Open Network Architecture Research Articles

Examining the Benefits and Drawbacks of Open Network Architectures in E-Commerce

The advent of digital commerce has revolutionized the way businesses operate and interact with customers. One of the key factors contributing to this transformation is the emergence of open network architectures, which facilitate seamless communication and data exchange between various entities within the e-commerce ecosystem. This research aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of digital commerce. And to investigate how open network architectures promote accessibility and interoperability among diverse systems, enabling seamless integration and collaboration within the e-commerce landscape. Explore the potential of open networks to foster innovation and scalability, empowering businesses to rapidly adapt to changing market demands and technological advancements. Evaluate the economic advantages of open network architectures, including reduced infrastructure costs, improved resource utilization, and streamlined operational processes. Examine the potential vulnerabilities and risks associated with open network architectures, such as data breaches, cyber threats, and privacy issues, and propose strategies to mitigate these challenges. Investigate the role of governance frameworks and industry standards in facilitating the effective implementation and management of open network architectures within the e-commerce domain. Identify and analyze the potential barriers to the widespread adoption for overcoming these challenges. This paper aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of e-commerce. Through real-world case studies, theoretical frameworks, and a multi-faceted approach encompassing technical, business, and strategic perspectives, we strive to equip e-commerce stakeholders with the insights necessary to navigate this complex landscape and make informed decisions that align with their organizational goals and market dynamics.

Cationic tantalum oxide nanoparticle contrast agent for micro computed tomography reveals articular cartilage proteoglycan distribution and collagen architecture alterations

ObjectiveCationic tantalum oxide nanoparticles (Ta2O5-cNPs), as a newly introduced contrast agent for computed tomography (CT) of cartilage, offer quantitative evaluation of proteoglycan content and biomechanical properties. However, knowledge on the depth-wise impact of cartilage constituents on NP diffusion, particularly the influence of the collagen network, is lacking. In this study, we aim to establish the depth-dependent relationship between Ta2O5-cNP diffusion and cartilage constituents (proteoglycan content, collagen content and network architecture). MethodsOsteochondral samples (n = 30) were harvested from healthy equine stifle joints (N = 15) and the diffusion of 2.55 nm diameter cationic Ta2O5-cNPs into the cartilage was followed with µCT imaging for up to 96 hours. The diffusion-related parameters, Ta2O5-cNP maximum partition (Pmax) and diffusion time constant, were compared against biomechanical and depth-wise structural properties. Biomechanics were assessed using a stress-relaxation and sinusoidal loading protocols, whereas proteoglycan content, collagen content and collagen network architecture were determined using digital densitometry, Fourier-transform infrared spectroscopy and polarized light microscopy, respectively. ResultsThe Pmax correlates with the depth-wise distribution of proteoglycans (bulk Spearman’s ρ = 0.87, p < 0.001). More open collagen network architecture at the superficial zone enhances intake of Ta2O5-cNPs, but collagen content overall decreases the intake. The Pmax values correlate with the equilibrium modulus (ρ = 0.80, p < 0.001) of articular cartilage. ConclusionThis study establishes the feasibility of Ta2O5-cNPs for the precise and comprehensive identification of biomechanical and structural changes in articular cartilage via contrast-enhanced µCT.

Design and deployment of optical x-haul for 5G, 6G, and beyond: progress and challenges [Invited

Sixth generation (6G) wireless technology, which is the next giant leap in the evolution of wireless communication, has been gaining great interest from academia, industry, and the general public due to its capabilities and the applications that are expected to be supported. Optical x-haul is becoming increasingly important as a key element of the 6G ecosystem due to the increasing demand for high data rates and low latency with the emergence of new 6G applications and use cases. The ability of optical x-haul to support high data rates and low latency along with its scalability and high-reliability performance makes it an essential transport technology for future wireless networks of 6G and beyond. However, optical and wireless technologies operate in different domains and with different standards. Therefore, integration of optical x-haul and 6G needs careful consideration with emphasis on bringing network intelligence softwarization. This is particularly important in supporting the quality of service requirements of emerging applications in the most cost-effective and sustainable manner. To this end, this paper reviews the research that has been carried out in the investigation of the optical x-haul network in a hybrid fiber–wireless system to cater to the next generation of wireless networks. Especially, we review the research carried out in open radio access network architecture, coordination functionalities in radio-over-fiber networks, optimization frameworks that can be used to jointly optimize wireless and optical network deployments, and reconfigurable optical x-haul. We conclude the paper by providing insights into research challenges that we need to overcome in integrating optical x-haul in the 6G ecosystem to support emerging applications.

Big data and data analytics in 5G mobile networks

In this paper, we study the features of big data and data analytics. We see how Big Data contributes to mobile networks. We give a term in which big data generally refers to a large amount of digital data. Also, we estimate that the amount processed by "Big Data" systems will double every two years. Hence, Big Data on mobile networks need to be analyzed in-depth according to retrieve exciting and useful information. Big Data provides unprecedented opportunities for internet service providers to understand the behavior and requirements of their users, which in turn enables real-time decision making across a wide range of applications. After that, we mention the dimensions often describe the 4Vs of Big Data. We continue with the study about the use of big data analytics in mobile networks. As we see, new technologies for managing big data in a highly scalable, cost-effective, and damage-resistant manner are required. So, beyond 2020 the system capacity and data rates in mobile networks must support thousands of times more traffic than 2010 levels. Furthermore, we mention the end-to-end latency, the massive number of connections, the cost, the Quality of Experience, the Issues, and finally, the big data management. We continue with the study about the big data analytics in 5G. The 5G networks standardizing and the 5G mobile optimization are crucial areas. There are new research areas were exploring new analytics techniques in big data according to discover new patterns and extract knowledge from the data are collected. Big data analytics can provide organizations with the ability to profile and segment customers based on distinct socioeconomic characteristics and increase customer satisfaction and retention levels. Also, Big Data analytics techniques can provide telecom providers with in-depth knowledge of networks before making informed decisions. Also, as we see, these analytics techniques can help Telecommunication providers to monitor and analyze various types of data as well as event messages on networks. Important information, like business intelligence, can be extracted from momentary and stored data. Hence, the mass adoption of smartphones, mobile broadband modems, tablets, and mobile data applications has been overwhelmingly wireless. Operators bend under the pressure and cost of continuously adding capacity and improving coverage while maximizing the use of the existing components of their range. Advanced radio access technologies, and all Internet Protocols, open internet network architectures must evolve smoothly from 4G systems. So those needs are leading us to make a study about the heterogeneous network or else HetNet for 5G networks. We are continuing with the challenges, and we mention about the curse of modularity, dimensions procedure, feature engineering, non-linearity, Bonferonni's principle, category report, variance and bias, data locality, data heterogeneity, noisy data, data availability, real-time processing, and streaming, data provenance, and data security.

Investigation of reconfigurable hardware platformsfor 5Gprotocol stack functions acceleration

Open RAN and 5G are two key technologies designed to qualitatively improve network infrastructure and provide greater flex-ibility and efficiency to mobile operators and users. 5G creates new capabilities for high-speed Internet, Internet of Things, telemedi-cine and many other applications, while Open RAN enables open and standardized network architectures, which reduces cost and risk for operators and promotes innovations. Given the growing number of users and data volumes, the purely software implementa-tion of certain functions of the 5G protocol, and especially computationally complex ones, requires significant computer resources and energy.These, for example, arelow-density parity-check (LDPC)coding,FFTandiFFT algorithms on physical (PHY)layer, and NEA and NIA security algorithms on Packet Data Convergence Protocol (PDCP)layer. Therefore, one of the activity areas in the development of means for 5G systems isthe hardware acceleration of such functions execution, which provides the possibility of processing large volumes of data in real time and with high efficiency. The high-performance hardware basis for implementing these functions today is field-programmable gate array(FPGA)integrated circuits.Along with this, the efficiency of the 5G protocol stack functions hardware acceleration depends significantly on the size of the data packets transmitted to the hardware accelerator. As ex-perience shows, for certain types of architecture of computer systems with accelerators, the acceleration value can take even a nega-tive value. This necessitates the search for alternative architectural solutions for the implementation of such systems.In this article the approaches for hardware acceleration using reconfigurable FPGA-based computing components are explored, their comparative analysis is performed, and architectural alternatives are evaluated for the implementation of a computing platform to perform the functions ofthe 5G protocol stack with hardware acceleration of PHY and medium access control(MAC)layers functions.

Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

The evolution of the future beyond-5G/6G networks toward a service-aware network is based on network slicing technology. With network slicing, communication service providers seek to meet all the requirements imposed by the verticals, including ultra-reliable low-latency communication (URLLC) services. In addition, the open radio access network (O-RAN) architecture paves the way for flexible sharing of network resources by introducing more programmability into the RAN. RAN slicing is an essential part of end-to-end network slicing since it ensures efficient sharing of communication and computation resources. However, due to the stringent requirements of URLLC services and the dynamics of the RAN environment, RAN slicing is challenging. In this article, we propose a two-level RAN slicing approach based on the O-RAN architecture to allocate the communication and computation RAN resources among URLLC end devices. For each RAN slicing level, we model the resource slicing problem as a single-agent Markov decision process and design a deep reinforcement learning algorithm to solve it. Simulation results demonstrate the efficiency of the proposed approach in meeting the desired quality of service requirements.

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

PurposeComputer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.MethodsThe deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).ResultsMean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen.ConclusionIn this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

Applying Federated Learning in Software-Defined Networks: A Survey

Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

Toward an AI-Enabled O-RAN-based and SDN/NFV-driven 5Ｇ&amp; IoT Network Era

Artificial Intelligent Technology has impacted tremendously in the areas of high performance computing, and network and communicatons industries. The advantages of a system applying AI includes performance improvent, optimization, and intelligent or smart AnFor intelligent fesure of 5G, network slicing, provided by Network equipment vendor by applying AI, softwarization and virtualization technologies to the network. For many other industries and applications such as healthcare, agriculture, finance, have benefited from AI technology in particular machine learning and deep learning within AL.With the integration of AI, 5G, and Inernet of Thngs, the industrial applications, smart farms, precision medicine.,smart city. This article focuses on the System architecture and design of open networking solution of the future of 5G, beyond-5G (B5G) or 6G. Among the challenges of an ON system solution, the propriety of radio access network (RAN) is one of essential challenges. The Open-RAN Alliance is formed through the integration of C-RAN Alliance and X-RAN Forum. The O-RAN Alliance mission&amp;rsquo;s is converting the radio access network industry to become an open networking intelligent, virtualized, and fully interoperable RAN. To realize B5G or 6G by applying O-RAN architecture and ecosystem is called O-RAN based B5G/6G The Integration of O-RAN based 5G RAN part and the SDN/NFV-based softwarization and virtualization of Core Network, Transport Network and Management functions, we can derive a stage of fully Open Networking architecture for the software (AI/M/DL) developers to work.

Detecting Coronary Artery Calcium on Chest Radiographs: Can We Teach an Old Dog New Tricks?

Detecting Coronary Artery Calcium on Chest Radiographs: Can We Teach an Old Dog New Tricks?

A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of a mobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied in many scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms.

Open and disaggregated optical transport networks for data center interconnects [Invited

In this paper, we present Alibaba’s practices and views on open and disaggregated optical transport networks, with a focus on metro data center interconnects (DCIs). We first discuss technology developments that have enabled open optical transport networks, followed by a presentation of our open optical network architecture for DCI networks. We then describe an overall data center network architecture and the application scenario of open and disaggregated DCI technology. Details of the point-to-point DCI network architecture and the need for reconfigurable optical add–drop multiplexers (ROADMs) in DCI networks are then discussed. We present the data models of DCI equipment, including optical terminal transponders, point-to-point optical line systems, and ROADMs, developed based on OpenConfig YANG models. The detailed design and implementation of a home-grown control and management software platform for DCI networks are also presented. We conclude with a discussion of the real deployment of the technology in Alibaba’s networks.

The SONATA data format for efficient description of large-scale network models.

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

Following in Situ the Degradation of Mesoporous Silica in Biorelevant Conditions: At Last, a Good Comprehension of the Structure Influence.

Mesoporous silica nanoparticles (MSNs) have seen a fast development as drug delivery carriers thanks to their tunable porosity and high loading capacity. The employ of MSNs in biomedical applications requires a good understanding of their degradation behavior both to control drug release and to assess possible toxicity issues on human health. In this work, we study mesoporous silica degradation in biologically relevant conditions through in situ ellipsometry on model mesoporous nanoparticle or continuous thin films, in buffer solution and in media containing proteins. In order to shed light on the structure/dissolution relationship, we performed dissolution experiments far from soluble silicate species saturation. Via a complete decorrelation of dissolution and diffusion contributions, we proved unambiguously that surface area of silica vectors is the main parameter influencing dissolution kinetics, while thermal treatment and open mesoporous network architecture have a minor impact. As a logical consequence of our dissolution model, we proved that the dissolution lag-time can be promoted by selective blocking of the mesopores that limits the access to the mesoporous internal surface. This study was broadened by studying the impact of the organosilanes in the silica structure, of the presence of residual structuring agents, and of the chemical composition of the dissolution medium. The presence of albumin at blood concentration was found affecting drastically the dissolution kinetics of the mesoporous structure, acting as a diffusion barrier. Globally, we could identify the main factors affecting mesoporous silica materials degradation and proved that we can tune their structure and composition for adjusting dissolution kinetics in order to achieve efficient drug delivery.

THE INVESTIGATION AND EVALUATION MULTISERVICE NETWORK NGN/IMS FOR MULTIMEDIA TRAFFIC

The subject of the study is multiservice communication network using the concept NGN (Next Generation Network) based on the open network architecture IMS (Internet Protocol Multimedia Subsystem), supporting a wide range services. The basis of this architecture is the IMS core, consisting of a set of specialized modules responsible for various functions for customer service. The purpose of the article is to analyze the existing technical capabilities of the IMS multimedia messaging subsystem and perspective solutions for the functioning of the NGN/IMS network efficiency in providing multimedia service. As the efficiency of the system, the capacity NGN/IMS networks is selected using the signaling system and protocols NGN. The capacity NGN/IMS networks during the establishment of a multimedia session was analyzed and the functional architecture of the IMS multimedia messaging subsystem that determine the interaction NGN signaling systems an protocols was explored. Manage the presentation Triple Play services to subscribers and simultaneously modify the media stream within the session allows the protocol for the initialization of the SIP and Diameter sessions, which are the main IMS signaling protocol. One of the important requirements for the IMS subsystem is the maintenance QoS (Quality of Service). A mathematical model for estimating the quality of communication services using a system GI/G/1/N based on the theory diffusion approximation is proposed. The research presented in this paper is very important for the theory queuing systems, since the article proposes a method for investigating multiservice communication networks with non-Poisson incoming flow and effective results are obtained for NGN/IMS networks. On the basis of the model analytical expressions are obtained, which allow evaluating the performance indicators of the Triple Play service. The proposed mathematical model can be used to solve a wide range of practical problems, including the management multiservice traffic in the process its transmission in multiservice telecommunications networks, taking into account the quality of service classes. Thus, studies NGN/IMS network capacity indicators using SIP protocols are relevant.

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Increasing availability of comprehensive experimental datasets in neuroscience and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational network models. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. We provide reference Application Programming Interfaces and model examples to catalyze support and adoption of the format. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

Exploring Network Softwarization and Virtualization by Applying SDN/NFV to 5G and IoT

The development trends of networking and wireless communications technologies have evolved toward Open Networking (ON), Software Defined Networks (SDN), Network Function Virtualization (NFV), and Disaggregation. ON and SDN/NFV are playing the key roles for softwarization and virtualization (S&V) of next generation networks such as 5 th Generation Communications and Networks (5G) and Internet of Things (IoT). This article explores the S&V of 5G & IoT and addresses the challenging issues related to applying SDN/NFV to 5G and IoT. These issues include system performance, end-to-end interoperability, software compatibility, global deployment, integration of SDN and NFV , and shortage of talents. In addition, we describe the research and development status of adapting ON architecture and applying SDN/NFV to 5G and IoT in Taiwan. Finally we report the collaboration between National Chiao Tung University (NCTU) and networking industry in the areas of programmable switches built on top of silicon with Programming Protocol-independent Packet Processor (P4) high-level script language and prototyping an OpenAirIntrface (OAI)-based M-CORD.

Hiding in the Open (Part II): How Tech Network Policies Can Inform 'Openness by Design' (and Vice Versa)

Hiding in the Open (Part II): How Tech Network Policies Can Inform Openness by Design (and Vice Versa) (TPRC 46 Paper Abstract: revised July 9, 2018) Open source. Open APIs. Open data. Open Internet. Open science. Open government. The concept of seemingly is everywhere in the information technology space. For the most part, is seen by its backers as an unalloyed virtue, a way of meritoriously spreading the innovative and social value of a particular online resource. And yet, surprisingly, no rigorous conceptual framework currently exists for assessing the relative value of open systems, especially as compared to their more proprietary counterparts. The author's thesis is that it is critical for stakeholders to fully understand at its deepest levels, in order to be able to participate in searching discussions of its merits in technology policy. This paper is the first in a Mozilla Foundation-sponsored series to examine across all layers and functionalities of the Internet. Here the focus is on modern day telecommunications and information networks, and their treatment by U.S. policymakers and other stakeholders. Future papers will explore the concept of in other layers of the Internet, including data platforms, software (such as open source), hardware, computational systems, applications (such as open APIs), and content. Three relevant industry sectors are reviewed in the paper: -- First, we will examine the architectural basis of the open Internet in its Middle Layers software elements -- including in particular the end-to-end principle, network interconnection, agnostic bearer protocols, and modularity. -- Second, we will turn to telephony network access, from the FCC’s Part 68 “Carterfone” rules, to the Computer Inquiry's basic telecommunications/enhanced information services dichotomy, and the Telecommunications Act/Computer III concepts of unbundling and Open Network Architecture. -- Third, we will analyze the broadband access debates -- from open access to network neutrality -- from the perspective of openness. The paper then provides some initial observations about in a networked environment. The framework of “openness by design” will be introduced, as an analytical tool for understanding the concept as a complex systems trade-off. In particular, should not be seen as an absolute value, or always advantageous, or even the clearly optimal default position. Instead, complete exists at one pole of a recognized systems polarity with complete enclosure. These two poles form the basis of an ever-shifting continuum between control and freedom, as played out in this instance with regard to networked resources. Moreover, an taxonomy can include structural, functional, behavioral, informational, participatory, and perceptual forms. The paper concludes with observing how is constantly at play throughout the Internet's software-based overlays and hardware-based underlays. Preliminary thoughts can be articulated on how to apply openness by design principles to all layers of the Internet ecosystem, including the data, software, computational, cloud, devices, and applications layers.

The Effectiveness NGN/IMS Networks in the Establishment of a Multimedia Session

The subject of the study is multiservice communication network using the concept NGN (Next Generation Network) based on the open network architecture IMS (Internet Protocol Multimedia Subsystem), supporting a wide range of services. The basis of this architecture is the IMS core, consisting of a set of specialized modules responsible for various functions for customer service. The purpose of the article is to analyze the existing technical capabilities of the IMS multimedia messaging subsystem and perspective solutions for the functioning of the NGN/IMS network efficiency in providing multimedia services. The effectiveness NGN/IMS networks during the establishment of a multimedia session was analyzed and the functional architecture of the IMS multimedia messaging subsystem that determine the interaction of NGN signaling systems an protocols was explored. One of the important requirements for the IMS subsystem is the maintenance QoS (Quality of Service). A mathematical model for estimating the quality of communication services using a system <i>GI</i>/<i>G</i>/1/<i>N_6H</i> based on the theory of diffusion approximation is proposed. On the basis of the model analytical expressions are obtained, which allow evaluating the performance indicators of the Triple Play service.

Privacy Protection for Data-Driven Smart Manufacturing Systems

The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. The transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets. The loss of sensitive information can lead to significant business loss and cause reputational damage. In this paper, the authors discuss emerging issues that are related to IIoT data sharing, investigate possible technological solutions to hide sensitive information and discuss some privacy management techniques in smart manufacturing systems.