Real time control of connected industry devices such as mobile robots constituting Industrial Internet of Things (IIoT) has made time-sensitive communications over Wi-Fi increasingly important. In order to provide support for time-sensitive services over Wi-Fi, key features of the IEEE 802.11 standard such as restricted Target Wake Time (rTWT) and multi-user Orthogonal Frequency Division Multiple Access (OFDMA) can be exploited. However, even with rTWT and OFDMA, Time-Sensitive Networking (TSN) over Wi-Fi is still a challenge given the unpredictability of wireless channels and the increasing number of Wi-Fi-enabled devices. In this paper, we present a comprehensive network calculus-based analysis of the delay bounds achievable in Wi-Fi networks, leveraging these IEEE 802.11 enhancements alongside synchronized priority queuing via IEEE 802.1Qbv. We propose PONTE, a novel Fully Polynomial Time Approximation Scheme (FPTAS) scheduler that guarantees strict non-violation probabilities (e.g., 99.99%) for inelastic, time-critical traffic over an 802.11 wireless network. Our extensive analysis and simulations in an IIoT scenario demonstrate that PONTE effectively manages dense, mixed traffic flows, achieving TSN objectives with minimal impact on fairness. Our approach is two orders of magnitude faster than the state-of-the-art.

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately ~15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

Decentralized learning scenarios offer the opportunity of a flexible cooperation between learning nodes; in other words, each node may cooperate with an arbitrary subset of its peers. In such scenarios, we tackle the problem of choosing the nodes that cooperate towards the training of a machine learning model, hence, tweaking the cooperation graph connecting the nodes themselves. We propose and evaluate a data-driven approach to the problem, by proposing three metrics to choose the edges to activate in the cooperation graph, and an efficient iterative algorithm exploiting them. Through our performance evaluation, which leverages state-of-the-art datasets and neural network architectures, we find that privacy-preserving metrics accounting for the difference between local datasets are very effective in identifying the best edges to activate to improve the efficiency of model training without hurting performance.

The advent of 5G deployments and the anticipation of 6G have initiated a concerted effort to define new network and application services that leverage these technologies. A key focus of this effort is deterministic networking, which provides the reliability, time sensitivity, and predictability essential for supporting critical services and applications. This article presents a solution to support multi-domain deterministic communications, integrating various technology-specific deterministic networking solutions---namely IEEE 802.11, IEEE 802.1 TSN, and 3GPP 5G domains---over an IETF DetNet data plane. Our goal is to create a realistic infrastructure for developing the next generation of deterministic services. Key contributions include the design, deployment, and performance characterization of the multi-domain data plane, as well as its integration into a real-life use case such as the remote control of a robotic dog.

The PREDICT-6G framework, a novel 6G network architecture aiming to provide reliable, predictable, and time sensitive networking across diverse applications, integrates a multi-technology, multi-domain network infrastructure, creating a homogeneous and deterministic E2E connectivity across wired and wireless elements. This architecture addresses the evolving needs of specialized applications, setting a new standard for future networking technologies. A key component is the AI-driven Multi-stakeholder Inter-domain Control-Plane, which ensures the seamless delivery of time-sensitive services.

Industry 4.0 applications impose the challenging demand of delivering packets with bounded latencies via a wireless network. This is further complicated if the network is not dedicated to the time critical application. In this paper we use network calculus analysis to derive closed form expressions of latency bounds for time critical traffic when 802.11 Target Wake Time (TWT) and 802.1Qbv work together in a shared 802.11 network.

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 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 20° in angle orientation which is compatible with Lidar when building indoor maps.

The traditional approach to distributed machine learning is to adapt learning algorithms to the network, e.g., reducing updates to curb overhead. Networks based on intelligent edge, instead, make it possible to follow the opposite approach, i.e., to define the logical network topology around the learning task to perform, so as to meet the desired learning performance. In this paper, we propose a system model that captures such aspects in the context of supervised machine learning, accounting for both learning nodes (that perform computations) and information nodes (that provide data). We then formulate the problem of selecting (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of epochs to run, in order to minimize the learning cost while meeting the target prediction error and execution time. After proving important properties of the above problem, we devise an algorithm, named DoubleClimb, that can find a$1+1/| \mathcal {I}|$-competitive solution (with$\mathcal {I}$being the set of information nodes), with cubic worst-case complexity. Our performance evaluation, leveraging a real-world network topology and considering both classification and regression tasks, also shows that DoubleClimb closely matches the optimum, outperforming state-of-the-art alternatives.

Ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the automation and optimization of the software-defined, virtualization-based next generation of networks such as 5G and beyond. Network services provided for autonomous vehicles, factory automation, e-health and cloud robotics often require strict delay bounds and reliability constraints influenced by the location of its composing Virtual Network Functions. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. Moreover, these devices are continuously on the move and might lose network connection or run out of battery, which further challenge service delivery in this dynamic environment. This work tackles the mobility, and battery restrictions; as well as the temporal aspects and conflicting traits of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a cost-minimizing Virtual Network Function placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios.

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.

In addition to CPRI, new functional splits have been defined in 5G creating diverse fronthaul transport bandwidth and latency requirements. These fronthaul requirements shall be fulfilled simultaneously together with the backhaul requirements by an integrated fronthaul and backhaul transport solution. In this paper, we analyze the technical challenges to achieve an integrated transport solution in 5G and propose specific solutions to address these challenges. These solutions have been implemented and verified with pre-commercial equipment. Our results confirm that an integrated fronthaul and backhaul transport dubbed Crosshaul can meet all the requirements of 5G fronthaul and backhaul in a cost-efficient manner.

Next 5G networks will force service and network providers to support a huge variety of final services with very different requirements in terms of bandwidth, latency, etc. In parallel, vertical industries, as customers, will use 5G networks as technical enablers to support their businesses, demanding appropriate mechanisms for a flexible access and control of network and computing resource slices in the locations where such vertical have business. Since the footprint and the availability of resources to support the variety of services could be limited on the primary provider side, open environments enabling the trading of resources in the form of slices are required to facilitate the sharing of infrastructure with the necessary isolation and scalability. Here, this is exemplified by the proposal of adaptations between two prevalent architectures being defined in the EU H2020 projects 5G-Crosshaul and 5GExchange for allowing the trading of crosshaul resources enabling 5G services for Verticals.

We are immerse in a world that becomes more and more mobile every day, with ubiquitous connectivity and increasing demand for mobile services. Current mobile terminals support several access technologies, enabling users to gain connectivity in a plethora of scenarios and favoring their mobility. However, the management of network connectivity using multiple interfaces is still starting to be deployed. The lack of smart connectivity management in multi interface devices forces applications to be explicitly aware of the variations in the connectivity state (changes in active interface, simultaneous access from several interfaces, etc.). In this paper, we analyze the present state of the connection management and handover capabilities in the three major mobile operating systems (OSes): Android, iOS and Windows. To this aim, we conduct a thorough experimental study on the connectivity management of each operating system, including several versions of the OS on different mobile terminals, analyzing the differences and similarities between them. Moreover, in order to assess how mobility is handled and how this can affect the final user, we perform an exhaustive experimental analysis on application behavior in intra- and inter-technology handover. Based on this experience, we identify open issues in the smartphone connectivity management policies and implementations, highlighting easy to deploy yet unimplemented improvements, as well as potential integration of mobility protocols.

The significant growth in mobile data traffic and the ever- increasing user's demand for high-speed, always connected networks continue challenging network providers and lead research towards solutions to enable faster, scalable and more flexible networks. In this paper we present the CROWD approach, a networking framework providing mechanisms to tackle the high densification and heterogeneity of wireless networks. The goal of CROWD is to design protocols and algorithms for very dense and heterogeneous wireless networks, which we call DenseNets. The mechanisms we propose include energy efficiency, MAC enhancements, connectivity management and backhaul configuration to contribute to the next generation of networks considering density as a resource instead of as an obstacle.

Mobile Internet traffic is growing steeply, mainly due to the deployment of new broadband wireless technologies and the ever increasing connectivity demand coming from new services being available to mobile users. Current mobile network architectures rely on centralized mobility protocols which intrinsically pose enormous burdens on the central anchors, both in terms of connectivity needs and user mobility management. In order to face these issues, a new paradigm, called Distributed Mobility Management, is being explored, based on flattening the network architecture by deploying multiple mobility anchors at the edge of the network. In this article we conduct an analytic and experimental evaluation of a network-based IP distributed mobility management solution that leverages Proxy Mobile IPv6 protocol operations. We develop an analytic model of the signaling and packet delivery costs, as well as the handover latency of both Proxy Mobile IPv6 and our distributed solution. We have also implemented a Linux-based prototype of our proposal, which has been used to experimentally assess the handover latency in a real IEEE 802.11 scenario. Finally, we use the results obtained from the analytic and experimental performance to evaluate the benefits that could be achieved by deploying a distributed mobility management solution.

In this paper, we propose to provide throughput guarantees in heterogeneous wireless mesh networks by jointly optimizing routing and Medium Access Control configuration. Our solution is based on the notion of linearized capacity region, which provides a technology-independent way of representing the capacity of a wireless link thereby hiding the technology specifics to the upper layers. From the available capacity of the underlying links as given by the linearized capacity region, we propose two routing algorithms based on multipath and single path that find optimal paths for all the flows in the network given their throughput requirements. The throughput allocation resulting from routing is then provided to each link, which uses this information to optimize its technology-specific Medium Access Control parameters. The proposed approach is evaluated in a heterogeneous scenario comprising Wireless Local Area Networks WLAN and Worldwide Interoperability for Microwave Access WiMAX technologies and is shown to outperform previous solutions by at least a factor of 2.Copyright © 2012 John Wiley & Sons, Ltd.

Current trends on mobile traffic show an exponential grow of the traffic consumed by users from smartphones and other portable devices. The explosion of traffic in cellular networks has forced operators to start deploying solutions to alleviate the congestion on their capacity-limited and expensive radio access networks. One of the solutions being discussed is the so called 3G offload that enables the terminals to use other technologies such as WiFi to offload some of the traffic. IP flow mobility is one mechanism providing 3G offload, by enabling selected flows to be moved among network interfaces. Although this is a very promising technology, it is not clear yet how it will affect the protocols currently in use to provide IP mobility in cellular networks, e.g., Proxy Mobile IPv6. The use of 3G offloading does not only benefits the operators, but also the final user, as it might extend the battery lifetime of its terminal. In this paper we first describe some network-based IP flow mobility extensions, highlighting important design choices. Secondly, we focus on providing experimental measurements showing how the use of this technology can result in an extended battery life for the case of 3G and WiFi enabled terminals.

In this demo we propose a novel architecture suitable for deployment in future mobile networks based on the distributed mobility management (DMM) paradigm. In DMM, the IP anchoring point gets closer to users, with the aim to flatten the architecture and to truly enable the fixed/mobile convergence. DMM allows operators to tackle the explosion of data traffic without burdening the core part of the network, offering a common IP framework for mobility and heterogeneous access. As a use case scenario, this demo has shown a possible deployment for a content delivery network's nodes, in order to exploit the DMM benefits.

Video traffic is foreseen to account for the majority of the Internet traffic in the near future. While the demand of video transmission keeps growing, the vast majority of wireless equipment deployed in the home environment, based on IEEE 802.11, cannot satisfy the amount of bandwidth that the video applications require. In order to cope with the increasing demand of multimedia traffic, the IEEE 802.11aa Task Group has recently standardized new mechanisms to allow efficient and robust transmission of multicast flows in Wireless LAN. However, the standard leaves open the choice of which one to use for a given scenario. In this paper, we explore the new mechanisms introduced by the 802.11aa Task Group, providing insights of the new choices for handling group addressed frames, by carrying out extensive simulations. Our results highlight the various trade-offs each mechanism has in terms of robustness, resource consumption and complexity, and provide a set of recommended guidelines for their use.

Wireless network access and the exchange of multimedia flows over the Internet are becoming more and more pervasive in the everyday life. However, simple technological advances in terms of improved network capacity cannot satisfy the increasing demand of such services, since a paradigm shift from the current Internet architecture is required. The EU FP7 MEDIEVAL project tackles this issue by addressing novel architectural frameworks and viable strategies to efficiently deliver video services in a wireless Internet context. This paper reviews the currently ongoing activities of the project for what concerns wireless access, in particular the identification of useful techniques for the considered access technologies (WLAN and LTE-A) and the general definition of architectural schemes to efficiently support video flows.

The aviation community is in the process of designing the next generation Aeronautical Telecommunications Network (ATN), based on Internet standards, to provide air- ground communications for the aircraft. Support for mobile networks in the current Internet architecture is provided by the NEtwork Mobility (NEMO) protocol. As currently defined, NEMO Basic Support protocol lacks of Route Optimization support which is an essential requirement for its adoption as part of the next generation ATN. This paper presents a novel security tool, the Crypto Prefixes, and their application to the Route Optimization in Nemo (CRYPTRON). The Crypto Prefixes are IPv6 prefixes with embedded cryptographic information that enable the Mobile Network Prefix proof-of ownership without any centralized trust infrastructure. In CRYPTRON, the Crypto Prefixes are used to protect the establishment of the bindings on the Correspondent Nodes for the whole Mobile Network Prefix.

This paper characterizes analytically the performance of REAchability Protocol (REAP), a network layer end-to-end recovery protocol for IPv6. REAP was developed by the IETF SHIM6 Working Group as part of its multihoming solution. The behavior of REAP is governed by a small number of parameters: three timers, a simple characterization of the application traffic, and the communication delay. The key figure of merit of REAP performance is the time to recover from a path failure as seen by the upper layers, figure that cannot be trivially obtained, despite the apparent simplicity of this reachability protocol. In this paper we provide upper bounds for the recovery time of REAP for different deployment scenarios, applying these analytical results to two interesting case studies, TCP and VoIP traffic.

When there is no wired connectivity, wireless mesh networks (WMNs) can provide Internet access with lower cost and greater flexibility than traditional approaches. This has motivated the design of new protocols and algorithms for WMNs, and recently the deployment of experimental prototypes. In this paper we add to these previous works with the performance evaluation of a first CARMEN deployment, with the following distinguishing features: i) it is an indoor deployment, ii) it is used by real users to connect to the Internet, and iii) it is built using off-the-shelf hardware. The results show that mesh technology can provide users with a satisfactory Internet experience, and motives further research along this line.

Current Internet use is evolving, users are becoming mobile and are expecting data services on the go. This fact presents big challenges and opportunities to operators, which see the increase in data services as a big market still to be exploited. However, current cellular technologies cannot accommodate the demand that will arise when the true Mobile Internet evolves. Addressing these challenges, we present the CARMEN project, the vision of which is to extend operators’ infrastructure by providing carrier grade services through a heterogeneous wireless mesh. The CARMEN architecture will provide enough bandwidth to cope with users’ expectations at a reduced cost, thereby generating major benefits to operators and users.

This paper evaluates, through an extensive simulation study, a flexible framework for centralized network-based handover control across wireless heterogeneous access. For handover decision-making algorithm, implemented in network elements, the approach encompasses events reported by the terminal side (e.g. radio conditions) as well as events reported by the network side (e.g. load change in the access). Based on standard contributions within the IEEE 802.21 Working Group we investigate and design the functionalities required for efficient network to network communication focusing on optimal device configuration and reliable transport. The study verifies that i) the signaling overhead introduced by the proposed framework does not impact negatively handover performance while allowing network reporting and ii) the implemented re-transmission mechanism reduces messages loss even under congestion conditions.

The availability of multiple technologies, with micro and macro wireless cells, for network access combined with terminals capable of exploiting such diversity in wireless access requires the development of new mechanisms for optimized handover procedures. Appealing solutions should support network controlled handovers through heterogeneous technologies, preferably combined with a cross-layers two/three design. The IEEE 802.21 working group is currently standardizing the methods and the protocol potentially able to provide such a solution. In this paper we analyze the impact of signaling timing on network controlled handovers execution and performance in this environment. Through an extensive simulation study, we obtain results, that can be exploited in both terminal and handover procedure designs.

WLAN hot-spots are becoming widely spread. This, combined with the availability of new multi-mode terminals integrating heterogeneous technologies, opens new business opportunities for mobile operators. Scenarios in which 3G coverage is complemented by WLAN deployments are becoming available. Thus, true all-IP based networks are ready to offer a new variety of services across heterogeneous access. However, to achieve this, some aspects still need to be analyzed. In particular, the effect of the terminal speed on the detection and selection process of the preferred access network is not yet well understood. In fact, efficiency of vertical handovers depends on the appropriate configuration of mobile devices. In this paper we present a simulation study of handover performance between 3G and WLAN access networks showing the impact of mobile users' speed. The mobile devices are based on the IEEE 802.21 cross layer architecture and use WLAN signal level thresholds as handover criteria. A novel algorithm to dynamically adjust terminals' configuration is presented.

An important requirement for Internet protocol (IP) networks to achieve the aim of ubiquitous connectivity is network mobility (NEMO). With NEMO support we can provide Internet access from mobile platforms, such as public transportation vehicles, to normal nodes that do not need to implement any special mobility protocol. The NEMO basic support protocol has been proposed in the IETF as a first solution to this problem, but this solution has severe performance limitations. This paper presents MIRON: Mobile IPv6 route optimization for NEMO, an approach to the problem of NEMO support that overcomes the limitations of the basic solution by combining two different modes of operation: a Proxy-MR and an address delegation with built-in routing mechanisms. This paper describes the design and rationale of the solution, with an experimental validation and performance evaluation based on an implementation