The potential offered by the abundance of sensors, actuators and communications in IoT era is hindered by the limited computational capacity of local nodes, making the distribution of computing in time and space a necessity. Several key challenges need to be addressed in order to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. Our research takes upon these challenges by dynamically distributing resources when the demand is rapidly time varying. We first propose an analytic mathematical dynamical modelling of the resources, offered workload, and networking environment, that incorporates phenomena met in wireless communications, mobile edge computing data centres, and network topologies. We also propose a new set of estimators for the workload and resources time-varying profiles that continuously update the model parameters. Building on this framework, we aim to develop novel resource allocation mechanisms that take explicitly into account service differentiation and context-awareness, and most importantly, provide formal guarantees for well-defined QoS/QoE metrics. Our research goes well beyond the state of the art also in the design of control algorithms for cyber-physical systems (CPS), by incorporating resource allocation mechanisms to the decision strategy itself. We propose a new generation of controllers, driven by a co-design philosophy both in the network and computing resources utilization. This paradigm has the potential to cause a quantum leap in crucial fields in engineering, e.g., Industry 4.0, collaborative robotics, logistics, multi-agent systems etc. To achieve these breakthroughs, we utilize and combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory and Network Theory, fields where the consortium has internationally leading expertise. Although researchers from Computer and Network Science, Control Engineering and Applied Mathematics have proposed various approaches to tackle the above challenges, our research constitutes the first truly holistic, multidisciplinary approach that combines and extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Our developed theory will be extensively tested on available experimental testbed infrastructures of the participating entities. The efficiency of the overall proposed framework will be tested and evaluated under three complex use cases involving mobile autonomous agents in IoT environments: (i) distributed remote path planning of a group of mobile robots with complex specifications, (ii) rapid deployment of mobile agents for distributed computing purposes in disaster scenarios and (iii) mobility-aware resource allocation for crowded areas with pre-defined performance indicators to reach.

The potential offered by the abundance of sensors, actuators and communications in IoT era is hindered by the limited computational capacity of local nodes, making the distribution of computing in time and space a necessity. Several key challenges need to be addressed in order to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. Our research takes upon these challenges by dynamically distributing resources when the demand is rapidly time varying. We first propose an analytic mathematical dynamical modelling of the resources, offered workload, and networking environment, that incorporates phenomena met in wireless communications, mobile edge computing data centres, and network topologies. We also propose a new set of estimators for the workload and resources time-varying profiles that continuously update the model parameters. Building on this framework, we aim to develop novel resource allocation mechanisms that take explicitly into account service differentiation and context-awareness, and most importantly, provide formal guarantees for well-defined QoS/QoE metrics. Our research goes well beyond the state of the art also in the design of control algorithms for cyber-physical systems (CPS), by incorporating resource allocation mechanisms to the decision strategy itself. We propose a new generation of controllers, driven by a co-design philosophy both in the network and computing resources utilization. This paradigm has the potential to cause a quantum leap in crucial fields in engineering, e.g., Industry 4.0, collaborative robotics, logistics, multi-agent systems etc. To achieve these breakthroughs, we utilize and combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory and Network Theory, fields where the consortium has internationally leading expertise. Although researchers from Computer and Network Science, Control Engineering and Applied Mathematics have proposed various approaches to tackle the above challenges, our research constitutes the first truly holistic, multidisciplinary approach that combines and extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Our developed theory will be extensively tested on available experimental testbed infrastructures of the participating entities. The efficiency of the overall proposed framework will be tested and evaluated under three complex use cases involving mobile autonomous agents in IoT environments: (i) distributed remote path planning of a group of mobile robots with complex specifications, (ii) rapid deployment of mobile agents for distributed computing purposes in disaster scenarios and (iii) mobility-aware resource allocation for crowded areas with pre-defined performance indicators to reach.

"The energy consumption of mobile networks has been the source of animated debates in the recent period, with the deployment of 5G technologies. However, the energy consumption estimations put forward by the different parties in the debate showed significant differences, up to two orders of magnitude. This is a result of a lack of accurate models and meaningful metrics in this field. More precisely, the control plane of a mobile network represents a significant share of the traffic exchanged between the user and the network infrastructure, much more than in any other network technology, and this role will become even more important with the development of network function virtualisation and orchestration. Models focusing on the application-level traffic are bound to make harsh approximations, leading to results that can not really help the involved parties. Project ECOMOME addresses this problem of accurately modelling and optimising the energy consumption of a mobile network, with a focus on 4G and 5G technologies. This will be achieved through three main research axes. The first contribution will be represented by the first independent measurement study of energy consumption in a mobile network. We will address both user equipment and the radio access network, conducting a network metrology study on real operational networks and on experimental testbeds. The measurement data collected in this campaign will represent the input for other contributions in the project, but it will also be made openly available to the research community. The second objective of the project is to use this measurement data in order to design accurate energy consumption models for mobile networks. In this sense, we take an original approach with respect to the literature, by focusing on modelling the impact of the building blocks of the mobile network, a series of ""atomic"" network mechanisms and functions which practically compose any service scenario and any user context. Modelling these atomic network mechanisms requires a detailed knowledge of the way a mobile network functions, but then allows the accurate modelling of any general scenario. Finally, the project also targets the proposal of energy efficient networking solutions. Indeed, the measurement data and the energy consumption models will allow us to detect the most energy-hungry phases in a mobile network. To reduce their impact, we will propose network intelligence solutions, which are based on observing the traffic transported by the network, detecting whenever the network settings are over-consuming, and adapting the network configuration with energy efficiency metrics in mind."

The energy consumption of mobile networks has been the source of animated debates in the recent period, with the deployment of 5G technologies. However, the energy consumption estimations put forward by the different parties in the debate showed significant differences, up to two orders of magnitude. This is a result of a lack of accurate models and meaningful metrics in this field. More precisely, the control plane of a mobile network represents a significant share of the traffic exchanged between the user and the network infrastructure, much more than in any other network technology, and this role will become even more important with the development of network function virtualisation and orchestration. Models focusing on the application-level traffic and presenting energy consumption as Joules/bit are bound to make harsh approximations and assumptions, leading to results that can not really help the involved parties, be it industrial stake-holders, policy makers or the general public. Project ECOMOME addresses this problem of accurately modelling and optimising the energy consumption of a mobile network, with a focus on 4G and 5G technologies. This will be achieved through three main research axes. The first contribution will be represented by the first independent measurement study of energy consumption in a mobile network. We will address both user equipment and the radio access network, conducting a network metrology study on real operational networks and on experimental testbeds. The measurement data collected in this campaign will represent the input for other contributions in the project, but it will also be made openly available to the research community. The second objective of the project is to use this measurement data in order to design accurate energy consumption models for mobile networks. In this sense, we take an original approach with respect to the literature, by focusing on modelling the impact of the building blocks of the mobile network, a series of "atomic" network mechanisms and functions which practically compose any service scenario and any user context. Modelling these atomic network mechanisms requires a detailed knowledge of the way a mobile network functions, but then allows the accurate modelling of any general scenario. Finally, the project also targets the proposal of energy efficient networking solutions. Indeed, the measurement data and the energy consumption models will allow us to detect the most energy-hungry phases in a mobile network. To reduce their impact, we will propose network intelligence solutions, which are based on observing the traffic transported by the network, detecting whenever the network settings are over-consuming, and adapting the network configuration with energy efficiency metrics in mind. To achieve these objectives, the ECOMOME project brings together 4 partners with a significant expertise on different topics related to mobile networks: cellular network architectures (ETS Montreal), network metrology (INSA Lyon), energy consumption (UP Timisoara) and network intelligence (IMDEA Networks Madrid). The results of the project will have a triple utility: 1) they will provide a new modelling approach and new network intelligence solutions to the academic and industrial community working on mobile networks; 2) they will help policy makers in their decisions regarding the future evolution and deployment of mobile network technologies, and 3) they will allow the general public to easily and intuitively assess the energy consumption of their mobile equipment and of the network infrastructure in a variety of scenarios.

Manufacturing in Europe should urgently address the following challenges towards the adoption of the Manufacturing as a Service paradigm: product customisation, circularity and sustainability, minimal downtime, predictive maintenance, seamless communication, reliability and robustness to uncertainties in demand and variability of resources, and cost reduction and performance optimisation. Unified Modeling and Automated Scheduling for Manufacturing as a Service (UniMaaS) project will develop a platform with a set of advanced technologies for offering flexible and decentralized manufacturing resources and supply chains as a Service to European SMEs and Industries. To realize this vision, the proposed platform will deploy 3 integrated Suites: (i) Data Modelling Suite: offering manufacturing dataspaces, cloud-based resource monitoring, trusted cross-company data exchange and digital product passport, (ii) Modelling Suite: offering modular modeling of manufacturing resources, intent-based servitization , AI-based estimations and Zero-X analysis, and (iii) Decision-Making Suite: offering scheduling and planning of manufacturing resources, circularity and sustainability optimization. The platform will adopt advanced digital technologies such as cloud computing, digital twins and trustworthy AI, as key enablers towards fulfilling its objectives. UniMaaS platform will provide easy access to customized manufacturing service chain and will support the entire life-cycle management of processes and products. The UniMaaS Suites and the Platform will be demonstrated in 4 Pilots including aircraft maintenance, automotive seating, 3D construction printing and logistics/warehouse management.