Abstract
The increasing number of IoT deployment scenarios and applications fostered the development of a multitude of specially crafted communication solutions, several proprietary, which are erecting barriers to IoT interoperability, impairing their pervasiveness. To address such problems, several middleware solutions exist to standardize IoT communications, hence promoting and facilitating interoperability. Although being increasingly adopted in most IoT systems, it became clear that there was no one size fits all solution that could address the multiple Quality-of-Service heterogeneous IoT systems may impose. Consequently, we witness new interoperability challenges regarding the usage of diverse middleware. In this work, we address this issue by proposing a novel architecture - the PolyglIoT, that can effectively interconnect diverse middleware solutions while considering the delivery QoS requirements alongside the proposed translation. We analyze the performance and robustness of the solution and show that such Multiprotocol Translator is feasible and can achieve a high performance, thus becoming a fundamental piece to enable future highly heterogeneous IoT systems of systems.

Abstract
Electromobility is a critical component of Europe’s strategy to create a more sustainable society and support the European Green Transition while enhancing quality of life. Electrification also plays an important role in securing Europe’s position in the growing market of electric and autonomous vehicles (EAV). The EU-funded OPEVA project aims to take a big step towards deployment of sustainable electric vehicles by means of optimising their support in an ecosystem. Specifically, the project focuses on analysing and designing optimisation architecture, reducing battery charging time, and developing infrastructure, as well as reporting on the driver-oriented human factors. Overall, OPEVA’s goal is to enhance EAV market penetration and adoption, making them more accessible and convenient. The aim of this paper is to inform the European automotive, transportation, energy and mobility community be presenting the OPEVA manifestation, and the overall solution strategy solidified through the progress throughout the first year of the project.

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Abstract
Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. © The Author(s) 2025.

Abstract
The rising adoption rates and integration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the energy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, where each prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local EVs and other energy assets but also fosters REC wide optimization. EnergAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives.