14 research outputs found
Efficient Discovery and Utilization of Radio Information in Ultra-Dense Heterogeneous 3D Wireless Networks
Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization and network planning replacing them with more accurate 3-Dimensional (3D) network concepts while utilizing spatially distributed location-specific radio characteristics. Empowering this initiative, initially a framework is developed to accurately estimate the location-specific path loss parameters under dynamic environmental conditions in a 3D small cell (SC) heterogeneous networks (HetNets) facilitating efficient radio resource management schemes using crowdsensing data collection principle together with Linear Algebra (LA) and machine learning (ML) techniques. According to the results, the gradient descent technique is with the highest path loss parameter estimation accuracy which is over 98%. At a latter stage, receive signal power is calculated at a slightly extended 3D communication distances from the cluster boundaries based on already estimated propagation parameters with an accuracy of over 74% for certain distances. Coordination in both device-network and network-network interactions is also a critical factor in efficient radio resource utilization while meeting Quality of Service (QoS) requirements in heavily congested future 3D SCs HetNets. Then, overall communication performance enhancement through better utilization of spatially distributed opportunistic radio resources in a 3D SC is addressed with the device and network coordination, ML and Slotted-ALOHA principles together with scheduling, power control and access prioritization schemes. Within this solution, several communication related factors like 3D spatial positions and QoS requirements of the devices in two co-located networks operated in licensed band (LB) and unlicensed band (UB) are considered. To overcome the challenge of maintaining QoS under ongoing network densification and with limited radio resources cellular network traffic is offloaded to UB. Approximately, 70% better overall coordination efficiency is achieved at initial network access with the device network coordinated weighting factor based prioritization scheme powered with the Q-learning (QL) principle over conventional schemes. Subsequently, coverage information of nearby dense NR-Unlicensed (NR-U) base stations (BSs) is investigated for better allocation and utilization of common location-specific spatially distributed radio resources in UB. Firstly, the problem of determining the receive signal power at a given location due to a transmission done by a neighbor NR-U BS is addressed with a solution based on a deep regression neural network algorithm enabling to predict receive signal or interference power of a neighbor BS at a given location of a 3D cell. Subsequently, the problem of efficient radio resource management is considered while dynamically utilizing UB spectrum for NR-U transmissions through an algorithm based on the double Q-learning (DQL) principle and device collaboration. Over 200% faster algorithm convergence is achieved by the DQL based method over conventional solutions with estimated path loss parameters
Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas
Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing
ubiquitous,
exible, and globally accessible services capable of low-cost
and unrestrained device targeting. In spite of its appeal, the underlying delivery
architecture must be carefully planned and optimized to maintain a high Qualityof-
Experience (QoE) and rational resource usage, especially when migrating from
services running on managed networks with established quality guarantees. To address
the lack of holistic research works on OTT multimedia delivery systems, this
Thesis focuses on an end-to-end optimization challenge, considering a migration
use-case of a popular Catch-up TV service from managed IP Television (IPTV)
networks to OTT. A global study is conducted on the importance of Catch-up
TV and its impact in today's society, demonstrating the growing popularity of
this time-shift service, its relevance in the multimedia landscape, and tness as
an OTT migration use-case. Catch-up TV consumption logs are obtained from
a Pay-TV operator's live production IPTV service containing over 1 million subscribers
to characterize demand and extract insights from service utilization at a
scale and scope not yet addressed in the literature. This characterization is used
to build demand forecasting models relying on machine learning techniques to enable
static and dynamic optimization of OTT multimedia delivery solutions, which
are able to produce accurate bandwidth and storage requirements' forecasts, and
may be used to achieve considerable power and cost savings whilst maintaining a
high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed,
implemented, and shown to outperform established caching algorithms in both
simulation and experimental scenarios. The need for accurate QoE measurements
in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation
of a new QoE model capable of taking into account the impact of key HAS
aspects. By addressing the complete content delivery pipeline in the envisioned
content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates
that signi cant improvements are possible in next-generation multimedia delivery
solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta
atractiva para fornecer um serviço flexÃvel e globalmente acessÃvel, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter nÃveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de
migração para cenários OTT. São obtidos registos de consumos de um serviço
de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes,
para caracterizar e extrair informação de consumos numa escala e âmbito
não contemplados ate a data na literatura. Esta caracterização e utilizada para
construir modelos de previsão de carga, tirando partido de sistemas de machine
learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega
de conteúdos em OTT através de previsões das necessidades de largura de banda e
armazenamento, potenciando ganhos significativos em consumo energético e custos.
Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um
desempenho superior as soluções de referencia, quer em cenários de simulação quer
experimentais. A necessidade de medição exacta da QoE em streaming adaptativo
HTTP motiva a criaçao de um modelo capaz de endereçar aspectos especÃficos
destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através
de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são
possÃveis melhorias de desempenho muito significativas nas redes de entregas de
conteúdos em OTT de próxima geração
Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band III
Ãœbersicht der Teilkonferenzen Band III
• Service Systems Engineering
• Sicherheit, Compliance und Verfügbarkeit von Geschäftsprozessen
• Smart Services: Kundeninduzierte Kombination komplexer Dienstleistungen
• Strategisches IT-Management
• Student Track
• Telekommunikations- und Internetwirtschaft
• Unternehmenssoftware – quo vadis?
• Von der Digitalen Fabrik zu Industrie 4.0 – Methoden und Werkzeuge für die Planung und Steuerung von intelligenten Produktions- und Logistiksystemen
• Wissensmanagemen
Safety and Reliability - Safe Societies in a Changing World
The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management
- mathematical methods in reliability and safety
- risk assessment
- risk management
- system reliability
- uncertainty analysis
- digitalization and big data
- prognostics and system health management
- occupational safety
- accident and incident modeling
- maintenance modeling and applications
- simulation for safety and reliability analysis
- dynamic risk and barrier management
- organizational factors and safety culture
- human factors and human reliability
- resilience engineering
- structural reliability
- natural hazards
- security
- economic analysis in risk managemen
A Practical Hardware Implementation of Systemic Computation
It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off
Shut Up and Take My Money: Engaging Facebook Communities to Build the Brand Narrative
Social media is revolutionizing the way firms manage their brands. A broad variety of platforms provide businesses an opportunity to engage in bidirectional dialogue with their customers, the effect of which is to enhance the brand/consumer relationship. This paper contends that virtual brand communities of the type that form through social media have an important role to play in the development of a brand’s personality. Using a case study approach we demonstrate how one firm has adopted Facebook to provide their brand community with a voice which contributes to the development of the brand narrative. We find that by using well-crafted content the firm elicits the support of their community and that this interaction contributes to the development of the brand ethos