ICONET will significantly extend state of the art research and development around the PI concept in pursuit of a new networked architecture for interconnected logistics hubs that combine with IoT capabilities and aiming towards commercial exploitation of results. ICONET strives to achieve the end commercial goal of allowing shipments to be routed towards final destinations automatically, by using collaborative decisions inspired by the information centric networking paradigm, and optimizing efficiency and customer service levels across the whole network. According to this vision, cargo regarded as smart physical packets will flow between hubs based on ‘content’ of the cargo influencing key commercial imperatives such as cost, optimisation, routing, efficiency and advancing EU's Green agenda. Consequently, the consortium are discernibly aimed at three (3) avenues of commercialisation and exploitation from the ICONET innovation, specifically targeted in the areas of (a) Warehousing as a service, (b) E-commerce fulfillment as a service, and (c) Synchromodality as a service. PI based logistic configurations will be simulated, prototyped and validated in the project . Modelling and analysis techniques will be combined with serious game type simulation, physical and digital prototyping, using living lab (LL) requirements scenarios and data. With analyses and simulations, optimal topologies and distribution policies for PI will be determined. The project implementation will be based on a succession of phases of modelling and design/prototyping, learning and experimentation and feedback and interaction with the wider business community, including the ALICE logistics platform as well as members of the partner Associations ESC, UIRR and ELUPEG. Through its Living Labs, the project will address under the PI paradigm both Supply Network Collaboration and Supply Network Coordination.

Electric motors are one of the main components in industries, thus knowing their health state exploiting predictive maintenance programs is nowadays more than necessary in the vision of the Factory of the Future. The correct application of these programs will reduce the repairing costs and the unplanned downtime, will generate savings in employees’ time and will optimise the energy consumption. So far, only expensive and bulky monitoring equipments, targeted to costly electric motors (e.g., carbon mills on power plants), are available on the market. These systems provide raw data that can be only manipulated by specialists. For these reasons, large firms currently may only implement predictive maintenance programs on costly electric motors and in that case they must employ experts capable of understanding the data provided by these systems. However, the large part of active industrial motors is small-medium sized and, for these reasons, are unmonitored. These motors are usually a key asset in SMEs’ production process. In this proposal, we aim at the “predictive maintenance democratisation”, i.e. a shift of its benefits also to smaller size motors. This can be done with a low-cost and easy-to-use system based on Internet of Things technologies. Particularly, we aim at improving and evaluating the market potential of a system capable to process, automatically understand motors’ health states and warn the end user in a simple manner, thus providing its benefits to both large firms and SMEs.

IW-NET will deliver a multimodal optimisation process across the EU Transport System, increasing the modal share of IWT and supporting the EC’s ambitions to reduce transport GHG emissions by two thirds by 2050. Enablers for sustainable infrastructure management and innovative vessels will support an efficient and competitive IWT sector addressing infrastructure bottlenecks, insufficient IT integration along the chain and slow adoption of technologies such as new vessel types, alternative fuels, automation, IoT, machine learning. The Living Lab will apply user-centered application scenarios in important TEN-T corridors demonstrating and evaluating the impacts in simulations and tests covering technological, organisational, legal, economical, ecological, and safety/security issues: 1) Digitalisation: optimised planning of barge operations serving dense urban areas with predictive demand routing (Brussels-Antwerp-Courtrai-Lille-Valenciennes); data driven optimisation on navigability in uncertain water conditions (Danube). 2) Sustainable Infrastructure and Intelligent Traffic Management: lock forecasting reducing uncertainty in voyage planning; lock planning; management of fairway sections where encounters are prohibited; berth planning with mandatory shore power supply and other services (hinterland of Bremerhaven via Weser/Mittelland Canal). 3) Innovative vessels: new barge designs fitting corridor conditions and target markets: barges with a high degree of automation for urban distribution (East Flanders-Ghent); new barge for push boats capable with low/high water levels optimising capacities (Danube from Austria to Romania); use of GALILEO services for advanced driver assistance like guidance, bridge height warning and automatic lock entering (Spree-Oder waterway close to Berlin). Accompanying activities are stakeholder engagement, capacity building, and the delivery of a European IWT development roadmap with policy recommendations for increasing the IWT share.

PLANET addresses the challenges of assessing the impact of emerging global trade corridors on the TEN-T network and ensuring effective integration of the European to the Global Network by focusing in two key R&D pillars: • A Geo-economics approach, modelling and specifying the dynamics of new trade routes and its impacts on logistics infrastructure & operations, with specific reference to TEN-T, including peripheral regions and landlocked developing countries; • An EU-Global network enablement through disruptive concepts and technologies (IoT, Blockchain and PI, 5G, 3D printing, autonomous vehicles /automation, hyperloop) which can shape its future and address its shortcomings, aligned to the DTLF concept of a federated network of T&L platforms. PLANET goes beyond strategic transport studies, and ICT for transport research, by rigorously modelling, analysing, demonstrating & assessing their interactions and dynamics thus, providing a more realistic view of the emerging T&L environment. The project employs 3 EU-global real-world corridor Living Labs including sea and rail for intercontinental connection and provides the experimentation environment for designing and exploiting future PI-oriented Integrated Green EU-Global T&L Networks [EGTN]. To facilitate this process, PLANET delivers a Symbiotic Digital Clone for EGTNs, as an open collaborative planning tool for TEN-T Corridor participants, infrastructure planners, and industry/technology strategists. PLANET also delivers an Active Blueprint and Road Map, providing guidance and building public & private actor capacity towards the realisation of EGTNs, and facilitating the development of disadvantaged regions. The project engages major T&L stakeholders, contributing to both strategy and technology and (importantly) has the industry weight and influence to create industry momentum in Federated Logistics and TEN-T’s integration into the Global Network.

The vision of MODALSHIFT lies in the creation of a transport network and traffic management optimisation framework, trusted and valuable for local stakeholders, that bridges the data from infrastructures, logistics and mobility operators. New IoT devices - a smart box enabling Capacity-as-a-Service, and a e-subscription device for public transport access for vulnerable people, will increase the sources for data collection. A mobility data space, associated to novel geolocation data anonymisation, will be set up in the 3 Case Studies (Bulgaria, Italy, Spain) to ensure trusted and secure data exchange between data providers and users. This multisource data will enhance traffic state forecasting and increase the detection rate of events by 15%. On this basis, predictive and prescriptive analytics and synchromodality-based scenarios, tested in digital twins and early pilots, will identify optimal actions of transport stakeholders for adjusting their operations, towards a reduction of 25% of the interconnection or transshipment delays. Agent-based modelling will identify how a modal shift towards low-carbon, active and shared mobility services can be acceptable by end-users and support a reshape of the public transport services and the use of urban space. A multimodal traffic management platform will orchestrate, upon the data space, the cooperation of stakeholders at network and multimodal hub scales. It enables the connection of dynamic optimisation algorithms to operational drivers’ tool for mobility operators, and of static models to visual interface for transport planners. The determination of governance models, values for each stakeholder, dynamic pricing and business models, will steer the participation of 8 stakeholders for each Case Study in the multimodal traffic management system. With this approach, MODALSHIFT stimulates new uses of the transport network to reduce traffic congestion for low-carbon and inclusive mobility, avoiding pernicious rebound effects.