The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particularly Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms, have shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real-world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state-of-charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation-to-reality gap. The framework incorporates EnergAIze, a MADDPG-based multi-agent control strategy, and specifically addresses challenges related to real-world data collection, system integration, and user behavior modeling. Preliminary results collected from a real-world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9% reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behaviors. These outcomes underscore the framework’s effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.

The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents Percepta, a lightweight Data Stream Processing (DSP) system tailored to support AI workloads at the edge, with a particular focus on such as Reinforcement Learning (RL). It introduces specialized features such as reward function computation, data storage for model retraining, and real-time data preparation to support continuous decision-making. Additional functionalities include data normalization, harmonization across heterogeneous protocols and sampling rates, and robust handling of missing or incomplete data, making it well-suited for the challenges of edge-based AI deployment.

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.

Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerful platforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach.

This paper reports the design, implementation, and experimental phases of the EU H2020 QualiChain pilot “Staffing the Public Sector—The Case of Portugal”. The overall purpose of this pilot is to ensure the authenticity and integrity of the diplomas for all involved stakeholders and therefore to contribute to solving the diploma counterfeiting and/or falsification that is a great threat to the recruitment of qualified personnel. The main innovative aspect of this solution is the integration that is offered between an Academic Management System (the Fenix.edu platform) and a Blockchain (Ethereum) to automatically deploy diplomas. This solution helps the involved stakeholders trust the diplomas provided. The case study involves four different stakeholders and studies, specifically, the increase in their satisfaction in terms of diploma control, diploma veracity, and diploma credibility. The developed system was tested with external participants who were asked to follow a set of guidelines and complete a survey to assess their perceptions. All system interactions were recorded, and the data were analyzed. The results indicated that the participants successfully executed the guidelines and that a perception increase toward diploma control, veracity, and credibility was identified.