26 research outputs found

    Network-Based UE Mobility Estimation in Mobile Networks

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    International audienceThe coexistence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively

    Performance Analysis of Spotify® for Android with Model-Based Testing

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    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    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

    Sensors and Systems for Indoor Positioning

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    This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

    Crowdfunding Non-fungible Tokens on the Blockchain

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    Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs

    Fake Malware Generation Using HMM and GAN

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    In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results

    WSN based sensing model for smart crowd movement with identification: a conceptual model

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    With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way

    Novel Models and Algorithms Paving the Road towards RF Convergence

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    After decades of rapid evolution in electronics and signal processing, the technologies in communications, positioning, and sensing have achieved considerable progress. Our daily lives are fundamentally changed and substantially defined by the advancement in these technologies. However, the trend is challenged by a well-established fact that the spectrum resources, like other natural resources, are gradually becoming scarce. This thesis carries out research in the field of RF convergence, which is regarded as a mean to intelligently exploit spectrum resources, e.g., by finding novel methods of optimising and sharing tasks between communication, positioning, and sensing. The work has been done to closely explore opportunities for supporting the RF convergence. As a supplement for the electromagnetic waves propagation near the ground, ground-to-air channel models are first proposed and analysed, by incorporating the atmospheric effects when the altitude of aerial users is higher than 300 m. The status quos of techniques in communications, positioning, and sensing are separately reviewed, and our newly developments in each field are briefly introduced. For instance, we study the MIMO techniques for interference mitigation on aerial users; we construct the reflected echoes, i.e., the radar receiving, for the joint sensing and communications system. The availability of GNSS signals is of vital importance to the GNSS-enabled services, particularly the life-critical applications. To enhance the resilience of GNSS receivers, the RF fingerprinting based anti-spoofing techniques are also proposed and discussed. Such a guarantee on GNSS and ubiquitous GNSS services drive the utilisation of location information, also needed for communications, hence the proposal of a location-based beamforming algorithm. The superposition coding scheme, as an attempt of the waveform design, is also brought up for the joint sensing and communications. The RF convergence will come with many facets: the joint sensing and communications promotes an efficient use of frequency spectrum; the positioning-aided communications encourage the cooperation between systems; the availability of robust global positioning systems benefits the applications relying on the GNSS service

    Personalized Interaction with High-Resolution Wall Displays

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    Fallende Hardwarepreise sowie eine zunehmende Offenheit gegenüber neuartigen Interaktionsmodalitäten haben in den vergangen Jahren den Einsatz von wandgroßen interaktiven Displays möglich gemacht, und in der Folge ist ihre Anwendung, unter anderem in den Bereichen Visualisierung, Bildung, und der Unterstützung von Meetings, erfolgreich demonstriert worden. Aufgrund ihrer Größe sind Wanddisplays für die Interaktion mit mehreren Benutzern prädestiniert. Gleichzeitig kann angenommen werden, dass Zugang zu persönlichen Daten und Einstellungen — mithin personalisierte Interaktion — weiterhin essentieller Bestandteil der meisten Anwendungsfälle sein wird. Aktuelle Benutzerschnittstellen im Desktop- und Mobilbereich steuern Zugriffe über ein initiales Login. Die Annahme, dass es nur einen Benutzer pro Bildschirm gibt, zieht sich durch das gesamte System, und ermöglicht unter anderem den Zugriff auf persönliche Daten und Kommunikation sowie persönliche Einstellungen. Gibt es hingegen mehrere Benutzer an einem großen Bildschirm, müssen hierfür Alternativen gefunden werden. Die daraus folgende Forschungsfrage dieser Dissertation lautet: Wie können wir im Kontext von Mehrbenutzerinteraktion mit wandgroßen Displays personalisierte Schnittstellen zur Verfügung stellen? Die Dissertation befasst sich sowohl mit personalisierter Interaktion in der Nähe (mit Touch als Eingabemodalität) als auch in etwas weiterer Entfernung (unter Nutzung zusätzlicher mobiler Geräte). Grundlage für personalisierte Mehrbenutzerinteraktion sind technische Lösungen für die Zuordnung von Benutzern zu einzelnen Interaktionen. Hierzu werden zwei Alternativen untersucht: In der ersten werden Nutzer via Kamera verfolgt, und in der zweiten werden Mobilgeräte anhand von Ultraschallsignalen geortet. Darauf aufbauend werden Interaktionstechniken vorgestellt, die personalisierte Interaktion unterstützen. Diese nutzen zusätzliche Mobilgeräte, die den Zugriff auf persönliche Daten sowie Interaktion in einigem Abstand von der Displaywand ermöglichen. Einen weiteren Teil der Arbeit bildet die Untersuchung der praktischen Auswirkungen der Ausgabe- und Interaktionsmodalitäten für personalisierte Interaktion. Hierzu wird eine qualitative Studie vorgestellt, die Nutzerverhalten anhand des kooperativen Mehrbenutzerspiels Miners analysiert. Der abschließende Beitrag beschäftigt sich mit dem Analyseprozess selber: Es wird das Analysetoolkit für Wandinteraktionen GIAnT vorgestellt, das Nutzerbewegungen, Interaktionen, und Blickrichtungen visualisiert und dadurch die Untersuchung der Interaktionen stark vereinfacht.An increasing openness for more diverse interaction modalities as well as falling hardware prices have made very large interactive vertical displays more feasible, and consequently, applications in settings such as visualization, education, and meeting support have been demonstrated successfully. Their size makes wall displays inherently usable for multi-user interaction. At the same time, we can assume that access to personal data and settings, and thus personalized interaction, will still be essential in most use-cases. In most current desktop and mobile user interfaces, access is regulated via an initial login and the complete user interface is then personalized to this user: Access to personal data, configurations and communications all assume a single user per screen. In the case of multiple people using one screen, this is not a feasible solution and we must find alternatives. Therefore, this thesis addresses the research question: How can we provide personalized interfaces in the context of multi-user interaction with wall displays? The scope spans personalized interaction both close to the wall (using touch as input modality) and further away (using mobile devices). Technical solutions that identify users at each interaction can replace logins and enable personalized interaction for multiple users at once. This thesis explores two alternative means of user identification: Tracking using RGB+depth-based cameras and leveraging ultrasound positioning of the users' mobile devices. Building on this, techniques that support personalized interaction using personal mobile devices are proposed. In the first contribution on interaction, HyDAP, we examine pointing from the perspective of moving users, and in the second, SleeD, we propose using an arm-worn device to facilitate access to private data and personalized interface elements. Additionally, the work contributes insights on practical implications of personalized interaction at wall displays: We present a qualitative study that analyses interaction using a multi-user cooperative game as application case, finding awareness and occlusion issues. The final contribution is a corresponding analysis toolkit that visualizes users' movements, touch interactions and gaze points when interacting with wall displays and thus allows fine-grained investigation of the interactions
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