Taking advantage of the multimode ability of modern mobile terminals, cellular operators are investing in alternate access networks (e.g., IEEE802.11 and IEEE802.16) to complement the increasingly overloaded cellular accesses. Complementing this, IP mobility support is gradually being introduced into network architectures, providing session continuity. To facilitate and optimize handover procedures between different access technologies in a seamless way, the IEEE 802.21 Media Independent Handover Services standard provides technology agnostic mechanisms to obtain information and control link layers. Implementations of the standard are appearing, but since support of IEEE 802.21 primitives directly at the link driver level is not yet realized, they have to revert to proprietary wrapping mechanisms or use specific operating system solutions to circumvent this issue. In this article we present ODTONE, an IEEE 802.21 implementation that is operating-system-independent and open source, and offers a novel approach to interfacing with different link layers that facilitates the deployment of IEEE 802.21 mobility mechanisms in multiple scenarios. We present a scenario where we integrate our middleware with an IP mobility management protocol, Proxy MIP IPv6, highlighting ODTONE¿s supporting features and impact while providing an open handover control architecture for network- based localized mobility management.

Private 5G networks has become a popular choice of various vertical industries to build dedicated and secure wireless networks in industry environments to deploy their services with enhanced service flexibility and device connectivity to foster industry digitalization. This article proposes multiple multi-domain solutions to deploy private 5G networks for vertical industries across their local premises and interconnecting them with the public networks. Such scenarios open up a new market segment for various stakeholders, and break the current operators’ business and service provisioning models. This, in turn, demands new interactions among the different stakeholders across their administrative domains. To this aim, three distinct levels of multi-domain solutions for deploying vertical’s 5G private networks are proposed in this work, which can support interactions at different layers among various stakeholders, allowing for different levels of service exposure and control. Building on a set of industry verticals (comprising Industry 4.0, Transportation and Energy), different deployment models are analyzed and the proposed multi-domain solutions are applied. These solutions are implemented and validated through two proof-of-concept prototypes integrating a 5G private network platform (5Growth platform) with public ones. These solutions are being implemented in three vertical pilots conducted with real industry verticals. The obtained results demonstrated the feasibility of the proposed multi-domain solutions applied at the three layers of the system enabling various levels of interactions among the different stakeholders. The achieved end-to-end service instantiation time across multiple domains is in the range of minutes, where the delay impact caused by the resultant multi-domain interactions is considerably low. The proposed multi-domain approaches offer generic solutions and standard interfaces to support the different private network deployment models.

Over the past decade, there has been a significant and rapid progression in satellite technology, mostly motivated by a wide deployment of Low Earth Orbit (LEO) mega-constellations and, more recently, emerging technologies like Inter-Satellite (Laser) Links (ISL) and multi-orbital constellations approaching maturity. This has rekindled interest towards the exploitation of satellite systems for a variety of tasks, including their use as computational servers similar to traditional Edge and Cloud infrastructures. Such paradigm, known as Satellite Computing, can serve any area on Earth in a holistic manner, enhancing applications anywhere and anytime in several distinct domains (e.g., environmental monitoring, emergency response and intelligent transportation). Despite being a highly active research area, Satellite Computing is still in early stages and, therefore, most research works are insular and target specific aspects and use cases. Moreover, the concept of Satellite Computing has not been standardized yet, thus its role in future network systems (e.g., beyond 5G/6G) remains unclear. This work aims to fill these gaps by providing a comprehensive survey of the state of the art. First, it discusses the network-and computing-related aspects that impact the realization of Satellite Computing in future systems. And second, it overviews specific use cases and applications enabled by Satellite Computing. Based on this two-fold analysis, a taxonomy of applications is derived from each application’s requirements and key networking design choices. Finally, it provides an overview from an industrial perspective, while identifying open challenges in the Satellite Computing area in order to boost and steer possible pathways for future research.

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.

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.