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5G network is expected to be highly flexible, enabling its adjustment according to user requirements. To achieve this, 5G leverages network slicing, allowing the division of the network infrastructure into different slices with completely different communication performances. However, this flexibility is not easily achieved, requiring the use of available 5G features, Quality of Service (QoS) parameters, and technologies to adapt the communication performance according to the user requirements. Furthermore, to foster wider adoption of 5G by verticals, it is crucial to simplify the network configuration process by providing common interfaces that accept attributes familiar to vertical users. In this line, having a vertical-oriented network slice manager capable of automating the deployment of slices in 5G networks is crucial. One of the key elements involved in creating such a system is a mapping between common communication attributes and available 5G configuration parameters and features. This mapping facilitates the adjustment of communication performance to meet vertical requirements. With this in mind, the document explores the realisation of such a mapping. First, it analyses the communication attributes defined by well-known organisations (e.g., 5G-ACIA, and 5GDNA); these attributes define the performance requirements of network slices or industrial use cases. Then, it maps these attributes to standardised R17 5G functionalities. The mapping is followed by a validation using the 5GAIner infrastructure (part of the IMAGINE-B5G experimental facilities), a real-world commercial-grade 5G Standalone (SA) network. The results showcase the feasibility of using this mapping to realize vertical slices.

5G technologies are considered a cornerstone of the advent of the next industrial revolution. Promising performance improvements, along with advanced features and assurances in terms of reliability, flexibility and isolation, are expected to enable the realization of diverse and novel use cases, fostering industrial automation with optimized production lines and manufacturing systems. This document shares the experience and knowledge using a 5G SA network for industrial applications. Concretely, the paper examines whether and how the available technology could fulfil the demanding industry requirements, namely in terms of isolation, flexibility and performance. This gap analysis revealed 5G QoS mechanisms as a key driver towards 5G for industry. Thus, a comprehensive analysis of the existing mechanisms and their impact on the network performance are presented, serving as a reality check of 5G SA Release 15 technologies. Although results showed promising possibilities to support industrial deployments, there is still a gap between what’s achievable and what is expected from 5G that will be gradually filled by the introduction of novel features in the upcoming releases. In general, the contributions and insights presented in this paper are considered to be valuable for industry, standards development organizations, manufacturers, and the wider 5G ecosystem. Moreover, this paper serves as a foundational component within a larger endeavour of automating network slicing mechanisms for industrial applications.

5G has been heralded as a critical, decisive technology for verticals’ digital transformation. It targets innovative technical capabilities, allowing dedicated and improved communication mechanisms, going beyond the mere performance increase experienced by past generations’ evolution. Despite the literature already envisaging the application of 5G-enabled solutions in vertical-based deployments, there is still a lack of experimental insights shedding light on the actual feasibility of 5G capabilities in specific vertical deployments. This gap is particularly evident in verticals that did not traditionally rely on mobile network solutions for their communication needs, such as safety-related control signals in railway operations. This paper aims to fill this gap by delivering an extensive empirical analysis performed through a 5G-enabled railway deployment. We designed and implemented a joint testbed, which allows us to faithfully reproduce two important transportation railway use cases that leverage 5G communications involving the vertical, communication service solutions operator, and integration actors. There, we expose the necessary integration engineering behind the enablement of 5G capabilities in such scenarios, considering the highly safety-certified and standardized environment of railway vertical sector regulations (which never considered the utilization of mobile networks in their development). We empirically demonstrate the performance capability achieved by the 5G-enabled network using two different systems, a carrier-grade one and an open-development one, in light of the required key performance indicators. To the best of our knowledge, this is the very first field trial and empirical evaluation of the suitability of 5G with the implementation of a 5G-enabled railway cross-level scenario within the transportation context.

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

Fault-tolerance methods are required to ensure high availability and high reliability in cloud computing environments. In this survey, we address fault-tolerance in the scope of cloud computing. Recently, cloud computing-based environments have presented new challenges to support fault-tolerance and opened new paths to develop novel strategies, architectures, and standards. We provide a detailed background of cloud computing to establish a comprehensive understanding of the subject, from basic to advanced. We then highlight fault-tolerance components and system-level metrics and identify the needs and applications of fault-tolerance in cloud computing. Furthermore, we discuss state-of-the-art proactive and reactive approaches to cloud computing fault-tolerance. We further structure and discuss current research efforts on cloud computing fault-tolerance architectures and frameworks. Finally, we conclude by enumerating future research directions specific to cloud computing fault-tolerance development.

The next generation of mobile broadband communication, 5G, is seen as a driver for the industrial Internet of things (IIoT). The expected 5G-increased performance spanning across different indicators, flexibility to tailor the network to the needs of specific use cases, and the inherent security that offers guarantees both in terms of performance and data isolation have triggered the emergence of the concept of public network integrated non-public network (PNI-NPN) 5G networks. These networks might be a flexible alternative for the well-known (albeit mostly proprietary) Ethernet wired connections and protocols commonly used in the industry setting. With that in mind, this paper presents a practical implementation of IIoT over 5G composed of different infrastructure and application components. From the infrastructure perspective, the implementation includes a 5G Internet of things (IoT) end device that collects sensing data from shop floor assets and the surrounding environment and makes these data available over an industrial 5G Network. Application-wise, the implementation includes an intelligent assistant that consumes such data to generate valuable insights that allow for the sustainable operation of assets. These components have been tested and validated in a real shop floor environment at Bosch Termotecnologia (Bosch TT). Results show the potential of 5G as an enhancer of IIoT towards smarter, more sustainable, green, and environmentally friendly factories.

In this work, we share the dataset collected during the Smart Green Homes (SGH) project. The project’s goal was to develop integrated products and technology solutions for households, as well as to improve the standards of comfort and user satisfaction. This was to be achieved while improving household energy efficiency and reducing the usage of gaseous pollutants, in response to the planet’s sustainability issues. One of the tasks executed within the project was the collection of data from volunteers’ homes, including environmental information and the level of comfort as perceived by the volunteers themselves. While used in the original project, the resulting dataset contains valuable information that could not be explored at the time. We now share this dataset with the community, which can be used for various scenarios. These may include heating appliance optimisation, presence detection and environmental prediction.

This article presents a framework for improved and efficient video delivery in scenarios featuring users moving at high speed (e.g., trains), leveraging on dynamic Multi-access Edge Computing (MEC) enabled 5G network capabilities. The framework is location-aware and it allows the content to efficiently follow the users, conserving load usage on network and computational resources, by placing virtualized Content Delivery Network (vCDN) nodes at edge sites. The nodes are controlled by the framework's centralized control unit, which is able to dynamically and preemptively deploy virtulized resources, as the train moves. The framework is capable of segmenting video content and placing the specific portion of content that a user is likely to consume across a set of dynamically deployed vCDN nodes, associated to the coverage section the train is currently passing. A proof of concept was implemented and evaluated, where the benefits of using this framework are assessed. Results showed that the proposed system was able to reduce the load on the core network by 10.9 percent and maximize the cache hit ratio to a value of 99.8 percent.

5G systems are envisioned to support numerous delay-sensitive applications such as the tactile Internet, mobile gaming, and augmented reality. Such applications impose new demands on service providers in terms of the quality of service (QoS) provided to the end-users. Achieving these demands in mobile 5G-enabled networks represent a technical and administrative challenge. One of the solutions proposed is to provide cloud computing capabilities at the edge of the network. In such vision, services are cloudified and encapsulated within the virtual machines or containers placed in cloud hosts at the network access layer. To enable ultrashort processing times and immediate service response, fast instantiation, and migration of service instances between edge nodes are mandatory to cope with the consequences of user's mobility. This paper surveys the techniques proposed for service migration at the edge of the network. We focus on QoS-aware service instantiation and migration approaches, comparing the mechanisms followed and emphasizing their advantages and disadvantages. Then, we highlight the open research challenges still left unhandled.

Network operators are under pressure to offer efficient network-based services while keeping service deployment costs to a minimum. Network functions virtualization (NFV) can potentially revolutionize network-based services bringing low-deployment costs for network operators. The NFV has been introduced to ultimately extend the non-proprietary and open-standard-based model to network and service deployments, significant improvements to today's proprietary locked implementations. Notwithstanding the continuous efforts of both academia and industry to support the NFV paradigm, the current NFV solutions offered are still in its infancy. In this survey, we provide a detailed background of NFV to establish a comprehensive understanding of the subject, ranging from the basics to more advanced topics. Moreover, we offer a comprehensive overview of the NFV main concepts, standardization efforts, the benefits of NFV, and discussions of the NFV architecture as defined by the European telecommunications standardization institute (ETSI). Furthermore, we discuss the NFV applicability and current open source projects. We then highlight NFV requirements, design considerations, and developmental architectural impairments and barriers to commercial NFV deployments. Finally, we conclude enumerating future directions for NFV development.

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

The function and performance of neural networks are largely determined by the evolution of their weights and biases in the process of training, starting from the initial configuration of these parameters to one of the local minima of the loss function. We perform the quantitative statistical characterization of the deviation of the weights of two-hidden-layer feedforward ReLU networks of various sizes trained via Stochastic Gradient Descent (SGD) from their initial random configuration. We compare the evolution of the distribution function of this deviation with the evolution of the loss during training. We observed that successful training via SGD leaves the network in the close neighborhood of the initial configuration of its weights. For each initial weight of a link we measured the distribution function of the deviation from this value after training and found how the moments of this distribution and its peak depend on the initial weight. We explored the evolution of these deviations during training and observed an abrupt increase within the overfitting region. This jump occurs simultaneously with a similarly abrupt increase recorded in the evolution of the loss function. Our results suggest that SGD’s ability to efficiently find local minima is restricted to the vicinity of the random initial configuration of weights.

The design and deployment of a novel mobile network architecture, motivated by the challenges deriving from the explosive increase in data traffic on operator networks, are a pressing problem in today's telecommunications. Distributed mobility management (DMM) introduces a key idea to tackle the traffic bottlenecks that impact current mobile networks by proposing the deployment of distributed mobility anchor points close to terminal locations. There have been many proposals to build DMM solutions, with different focuses and merits for future mobile networks, aimed at overcoming the limitations from current centralized mobility management solutions. To the best of our knowledge, a comprehensive analysis and comparison study systematically analyzing available design options have not yet been provided. In this paper, we identify essential design considerations and their underlying options, comparing their impact on user and network performance. In addition, we provide the recent advances of DMM with emerging trends such as control-/data-plane separation and mobile cloud, which will impact the next generation network paradigm.

5G systems are putting increasing pressure on Telecom operators to enhance users’ experience, leading to the development of more techniques with the aim of improving service quality. However, it is essential to take into consideration not only users’ demands but also service providers’ interests. In this work, we explore policies that satisfy both views. We first formulate a mathematical model to compute End-to-End (E2E) delay experienced by mobile users in Multi-access Edge Computing (MEC) environments. Then, dynamic Virtual Machine (VM) allocation policies are presented, with the objective of satisfying mobile users Quality of Service (QoS) requirements, while optimally using the cloud resources by exploiting VM resource reuse.Thus, maximizing the service providers’ profit should be ensured while providing the service required by users. We further demonstrate the benefits of these policies in comparison with previous works.

Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

Over the last few years, pervasive systems have seen some interesting development. Nevertheless, human−human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this work, we have developed a pervasive system − named CLASSY − that is aware of the conversational context and suggests documents potentially useful to the users based on an Information Retrieval system, and proposed a new scoring approach that uses semantics and distance based on proximity data in order to classify the relationship between tokens.

New challenging applications and scenarios for converged fixed and mobile Internet infrastructures, require efficient technologies adapting the multicast services for fixed IPv6 infrastructures to heterogeneous mobile environment. This paper discusses architecture aimed to support QoS based mobile multicast in heterogeneous mobile IPv6 environmentusing context transfer between access routers for seamlesshandover of mobile multicast applications. The architecture includes components for policy based QoS management of mobile multicast, reliable mobile multicast support at access routers and optimised handover based on learning of capabilities of access routers and networks.

This paper describes an experimental evaluation of a multiservice ad hoc network, aimed to be interconnected with an infrastructure, operator-managed network. This network supports the efficient delivery of services, unicast and multicast, legacy and multimedia, to users connected in the ad hoc network. It contains the following functionalities: routing and delivery of unicast and multicast services; distributed QoS mechanisms to support service differentiation and resource control responsive to node mobility; security, charging, and rewarding mechanisms to ensure the correct behaviour of the users in the ad hoc network. This paper experimentally evaluates the performance of multiple mechanisms, and the influence and performance penalty introduced in the network, with the incremental inclusion of new functionalities. The performance results obtained in the different real scenarios may question the real usage of ad-hoc networks for more than a minimal number of hops with such a large number of functionalities deployed.