SymbioCity: Smart Cities for Smarter Networks

The "Smart City" (SC) concept revolves around the idea of embodying cutting-edge ICT solutions in the very fabric of future cities, in order to offer new and better services to citizens while lowering the city management costs, both in monetary, social, and environmental terms. In this framework, communication technologies are perceived as subservient to the SC services, providing the means to collect and process the data needed to make the services function. In this paper, we propose a new vision in which technology and SC services are designed to take advantage of each other in a symbiotic manner. According to this new paradigm, which we call "SymbioCity", SC services can indeed be exploited to improve the performance of the same communication systems that provide them with data. Suggestive examples of this symbiotic ecosystem are discussed in the paper. The dissertation is then substantiated in a proof-of-concept case study, where we show how the traffic monitoring service provided by the London Smart City initiative can be used to predict the density of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging Telecommunications Technologie

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

Minimizing power consumption in virtualized cellular networks

Cellular network nodes should be dynamically switched on/off based on the load requirements of the network, to save power and minimize inter-cell interference. This should be done keeping into account global interference effects, which requires a centralized approach. In this paper, we present an architecture, realized within the Flex5GWare EU project, that manages a large-scale cellular network, switching on and off nodes based on load requirements and context data. We describe the architectural framework and the optimization model that is used to decide the activity state of the nodes. We present simulation results showing that the framework adapts to the minimum power level based on the cell loads

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Energy Efficient Healthcare Monitoring System using 5G Task Offloading

Healthcare expenses can be significantly reduced, and lives saved byenabling the continuous monitoring of patient health remotely using WirelessBody Sensor Networks (WBSN). However, an energy efficient mobile gateway(e.g. 5G smartphone) is required which moves with the patient in real time toprocess the data from the bio sensors without depleting the battery. Thispaper proposes a 5G based healthcare cardiovascular disease RemoteMonitoring system called 5GREM using Electrocardiogram (ECG) biosensor as a BSN device. The aim is to monitor and analyse the patient’s heartrhythms and send emergency alerts during irregularities to the nearestcaregivers, ambulance or physician to minimize heart attacks and heartfailures while saving energy. Since ECG signal execution is computerintensive, requests from the ECG sensor are either executed locally on thegateway, offloaded to nearby mobile devices or to the 5G edge whileconsidering the battery level, CPU level, transmission power, delays and taskfail rate

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

Dual-battery empowered green cellular networks

With awareness of the potential harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology (ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase sustainedly. It is essential to leverage green energy to power BSs to reduce their on-grid brown energy consumption. However, in order to furthest save on-grid brown energy and decrease the on-grid brown energy electricity expenses, most existing green energy related works only pursue to maximize the green energy utilization while compromising the services received by the mobile users. In reality, dissatisfaction of services may eventually lead to loss of market shares and profits of the network providers. In this research, a dual-battery enabled profit driven user association scheme is introduced to jointly consider the traffic delivery latency and green energy utilization to maximize the profits for the network providers in heterogeneous cellular networks. Since this profit driven user association optimization problem is NP-hard, some heuristics are presented to solve the problem with low computational complexity. Finally, the performance of the proposed algorithm is validated through extensive simulations. In addition, the Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies utilizing unlicensed bands cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local BSs will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks, is introduced. By optimally allocating the network resource, the introduced resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization is maximized; this furthest saves the on-grid energy. Finally, the performance of the proposed architecture is validated through extensive simulations. Furthermore, the mobile devices serve the important roles in cellular networks and IoT. With the ongoing worldwide development of IoT, an unprecedented number of edge devices imperatively consume a substantial amount of energy. The overall IoT mobile edge devices have been predicted to be the leading energy guzzler in ICT by 2020. Therefore, a three-step green IoT architecture is proposed, i.e., ambient energy harvesting, green energy wireless transfer and green energy balancing, in this research. The latter step reinforces the former one to ensure the availability of green energy. The basic design principles for these three steps are laid out and discussed. In summary, based on the dual-battery architecture, this dissertation research proposes solutions for the three aspects, i.e., green cellular BSs, green D2D communications and green devices, to hopefully and eventually actualize green cellular access networks, as part of the ongoing efforts in greening our society and environment