775 research outputs found

    Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems

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    Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.publishedVersio

    Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop

    A Cross-layer Approach for MPTCP Path Management in Heterogeneous Vehicular Networks

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    Multipath communication has recently arisen as a promising tool to address reliable communication in vehicular networks. The architecture of Multipath TCP (MPTCP) is designed to facilitate concurrent utilization of multiple network interfaces, thereby enabling the system to optimize network throughput. In the context of vehicular environments, MPTCP offers a promising solution for seamless roaming, as it enables the system to maintain a stable connection by switching between available network interfaces. This paper investigates the suitability of MPTCP to support resilient and efficient Vehicleto-Infrastructure (V2I) communication over heterogeneous networks. First, we identify and discuss several challenges that arise in heterogeneous vehicular networks, including issues such as Head-of-Line (HoL) blocking and service interruptions during handover events. Then, we propose a cross-layer path management scheme for MPTCP, that leverages real-time network information to improve the reliability and efficiency of multipath vehicular communication. Our emulation results demonstrate that the proposed scheme not only achieves seamless mobility across heterogeneous networks but also significantly reduces handover latency, packet loss, and out-of-order packet delivery. These improvements have a direct impact on the quality of experience for vehicular users, as they lead to lower application layer delay and higher throughput

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 173-194)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022This works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Introduction -- Matrix exponential and deep learning neural network modeling of cellular handovers -- Survivability modeling in cellular networks -- Multi connectivity based handover enhancement and adaptive fractional packet duplication in 5G cellular networks -- Deepslice and Secure5G: a deep learning framework towards an efficient, reliable and secure network slicing in 5G networks -- Conclusion and future scop

    Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

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    Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

    Situation-aware Edge Computing

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    Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

    Seamless coverage for the next generation wireless communication networks

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    Data demand has exponentially increased due to the rapid growth of wireless and mobile devices traffic in recent years. With the advent of the fifth generation, 5G, and beyond networks, users will be able to take advantage of additional services beyond the capability of current wireless networks while maintaining a highquality experience. The exploitation of millimeter-wave (mm-wave) frequency in 5G promises to meet the demands of future networks with the motto of providing high data rate coverage with low latency to its users, which will allow future networks to function more efficiently. However, while planning a network using mm-wave frequencies, it is important to consider their small coverage footprints and weak penetration resistance. Heterogeneous network planning with the dense deployment of the small cells is one way of overcoming these issues, yet, without proper planning of the integrated network within the same or different frequencies could lead to other problems such as coverage gaps and frequent handovers; due to the natural physics of mm-wave frequencies. Therefore this thesis focuses on bringing ultra-reliable low-latency communication for mm-wave indoor users by increasing the indoor coverage and reducing the frequency of handovers. Towards achieving this thesis’s aim, a detailed literature review of mm-wave coverage is provided in Chapter 2. Moreover, a table that highlights the penetration loss of materials at various frequencies is provided as a result of thorough research in this field, which will be helpful to the researchers investigating this subject. According to our knowledge, this is the first table presenting the most studies that have been conducted in this field. Chapter 3 examines the interference effect of the outdoor base station (BS) inside the building in the context of a heterogeneous network environment. A single building model scenario is created, and the interference analysis is performed to observe the effects of different building materials used as walls. The results reveal the importance of choosing the material type when outdoor BS is close to the building. Moreover, the interference effect of outdoor BS should be minimized when the frequency re-use technique is deployed over very short distances. Chapter 4 presents two-fold contributions, in addition to providing a comprehensive handover study of mm-wave technology. The first study starts with addressing the problem of modelling users’ movement in the indoor environment. Therefore, a user-based indoor mobility prediction via Markov chain with an initial transition matrix is proposed, acquired from Q-learning algorithms. Based on the acquired knowledge of the user’s mobility in the indoor environment, the second contribution of this chapter provides a pre-emptive handover algorithm to provide seamless connection while the user moves within the heterogeneous network. The implementation and evaluation of the proposed algorithm show a reduction in the handover signalling costs by more than 50%, outperforming conventional handover algorithms. Lastly, Chapter 5 contributes to providing robust signal coverage for coverage blind areas and implementing and evaluating the proposed handover algorithm with the intelligent reflective surface. The results show a reduction in the handover signalling costs by more than 33%, outperforming conventional handover algorithms with the pre-emptive handover initiation

    Evolution of 5G Network: A Precursor towards the Realtime Implementation of VANET for Safety Applications in Nigeria

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      A crucial requirement for the successful real-time design and deployment of Vehicular Adhoc Networks (VANET) is to ensure high speed data rates, low latency, information security, and a wide coverage area without sacrificing the required Quality of Service (QoS) in VANET. These requirements must be met for flawless communication on the VANET. This study examines the generational patterns in mobile wireless communication and looks into the possibilities of adopting fifth generation (5G) network technology for real-time communication of road abnormalities in VANET. The current paper addresses the second phase of a project that is now underway to develop real-time road anomaly detection, characterization, and communication systems for VANET. The major goal is to reduce the amount of traffic accidents on Nigerian roadways. It will also serve as a platform for the real-time deployment and testing of various road anomaly detection algorithms, as well as schemes for communicating such detected anomalies in the VANET.   &nbsp
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