This work presents a video surveillance platform for smart cities designed to exploit the facilities offered by SDN/NFV networks to achieve gain in terms of scalability, flexibility, OpEx and CapEx reduction. The main elements of this platform are the Smart Access Nodes (SAN) and the Network Access Nodes (NAN). SAN devices are distributed on the territory to cover small/medium areas and provide connectivity to Data Flow Senders and Users. Data flow Senders may be IP cameras, sensors and camera-equipped drones. Thanks to the platform, they can be installed with no particular configuration setup.

The advent of advanced mobile communication systems like 5G and beyond holds promise for vertical sectors, particularly in transport & logistics (T&L), by enhancing network performance and ensuring high levels of quality of service (QoS), crucial for automating and optimizing T&L processes. However, assessing the impact of 5G requires investments in network infrastructure, posing time, and return on investment uncertainties. To address this, the European project VITAL-5G offers an open, virtualized, and flexible experimentation platform that provides 3rd party vertical stakeholders the opportunity to test and validate 5G-enabled services under realistic conditions. This paper explores the VITAL-5G platform’s capabilities, detailing the infrastructure that supports 5G standalone (SA) testing across multiple trial sites, including port and logistics environments. We highlight a subset of performed activities and experimentation campaigns, technical and operational challenges faced by 3rd party experimenters, and the potential business opportunities that emerge from 5G-enhanced services in the T&L sector. Finally, we present insights into future developments and opportunities for extending these capabilities to other vertical sectors.

The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. This paper addresses these issues by proposing a novel modular and resilient FL framework. In this context, resilience refers to the system’s ability to maintain operation and performance despite disruptions. The framework is built on decoupled modules handling core FL functionalities, allowing flexibility in integrating various algorithms, communication protocols, and resilience strategies. Results demonstrate the framework’s ability to integrate different communication protocols and FL paradigms, showing that protocol choice significantly impacts performance, particularly in high-volume communication scenarios, with Zenoh and MQTT exhibiting lower overhead than Kafka in tested configurations, and Zenoh emerging as the most efficient communication option. Additionally, the framework successfully maintained model training and achieved convergence even when simulating probabilistic worker failures, achieving a MCC of 0.9453.

The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.