune and cancer cells) living in a microfluidic chip environment. The main issues faced in this work are the introduction of mass-preserving and positivity-preserving conditions, involving the balancing of incoming and outgoing fluxes passing through interfaces between 2D and 1D domains of the chip and the development of mass-preserving and positivity preserving numerical conditions at the external boundaries and at the interfaces between 2D and 1D domains.

Multicast services provide an efficient way of conserving resources and reducing network traffic for multicast senders. However, ensuring security, particularly source authentication, is crucial for applications such as online news, IPTV, video streaming, and stock quote distribution. Previous research efforts have attempted to provide source authentication for multicast applications, but they often struggle to handle the dynamic nature of multicast mobile receivers and multi-domain networks. This paper provides an analysis of research conducted over 22 years on source authentication mechanisms with non-repudiation. Most of these mechanisms fail to address the dynamic nature of mobile users and multi-domain networks. To address this issue, a new multicast source authentication scheme for multi-domain Software Defined Mobile Networks (SDMN) is proposed. This approach uses the global view of the controller in SDMN to operate in dynamic environments, provide non-repudiation, and tolerate packet loss. Simulation results indicate that the proposed mechanism uses resources efficiently, reduces communication and delay overhead, and performs well in multi-domain and dynamic networks.

As an emerging network technology, Network Function Virtualization (NFV) enables network functions decoupling from dedicated hardware by replacing traditional middleboxes with software implemented Virtual Network Functions (VNFs). In NFV-enabled Internet of Things (IoT) networks, each IoT service can be represented as an ordered sequence of VNFs, referred to as Service Function Chain (SFC). Through NFV, operating expenditure and capital expenditure can be significantly reduced, thereby achieving flexible provisioning of IoT services. However, with the arriving of 6G era, the network scale of IoTs continuously expands, and service requirements of IoT users become more diversified. Particularly, 6G enabled IoT services have stringent delay requirements. How to efficiently place the SFCs in multi-domain IoT networks to satisfy the specific delay requirements while guaranteeing quality of service becomes a serious challenge. To this end, in this paper, we investigate the problem of delay guaranteed SFC placement in multi-domain IoT networks. Specifically, by taking in account QoS requirements and VNF dependency relationships, we formulate the problem of delay guaranteed SFC placement in multi-domain IoT networks as a multi-objective optimization model to maximize service acceptance ratio and minimize operational cost, while satisfying the delay requirements of SFC requests. To solve the problem, we further design a Delay Guaranteed heuristic SFC Placement (DGSP) algorithm with VNF parallelization. In the proposed DGSP algorithm, the VNFs without dependency relationships are placed in parallel in an adaptive and cost efficient manner, and virtual link mapping is performed based on the shortest path algorithm. Finally, we conduct simulation experiments for performance evaluation, and simulation results demonstrate the proposed DGSP algorithm can get higher service acceptance ratio and lower operational cost than comparison algorithms.

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies.

Major treats to visual health includes diabetic macular edema (DME), age-related macular degeneration (AMD) and retinal vein occlusion (RVO), which require prompt and correct interpretation for effective treatment. Optical coherence tomography (OCT) is an imaging modality, providing intense cross-sectional views of the retina to aid in diagnosis. Diagnosis and localization of retinal diseases were complicated by the structure of retinal fluids. In order to cope with these challenges, a deep learning architecture, the Adaptive Multi-Domain Fusion Network (AMDF-Net), is initiated to improve the detection of retinal diseases. AMDF-Net assimilates state of the art modules like Hybrid Spectral-Spatial Transformer (HSST) to gain insight about global and local features effectively. Moreover, the Dynamic Attention Fusion (DAF) module enhances the work of the network by specifying the features unique to retinal fluids, and Disease-Inclusive Segmentation (DIS) module makes it easier to accurately diagnose primary fluids. Extensive analyses of publicly available and real-time data reveal that AMDF-Net shows notable results with Dice coefficient of 98. 87% and classification accuracy of 98. 12%. These remarks highlight the potential of AMDF-Net to elevate automated retinal disease analysis and provide valuable assistance in the development of decisions focused on treatment.

Currently, network applications, such as audio, video, and augmented reality, have different stringent service requirements. They require service provision through end-to-end connections via other networks with different operating environments or service conditions. Therefore, network operators require information on their own and other networks to provide end-to-end services traversing several networks while guaranteeing their quality of service (QoS) requirements. This study proposes an inter-domain flow decision method that satisfies QoS requirements using a directed acyclic graph (DAG) in multi-domain and hierarchical software defined networking (SDN) networks. There are multiple local networks with SDN controllers that are connected to the global SDN controller. The flow decision in the proposed method is based on the effective bandwidth theory of the martingale process. The effectiveness of the proposed method is demonstrated by comparing it with existing SDN-based path selection methods using the Riverbed Modeler (older, OPNET) and OpenDaylight SDN controllers.