Algorithms for the Design of 5G networks with VNF-based Reusable Functional Blocks Annals of Telecommunications

We face the problem of designing a 5G network composed of Virtual Network Function (VNF)-based entities, called Reusable Functional Blocks (RFBs). RFBs provide a high level of flexibility and scalability, which are recognized as core functions for the deployment of the forthcoming 5G technology. Moreover, the RFBs can be run on different HardWare (HW) and SoftWare (SW) execution environments located in 5G nodes, in line with the current trend of network softwarization. After overviewing the considered RFB-based 5G network architecture, we formulate the problem of minimizing the total costs of a 5G network composed of RFBs and physical 5G nodes. Since the presented problem is NP-Hard, we derive two algorithms, called SFDA and 5G-PCDA, to tackle it. We then consider a set of scenarios located in the city of San Francisco, where the positions of the users and the set of candidate sites to host 5G nodes have been derived from the WeFi app. Our results clearly show the trade-offs that emerge between (i) the total costs incurred by the installation of the 5G equipment, (ii) the percentage of users that are served, and (iii) the minimum downlink traffic provided to the users

Towards 5G: scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure

Moving from 4G LTE to 5G is an archetypal example of technological change. Mobile Network Operators (MNOs) who fail to adapt will likely lose market share. Hitherto, qualitative frameworks have been put forward to aid with business model adaptation for MNOs facing on the one hand increasing traffic growth, while on the other declining revenues. In this analysis, we provide a complementary scenario-based assessment of 5G infrastructure strategies in relation to mobile traffic growth. Developing and applying an open-source modelling framework, we quantify the uncertainty associated with future demand and supply for a hypothetical MNO, using Britain as a case study example. We find that over 90% of baseline data growth between 2016 and 2030 is driven by technological change, rather than demographics. To meet this demand, spectrum strategies require the least amount of capital expenditure and can meet baseline growth until approximately 2025, after which new spectrum bands will be required. Alternatively, small cell deployments provide significant capacity but at considerable cost, and hence are likely only in the densest locations, unless MNOs can boost revenues by capturing value from the Internet of Things (IoT), Smart Cities or other technological developments dependent on digital connectivity.Edward Oughton, Zoraida Frias, Tom Russell and David Cleevely would like to express their gratitude to the UK Engineering and Physical Science Research Council for funding via grant EP/N017064/1: Multi-scale InfraSTRucture systems AnaLytics (Mistral). Zoraida Frias would like to thank the Universidad Politécnica de Madrid for their support through the mobility program scholarship

Bi-criteria network optimization: problems and algorithms

Several approaches, exact and heuristics, have been designed in order to generate the Pareto frontier for multi-objective combinatorial optimization problems. Although several classes of standard optimization models have been studied in their multi- objective version, there still exists a big gap between the solution techniques and the complexity of the mathematical models that derive from the most recent real world applications. In this thesis such aspect is highlighted with reference to a specific application field, the telecommunication sector, where several emerging optimization problems are characterized by a multi-objective nature. The study of some of these problems, analyzed and solved in the thesis, has been the starting point for an assessment of the state of the art in multicriteria optimization with particular focus on multi-objective integer linear programming. A general two-phase approach for bi-criteria integer network flow problems has been proposed and applied to the bi-objective integer minimum cost flow and the bi-objective minimum spanning tree problem. For both of them the two-phase approach has been designed and tested to generate a complete set of efficient solutions. This procedure, with appropriate changes according to the specific problem, could be applied on other bi-objective integer network flow problems. In this perspective, this work can be seen as a first attempt in the direction of closing the gap between the complex models associated with the most recent real world applications and the methodologies to deal with multi-objective programming. The thesis is structured in the following way: Chapter 1 reports some preliminary concepts on graph and networks and a short overview of the main network flow problems; in Chapter 2 some emerging optimization problems are described, mathematically formalized and solved, underling their multi-objective nature. Chapter 3 presents the state of the art on multicriteria optimization. Chapter 4 describes the general idea of the solution algorithm proposed in this work for bi-objective integer network flow problems. Chapter 5 is focused on the bi-objective integer minimum cost flow problem and on the adaptation of the procedure proposed in Chapter 4 on such a problem. Analogously, Chapter 6 describes the application of the same approach on the bi-objective minimum spanning tree problem. Summing up, the general scheme appears to adapt very well to both problems and can be easily implemented. For the bi-objective integer minimum cost flow problem, the numerical tests performed on a selection of test instances, taken from the literature, permit to verify that the algorithm finds a complete set of efficient solutions. For the bi-objective minimum spanning tree problem, we solved a numerical example using two alternative methods for the first phase, confirming the practicability of the approach

Human in the Loop: Distributed Deep Model for Mobile Crowdsensing

With the proliferation of mobile devices, crowdsensing has become an appealing technique to collect and process big data. Meanwhile, the rise of fifth generation wireless systems, especially the new cellular base stations with computing ability, brings about the revolutionary edge computing. Although many approaches regarding the mobile crowdsensing have emerged in the last few years, very few of them are focused on the combination of edge computing and crowdsensing. In this paper, we adopt the state-of-the-art edge computing method to solve the crowdsensing problem with the real-time sensing data, and more importantly, make human be in the loop again, in order to respect the users’ willing and privacy. A distributed deep learning model is adopted to extract features from the captured data, which is not only a compression process to reduce the communication cost, but an encryption procedure for safety protection. The proposed model enables the crowdsensing system to fully harness the computing capacity of edge nodes and devices, and obtain a strong data analysis ability to process the captured data. Simulations demonstrate that our approach is robust and efficient, and outperforms other strategies in several related tasks

From Logic to Realism to Brighter Future for Humanity

This collection of articles explores a wide range of subject, from Godel’s incompleteness theorem, to possible technocalypse and neutrofuturology. Articles on historical debates on irrational number to electroculture, on vortex particle, or on different Neutrosophic applications are included

Transformative Effects of IoT, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges

Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesised to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing