A flexible network architecture for 5G systems

In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the E

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Identifying 5G system enhancements: enabling technologies for multi-service networks

Proceeding of: 2018 IEEE Conference on Standards for Communications and Networking (CSCN)The fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, we provide a gap analysis of current 5G system (5GS) with respect to some specific enhancements and detail our insights on the enabling innovations that can fill the identified gaps. We will then discuss the essential building blocks and design principles of an evolved 5G baseline architecture capitalizing on the innovations that are being developed.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

A new self-planning methodology based on signal quality and user traffic in Wi-Fi networks

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-19909-8_2Wi-Fi networks have become one of the most popular technologies for the provisioning of multimedia services. Due to the exponential increase in the number of Access Points (AP) in these networks, the automation of the planning, configuration, optimization and management tasks has become of prime importance. The efficiency of these automated processes can be improved with the inclusion of data analytics mechanisms able to process the large amount of data that can be collected from Wi-Fi networks by powerful monitoring systems. This paper presents a new self-planning methodology that collects historical network measurements and extracts knowledge about user signal quality and traffic demands to determine adequate AP relocations. The performance of the proposed AP relocation methodology based on a genetic algorithm is validated in a real Wi-Fi network. The proposed approach can be easily adapted to other contexts such as small cell networks.Peer ReviewedPostprint (author's final draft

A new self-planning methodology based on signal quality and user traffic in Wi-Fi networks

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-19909-8_2Wi-Fi networks have become one of the most popular technologies for the provisioning of multimedia services. Due to the exponential increase in the number of Access Points (AP) in these networks, the automation of the planning, configuration, optimization and management tasks has become of prime importance. The efficiency of these automated processes can be improved with the inclusion of data analytics mechanisms able to process the large amount of data that can be collected from Wi-Fi networks by powerful monitoring systems. This paper presents a new self-planning methodology that collects historical network measurements and extracts knowledge about user signal quality and traffic demands to determine adequate AP relocations. The performance of the proposed AP relocation methodology based on a genetic algorithm is validated in a real Wi-Fi network. The proposed approach can be easily adapted to other contexts such as small cell networks.Peer ReviewedPostprint (author's final draft

Ensemble-based network edge processing

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio