Towards personalised and adaptive QoS assessments via context awareness

Quality of Service (QoS ) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically

Adaptive Screen Generation for Mobile Devices

When one looks at any learning software - eLearning based or not - one cannot sometims help but think why the authors could not have organized the screens (or the means of navigation) in a way similar to some other piece of software which the user has used or come accustomed to using. In this paper it is hoped to be able to achieve just that. That is, to accommodate the adaptive screen design by building up a profile about the user which can capture the learning styles of the user and personal preferences, and to be able to have the same navigation process over multiple devices and transmission types. With this as the design backbone it is also planned to offer user driven material so as to be able to take full advantage of the user requirements

The value of QoE-based adaptation approach in educational hypermedia: Empirical evaluation

Abstract. This paper reports the results of a comparison-based empirical study on the applicability of the end-user Quality of Experience-based content adaptation mechanism in adaptive educational hypermedia. The focus of the paper will be the experiment itself: the initial settings, testing scenarios and the results. We will show that for low bit rate connections the QoE-based adaptation decreases study session time, information processing time per page and the number of re-visits to a page, it maintains similar learning outcomes while also improving the user quality of experience and satisfaction with the system. Finally we will comment on the results and interpret them

Design of a QoS-based Framework for Service Ranking and Selection in Cloud E-marketplaces

In most existing commercial cloud e-marketplaces, finding a suitable cloud service to perform user's objectives can be cognitively demanding and potentially affects the user satisfaction of both the process and outcome of decision making. Most existing cloud selection techniques have not sufficiently addressed the problem of service choice overload in a manner, that provides means that elicits subjective user preferences. Besides, only a few of these techniques suffice in situations where there are a large number of services to be evaluated and the results are presented in textual formats, either in a list or tables, which does not provide any means of comparison of results returned. Based on a comparative review of existing service selection techniques, a set of requirements was identified to guide the design of cloud service selection framework that would suffice in a cloud e-marketplace context. A cloud service selection framework was formulated that encapsulates the set of requirements. The increase in the number of available services on the e-marketplace leaves the users in the dilemma of which service to select, particularly when the services perform equivalent functionalities and may only differ with respect to their quality of service (QoS) attributes. The proposed framework is a viable proposition for the reduction service choice overload in cloud service e-marketplaces

Adaptive multimedia streaming control algorithm in wireless LANs and 4G networks

E-learning has become an important service offered over the Internet. Lately many users are accessing learning content via wireless networks and using mobile devices. Most content is rich media-based and often puts significant pressure on the existing wireless networks in order to support high quality of delivery. In this context, offering a solution for improving user quality of experience when multimedia content is delivered over wireless networks is already a challenging task. Additionally, to support this for mobile e-learning over wireless LANs becomes even more difficult. If we want to increase the end-used perceived quality, we have to take into account the users’ individual set of characteristics. The fact that users have subjective opinions on the quality of a multimedia application can be used to increase their QoE by setting a minimum quality threshold below which the connection is considered to be undesired. Like this, the use of precious radio resources can be optimized in order to simultaneously satisfy an increased number of users. In this thesis a new user-oriented adaptive algorithm based on QOAS was designed and developed in order to address the user satisfaction problem. Simulations have been carried out with different adaptation schemes to compare the performances and benefits of the DQOAS mechanism. The simulation results are showing that using a dynamic stream granularity with a minimum threshold for the transmission rate, improves the overall quality of the multimedia delivery process, increasing the total number of satisfied users and the link utilization The good results obtained by the algorithm in IEEE 802.11 wireless environment, motivated the research about the utility of the newly proposed algorithm in another wireless environment, LTE. The study shows that DQOAS algorithm can obtain good results in terms of application perceived quality, when the considered application generates multiple streams. These results can be improved by using a new QoS parameters mapping scheme able to modify the streams’ priority and thus allowing the algorithms decisions to not be overridden by the systems’ scheduler

The quality of experience of next generation audio :exploring system, context and human influence factors

PhD ThesisThe next generation of audio reproduction technology has the potential to deliver immersive and personalised experiences to the user; multichannel with-height loudspeaker arrays and binaural techniques offer 3D audio experiences, whereas objectbased techniques offer possibilities of adapting content to suit the system, context and user. A fundamental process in the advancement of such technology is perceptual evaluation. It is crucial to understand how listeners perceive new technology in order to drive future developments. This thesis explores the experience provided by next generation audio technology by taking a quality of experience (QoE) approach to evaluation. System, context and human factors all influence QoE and in this thesis three case studies are presented to explore the role of these categories of influence factors (IFs) in the context of next generation audio evaluation. Furthermore, these case studies explore suitable methods and approaches for the evaluation of the QoE of next generation audio with respect to its various IFs. Specific contributions delivered from these individual studies include a subjective comparison between soundbar and discrete surround sound technology, the application of the Open Profiling of Quality method to the field of audio evaluation, an understanding of both how and why environmental noise influences preferred audio object balance, an understanding of how the influence of technical audio quality on overall listening experience is related to a range of psychographic variables and an assessment of the impact of binaural processing on overall listening experience. When considering these studies as a whole, the research presented here contributes the thesis that to effectively evaluate the perceived quality of next generation audio, a QoE mindset should be taken that considers system, context and human IFs.Engineering and Physical Sciences Research Council (EPSRC) and the British Broadcasting Corporation Research & Development department (BBC R&D