6G goals, such as improved coverage, flexibility to different network scenarios, increased throughput, higher robustness and reliability could be advanced with the use of new access and flexible topologies. In this contribution, three enablers of new access and flexible topologies, which contribute to the aforementioned goals, are presented, namely network of networks, multi-connectivity and E2E context awareness management. Relevant studies of each enabler that impact the system architecture are described, along with the corresponding overviews of potential solutions. In addition, each enabler has been mapped to the 6G E2E system blueprint proposed by the Hexa-X-II project and its impact to the system, as well as the relevant stakeholders have been identified.

Wireless communication has profoundly transformed the way we experience the world. For instance, at most events, attendees commonly utilize their smartphones to document and share their experiences. This shift in user behavior largely stems from the cellular network’s capacity for communication. However, as networks become increasingly sophisticated, new opportunities arise to leverage the network for services beyond mere communication, collectively termed Beyond Communication Services (BCS). These services encompass joint communications and sensing, network as a service, and distributed computing. This paper presents examples of BCS and identifies the enablers necessary to facilitate their realization in sixth generation (6 G). These enablers encompass exposing data and network capabilities, optimizing protocols and procedures for BCS, optimizing compute offloading protocols and signalling, and employing application and device-driven optimization strategies.

A ML function orchestrator deploying secure ML pipelines to support near-real-time control of network services is demonstrated. A distributed ledger supports the initial key exchange to establish secure connectivity among the agents in the pipeline.

In addition to the many services deployed with the predecessor cellular generation, 6G is envisioned to host a large number of new services with strict KPI and QoS requirements which require the various network resources to be optimized accordingly. For example, 6G is expected to be the first generation of cellular systems with built-in sensing capabilities. The coexistence of these services on a common network infrastructure requires improvements in the network architecture to meet the required flexibility, efficiency, and autonomy. In order to provide a seamless and effective coexistence among different services, the 6G system needs to evolve the 5G Radio Access Network (RAN) and Core Network (CN). This paper discusses the different ongoing research activities on the 6G architecture and outlines the potential evolution of the different parts of the network. Moreover, the paper discusses the standardisation implications of these evolutionary changes.

Last years we have witnessed the uprising of location based applications, which depend on the devices ability to accurately obtain their position. IEEE 802.11, foretelling the need for such applications, started the IEEE 802.11az work on Next Generation Positioning. Although this standard provides positioning enhancements for sub-6GHz and mmWave bands, high accuracy in the order of centimeters can only be obtained in the latter band, thanks to the beamforming information available at mmWave operation. This work presents a detailed analysis on the new techniques provided by IEEE 802.11az for enhanced secured positioning in the mmWave band, assessing them through experimentation.

Edge computing has established itself as the foundation for next-generation mobile networks, IT infrastructure, and industrial systems thanks to promised low network latency, computation offloading, and data locality. These properties empower key use-cases like Industry 4.0, vehicular communication, and Internet of Things. Nowadays, implementation of Edge computing is based on extensions to available Cloud computing software tools. While this approach accelerates adoption, it hinders the deployment of the aforementioned use-cases, which requires an infrastructure largely more decentralized than Cloud data centers, notably in the far-Edge of the network. In this context, this work aims at: (i) analyzing the differences between Cloud and Edge infrastructures, (ii) analyzing the architecture adopted by the most prominent open-source Edge computing solutions, and (iii) evaluating those solutions in terms of scalability and service instantiation time in a medium-size far-Edge system. Results show that mainstream Edge solutions require powerful centralized controllers and always-on connectivity, making them unsuitable for highly decentralized scenarios in the far-Edge where stable and high-bandwidth links are not ubiquitous.

Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don’t Let Me Down! (DLMD), an algorithm that promotes offloading robot functions to the Cloud when possible to minimize the consumption of Edge resources. Additionally, DLMD takes the appropriate migration, traffic steering, and radio handover decisions to meet robotic service requirements as strict latency constraints. In the paper, we formulate the optimization problem that DLMD aims to solve, compare DLMD performance against the state of the art, and perform stress tests to assess DLMD performance in small & large networks. Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is optimal in a local warehousing use case; and (iii) consumes only 5% of the Edge resources upon network stress.

This paper provides an extensive analysis of Reliable and Available Wireless (RAW) enhancements and solutions needed to manage industrial environments more effectively. Starting from the description of a representative industrial use case, an analysis of gaps and promising new extensions is performed. Namely, the need to (i) support multi-domain operation, at both technology and administrative levels; (ii) integrate RAW with edge architectures; and, (iii) increase the mobility support in RAW networks. The identified gaps are indeed not yet tackled by the relevant standardization development organizations, mainly the Internet Engineering Task Force (IETF), and are thus object of our future work.

Cloud-based robotics systems leverage a wide range of Information Technologies (IT) to offer tangible benefits like cost reduction, powerful computational capabilities, data offloading, etc. However, the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies (OT) nowadays used in robotics systems that require strict real-time guarantees and security. Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users. The goal of this work is hence threefold: i) to analyze the current edge computing and fog computing landscape in the context of robotics systems, ii) to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature, and iii) to experimentally identify current benefits and open challenges of edge computing and fog computing. Results show that, in the case of an exemplary delivery application comprising two mobile robots, the robot coordination and range can be improved by consuming real-time radio information available at the edge. However, our evaluation highlights that the existing software, wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20deg in angle orientation which is compatible with Lidar when building indoor maps.

The stringent low-latency, high reliability, availability and resilience requirements of 6G use cases will present challenges to cloud providers. Currently, cloud providers lack simple, efficient, and secure implementation of provisioning solutions that meet these challenges. Multi-cloud federation is a promising approach. In this paper, we evaluate the application of private and public blockchain networks for multi-cloud federation. We compare the performance of blockchain-based federation in private and public blockchain networks and their integration with a production-ready orchestration solution. Our results show that the public blockchain needs approximately 91 seconds to complete the federation procedure compared to the 48 seconds in the private blockchain scenario.

—In this letter, we formulate the orchestration prob- lem for networked robotic applications considering contextual information. Any solution to the proposed formulation provides adequate routing updates, migration and radio handover decisions as the robot moves. We prove the NP-hard nature of the problem, and solve it for a remote driving robotic application with or without some contextual information, as in state-of- the-art. Results show that without contextual information it is impossible to meet the latency requirements of a remote driving robotic application.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated. In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their ion-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements.

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains, as machinery and other components not to be restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that upon interference FoReCo reduces the trajectory error by more than a 34.35% in both simulation, and experimentation. We also show that FoReCo is sufficiently lightweight to be deployed in existing hardware.

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve physical layer performance, including wireless coverage, achievable data rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate on centralized, distributed, and hybrid artificial-in-telligence-assisted MAC architectures in RIS-aid-ed multi-user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design.

Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.

Edge computing have received considerable attention as a promising candidate for the evolution of robotic systems. In this work, we propose COTORRA, an Edge driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being applied in a production environment. We have tested COTORRA in a controlled university environment as an easy applicable, serverless, and modular testbed on top of commodity network infrastructure. COTORRA supports pluggable robotic applications. To verify its feasibility and assess its performance, we ran a set of experiments that show how autonomous navigation applications can achieve target latencies bellow 15 ms, and perform an inter-domain Distributed Ledger Technology (DLT) federation within 19 seconds.

Edge computing have received considerable attention as a promising candidate for the evolution of robotic systems. In this work, we propose COTORRA, an Edge driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being applied in a production environment. We have tested COTORRA in a controlled university environment as an easy applicable, serverless, and modular testbed on top of commodity network infrastructure. COTORRA supports pluggable robotic applications. To verify its feasibility and assess its performance, we ran a set of experiments that show how autonomous navigation applications can achieve target latencies bellow 15 ms, and perform an inter-domain Distributed Ledger Technology (DLT) federation within 19 seconds.

This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.