Biblio

Control room video surveillance is an important source of information for ensuring public safety. To facilitate the process, a Decision-Support System (DSS) designed for the security task force is vital and necessary to take decisions rapidly using a sea of information. In case of mission critical operation, Situational Awareness (SA) which consists of knowing what is going on around you at any given time plays a crucial role across a variety of industries and should be placed at the center of our DSS. In our approach, SA system will take advantage of the human factor thanks to the reinforcement signal whereas previous work on this field focus on improving knowledge level of DSS at first and then, uses the human factor only for decision-making. In this paper, we propose a situational awareness-centric decision-support system framework for mission-critical operations driven by Quality of Experience (QoE). Our idea is inspired by the reinforcement learning feedback process which updates the environment understanding of our DSS. The feedback is injected by a QoE built on user perception. Our approach will allow our DSS to evolve according to the context with an up-to-date SA.

The evolution of Service-Oriented Computing (SOC) provides more efficient software development methods for building and engineering new value-added service-based applications. SOC is a computing paradigm that relies on Web services as fundamental elements. Research and technical advancements in Web services composition have been considered as an effective opportunity to develop new service-based applications satisfying complex requirements rapidly and efficiently. In this paper, we present a novel approach enhancing the composition of semantic Web services. The novelty of our approach, as compared to others reported in the literature, rests on: i) mapping user's/organization's requirements with Business Process Modeling Notation (BPMN) and semantic descriptions using ontologies, ii) considering functional requirements and also different types of non-functional requirements, such as quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz), iii) using Formal Concept Analysis (FCA) technique to select the optimal set of Web services, iv) considering composability levels between sequential Web services using Relational Concept Analysis (RCA) technique to decrease the required adaptation efforts, and finally, v) validating the obtained service-based applications by performing an analytical technique, which is the monitoring. The approach experimented on an extended version of the OWLS-TC dataset, which includes more than 10830 Web services descriptions from various domains. The obtained results demonstrate that our approach allows to successfully and effectively compose Web services satisfying different types of user's functional and non-functional requirements.

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this paper, a user-centric approach and a novel methodology for anomaly detection is proposed, where the Quality of Experience (QoE) metric is used to evaluate the end-user experience. The system model demonstrates how dysfunctional serving eNodeBs are successfully detected by implementing a parametric QoE model using machine learning for prediction of user QoE in a network scenario created by the ns-3 network simulator. This approach can play a vital role in the future ultra-dense and green mobile communication networks that are expected to be both self- organizing and self-healing.

Network quality-of-service (QoS) does not always directly translate to users' quality-of-experience (QoE), e.g., changes in a video streaming app's frame rate in reaction to changes in packet loss rate depend on various factors such as the adaptation strategy used by the app and the app's use of forward error correction (FEC) codes. Therefore, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system (OS). However, today, entities such as ISPs and OSes that implement these optimizations typically do not have a convenient way of obtaining input from applications on user QoE. To address this problem, we propose offline generation of per-application models mapping application-independent QoS metrics to corresponding application-specific QoE metrics, thereby enabling entities (such as ISPs and OSes) that can observe a user's network traffic to infer the user's QoE, in the absence of direct input. In this paper, we describe how such models can be generated and present our results from two popular video applications with significantly different QoE metrics. We also showcase the use of these models for ISPs to perform QoE-aware traffic management and for the OS to offer an efficient QoE diagnosis service.

Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before. Downloading and watching video content on mobile devices is currently a growing trend among users, that is causing a demand for higher bandwidth and better provisioning throughout the network infrastructure. At the same time, popular demand for privacy has led many online streaming services to adopt end-to-end encryption, leaving providers with only a handful of indicators for identifying QoE issues. In order to address these challenges, we propose a novel methodology for detecting video streaming QoE issues from encrypted traffic. We develop predictive models for detecting different levels of QoE degradation that is caused by three key influence factors, i.e. stalling, the average video quality and the quality variations. The models are then evaluated on the production network of a large scale mobile operator, where we show that despite encryption our methodology is able to accurately detect QoE problems with 72\textbackslash%-92\textbackslash% accuracy, while even higher performance is achieved when dealing with cleartext traffic