Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.Peer ReviewedPostprint (author's final draft

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

Big Data Analysis

The value of big data is predicated on the ability to detect trends and patterns and more generally to make sense of the large volumes of data that is often comprised of a heterogeneous mix of format, structure, and semantics. Big data analysis is the component of the big data value chain that focuses on transforming raw acquired data into a coherent usable resource suitable for analysis. Using a range of interviews with key stakeholders in small and large companies and academia, this chapter outlines key insights, state of the art, emerging trends, future requirements, and sectorial case studies for data analysis

EVENTR - DESIGN OF AN EVENT MANAGEMENT MOBILE APPLICATION USING ONTOLOGY

An event is a special occasion that occurs when few or more people gather together to fulfill their personal, cultural or organizational objectives. With many events happening every day, few handy tools are available to help an event organizer to initiate, track and propagate an event. Often important data such as contacts, mails are saved by the organizer in some apps like Excel, calendar, mobile contacts, and mailbox, but not easily retrieved when needed for a follow-up event. Some of the information belongs to common events are not been grouped categorically, so it could not be suitable to relate the similar actions with respect to events which are associated to each other. Follow-up events usually use almost similar data from the previous season, but every time the organizer ends up spending more time searching for the previous records and sometimes fail to retrieve. Event based apps are available in the mobile environment very few in number, and especially such follow-up concentrated event tracker application is not available. Hence, Eventr is designed to fulfill this purpose. Eventr will solve this issue by initiating event, automating simple tasks, record critical data till the event day, this information are stored in the form of Eventr ontology. Since it is ontology based event representation, it would make possible to identify the intermediate tasks of a particular event and to retrieve the tasks associated with the event to the event organizer for the next season. Â

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

BCOOL: A novel blockchain congestion control architecture using dynamic service function chaining and machine learning for next generation vehicular networks

ABSTRACT: This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL’s first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fogbased Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users’ dynamic QoS priority demands. BCOOL exhibits a linear complexity and a strong resilience to failures. Simulation results show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. This yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results prove that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs

Problem solving methods in a global networked age

We believe that the future for problem solving method (PSM) derived work is very promising. In short, PSMs provide a solid foundation for creating a semantic layer supporting planetary-scale networks. Moreover, within a world-scale network where billions services are used and created by billions of parties in ad hoc dynamic fashion we believe that PSM-based mechanisms provide the only viable approach to dealing the sheer scale systematically. Our current experiments in this area are based upon a generic ontology for describing Web services derived from earlier work on PSMs. We outline how platforms based on our ontology can support large-scale networked interactivity in three main areas. Within a large European project we are able to map business level process descriptions to semantic Web service descriptions, to enable business experts to manage and use enterprise processes running in corporate information technology systems. Although highly successful, Web service-based applications predominately run behind corporate firewalls and are far less pervasive on the general Web. Within a second large European project we are extending our semantic service work using the principles underlying the Web and Web 2.0 to transform the Web from a Web of data to one where services are managed and used at large scale. Significant initiatives are now underway in North America, Asia, and Europe to design a new Internet using a 'clean-slate' approach to fulfill the demands created by new modes of use and the additional 3 billion users linked to mobile phones. Our investigations within the European-based Future Internet program indicate that a significant opportunity exists for our PSM-derived work to address the key challenges currently identified: scalability, trust, interoperability, pervasive usability, and mobility. We outline one PSM-derived approach as an exemplar

Leveraging Cloud-based NFV and SDN Platform Towards Quality-Driven Next-Generation Mobile Networks

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand for network services and serve as an enabler for key emerging technologies such as the Internet of Things (IoT) and 5G networking. The thesis considers different approaches and platforms to serve as an NFV/SDN based cloud applications while closely considering how such an environment deploys its virtualized services to optimize the network and reducing their costs. The thesis starts first by defining the standards of adopting microservices as architecture for NFV. Then, it focuses on the latency-aware deployment approach of virtual network functions (VNFs) forming service function chains (SFC) in a cloud environment. This approach ensures that NSPs still meet their strict quality of service and service level agreements while considering both functional and non-functional constraints of the NFV-based applications such as, delay, resource allocation, and intercorrelation between VNF instances. In addition, the thesis proposes a detailed approach on recovering and handling of those instances by optimizing the decision of migrating or re-instantiating the virtualized services upon a sudden event (failure/overload…). All the proposed approaches contribute to the orchestration of NFV applications to meet the requirements of the IoT and NGNs era

Occupational Therapy Practice Trends for Occupations Impacted by Puberty in Children with ASD

Introduction: Adolescents face physical, social, cognitive, and emotional changes during the developmental stage of puberty. Pubertal changes can be difficult for neurotypical adolescents to cope with, but especially for individuals with autism spectrum disorder (ASD). Specifically, adolescents with ASD experience challenges with adapting to physical and emotional changes, managing new hygiene routines, abiding to social norms, and inappropriate sexual behaviors (Beddows & Brooks, 2016; Cridland, Caputi, Jones, & Magee, 2015; Cridland, Jones, Caputi, & Magee, 2014). While occupational therapy plays a role in helping adolescents with ASD, a lack of occupational therapy literature on this topic leads to unknown practice trends for practitioners to address puberty-related challenges in individuals with ASD. The purpose of this independent study was to understand the trends of occupational therapy practitioners in addressing developmental changes during puberty for adolescents with ASD. Methodology: A descriptive survey design was used. The Occupational Therapy for Autism and Puberty Survey (OT-APS) was developed based upon a literature review to understand best practices in ASD. Stratified random sampling and convenience sampling were employed with 71 occupational therapy practitioners responding to the descriptive survey. Results and Conclusions: Practice trends gleaned from the survey include: a) The most common assessments used were the Sensory Profile 2 (69.0%), the Sensory Processing vii Measure (63.4%), and the BOT-2 (62.0%). b) The most common interventions practitioners have been trained in were behavioral strategies (94.4%), sensory processing integration (81.7%), and social learning approaches (80.3%). c) The interventions most often used included social learning approaches (M= 5.30, SD = 2.86) and behavioral strategies (M = 5.28, SD = 2.65). d) Practitioners were most comfortable addressing emotional regulation (54.9%) and personal hygiene and self-care (58%) and least comfortable addressing masturbation (88. 7%) and menstruation ( 42.8% ). e) Practitioners were most comfortable using behavioral strategies (56.3%) and social learning approaches (46.5%). Significance: This research study provided evidence regarding the training completed, challenges addressed, interventions used, and comfortability of occupational therapy practitioners to address puberty related changes and challenges with children and adolescents age 8-16 with ASD. The findings provide an understanding of current practice trends and can be compared with best practice approaches and help identify evidence-based interventions that are being more or less utilized. Further research is needed better understand how these interventions are being provided in practice and why interventions are more often or rarely used. The information can be used in determining whether occupational therapy practitioners require further training in and understanding of puberty related changes and challenges within this population

Efficient learning of sequential tasks for collaborative robots: a neurodynamic approach

Dissertação de mestrado integrado em Engenharia Eletrónica, Industrial e ComputadoresIn the recent years, there has been an increasing demand for collaborative robots able to interact and co operate with ordinary people in several human environments, sharing physical space and working closely with people in joint tasks, both within industrial and domestic environments. In some scenarios, these robots will come across tasks that cannot be fully designed beforehand, resulting in a need for flexibility and adaptation to the changing environments. This dissertation aims to endow robots with the ability to acquire knowledge of sequential tasks using the Programming by Demonstration (PbD) paradigm. Concretely, it extends the learning models - based on Dynamic Neural Fields (DNFs) - previously developed in the Mobile and Anthropomorphic Robotics Laboratory (MARLab), at the University of Minho, to the collaborative robot Sawyer, which is amongst the newest collaborative robots on the market. The main goal was to endow Sawyer with the ability to learn a sequential task from tutors’ demonstrations, through a natural and efficient process. The developed work can be divided into three main tasks: (1) first, a previously developed neuro-cognitive control architecture for extracting the sequential structure of a task was implemented and tested in Sawyer, combined with a Short-Term Memory (STM) mechanism to memorize a sequence in one-shot, aiming to reduce the number of demonstration trials; (2) second, the previous model was extended to incorporate workspace information and action selection in a Human-Robot Collaboration (HRC) scenario where robot and human co worker coordinate their actions to construct the structure; and (3) third, the STM mechanism was also extended to memorize ordinal and temporal aspects of the sequence, demonstrated by tutors with different behavior time scales. The models implemented contributed to a more intuitive and practical interaction with the robot for human co-workers. The STM model made the learning possible from few demonstrations to comply with the requirement of being an efficient method for learning. Moreover, the recall of the memorized information allowed Sawyer to evolve from being in a learning position to be in a teaching one, obtaining the capability of assisting inexperienced co-workers.Nos últimos anos, tem havido uma crescente procura por robôs colaborativos capazes de interagir e cooperar com pessoas comuns em vários ambientes, partilhando espaço físico e trabalhando em conjunto, tanto em ambientes industriais como domésticos. Em alguns cenários, estes robôs serão confrontados com tarefas que não podem ser previamente planeadas, o que resulta numa necessidade de existir flexibilidade e adaptação ao ambiente que se encontra em constante mudança. Esta dissertação pretende dotar robôs com a capacidade de adquirir conhecimento de tarefas sequenciais utilizando técnicas de Programação por Demonstração. De forma a continuar o trabalho desenvolvido no Laboratório de Robótica Móvel e Antropomórfica da Universidade do Minho, esta dissertação visa estender os modelos de aprendizagem previamente desenvolvidos ao robô colaborativo Sawyer, que é um dos mais recentes no mercado. O principal objetivo foi dotar o robô com a capacidade de aprender tarefas sequenciais por demonstração, através de um processo natural e eficiente. O trabalho desenvolvido pode ser dividido em três tarefas principais: (1) em primeiro lugar, uma arquitetura de controlo baseada em modelos neurocognitivos, desenvolvida anteriormente, para aprender a estrutura de uma tarefa sequencial foi implementada e testada no robô Sawyer, conjugada com um mecanismo de Short Term Memory que permitiu memorizar uma sequência apenas com uma demonstração, para reduzir o número de demonstrações necessárias; (2) em segundo lugar, o modelo anterior foi estendido para englobar informação acerca do espaço de trabalho e seleção de ações num cenário de Colaboração Humano-Robô em que ambos coordenam as suas ações para construir a tarefa; (3) em terceiro lugar, o mecanismo de Short-Term Memory foi também estendido para memorizar informação ordinal e temporal de uma sequência de passos demonstrada por tutores com comportamentos temporais diferentes. Os modelos implementados contribuíram para uma interação com o robô mais intuitiva e prática para os co-workers humanos. O mecanismo de Short-Term Memory permitiu que a aprendizagem fosse realizada a partir de poucas demonstrações, para cumprir com o requisito de ser um método de aprendizagem eficiente. Além disso, a informação memorizada permitiu ao Sawyer evoluir de uma posição de aprendizagem para uma posição em que é capaz de instruir co-workers inexperientes.This work was carried out within the scope of the project “PRODUTECH SIF - Soluções para a Indústria do Futuro”, reference POCI-01-0247-FEDER-024541, cofunded by “Fundo Europeu de Desenvolvimento Regional (FEDER)”, through “Programa Operacional Competitividade e Internacionalização (POCI)”