The Quality of Service (QoS) routing approach is gaining an increasing interest in the Internet community due to the new emerging Internet applications such as real-time multimedia applications. These applications require better levels of quality of services than those supported by best effort networks. Therefore providing such services is crucial to many real time and multimedia applications which have strict quality of service requirements regarding bandwidth and timeliness of delivery. QoS routing is a major component in any QoS architecture and thus has been studied extensively in the literature. Scalability is considered one of the major issues in designing efficient QoS routing algorithms due to the high cost of QoS routing both in terms of computational effort and communication overhead. Localized quality of service routing is a promising approach to overcome the scalability problem of the conventional quality of service routing approach. The localized quality of service approach eliminates the communication overhead because it does not need the global network state information. The main aim of this thesis is to contribute towards the localised routing area by proposing and developing some new models and algorithms. Toward this goal we make the following major contributions. First, a scalable and efficient QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated. Second, we have developed a path selection technique that can be used with existing localized QoS routing algorithms to enhance their scalability and performance. Third, a scalable and efficient hierarchical QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated.

Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have / Ministry of Higher Education in Egypt and the Egyptian Cultural Centre and Educational Bureau in London

While there has always been a tremendous demand for streaming video over Wireless networks, the nature of the application still presents some challenging issues. These applications that transmit coded video sequence data over best-effort networks like the Internet, the application must cope with the changing network behaviour; especially, the source encoder rate should be controlled based on feedback from a channel estimator that explores the network intermittently. The arrival of powerful video compression techniques such as H.264, which advance in networking and telecommunications, opened up a whole new frontier for multimedia communications. The aim of this research is to transmit the H.264 coded video frames in the wireless network with maximum reliability and in a very efficient manner. When the H.264 encoded video sequences are to be transmitted through wireless network, it faces major difficulties in reaching the destination. The characteristics of H.264 video coded sequences are studied fully and their capability of transmitting in wireless networks are examined and a new approach called Optimal Packet Fragmentation (OPF) is framed and the H.264 coded sequences are tested in the wireless simulated environment. This research has three major studies involved in it. First part of the research has the study about Long Range Dependence (LRD) and the ways by which the self-similarity can be estimated. For estimating the LRD a few studies are carried out and Wavelet-based estimator is selected for the research because Wavelets incarcerate both time and frequency features in the data and regularly provides a more affluent picture than the classical Fourier analysis. The Wavelet used to estimate the self-similarity by using the variable called Hurst Parameter. Hurst Parameter tells the researcher about how a data can behave inside the transmitted network. This Hurst Parameter should be calculated for a more reliable transmission in the wireless network. The second part of the research deals with MPEG-4 and H.264 encoder. The study is carried out to prove which encoder is superior to the other. We need to know which encoder can provide excellent Quality of Service (QoS) and reliability. This study proves with the help of Hurst parameter that H.264 is superior to MPEG-4. The third part of the study is the vital part in this research; it deals with the H.264 video coded frames that are segmented into optimal packet size in the MAC Layer for an efficient and more reliable transfer in the wireless network. Finally the H.264 encoded video frames incorporated with the Optimal Packet Fragmentation are tested in the NS-2 wireless simulated network. The research proves the superiority of H.264 video encoder and OPF¿s master class.

Radio spectrum has become increasingly scarce due to the proliferation of new wireless communication services. This problem has been exacerbated by fixed bandwidth licensing policies that often lead to spectral underutilization. Cognitive radio networks (CRN) can address this issue using flexible spectrum management that permits unlicensed (secondary) users to access the licensed spectrum. Supporting real-time quality-of-service (QoS) in CRNs however, is very challenging, due to the random spectrum availability induced by the licensed (primary) user activity. This thesis considers the problem of real-time voice transmission in CRNs with an emphasis on secondary network ``friendliness''. Friendliness is measured by the secondary real-time voice capacity, defined as the number of connections that can be supported, subject to typical QoS constraints. The constant bit rate (CBR) air interface case is first assumed. An offline scheduler that maximizes friendliness is derived using an integer linear program (ILP) that can be solved using a minimum cost flow graph construction. Two online primary scheduling algorithms are then introduced. The first algorithm is based on shaping the primary spectral hole patterns subject to primary QoS constraints. The second applies real-time scheduling to both primary traffic and virtual secondary calls. The online scheduling algorithms are found to perform well compared to the friendliness upper bound. Extensive simulations of the primary friendly schedulers show the achievable secondary voice capacity for a variety of parameters compared to non-friendly primary scheduling. The thesis then considers the variable bit rate (VBR) air interface option for primary transmissions. Offline and online approaches are taken to generate a primary VBR traffic schedule that is friendly to secondary voice calls. The online VBR schedulers are found to perform well compared to the friendliness upper bound. Simulation results are presented that show the effect of the primary traffic load and primary network delay tolerance on the primary network friendliness level towards potential secondary voice traffic. Finally, secondary user friendliness is considered from an infrastructure deployment point of view. A cooperative framework is proposed, which allows the primary traffic to be relayed by helper nodes using decode-and-forward (DF) relaying. This approach decreases the primary traffic channel utilization, which, in turn, increases the capacity available to potential secondary users. A relay selection optimization problem is first formulated that minimizes the primary channel utilization. A greedy algorithm that assigns relay nodes to primary data flows is introduced and found to perform well compared to the optimum bound. Results are presented that show the primary network friendliness for different levels of primary channel utilization. / Dissertation / Doctor of Philosophy (PhD)

Yes / In this paper, a novel multilink selection framework is developed for different applications with various quality of service (QoS) requirements in avionic systems, based on the multi-attribute decisionmaking model. Two metaheuristic algorithms are proposed to solve this model while optimizing the multilink selection performances. Multilink configuration and multi-homing capabilities are generally required for aircrafts operating in a heterogeneous wireless network environment. The first algorithm, called Analytic Hierarchy Process and Simulated Annealing (AHP-SA), utilises a two-phase process. In Phase one, an analytic hierarchy process (AHP) is used to choose the decision weight factors. Then, in Phase two, a simulated annealing process is applied to select suitable networks, for various service requests, based on the weights obtained from first phase. Further, to improve customer satisfaction, Simulated Annealing algorithm for Simultaneous Weights and Network Selection Optimisation (SA-SWNO) is developed, in which a simulated annealing algorithm is applied to dynamically optimise weight factors of objective functions and the request-to-network assignment matrix. Simulation results demonstrates that both proposed algorithms outperform the commonly used price-based or QoS-based network selection scheme with much higher averaged satisfaction degree and lower computational complexity. / Cockpit NetwOrk CoMmunications Environment Testing (COMET) Project under the European Commission’s Program Clean Sky2 in partnership with the European Aeronautical Industry

No description available.

Objetivo: Determinar la influencia de la calidad de servicio en la satisfacción y lealtad del cliente de una clínica de fertilidad en Chiclayo, 2021. Materiales y métodos: Por medio de un estudio de enfoque cuantitativo y diseño no experimental transversal, se realizó una encuesta a una muestra de 132 clientes de la clínica aplicando la metodología SERVPERF, evaluando las variables en estudio y posteriormente calculando su nivel de relación a través del estadístico Rho de Spearman. Resultado: Se encontró que el nivel de las 3 variables es alto, sin embargo, al buscar la relación de la satisfacción y lealtad con la calidad de servicio se determinó un nivel de relación moderado, a causa de la importancia del resultado final para el cliente, antes que alguna de las variables en estudio. Conclusiones: La clienta valora más el resultado final, quedar embarazada, para su satisfacción y lealtad, antes que la calidad, por lo que se recomienda realizar un estudio más profundo de las variables mencionadas y así mejorar el servicio. / Objective: To determine the influence of service quality on customer satisfaction and loyalty in a fertility clinic in Chiclayo, 2021. Materials and methods: Through a quantitative approach and a non-experimental cross-sectional design, a survey was conducted on a sample of 132 clients of the clinic applying the SERVPERF methodology, evaluating the variables under study and subsequently calculating their level of relationship through Spearman's Rho statistic. Result: It was found that the level of the 3 variables is high, however, when looking for the relationship between satisfaction and loyalty with the quality of service, a moderate level of relationship was determined, due to the importance of the final result for the client, before any of the variables under study. Conclusions: The client values more the final result, getting pregnant, for her satisfaction and loyalty, before quality, so it is recommended to carry out a deeper study of the mentioned variables and improve the service.

Cette thèse étudie les techniques de gestion intelligente du spectre et de topologie des réseaux via une approche radio intelligente dans le but d’améliorer leur capacité, leur qualité de service (QoS – Quality of Service) et leur consommation énergétique. Les techniques d’apprentissage par renforcement y sont utilisées dans le but d’améliorer les performances d’un système radio intelligent. Dans ce manuscrit, nous traitons du problème d’accès opportuniste au spectre dans le cas de réseaux intelligents sans infrastructure. Nous nous plaçons dans le cas où aucune information n’est échangée entre les utilisateurs secondaires (pour éviter les surcoûts en transmissions). Ce problème particulier est modélisé par une approche dite de bandits manchots « restless » markoviens multi-utilisateurs (multi-user restless Markov MAB -multi¬armed bandit). La contribution principale de cette thèse propose une stratégie d’apprentissage multi-joueurs qui prend en compte non seulement le critère de disponibilité des canaux (comme déjà étudié dans la littérature et une thèse précédente au laboratoire), mais aussi une métrique de qualité, comme par exemple le niveau d’interférence mesuré (sensing) dans un canal (perturbations issues des canaux adjacents ou de signaux distants). Nous prouvons que notre stratégie, RQoS-UCB distribuée (distributed restless QoS-UCB – Upper Confidence Bound), est quasi optimale car on obtient des performances au moins d’ordre logarithmique sur son regret. En outre, nous montrons par des simulations que les performances du système intelligent proposé sont améliorées significativement par l’utilisation de la solution d’apprentissage proposée permettant à l’utilisateur secondaire d’identifier plus efficacement les ressources fréquentielles les plus disponibles et de meilleure qualité. Cette thèse propose également un nouveau modèle d’apprentissage par renforcement combiné à un transfert de connaissance afin d’améliorer l’efficacité énergétique (EE) des réseaux cellulaires hétérogènes. Nous formulons et résolvons un problème de maximisation de l’EE pour le cas de stations de base (BS – Base Stations) dynamiquement éteintes et allumées (ON-OFF). Ce problème d’optimisation combinatoire peut aussi être modélisé par des bandits manchots « restless » markoviens. Par ailleurs, une gestion dynamique de la topologie des réseaux hétérogènes, utilisant l’algorithme RQoS-UCB, est proposée pour contrôler intelligemment le mode de fonctionnement ON-OFF des BS, dans un contexte de trafic et d’étude de capacité multi-cellulaires. Enfin une méthode incluant le transfert de connaissance « transfer RQoS-UCB » est proposée et validée par des simulations, pour pallier les pertes de récompense initiales et accélérer le processus d’apprentissage, grâce à la connaissance acquise à d’autres périodes temporelles correspondantes à la période courante (même heure de la journée la veille, ou même jour de la semaine par exemple). La solution proposée de gestion dynamique du mode ON-OFF des BS permet de diminuer le nombre de BS actives tout en garantissant une QoS adéquate en atténuant les fluctuations de la QoS lors des variations du trafic et en améliorant les conditions au démarrage de l’apprentissage. Ainsi, l’efficacité énergétique est grandement améliorée. Enfin des démonstrateurs en conditions radio réelles ont été développés pour valider les solutions d’apprentissage étudiées. Les algorithmes ont également été confrontés à des bases de données de mesures effectuées par un partenaire dans la gamme de fréquence HF, pour des liaisons transhorizon. Les résultats confirment la pertinence des solutions d’apprentissage proposées, aussi bien en termes d’optimisation de l’utilisation du spectre fréquentiel, qu’en termes d’efficacité énergétique. / Future cellular network technologies are targeted at delivering self-organizable and ultra-high capacity networks, while reducing their energy consumption. This thesis studies intelligent spectrum and topology management through cognitive radio techniques to improve the capacity density and Quality of Service (QoS) as well as to reduce the cooperation overhead and energy consumption. This thesis investigates how reinforcement learning can be used to improve the performance of a cognitive radio system. In this dissertation, we deal with the problem of opportunistic spectrum access in infrastructureless cognitive networks. We assume that there is no information exchange between users, and they have no knowledge of channel statistics and other user's actions. This particular problem is designed as multi-user restless Markov multi-armed bandit framework, in which multiple users collect a priori unknown reward by selecting a channel. The main contribution of the dissertation is to propose a learning policy for distributed users, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret. Moreover, numerical studies show that the performance of the cognitive radio system can be significantly enhanced by utilizing proposed learning policies since the cognitive devices are able to identify the appropriate resources more efficiently. This dissertation also introduces a reinforcement learning and transfer learning frameworks to improve the energy efficiency (EE) of the heterogeneous cellular network. Specifically, we formulate and solve an energy efficiency maximization problem pertaining to dynamic base stations (BS) switching operation, which is identified as a combinatorial learning problem, with restless Markov multi-armed bandit framework. Furthermore, a dynamic topology management using the previously defined algorithm, RQoS-UCB, is introduced to intelligently control the working modes of BSs, based on traffic load and capacity in multiple cells. Moreover, to cope with initial reward loss and to speed up the learning process, a transfer RQoS-UCB policy, which benefits from the transferred knowledge observed in historical periods, is proposed and provably converges. Then, proposed dynamic BS switching operation is demonstrated to reduce the number of activated BSs while maintaining an adequate QoS. Extensive numerical simulations demonstrate that the transfer learning significantly reduces the QoS fluctuation during traffic variation, and it also contributes to a performance jump-start and presents significant EE improvement under various practical traffic load profiles. Finally, a proof-of-concept is developed to verify the performance of proposed learning policies on a real radio environment and real measurement database of HF band. Results show that proposed multi-armed bandit learning policies using dual criterion (e.g. availability and quality) optimization for opportunistic spectrum access is not only superior in terms of spectrum utilization but also energy efficient.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)

IEEE 802.11 wireless LANs operating in the infrastructure mode are extremely popular and have seen widespread deployment because of their convenience and cost efficiency. A large number of research studies have investigated the performance of DCF, the default MAC protocol in 802.11 WLANs. Previous studies have pointed out several performance problems caused by the interaction of DCF in infrastructure-based WLANs. This thesis addresses a few of these issues. In the first part of the thesis, we address the issue of head-of-line (HOL) blocking at the Access Point (AP) in infrastructure WLANs. We use a cooperative ARQ scheme to resolve the obstruction at the AP queue. We analytically study the performance of our scheme in a single cell IEEE 802.11 infrastructure WLAN under a TCP controlled file download scenario and validate our analysis by extensive simulations. Both analysis and simulation results show considerable increase in system throughput with the cooperative ARQ scheme. We further examine the delay performance of the ARQ scheme in the presence of both elastic TCP traffic and delay sensitive VoIP traffic. Simulations results show that our scheme decreases the delay in the downlink for VoIP packets significantly while simultaneously providing considerable gains in the TCP download throughput. Next, we propose a joint uplink/downlink opportunistic scheduling scheme for maximising system throughput in infrastructure WLANs. We first solve the uplink/downlink unfairness that exists in infrastructure WLANs by maintaining a separate queue and a backoff timer at the AP for each mobile station (STA). We also increase the system throughput by making the backoff timer a function of the channel gains. We analyse the I performance of our scheme under symmetric UDP traffic with i. i. d. channel conditions. Finally, we discuss several opportunistic scheduling policies which aim to increase the system throughput while satisfying certain Quality of Service (QoS) objectives. The standard IEEE 802.11 DCF protocol only offers best-effort services and does not provide any QoS guarantees. Providing QoS in 802.11 networks with time varying channel conditions has proven to be a challenge. We show by simulations that by an appropriate choice of the scheduling metric in our opportunistic scheduling scheme, different QOS objectives like maximizing weighted system sum throughput, minimum rate guarantees and throughput optimality can be attained.