Final report on dissemination, regulation, standardization, exploitation & training : D6.3

In D6.1 deliverable project dissemination, exploitation and training plans, as well as standardization & regulatory approach strategy was presented. The D6.2 reported on the necessary updates of these strategies and the actions taken by the partners in line with them, as well as the obtained results. In this D6.3 deliverable, a full set of project dissemination activities, standardization & regulatory contributions as well as an operator’s “cook book” outlining steps necessary for full deployment of ON functionality and services, are presented.Deliverable D6.3 del projecte OneFITPostprint (author’s final draft

Business scenarios, technical challenges and system requirements - D2.1

Deliverable D2.1 del projecte Europeu OneFIT (ICT-2009-257385)Preprin

Formulation, implementation considerations, and first performance evaluation of algorithmic solutions - D4.1

Deliverable D4.1 del projecte Europeu OneFIT (ICT-2009-257385)This deliverable contains a first version of the algorithmic solutions for enabling opportunistic networks. The presented algorithms cover the full range of identified management tasks: suitability, creation, QoS control, reconfiguration and forced terminations. Preliminary evaluations complement the proposed algorithms. Implementation considerations towards the practicality of the considered algorithms are also included.Preprin

Results analysis and validation - D5.3

Deliverable D5.3 del projecte OneFITPostprint (author’s final draft

Performance evaluation of synergic operation of algorithms enabling opportunistic networks - D4.3

Deliverable D4.3 del projecte OneFITPreprin

Validation platform specification – D5.1

Deliverable D5.1 del projecte Europeu OneFIT (ICT-2009-257385)The present deliverable introduces the OneFIT Proof-of-Concept (PoC) Architecture which will be used as a basis for the validation platform development throughout the project. This PoC Architecture proposal is validated by identifying the roles of the various components in the framework of the OneFIT Scenarios as derived and detailed in WP2. The applied methodology ensures that all required features are appropriately considered. Furthermore, the hardware components available to the project are detailed which are the basis for the development of an integrated validation platform. Their role is highlighted by an instantiation step which maps the PoC Architecture components to the identified hardware components. Finally, a scenario instantiation is derived which illustrates the role of the various hardware components for the validation of selected OneFIT scenarios.Postprint (published version

OneFIT functional and system architecture - D2.2

Deliverable D2.2 del projecte Europeu OneFIT (ICT-2009-257385)This document presents the OneFIT functional and system architecture for the management and control of infrastructure coordinated opportunistic networks (ONs). The most relevant building blocks "Cognitive management System for the Coordination of the Infrastructure" (CSCI) and the "Cognitive Management system for the Opportunistic Network" (CMON) are described.Postprint (published version

Knowledge generation from telecommunications big data for enabling cognitive infrastructure management

The continuously growing use of Internet and the optimization of the services, in terms of offering more capabilities to the users, result in the increased need for spectrum/bandwidth, a rather limited resource, and processing capabilities in core and access networks. To this end, Cognitive Radio Systems (CRSs) have been proposed for enhancing the resource allocation and utilization, and thus bridge this gap while preserving, if not enhancing, the Quality of Services (QoS) and the Quality of Experience (QoE). Moreover, the availability of large amounts of unstructured data, which come from various sources, is seen as highly promising for deriving high level information and new insights for the business world while easier access to them through the Web facilitates the research towards this direction. However, the velocity of them being changed requires exceptional technology to efficiently process large quantities of data within tolerable timeframes. Data characterized by high volume, variety and velocity are commonly known as Big Data. These data need to be efficiently managed, handled and exploited by the Network Operators (NOs) and/or Service Providers (SPs) but human resources are not sufficient. Knowledge building mechanisms are often proposed for addressing both of the above challenges. In particular, cognitive network management can offer solutions to the challenges posed by future networks but this requires the incorporation of knowledge that is dynamically built from its own mechanisms. Dynamically built knowledge exploits context information and allows quicker and more complex data analysis so as to better comply with the volume, the velocity and the variety of the produced Big Data. In order to build knowledge that enhances the decisions of the network, the network monitors its current state and senses information with respect to the context it functions, it collects information regarding the results of its decisions – whether the state in which it evolved allows it to have better or worse performance – and is dynamically trained to select the state with the highest performance when in similar context. During the decision making process, rules and policies of the NO and/or the SP are combined with the knowledge built from the past experience of the network so as to be respected.To this end, this dissertation studies, designs, proposes and evaluates knowledge building mechanisms that can exploit (Big) data and enhance the decision making processes of a CRS.Η συνεχώς αυξανόμενη χρήση του Διαδικτύου και η βελτιστοποίηση των υπηρεσιών, υπό την έννοια της προσφοράς περισσότερων δυνατοτήτων στους χρήστες, έχει ώς αποτέλεσμα την αυξανόμενη ανάγκη ραδιοσυχνότητων, μίας περιορισμένης φυσικής πηγής, και των επεξεργαστικών δυνατοτήτων των δικτύων. Τα συστήματα γνωσιακής διαχείρισης έχουν την ικανότητα να βελτιώνουν την κατανομή και την χρησιμοποίηση των πόρων ενώ παράλληλα διατηρούν, αν όχι βελτιώνουν, την ποιότητα των υπηρεσιών (QoS) και την ποιότικα της εμπειρίας των χρηστών. Παράλληλα, η μεγάλη διαθεσιμότητα της αδόμητης πληροφορίας από διαφορετικές πηγές παρέχει την δυνατότητα της δημιουργίας γνωσης αλλά η μεγάλη ταχύτητα με την οποία η πληροφορία αυτή αλλάζει απαιτεί τέτοια τεχνολογία που να μπορεί να επεξεργάζεται μεγάλο όγκο δεδομένων σε μικρά χρονικά διαστήματα. Τα δεδομένα που χαρακτηρίζοντια από μεγάλο όγκο, ποικιλομορφία και ταχύτητα είναι γνωστά ως Big Data. Οι μηχανισμοί δημιουργίας γνώσης αναφέρονται συχνά ώς η διέξοδος και στις 2 παραπάνω προκλήσεις των μελλοντικών δικτύων. Συγκεκριμένα, οι μηχανισμοί δημιουργίας γνώσης παράγουν δυναμικά την γνώση που περιλαμβάνει την πρότερη εμπειρία του δικτύου και μπορεί να καθοδηγήσει τις αποφάσεις του δικτύου. Συγκεκριμένα, παρακολουθούν την κατάσταση του δικτύου, συλλέγουν πληροφορίες από το περιβαλλον τους και σχετικά με την απόδοση των αποφάσεών τους και εκπαιδεύονται δυναμικά ώστε να επιλέγουν την καταλληλότερη των αποφάσεων δεδομένης της κατάστασης του δικτύου.Κατά την διαδικασία λήψης αποφάσεων για το δίκτυο, οι κανόνες και η πολιτική διαχείρισης του δικτύου συνυπολογίζονται. Προς αυτήν την κατεύθυνση, η εν λόγω διατριβή μελετά, σχεδιάζει, προτείνει και αξιολογεί μηχανισμούς δημιουργίας γνώσης που μπορούν να αξιοποιήσουν δεδομένα μεγάλης κλίμακας και να βελτιώσει τις διαδικασίες λήψης αποφάσεων των συστημάτων γνωσιακής διαχείρισης