A Novel Design for Advanced 5G Deployment Environments with Virtualized Resources at Vehicular and MEC Nodes

IoT and edge computing are profoundly changing the information era, bringing a hyper-connected and context-aware computing environment to reality. Connected vehicles are a critical outcome of this synergy, allowing for the seamless interconnection of autonomous mobile/fixed objects, giving rise to a decentralized vehicle-to-everything (V2X) paradigm. On this front, the European Telecommunications Standards Institute (ETSI) proposed the Multi-Access Edge Computing (MEC) standard, addressing the execution of cloud-like services at the very edge of the infrastructure, thus facilitating the support of low-latency services at the far-edge. In this article, we go a step further and propose a novel ETSI MEC-compliant architecture that fully exploits the synergies between the edge and far-edge, extending the pool of virtualized resources available at MEC nodes with vehicular ones found in the vicinity. In particular, our approach allows vehicle entities to access and partake in a negotiation process embodying a rewarding scheme, while addressing resource volatility as vehicles join and leave the resource pool. To demonstrate the viability and flexibility of our proposed approach, we have built an ETSI MEC-compliant simulation model, which could be tailored to distribute application requests based on the availability of both local and remote resources, managing their transparent migration and execution. In addition, the paper reports on the experimental validation of our proposal in a 5G network setting, contrasting different service delivery modes, by highlighting the potential of the dynamic exploitation of far-edge vehicular resources

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Aplicações de IoT no contexto de uma cidade inteligente

Over the last few years, Smart City solutions mature very rapidly alongside IoT and cloud computing. These technologies made it easier to create services and incorporate applications devoted to improving citizen’s quality of life and offer ways for businesses to implement their solutions. Through rapid advances in the quality of sensors, new methods emerged, combining different types of devices to create a better picture of the environment. The purpose of this dissertation is to provide useful information thought public services, that can be accessed by people visiting or residing in the beach area of Costa Nova and Barra. It also provides a solution for the traffic classification problem that projects based on radar data tend to face. These applications take advantage of the devices implemented in the PASMO project, such as parking sensors, radars, and CCTV cameras. By making the service public, businesses have the opportunity to build applications on top of it, utilizing the sensor data without being directly connected to the data storage. The example developed in this dissertation offers a dashboard experience where users can navigate through charts that provide a variety of data and real-time maps. It also provides a public API that researchers and businesses can use to develop new applications in the context of PASMO. The other area tackled in this document is traffic classification. Although the data provided is reliable for the most part, one big issue is the accuracy of vehicle classification provided by the radar. Still, this device offers precise values when it comes to detection, with the cameras doing a good job in classifying traffic. The goal is to combine these two devices to present much precise information, using state-of-the-art object detection algorithms and sensor fusion methods. In the end, the system will enrich the PASMO project by making its data easily available to the public while correcting the accuracy problems of some devices.Nos últimos anos, as soluções Smart City amadurecem muito rapidamente em conjunto com IoT e serviços na cloud. Estas tecnologias facilitam a criação de serviços e a incorporação de aplicações direcionados á melhoria da qualidade de vida do cidadão, oferecendo formas das empresas implementarem suas soluções. Por meio de rápidos avanços na qualidade dos sensores, novos métodos surgiram, combinando diferentes tipos de dispositivos para criar uma melhor imagem da realidade. O objetivo desta dissertação é fornecer informações úteis através de serviços públicos, que podem ser acedidos por pessoas que visitam ou residem na Costa Nova e Barra. Também fornece uma solução para o problema de classificação de tráfego que projetos baseados em dados de radar tendem a enfrentar. Estas aplicações beneficiam dos dispositivos implementados no projeto PASMO, como sensores de estacionamento, radares e câmeras de CFTV. Ao disponibilizar os serviços publicamente, as empresas têm a oportunidade de construir as suas próprias aplicações em cima destes, usando os dados dos sensores sem estar diretamente conectado ao armazenamento de dados. O exemplo desenvolvido nesta dissertação oferece uma experiência de dashboard onde os utilizadores podem navegar por gráficos que fornecem uma variedade de dados e mapas em tempo real. Também fornece uma API pública que os investigadores e empresas podem usar para desenvolver novos aplicativos no contexto do PASMO. A outra área abordada neste documento é a classificação de tráfego. Embora os dados fornecidos sejam confiáveis, um grande problema provém da precisão da classificação dos veículos fornecida pelo radar. Ainda assim, este dispositivo oferece valores precisos quando se trata de detecção, com as câmeras fazendo um bom trabalho na parte de classificação do tráfego. O objetivo é combinar estes dois dispositivos para apresentar informações corretas, usando algoritmos de detecção de objetos e métodos de fusão de sensores. No final, o sistema irá enriquecer o projeto PASMO, tornando seus dados facilmente disponíveis ao público e corrigindo problemas de precisão de alguns dispositivos.Mestrado em Engenharia de Computadores e Telemátic