3,015 research outputs found
Quality of experience and access network traffic management of HTTP adaptive video streaming
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming
Adaptive vehicular networking with Deep Learning
Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities.
In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects.
The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup.
The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios.
The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
A new algorithm to enhance security against cyber threats for internet of things application
One major problem is detecting the unsuitability of traffic caused by a distributed denial of services (DDoS) attack produced by third party nodes, such as smart phones and other handheld Wi-Fi devices. During the transmission between the devices, there are rising in the number of cyber attacks on systems by using negligible packets, which lead to suspension of the services between source and destination, and can find the vulnerabilities on the network. These vulnerable issues have led to a reduction in the reliability of networks and a reduction in consumer confidence. In this paper, we will introduce a new algorithm called rout attack with detection algorithm (RAWD) to reduce the affect of any attack by checking the packet injection, and to avoid number of cyber attacks being received by the destination and transferred through a determined path or alternative path based on the problem. The proposed algorithm will forward the real time traffic to the required destination from a new alternative backup path which is computed by it before the attacked occurred. The results have showed an improvement when the attack occurred and the alternative path has used to make sure the continuity of receiving the data to the main destination without any affection
Telecommunications Wireless Generations: Overview, Technological Differences, Evolutional Triggers, and the Future
This study expands on prior studies on wireless telecommunication generations by examining the technological differences and evolutional triggers that characterise each Generation (from 1G to 5G). Based on a systematic literature review approach, this study examines fifty (50) articles to enhance our understanding of wireless generation evolution. Specifically, this study analyses i) the triggers that necessitated the evolution of wireless telecommunication generations and ii) makes a case regarding why it is imperative to look beyond the fifth Generation (5G) network technologies. The authors propose areas for future research
Оброблення зображень та відео сигналів в системах передачі мультимедійних даних
Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник дипломної роботи: доцент кафедри ТКРС, Терентьєва І.Є.Об’єкт дослідження - є дослідження методів оброблення зображень та відео сигналів в системах передачі мультимедійних даних Предмет дослідження - дослідження проксі-сервера DASH, та побудова
механізму, який розв'язує проблеми забезпечення надійної доставки мультимедійного вмісту, уникнення небажаних збоїв у роботі служби та зменшення затримки обслуговування. Мета кваліфікаційної роботи - створення механізму, який дозволяє вибиратиальтернативні представлення у випадку сегментів які потрібно відновити за допомогою HTTP. Метод дослідження - дослідження методів, які допоможуть уникнути перебоїв у службі та зменшити затримку послуги для послуг мультимедійного мовлення LTE
шляхом додавання можливостей динамічної адаптації до процесу відновлення помилок одноадресної адреси
Forecasting Network Traffic: A Survey and Tutorial with Open-Source Comparative Evaluation
This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions
Predictive QoS for cellular connected UAV payload communication
Unmanned aerial vehicles (UAVs), or drones, are revolutionizing industries due to their versatility, affordability and applicability. Reliable communication links are essential for UAV operations, especially for beyond visual line of sight scenarios where drones are flown beyond the operator’s line of sight. Cellular networks, particularly in the context of 5G and beyond, offer potential solutions to meet the data-intensive demands of UAV applications. This study explores the feasibility of predictive quality of service for forecasting uplink (UL) throughput quality of service (QoS) parameter in UAV payload communication links. Comprehensive field tests were conducted to ensure accurate real-world results, as simulations may not fully capture real-world complexities. Field trial measurements were conducted in a sub-urban area to evaluate drone performance at various altitudes and bands. This sheds light on potential challenges and trade-offs for cellular-connected drones and their coexistence with terrestrial users. Drones flying at high altitudes often experience line of sight propagation, causing them to undergo frequent handovers between multiple base stations. Field trials demonstrated that drones connected to a 700 MHz signal encountered minimal interference and no handovers. Conversely, drones connected to the 3500 MHz frequency band faced multiple handovers, highlighting the complexities of UAV-cellular integration and emphasizing the significance of frequency band selection in drone applications. By harnessing machine learning (ML) models and comparative analysis of centralized and federated learning methods, this research investigates ML model performances in forecasting UL throughput based on prediction accuracy. The findings emphasize the importance of diverse training data and highlight the impact of frequency bands on UAV communication. These insights lay the groundwork for addressing UAV communication complexities and advancing the integration of machine learning and network dynamics for improving UAV operations
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