Search Results (1,235)

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features. Full article

Diabetic cardiomyopathy is a major etiological factor in heart failure in diabetic patients, characterized by mitochondrial oxidative metabolism dysfunction, myocardial fibrosis, and marked glycogen elevation. The aim of the present study is to evaluate the effect of endurance training and prebiotic xylooligosaccharide (XOS) on the activity of key oxidative enzymes, myocardial collagen, and glycogen distribution as well as some serum biochemical risk markers in streptozotocin-induced type 1 diabetic rats. Male Wistar rats (n = 36) were divided into four diabetic groups (n = 9): sedentary diabetic rats on a normal diet (SDN), trained diabetic rats on a normal diet (TDN), trained diabetic rats on a normal diet with an XOS supplement (TD-XOS), and sedentary diabetic rats with an XOS supplement (SD-XOS). The results show that aerobic training managed to increase the enzyme activity of respiratory Complex I and II and the lactate dehydrogenase in the cardiomyocytes of the diabetic rats. Furthermore, the combination of exercise and XOS significantly decreased the collagen and glycogen content. No significant effects on blood pressure, heart rate or markers of inflammation were detected. These results demonstrate the beneficial effects of exercise, alone or in combination with XOS, on the cardiac mitochondrial enzymology and histopathology of diabetic rats. Full article

Software-defined networking (SDN) is an emerging networking technology with a central point, called the controller, on the control plane. This controller communicates with the application and data planes. In fifth-generation (5G) mobile wireless networks and beyond, specific levels of service quality are defined for different traffic types. Ultra-reliable low-latency communication (URLLC) is one of the key services in 5G. This paper presents a fuzzy logic (FL)-based dynamic routing (FLDR) mechanism with congestion avoidance for URLLC on SDN-based 5G networks. By periodically monitoring the network status and making forwarding decisions on the basis of fuzzy inference rules, the FLDR mechanism not only can reroute in real time, but also can cope with network status uncertainty owing to FL’s fault tolerance capabilities. Three input parameters, normalized throughput, packet delay, and link utilization, were employed as crisp inputs to the FL control system because they had a more accurate correlation with the network performance measures we studied. The crisp output of the FL control system, i.e., path weight, and a predefined threshold of packet loss ratio on a path were applied to make routing decisions. We evaluated the performance of the proposed FLDR mechanism on the Mininet simulator by installing three additional modules, topology discovery, monitoring, and rerouting with FL, on the traditional control plane of SDN. The superiority of the proposed FLDR over the other existing FL-based routing schemes was demonstrated using three performance measures, system throughput, packet loss rate, and packet delay versus traffic load in the system. Full article

Photon counting computed tomography (PCCT) represents a paradigm shift from conventional CT imaging, propelled by a new generation of X-ray detectors capable of counting individual photons and measuring their energy. The first part of this narrative review is focused on the technical aspects of PCCT and describes its key advancements and benefits compared to conventional CT but also its limitations. By synthesizing the existing literature, the second part of the review seeks to elucidate the potential of PCCT as a valuable tool for assessing carotid artery disease. Thanks to the enhanced spatial resolution and image quality, PCCT allows for an accurate evaluation of carotid luminal stenosis. With its ability to finely discriminate between different tissue types, PCCT allows for detailed characterization of plaque morphology and composition, which is crucial for assessing plaque vulnerability and the risk of cerebrovascular events. Full article

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks. Full article

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications. Full article

This study examines the problem of minimizing the amount and distribution of time delays or latencies experienced by data as they travel from one point to another within a software-defined network (SDN). For this purpose, a model is proposed that seeks to represent the minimization of the distances between network switches in proportion to the total nodes in a network. The highlights of this study are the proposal of two mixed-integer quadratic models from a fractional initial version. The first is obtained by transforming (from the original fractional model) the objective function into equivalent constraints. The second one is obtained by splitting each term of the fraction with an additional variable. The two developed models have a relationship between switches and controllers with quadratic terms. For this reason, an algorithm is proposed that can solve these problems in a shorter CPU time than the proposed models. In the development of this research work, we used real benchmarks and randomly generated networks, which were to be solved by all the proposed models. In addition, a few additional random networks that are larger in size were considered to better evaluate the performance of the proposed algorithm. All these instances are evaluated for different density scenarios. More precisely, we impose a constraint on the number of controllers for each network. All tests were performed using our models and the computational power of the Gurobi solver to find the optimal solutions for most of the instances. To the best of our knowledge, this work represents a novel mathematical representation of the latency density management problem in an SDN to measure the efficiency of the network. A detailed analysis of the test results shows that the effectiveness of the proposed models is closely related to the size of the studied networks. Furthermore, it can be noticed that the performance of the second model compared to the first one presents better behavior in terms of CPU times, the optimal solutions obtained, and the reduced Mipgaps obtained using the solver. These findings provide a deep understanding of how the models operate and how the optimization dynamics contribute to improving the efficiency and performance of SDNs. Full article

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management. Full article

The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimized for different packet parsing performance. As modern network infrastructures evolve, the need for multi-tenant environments becomes increasingly critical. Multi-tenancy allows multiple independent users or organizations to share the same physical network resources while maintaining isolation and customized configurations. The rise of 5G and cloud computing has accelerated the demand for network slicing and virtualization technologies, enabling efficient resource allocation and management for multiple tenants. By leveraging P4-programmable packet parsers on FPGAs, our framework addresses these challenges by providing flexible and scalable solutions for multi-tenant network environments. The base parser offers a simple design for essential packet parsing, using minimal resources for high-speed processing. The overlay parser extends the base design for parallel processing, supporting various bus sizes and throughputs. The pipeline parser boosts throughput by segmenting parsing into multiple stages. The efficiency of the proposed approaches is evaluated through detailed resource consumption metrics measured on an Alveo U280 board, demonstrating throughputs of 15.2 Gb/s for the base design, 15.2 Gb/s to 64.42 Gb/s for the overlay design, and up to 282 Gb/s for the pipelined design. These results demonstrate a range of high performances across varying throughput requirements. The proposed approach utilizes a system that ensures low latency and high throughput that yields streaming packet parsers directly from P4 programs, supporting parsing graphs with up to seven transitioning nodes and four connections between nodes. The functionality of the parsers was tested on enterprise networks, a firewall, and a 5G Access Gateway Function graph. Full article

The success of CO₂ storage projects largely depends on addressing formation damage, such as salt precipitation, hydrate formation, and fines migration. While analytical models for reservoir behaviour during CO₂ storage in aquifers and depleted gas fields are widely available, models addressing formation damage and injectivity decline are scarce. This work aims to develop an analytical model for CO₂ injection in a layer-cake reservoir, considering permeability damage. We extend Dietz’s model for gravity-dominant flows by incorporating an abrupt permeability decrease upon the gas-water interface arrival in each layer. The exact Buckley-Leverett solution of the averaged quasi-2D (x, z) problem provides explicit formulae for sweep efficiency, well impedance, and skin factor of the injection well. Our findings reveal that despite the induced permeability decline and subsequent well impedance increase, reservoir sweep efficiency improves, enhancing storage capacity by involving a larger rock volume in CO₂ sequestration. The formation damage factor d, representing the ratio between damaged and initial permeabilities, varies from 0.016 in highly damaged rock to 1 in undamaged rock, resulting in a sweep efficiency enhancement from 1–3% to 50–53%. The developed analytical model was applied to predict CO₂ injection into a depleted gas field. Full article

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical–radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness. Full article

Parkinson’s disease (PD), the second most common neurodegenerative disease in the elderly, is characterized by selective loss of dopaminergic neurons and accumulation of α-synuclein (α-syn), mitochondrial dysfunction, Ca²⁺ dyshomeostasis, and neuroinflammation. Since current treatments for PD merely address symptoms, there is an urgent need to identify the PD pathophysiological mechanisms to develop better therapies. Increasing evidence has identified K_V3.4, a ROS-sensitive K_V channel carrying fast-inactivating currents, as a potential therapeutic target against neurodegeneration. In fact, it has been hypothesized that K_V3.4 channels could play a role in PD etiopathogenesis, controlling astrocytic activation and detrimental pathways in A53T mice, a well-known model of familial PD. Here, we showed that the A53T midbrain, primarily involved in the initial phase of PD pathogenesis, displayed an early upregulation of the K_V3.4 channel at 4 months, followed by its reduction at 12 months, compared with age-matched WT. On the other hand, in the A53T striatum, the expression of K_V3.4 remained high at 12 months, decreasing thereafter, in 16-month-old mice. The proteomic profile highlighted a different detrimental phenotype in A53T brain areas. In fact, the A53T striatum and midbrain differently expressed neuroprotective/detrimental pathways, with the variation of astrocytic p27^kip1, XIAP, and Smac/DIABLO expression. Of note, a switch from protective to detrimental phenotype was characterized by the upregulation of Smac/DIABLO and downregulation of p27^kip1 and XIAP. This occurred earlier in the A53T midbrain, at 12 months, compared with the striatum proteomic profile. In accordance, an upregulation of Smac/DIABLO and a downregulation of p27^kip1 occurred in the A53T striatum only at 16 months, showing the slowest involvement of this brain area. Of interest, HIF-1α overexpression was associated with the detrimental profile in midbrain and its major vulnerability. At the cellular level, patch-clamp recordings revealed that primary A53T striatum astrocytes showed hyperpolarized resting membrane potentials and lower firing frequency associated with K_V3.4 ROS-dependent hyperactivity, whereas primary A53T midbrain astrocytes displayed a depolarized resting membrane potential accompanied by a slight increase of K_V3.4 currents. Accordingly, intracellular Ca²⁺ homeostasis was significantly altered in A53T midbrain astrocytes, in which the ER Ca²⁺ level was lower than in A53T striatum astrocytes and the respective littermate controls. Collectively, these results suggest that the early K_V3.4 overexpression and ROS-dependent hyperactivation in astrocytes could take part in the different vulnerabilities of midbrain and striatum, highlighting astrocytic K_V3.4 as a possible new therapeutic target in PD. Full article

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed. Full article

The relentless growth of wireless networks coupled with the burgeoning demand for dynamic resource allocation has spurred research into innovative solutions. This paper presents an evaluation of Cloud-based Dynamic Network Scaling and Slicing (CDNSS) as a promising approach to meet the evolving demands of wireless networks. By leveraging cloud infrastructure and slicing techniques, CDNSS offers the flexibility to dynamically scale resources and allocate network slices tailored to diverse service requirements. The evaluation encompasses the performance of CDNSS in terms of scalability, resource utilisation and Quality of Service (QoS) provisioning. Through extensive simulations and analyses, the efficacy of CDNSS in addressing the challenges of resource management and service differentiation in wireless networks is demonstrated. The findings underscore the potential of CDNSS as a pivotal technology to enhance the efficiency and adaptability of wireless network architectures in the era of dynamic connectivity demands. Full article

The security requirements for SDN (Software-Defined Network) cross-domain communication are diverse and dynamically changing; thus, a security service negotiation function is required for the SDN interdomain. However, the SDN interdomain distributed communication environment leads to a lack of trustworthiness and security. Therefore, this paper proposes a blockchain-based SDN interdomain security service negotiation mechanism, BSSN-SDNs, to provide automatic, secure, and trustworthy SDN interdomain security service negotiation. BSSN-SDNs proposes a three-layer reference architecture that enables joint on-chain and off-chain work by extending the security service negotiation module and blockchain client on the controller and deploying security service negotiation smart contracts on the blockchain. It especially adopts non-interactive key exchange and the message authentication code to ensure the confidentiality of the secure service negotiated on-chain. Finally, the timeliness as well as security and trustworthiness of BSSN-SDNs are analyzed, and the FISCO BCOS-based experiment results show that the delay of BSSN-SDNs is acceptable and is positively correlated with the number of policies and the number of SDN domains involved in negotiation. Full article