Existing Network Architecture Research Articles

Internet Architecture: Current Limitations Leading Towards Future Internet Architecture

The original internet design principle was guided by the end-to-end principle in the early 1980s and formed the foundation for the existing internet architectural model. The priorities of the original internet designers do not match the needs of today actual users; rise in new players, demanding applications, erosion of trust and rights and responsibilities is pushing the internet to a new dimension. This paper presents the goals and principles behind the design of the original internet architecture, the resulting issues and limitations of the existing network architecture and the approaches that is driving the future internet architecture.

Development progress of slave nodes for HL-2M timing system

The maximum ion temperature of HL-2M core plasma will exceed 12 KeV, and the current intensity, volume, heating and driving power of plasma are 6 times, 3 times and 2.5 times that of HL-2A, respectively. Timing system is an important component of control and data acquisition system, ensures that each relatively independent subsystem will be put into operation more accurately according to the scheduled time during plasma discharge, and associates each sampled data of acquisition system with timestamp, which is a significant quality improvement for the control and experimental data analysis of HL-2M related subsystems. Last year, we tested the synchronization deviation of clocks and triggers of the timing system based on the PXI/PXIe hardware. The results showed that this network architecture can meet our accuracy requirements for the timing system. However, in addition to PXI/PXIe hardware, HL-2M contains a large number of subsystems with other bus interfaces. Therefore, we developed a new type of slave node RTS-8C-4T03 to support subsystems with non-PXI/PXIe hardware, and the device can be compatible with the existing network architecture of HL-2M timing system. In addition, we have formulated the communication commands of HL-2M timing system, and the configuration and operation of clocks and triggers of all nodes are strictly implemented according to the definition of the communication commands. The test results show that the clock synchronization and triggering deviation of current HL-2M timing system is within 65 ns, which can fulfill the relevant engineering requirements of HL-2M diagnostic systems and advanced divertor configurations.

Software-Defined Networking: An Evolving Network Architecture—Programmability and Security Perspective

Software-defined networking is an evolving network architecture beheading the traditional network architecture focusing its disadvantages in a limited perspective. A couple of decades before, programming and networking were viewed as different domains which today with the lights of SDN bridging themselves together. This is to overcome the existing challenges faced by the networking domain and an attempt to propose cost-efficient effective and feasible solutions. Changes to the existing network architecture are inevitable considering the volume of connected devices and the data being held together. SDN introduces a decoupled architecture and brings customization within the network making it easy to configure, manage, and troubleshoot. This paper focuses on the evolving network architecture, the software-defined networking. Unlike a generic view on the evolving network, which makes work as a review, this work addresses various perspectives of the architecture leaving it an intermediate work in between the review of the literature and implementation, contributing towards factors like the design, programmability, security, security behaviors, and security lapses. This paper also analyses various weak points of the architecture and evolves the attack vectors in each plane leaving a conclusion to further progress towards identifying the impacts of the attacks and proposing mitigation strategies.

Dense Residual Network: Enhancing global dense feature flow for character recognition

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models.

Optical Technology for NFV Converged Networks

5G and its evolution towards 6G is unlocking new use cases that will require the reconsideration of the existing network architectures and its operation. As the network will be required to support new service types and radio protocol splits, the traditional physical point to point connections will need to be replaced with a transport network up to the antenna site to guarantee low latency services and high bandwidth. Optical based transport is a key enabler to realize such a convergent network, where the traditional fixed infrastructure in use for mobile services and mobile infrastructure should also support enterprises services. The Software Defined Network (SDN) and Network Function Virtualization (NFV) technology plays a key role to evolve towards digitalization. It allows to simplify the creation of new services and to implement a real decoupling between the infrastructure and the network functions that run virtually, on generic processing units located everywhere in the network. Supporting automation is a key requirement that traditional optical technology is not able to meet. In this paper the reference scenarios for the access network are presented with the analysis of their requirements and the enabling optical solutions based on integrated silicon photonics.

Neural Architecture Transfer.

Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search for each deployment specification of hardware or objective. This is a computationally impractical endeavor given the potentially large number of application scenarios. In this paper, we propose Neural Architecture Transfer (NAT) to overcome this limitation. NAT is designed to efficiently generate task-specific custom models that are competitive under multiple conflicting objectives. To realize this goal we learn task-specific supernets from which specialized subnets can be sampled without any additional training. The key to our approach is an integrated online transfer learning and many-objective evolutionary search procedure. A pre-trained supernet is iteratively adapted while simultaneously searching for task-specific subnets. We demonstrate the efficacy of NAT on 11 benchmark image classification tasks ranging from large-scale multi-class to small-scale fine-grained datasets. In all cases, including ImageNet, NATNets improve upon the state-of-the-art under mobile settings ( ≤ 600M Multiply-Adds). Surprisingly, small-scale fine-grained datasets benefit the most from NAT. At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods. Overall, experimental evaluation indicates that, across diverse image classification tasks and computational objectives, NAT is an appreciably more effective alternative to conventional transfer learning of fine-tuning weights of an existing network architecture learned on standard datasets. Code is available at https://github.com/human-analysis/neural-architecture-transfer.

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

Congestion Control of Multi-layer Cellular Networks based on Modeling of Transmit Power

The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability.

Blockchain-based Secure Data Transmission for UAV Swarm using Modified Particle Swarm Optimization Path Planning Algorithm

With the rapid development of unmanned aerial vehicles assisted applications enabled with a communication system, they are open to various malicious attacks. As a new form of flying things, they can access the network for better communication via the aerial base station. Most of the Unmanned aerial vehicles assisted flying objects' optimal path selection schemes does not consider the path deviation. In path deviation attacks, secure data transmission are not addressed in existing works. The secure communication process between Unmanned aerial vehicles and base station are exploited through security-based attacks. Moreover, path loss issue leads to multicast packet loss and unsecured broadcast. The existing network architecture setup does not fulfill the secure data communication and privacy issues. In this paper, Blockchain is utilized to investigate the secure communication between Unmanned aerial vehicles to Wireless Unmanned aerial vehicles Base stations. Since the destination information is dynamic under an uncertain environment, it will cause a delay in data communication. Unmanned aerial vehicles are more vulnerable to security attacks. The proposed blockchain-based architecture supports secure data communication in Unmanned aerial vehicles uncertain environments. To improve network security, this paper designs a modified particle swarm optimization method for better path selection. Through these experimental results, a blockchain-based data communication scheme is outer performed concerning network security.

Distributed parallel genetic algorithm for online virtual network embedding

SummaryNetwork virtualization (NV) has emerged as a promising paradigm to address the constraints of implementing new protocols and services in existing network architecture by allowing the simultaneous coexistence of multiple heterogeneous virtual networks on a shared substrate infrastructure. Hence, NV is a critical technology for establishing future network architectures (e.g., 5G network and the smart Internet of Things [IoT]). Virtual network embedding (VNE) is a major challenge in NV since it is acknowledged as ‐hard. Many VNE solutions have been proposed over the past decade. However, the proposed solutions merely centralize VNE node mapping while recommending virtual link embedding for the shortest path method or multicommodity flow (MCF) mechanism. This research paper presents an intelligent virtual network orchestration based on genetic algorithm (GA) for the link mapping stage that implements distributed parallelism to significantly and efficiently reduce the operation time. Our extensive simulations have demonstrated that the proposed algorithm not only outperforms the state‐of‐the‐art VNE algorithm in all performance metrics but also achieves 44.01% faster embedding speed than the most well‐known, fastest link mapping method in VNE.

Criso: An Incremental Scalable and Cost-Effective Network Architecture for Data Centers

With the explosive data growth, an enormous number of computing and networking components (e.g., servers, switches, and wires) are continuously being augmented to data centers. Data center networks (DCNs) - maintaining a high network capacity - must be cost efficient, incrementally scalable, and fault-tolerant. To address these challenges, we propose in this study a new type of DCN architecture referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> . Different from the existing network architectures, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is designed hierarchically and recursively by employing two ports servers and commodity switches. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is constructed based on numerous isomorphic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pod</i> s, each of which leverages external interfaces supplied by switches to connect with neighboring pods. Additionally, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pod</i> -based and fault-tolerant routing algorithm is designed to handle multiple failures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> has an array of promising features, including being cost-efficient and delivering a high-network capacity that can be extended to millions of nodes. The analytic results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is significantly superior to the four state-of-the-art data center structures in terms of network capacity, scalability, cost, power consumption, and other static characteristics. Furthermore, the experimental results unveil that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> satisfies the fault-tolerant demands of modern data centers. Compared to the four existing topologies (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DCell</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BCube</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiConn</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fat-Tree</i> ) that have been widely investigated, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is adroit at maintaining a balanced performance in terms of throughput and latency.

Task-Oriented Intelligent Networking Architecture for the Space–Air–Ground–Aqua Integrated Network

As one of the most promising networks, the space–air–ground–aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, and unbalanced network load. In this article, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply the edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Second, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Third, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a preprocessing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This article presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.

A New Flow Entry Replacement Scheme Considering Traffic Characteristics in Software-Defined Networks

The Software Defined Network (SDN) decouples control and data planes of the existing network architecture and a controller carries out centralized management and control of the network. This provides flexibility for the networks and enables efficient operation, depending on their condition. However, when the performance of switches comprising the SDN varies, any switches with low performance can cause flow table overflow and subsequent bottleneck with overhead concentrating in the controller. Besides, packet processing can be delayed, declining user QoS. The paper proposes a flow entry replacement scheme that takes into account the flow features to address the issue. The proposed Short Flow First (SFF) scheme discriminates the flow features based on the matching period of flow entry and determines a subject of the flow entry replacement with the features. For evaluation, the paper built the SDN environment using the Mininet emulator and compared the performance of the proposed SFF scheme with the representative flow entry replacement schemes. According to the results, the proposed scheme showed a good performance in the flow entry miss rate and packet delay due to failure of matching the flow entry.

Design and preliminary application of regional control and prevention auxiliary information system under the attack of COVID-19 infectious disease

Objective To propose the concept of a novel regional control and prevention (RCP) system for the outbreak of COVID-19 infectious disease, design an emergency epidemic prevention information system based on the existing network architecture and information system in the region, and a remote intelligent medical consultation and remote office platform, research and develop the technology of risk assessment and early warning for people in the region, and improve the regions’prevention and control ability facing emergency of major infectious diseases. Methods Taking colleges, affiliated (teaching) hospitals, and cloud applications as typical RCP regional units, the existing local area network interaction methods between the cloud and universities and affiliated (teaching) hospitals are established to realize remote work in the network environment, remote medical imaging, psychological and ethical consultation and interaction; applying multi-agent propagation model based on complex network, combining Global Positioning System (GPS), Radio Frequency Identification (RFID), and electronic fence technology, to realize the risk classification and early warning of units and personnel in the area. Results In the RCP, a system architecture combining campus network, affiliated (teaching) hospital intranet, and the Internet is used. Dynamic connection is made using distributed technology and cloud storage. The data buffer mechanism of the intermediary database in the network realized telemedicine consultation and telecommuting. Relying on the platform, multi-agent propagation model based on complex network and cellular automaton model are used to realize the score and early warning of population exposure risk in the region by using GPS, RFID and electronic fence technology. Conclusions In the epidemic phase of major infectious diseases, the construction of RCP can improve the response speed of wartime epidemic prevention, provide reasonable data-based warnings and risk ratings, and reduce the exposure risk of susceptible people. The design and development of RCP is a systematic project that needs to combine regional structural and functional characteristics, and the foundation of the early informatization work in the region and the level of the emergency development team determine the development progress, maintenance, and actual application effects. It is recommended to establish a peacetime and wartime combined RCP mode and incorporate it into the government's disease control system to improve the national and regional level of prevention and control of major infectious diseases. Key words: COVID-19; Infectious disease prevention and control; Regional

Second-order Spectral Transform Block for 3D Shape Classification and Retrieval.

In this paper, we propose a novel network block, dubbed as second-order spectral transform block, for 3D shape retrieval and classification. This network block generalizes the second-order pooling to 3D surface by designing a learnable non-linear transform on the spectrum of the pooled descriptor. The proposed block consists of following two components. First, the second-order average (SO-Avr) and max-pooling (SOMax) operations are designed on 3D surface to aggregate local descriptors, which are shown to be more discriminative than the popular average-pooling or max-pooling. Second, a learnable spectral transform parameterized by mixture of power function is proposed to perform non-linear feature mapping in the space of pooled descriptors, i.e., manifold of symmetric positive definite matrix for SO-Avr, and space of symmetric matrix for SOMax. The proposed block can be plugged into existing network architectures to aggregate local shape descriptors for boosting their performance. We apply it to a shallow network for nonrigid 3D shape analysis and to existing networks for rigid shape analysis, where it improves the first-tier retrieval accuracy by 7.2% on SHREC'14 Real dataset and achieves state-of-the-art classification accuracy on ModelNet40. As an extension, we apply our block to 2D image classification, showing its superiority compared with traditional second-order pooling methods. We also provide theoretical and experimental analysis on stability of the proposed second-order spectral transform block.

Multipath Mobile Internet Protocol for Mobility Networks

The internet data traffic is rapidly increases by twofold or even more in the near future. Most of the data traffic are generated from mobile devices. Due to this explosion of data traffic, existing network architecture is incapable in handling such a massive data traffic. The Network Mobility Basic Support Protocol (NEMO-BSP) is an extended version of Mobile Internet Protocol (MIPv6) that is designed to support seamless communications between homogenous and heterogeneous network domains. But NEMO-BSP is suffer very high binding and tunnel cost. The Proxy Mobility Scheme Protocol (PMIPv6) was developed to overcome issues of binding and tunnel cost. There have been many attempts to reduce the binding and tunnel cost. In this research work a multipath mobile IP protocol is designed that divides data packets on multiple paths (data packers on multiple sub-flows) without tunnel formation in the mobile network environment. the Multiple Mobile Internet Protocol Scheme (MMIPS). The network is divided into domain zones called intra-domain and inter-domain zones. The proposed protocol is aimed to remove the tunnel burden and reduce the packet size which significantly reduce the binding and tunnel cost. An analytical result are show that the proposed scheme binding and packet cost is less than compared schemes.

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors.

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.

A comprehensive review of the impact of transmission technologies on the electrical grid

The electrical infrastructure needs the integration of communication technologies, and in recent decades the progress has been significant. Therefore, this work presents the latest advances in this subject, as well as new functionalities.The work brings together the advances in automation, from the early stages to the present day. At the moment, the Smart Grid needs to use communication technologies to enable a response to demand, which will allow a different relationship between customer and company. The work presents the existing network architectures and communication protocols used in the Smart Grid. The document presents the challenges of electrical infrastructure, and shows the benefits and drawbacks of different communication technologies. In summary, the paper shows the parallel evolution of the communication technologies and the electrical grid, as a basic aspect for the development of new functionalities and services for all the agents involved in the power generation-transmission-distribution system.

A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-Ground IoV Architecture

The 5G Internet of Vehicles has become a new paradigm alongside the growing popularity and variety of computation-intensive applications with high requirements for computational resources and analysis capabilities. Existing network architectures and resource management mechanisms may not sufficiently guarantee satisfactory Quality of Experience and network efficiency, mainly suffering from the coverage limitation of road side units, unsatisfactory computational resources and capabilities of onboard equipment, frequently changing network topologies, and ineffective resource management schemes. To meet the demands of such applications, in this article we first establish a novel architecture by integrating the satellite network with the 5G cloud-enabled Internet of Vehicles to efficiently support seamless coverage and efficient resource management. An incentive mechanism based joint optimization problem of opportunistic computation offloading under delay and cost constraints is formulated under the proposed 5G integrated satellite-ground framework, where a vehicular user can either be a service requestor allowed to offload workload to nearby vehicles via vehicle-to-vehicle channels while effectively controlling the cost, or a service provider who provides computing services while protecting profits. As the optimization problem is non-convex and NP-hard, simulated annealing based on the Markov Chain Monte Carlo as well as the metropolis algorithm is applied which can efficaciously obtain both high-quality and cost-effective approximations of global optimal solutions. The effectiveness of the proposed mechanism is corroborated through simulation results.

Prediction Based Vehicular Caching: Where and What to Cache?

The explosive growth of user demands for mobile data puts forward higher requirements for existing network architecture. In this case, many novel technologies, such as edge computing, ultra-dense network (UND), are recently proposed to alleviate the network burden of cellular macro base stations (MBSs). However, their performance is limited by dynamic user requests and uncertain user mobility since they highly rely on the fixed infrastructure. Along with the advent of autonomous driving era, autonomous vehicles possess strong computing and communication ability, which provide a new idea to resolve the limitation of infrastructure based network architecture. In this paper, we exploit the performance of a vehicular caching scheme where the role of moving vehicles is changed from service consumers in traditional networks to service providers and service consumers. As such, a prediction based vehicular caching scheme is proposed. Specifically, an optimization problem is firstly formulated by exploring the relationship of caching vehicles and mobile users, in order to optimize the network energy efficiency. The nonconvex optimization problem is solved by decomposing it into a nonlinear programming problem. By applying the Lyapunov method and autoregressive neural network (ANN), an online caching decision algorithm is finally proposed to make caching decisions. Extensive simulations are conducted to evaluate the caching scheme in different scenarios. Results show that the vehicular caching scheme can obviously improve network energy efficiency with different requests, but the increment is reduced under the scenario of intensive requests. The comparison between online and offline caching also shows the necessity of online caching decision making due to its benefit in resource utilization where system gain is increased from 8.4% to 59.24%.