Smart city infrastructures are evolving from centralized cloud systems to distributed Cyber-Physical Systems of Systems (CPSoS), requiring integration across heterogeneous administrative domains. This work presents a flexible, modular, multi-domain architecture for automated orchestration and management of IoT services across heterogeneous environments. It relies on a recursive federation model, where autonomous local domains manage their own resources while higher-level components coordinate cross-domain operations. Interoperability is achieved through standardized interfaces using TM Forum Open APIs and ETSI NGSI-LD, while a Secure Integration Fabric enables secure, policy-based coordination across public and private domains. The architecture is validated in a real-world Smart Waste Management pilot, demonstrating support for flexible workflows, cross-platform collaboration, real-time decision-making, and avoidance of vendor lock-in. Experimental results show that dynamic, context-driven service orchestration improves scalability, interoperability, and resource efficiency compared to static deployments.
Towards shipping decarbonisation, the maritime industry faces multiple challenges either in terms of reducing the energy demand of ships or in exploring alternatives to the dominant conventional fossil fuels. In both cases, industry stakeholders require strong proof of the technical and financial feasibility of possible decarbonisation options in order to gradually adopt them at a large scale. Research and innovation actions are focusing on specific technologies for improving their readiness and providing respective proof or are exploring a more holistic view on the various decarbonisation options. For the latter, introducing a regional approach can be more effective. For instance, the green corridors approach offers a promising option for reducing emissions from ships on specific routes. However, this approach is considered on a case-by-case basis, failing to address larger regions. This paper attempts to systematically explore green shipping pathways for a clean energy transition in the Mediterranean Sea basin, as part of the EU-funded GreenMED project. The methodological framework is based on bottom-up approaches, capitalising on AIS data, to provide an estimation of the fuel consumption and emissions of the fleet of scope, while employing a supply chain mapping to analyse the existing and expected fuel supply chains supporting the demand of the maritime activity in the region. The scope and framework of the study is presented to set the basis for its materialisation, while also offering a view on some early insights in terms of both the supply chain and energy demand.
The introduction of non-indigenous species (NIS) into marine ecosystems represents a significant challenge for biodiversity conservation, economic stability, and public health. The Mediterranean Sea, known for its rich marine diversity, has become one of the most affected regions globally, with over 1,000 alien species recorded (Ulman et al., 2017). These species, once introduced, can alternative ecosystems, outcompete local fauna, and cause substantial disruptions to fisheries, aquaculture, and tourism industries.
The FOR-FREIGHT (Flexible, multi-mOdal and Robust FREIGHt Transport) project represents a pioneering initiative aimed at revolutionizing multimodal logistics by optimizing transport capacity and enhancing sustainability and efficiency. This paper delves into the project's overarching objectives, methodologies, and anticipated impacts. With a primary focus on developing innovative solutions seamlessly integrated into existing logistics systems, FOR-FREIGHT strives to diminish the average cost of freight transport. The project's unique approach encompasses a comprehensive, end-to-end optimization of multimodal logistics services, addressing challenges prevalent in airports, ports, inland terminals, and logistics nodes. Central to FOR-FREIGHT's success is the creation of a cloud-based platform, combining IoT, AI/ML, and Big Data Management, designed to streamline logistics processes and facilitate decision-making. The paper also emphasizes the integration of legacy systems, ensuring the project's applicability to real-world scenarios. Furthermore, FOR-FREIGHT envisions an open marketplace and standardized interfaces, fostering collaboration and interoperability across diverse stakeholders. As the project advances, it promises to not only redefine multimodal logistics practices but also contribute to the establishment of sustainable and efficient standards within the industry.
In the domain of maritime security and safety, the surveillance and monitoring of vessels in ports are crucial for safeguarding against potential threats and ensuring the overall integrity of port operations. Current port surveillance methods, including radar systems, CCTV networks, and AIS, exhibit limitations, necessitating innovative solutions. The integration of drone-captured imagery holds promise, particularly for detecting non-cooperative vessels that may evade traditional surveillance methods. This paper presents VESSELimg dataset, a meticulously gathered and annotated collection of drone-captured images within port environments. This dataset serves as a benchmark for training and validating deep learning models, specifically tailored for automatic vessel detection, covering diverse vessel types. The implementation of a YOLO-based deep learning model for real-time inference demonstrates the practical applicability of the dataset, underscoring its potential for enhancing security and safety measures in port environments.
