Cloud infrastructures can provide resource sharing between many applications and usually can meet the requirements of most of them. However, in order to enable an efficient usage of these resources, automatic orchestration is required. Commonly, automatic orchestration tools are based on the observability of the infrastructure itself, but that is not enough in some cases. Certain classes of applications have specific requirements that are difficult to meet, such as low latency, high bandwidth and high computational power. To properly meet these requirements, orchestration must be based on multilevel observability, which means collecting data from both the application and the infrastructure levels. Thus in this work we developed a platform aiming to show how multilevel observability can be implemented and how it can be used to improve automatic orchestration in cloud environments. As a case study, an application of computer vision and robotics, with very demanding requirements, was used to perform two experiments and illustrate the issues addressed in this paper. The results confirm that cloud orchestration can largely benefit from multilevel observability by allowing specific application requirements to be met, as well as improving the allocation of infrastructure resources.

The research field of the Intelligent Spaces has experienced increasing attention in the last decade. As an instance of the ubiquitous computing paradigm, the general idea is to extract information from the ambient and use it to interact and provide services to the actors present in the environment. The sensory analysis is mandatory in this area and humans are usually the principal actors involved. In this sense, we propose a human detector to be used in an Intelligent Space based on a multi-camera network. Our human detector is implemented in the same paradigm of our Intelligent Space. As a contribution of the present work, the human detector is designed to be a service that is scalable, reliable and parallelizable. It is also a concern of our service to be flexible, less structured as possible, attending different Intelligent Space applications and services, as well as their requirements. As it can be found in different everyday environments, a multicamera system is used to overcome some difficulties traditionally faced by existing human detection approaches. To validate our approach, we implement three different applications that are proof of concept of many day-to-day real tasks. Two of these applications involve human-robot interaction. With respect to time and detection performance requirements, our human detection service has proved to be suitable for interacting with the other services of our Intelligent Space, in order to successfully complete the tasks of each application.

Real‐time and mission‐critical applications for Industry 4.0 demand fast and reliable communication. Therefore, knowing devices' location is essential, but GPS is of little use indoors, whereas electromagnetic impairments and interferences demand new approaches to ensure reliability. The challenges include real‐time feedback with end‐to‐end (E2E) low latency; high data density due to large number of IoT devices per area; and smaller communication cells, which increases the handover frequency and complexity. To tackle these issues, we introduce a programmable intelligent space (PIS) to deploy attocells, enable E2E programmability, and provide a precise computer vision localization system and networking programmability based on software‐defined networking. To validate our approach, experiments were conducted, controlling a mobile robot through a trajectory. We demonstrate the need for higher camera frame rate to achieve tighter precision, evaluating different trade‐offs on localization, bandwidth, and latency. Results have shown that PIS wireless attocell handover achieves seamlessly mobile communication, delivering packets within the deadline window, with similar performance to a no handover baseline.

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Lysa is a guide-robot whose goal is to assist people with visual impairment, improving their autonomy and mobility. Lysa is able to detect potholes, ground obstacles and risks of collision with high objects, warning the user of such dangers by voice. However Lysa is still unable to guide a person to a place within an environment. Therefore, the aim of this project will be to develop MobiLysa: a service to perform localization, navigation and control of the robot Lysa so it can guide a person to a desired location inside a building. That will be done using an Intelligent Space based on computer vision. The user will be able to indicate the desired destination, either by voice or other form of interaction, and be guided to the place independently and safely.

This paper provides a description of key results stemming from experiments conducted at the wireless/optical boundary. The main aim is to show the advantages of a converged optical/wireless network deployment, which uses very recent technologies like SDN, Virtualization, fog and cloud computing, and Radio-over fiber. The results are mainly taken out of an international collaborative research project called FUTEBOL (Federated Union of Telecommunications Research Facilities for an EU Brazil Open Laboratory).

É sabido que usar apenas odometria na localização de robôs móveis leva a um crescente acúmulo de erro. Esse problema torna-se maior quando se busca coordenar os movimentos de mais de um robô para atuarem em formação. Sendo assim, este trabalho propõe o uso de um espaço inteligente baseado em visão computacional como uma infraestrutura de serviços para aplicações de robótica móvel, como exemplo, o controle de formação de robôs. A arquitetura baseada em microserviços e computação em nuvem oferece à aplicação flexibilidade, baixa latência e escalabilidade. Os resultados obtidos demonstram a viabilidade de se utilizar espaços inteligentes como provedor e gerenciador desse tipo de aplicação.

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