Our Research Activities

Personalized Mobile Telecom based on big data & network function virtualization technologies

Mobile networks are nowadays architected to serve all users and all service types, with no specific tailoring to the individual needs of users or the specific requirements of services. However, statistics demonstrate that users exhibit significant diversity in their usage habits. Furthermore, current mobile networks are static; even if a mobile user moves far away from the mobile network infrastructure, he remains serviced by the same core network, a fact that may impact numerous emerging mobile services with strict latency and jitter requirements. To cope with the above, we aim for taking the concept of mobile networking a major leap forward, whereby both the mobile network and the mobile services it supports are personalized for individual users or customized for groups of users. To always ensure low latency, the mobility of both the mobile services and the personalized mobile networks will be seamlessly enabled as per the mobility features of users. Service personalization and mobile network personalization are interactively happening and will be offered as one atomic personalized service using big data technologies. The edification of such breakthrough concept yields a number of challenging scientific problems that we are duly addressing, leveraging and contributing to technologies such as Network Function Virtualization, Software Defined Networking, Network Slicing, and Mobile Edge Computing.

Drone-based platform for the delivery of Internet of Things services

The usage of Unmanned Arial Vehicles (UAVs), simply known as drones, for mail delivery, rescue team management, or disaster recovery operations is gaining lots of attention. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Whilst these drones would be deployed for specific objectives (e.g., mail delivery), they can be simultaneously used for offering numerous Value-Added Services (VASs), particularly in the area of Internet of Things (IoT), when they are equipped with suitable and remotely controllable sensors, cameras, and actuators. Indeed, integrating and orchestrating the different segments of drones (i.e., each manufactured with specific hardware and used for a specific purpose) would yield a potential Unmanned Arial System (UAS) that could be used as an important data transport platform, on the fly and in parallel to the existing Internet system. Sharing the infrastructure of this UAS for the provisioning of different IoT services would lower both capital and operational expenses, would encourage innovation giving birth to a plethora of new IoT services that can be offered only from height, and would create a novel ecosystem with new stakeholders. The edification of this self-* multi-purpose UAS along with its VAS and its orchestration system comes with a number of scientific challenges, that we are duly addressing, leveraging the team’s expertise in IoT device management, mobile networking, and cloud computing.

Social network-based Personalized Over the Top TV

The goals of this research activity are to create a new type of Internet service by building an agile online system, which enables a user to automatically create and manage an Over the Top TV channel, and to automatically create and manage the channel’s social network (i.e., target audience). In this research work, we aim at enhancing the TV viewing experience. Effortless personalization will be ensured by sophisticated content recommendations and sharing of channels by content providers and users. Here, we also envision creating a new paradigm for socializing around audio-visual content and aim at creating new forms of social engagement and collective content consumption by making the creation/management of a TV channel/audience a core social networking act. To achieve this vision, our research focus is on i) designing and implementing flexible and elastic cloud-based content delivery architecture/content placement and distribution algorithms for optimized delivery of personalized TV programs, and ii) on defining new personalization and group recommendation strategies using big data techniques.

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