The growing interest in precision agriculture has led, in recent years, to an increase in the adoption of Internet of Things (IoT) technologies in the service of smart agriculture to optimize agricultural production processes through the monitoring of environmental conditions and prevent food loss. This work stems from research conducted as part of the Tech4You project, where the enabling digital technologies developed in Spoke 6 contribute to the advanced solutions envisaged by Spoke 3 to facilitate the transition to a sustainable agrifood system. In particular, we present the design and evaluation of a multi-hop Device-to-Device (D2D) communication architecture that leverages Long Range (LoRa) technology, specifically designed for monitoring vineyards in the context of passito wine production. The proposed framework addresses the challenge of monitoring mobile containers for grapes during the drying phase, a critical stage in which inadequate temperatures and humidity can promote the growth of fungi and the formation of mycotoxins. The integration of simulation-based performance evaluation with a multi-layer system architecture is presented in this work. The objective is to compare the performance of different routing strategies in choosing data forwarding paths to the gateway. The simulation results show that the proposed routing strategy, which is based on learning but also focuses on energy consumption, offers good performance. In particular, it achieves packet delivery rates of over $92\%$ and preserves over $95\%$ of active nodes after 2 h of operation. Energy-aware routing strategies also perform well compared to those that only consider the distance from the destination, but overall, the proposed strategy achieves a better trade-off on the metrics analyzed.

The healthcare landscape is undergoing a transformative shift driven by the rapid advancements of sixth-generation (6G) cellular networks and Internet of Things (IoT) technologies. This convergence has led to the emergence of medical digital twins (MDTs) — innovative virtual replicas of physical entities or processes. MDTs enable safe monitoring and testing of therapeutic decisions, resulting in substantial cost savings and enhanced decision-making capabilities for physicians. Furthermore, they provide real-time assistance and facilitate comprehensive data analysis, revolutionizing patient care. However, their full potential is constrained by the intricate nature of human life cycles. Key factors include the choice of data types and the communication protocols used to connect physical and digital twins. For individuals with disabilities, integrating wearable devices with edge computing and WiFi-based Channel State Information (CSI) can effectively monitor health metrics and recognize activities. Although the integration of 6G and IoT technologies holds the promise of enabling seamless communication between physical and digital twins, this paper, which is developed in the framework of the HIPPOCRATES project, focuses on a currently implementable architecture for MDTs based on the Message Queuing Telemetry Transport (MQTT) protocol. The proposed system integrates CSI to augment health monitoring and intervention, particularly for individuals with disabilities.

Sixth-generation (6G) networks are expected to integrate intelligence as a native capability, enabling advanced verticals such as digital health (eHealth) supported by large-scale Internet of Medical Things (IoMT) deployments. In this context, Federated Learning (FL) is emerging as a promising paradigm for collaborative model training, allowing distributed medical devices to learn from data while preserving privacy. Nevertheless, conventional approaches such as Federated Averaging (FedAvg) often suffer from unstable convergence and performance degradation in non-independent and non-identically distributed (non-IID) environments, especially under partial and resource-constrained client participation. To address these challenges, we propose a Digital Twin (DT)-enabled FL framework in which the server evolves from a passive aggregator into an active orchestrator of the learning process. The DT consists of a virtual representation of participating devices and their learning dynamics, thereby enabling an informed selection of clients and coordinated updates based on both resource conditions and predictive uncertainty. Building on this orchestration layer, we introduce Twin2Twin (T2T), a hierarchical aggregation strategy that groups clients into similarity-based cohorts and combines their updates in a structured manner, improving training stability in dynamic environments. Specifically, the proposed framework integrates: (i) DT-driven client orchestration that accounts for both device heterogeneity and data informativeness, and (ii) a similarity-aware aggregation mechanism that reduces update variability across training rounds. Experimental results demonstrate that the proposed strategy achieves faster and more stable convergence compared to traditional FL, highlighting the potential of DT-driven orchestration to support reliable and scalable learning in IoMT systems deployed in 6G networks.