A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field

Towards edge intelligence in smart spaces

After more than two decades of existence, the internet of things has been revolutionizing the way we interact with the world around us. Although, in its origins, the adoption of a cloud computing paradigm supported this ubiquitous computing model, the increasing complexity of IoT systems has led to the gradual fading of the traditional hierarchical model of cloud computing. The search for solutions to the problems of latency, scalability and privacy has, in recent years, driven the movement of data processing and storage, from the cloud, to the edge of the network (edge computing). Starting from the particular case of edge computing that keeps the focus on extending the boundaries of artificial intelligence to the edge of the network - Edge intelligence - a survey of the current state of the art is carried out, culminating into the specification of an architecture to support edge intelligence applications. In order to validate the proposed architecture, two scenarios are presented. In the scope of waste management and energy recycling, a system for used cooking oil classification in a national domestic collection network is presented. With the local classification of the trustworthiness of each deposit, it was possible to significantly shorten the response times, with a direct impact on energy consumption levels. Aimed at smart cities, a second application scenario, proposes an approach based on computer vision and deep learning, for local detection of pedestrians on crosswalks. In this context, an edge intelligence paradigm allowed to overcome privacy related issues, as well as reducing response times by more than 80 times, when compared to a cloud computing based solution.Após mais de duas décadas de existência, a internet das coisas, tem vindo a revolucionar a forma como interagimos com o mundo que nos rodeia. Apesar de, nas suas origens, a adoção de um paradigma de computação em nuvem ter servido de suporte a este modelo de computação ubíqua, a crescente complexidade dos sistemas IoT tem conduzido ao paulatino esvanecer do tradicional modelo hierárquico da computação em nuvem. A procura por soluções para os problemas de latência, escalabilidade e garantia de qualidade de serviço tem, nos últimos anos, impulsionado a deslocação do processamento e armazenamento de dados, da nuvem, para a periferia da rede (computação periférica). Partindo do caso particular de computação periférica que mantém o foco no alargar das fronteiras da inteligência artificial para a periferia da rede - Periferia inteligente - um levantamento do atual estado da arte é levado a cabo, culminando na especificação de uma arquitetura de suporte a cenários de periferia inteligente. Com vista à validação da arquitetura proposta, dois cenários são apresentados. No âmbito da gestão de resíduos e reciclagem energética, um sistema para classificação de óleo alimentar usado, numa rede nacional de recolha doméstica é apresentado. Com classificação local da veracidade de cada depósito foi possível encurtar significativamente os tempos de resposta, com impacto direto nos níveis de consumo energético. Direcionado às cidades inteligentes, um segundo cenário de aplicação, propõe uma abordagem baseada em visão computacional e aprendizagem profunda, para deteção local de peões em passadeiras. Neste contexto, um paradigma de periferia inteligente permitiu ultrapassar questões relativas à privacidade na transmissão de dados, assim como reduzir em mais de 80 vezes os tempos de resposta, quando comparado com uma solução de computação em nuvem

Reconfigurable Antenna Systems: Platform implementation and low-power matters

Antennas are a necessary and often critical component of all wireless systems, of which they share the ever-increasing complexity and the challenges of present and emerging trends. 5G, massive low-orbit satellite architectures (e.g. OneWeb), industry 4.0, Internet of Things (IoT), satcom on-the-move, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, all call for highly flexible systems, and antenna reconfigurability is an enabling part of these advances. The terminal segment is particularly crucial in this sense, encompassing both very compact antennas or low-profile antennas, all with various adaptability/reconfigurability requirements. This thesis work has dealt with hardware implementation issues of Radio Frequency (RF) antenna reconfigurability, and in particular with low-power General Purpose Platforms (GPP); the work has encompassed Software Defined Radio (SDR) implementation, as well as embedded low-power platforms (in particular on STM32 Nucleo family of micro-controller). The hardware-software platform work has been complemented with design and fabrication of reconfigurable antennas in standard technology, and the resulting systems tested. The selected antenna technology was antenna array with continuously steerable beam, controlled by voltage-driven phase shifting circuits. Applications included notably Wireless Sensor Network (WSN) deployed in the Italian scientific mission in Antarctica, in a traffic-monitoring case study (EU H2020 project), and into an innovative Global Navigation Satellite Systems (GNSS) antenna concept (patent application submitted). The SDR implementation focused on a low-cost and low-power Software-defined radio open-source platform with IEEE 802.11 a/g/p wireless communication capability. In a second embodiment, the flexibility of the SDR paradigm has been traded off to avoid the power consumption associated to the relevant operating system. Application field of reconfigurable antenna is, however, not limited to a better management of the energy consumption. The analysis has also been extended to satellites positioning application. A novel beamforming method has presented demonstrating improvements in the quality of signals received from satellites. Regarding those who deal with positioning algorithms, this advancement help improving precision on the estimated position