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

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

Validation platform implementation description – D5.2

Deliverable D5.2 del projecte OneFITPostprint (published version

Proposal of C4MS and inherent technical challenges – D3.1

Deliverable D3.1 del projecte Europeu OneFIT (ICT-2009-257385)The scope of OneFIT is on Opportunistic etworks and Cognitive Management Systems for Efficient Application Provision in the uture Internet. This document contains a proposal of Control Channels for Coordination of Cognitive Management Systems (C4MS) which enables delivery of guidance/assistance information from infrastructure towards the Opportunistic Networks and provides means for the management of Opportunistic Networks. This document defines first messages and elementary procedures for the C4MS as well as it identifies a preliminary set of information which is to be conveyed over C4MS. The document introduces also the inherent technical challenges related to the C4MS proposal.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

Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents

207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Validation platform implementation description - D5.2

This deliverable describes different test-beds for the validation of the architecture, algorithms and protocols for the operator governed opportunistic networking as defined in the OneFIT Project. Further on, this deliverable provides a description of the implementation of the OneFIT cognitive management systems CSCI and CMON as well as the C4MS protocol. Also, implementation of the blocks supporting the OneFIT system (JRRM, CCM, DSONPM, and DSM) is described. This document also describes the implementation of the OneFIT scenarios for opportunistic coverage extension, opportunistic capacity extension, infrastructure supported ad-hoc networking and device-to-device communication as well as opportunistic resource aggregation in the backhaul network

Dynamic frequency planning for professional wireless microphone systems

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