The Internet of Things (IoT) adoption grows significantly and is successful in many different domains. Nevertheless, the ever-growing demand for more connected devices pushes the requirement for scalable IoT architectures capable of maintaining the security and privacy of collected data. The latter is a particularly critical aspect when considering sensitive data, e.g., medical records. One solution to address this challenge is to modify the centralized back-end model to one based on a Blockchain, changing the way IoT data is stored and shared by providing a decentralized peer-to-peer network. This technology enables naming and tracking for connected devices, and in the case of this article, features a high availability of Personal Health Records, yet protecting patients' privacy through the use of cryptography. Furthermore, the addition of Fog computing mechanisms helps to achieve real-time data processing, supports precision medicine, and avoids single points of failure. As a result, devices have a local and more resilient ecosystem for operation. In this context, this work proposes an architecture model named FogChain, which combines the technologies Blockchain, Fog computing, and the IoT for the healthcare domain. Our main contribution is the FogChain model itself, and its concept of overcoming IoT constraints by employing a differential approach, adding an intermediary Fog layer near to the edge to improve their capabilities and resources. Experiments demonstrate that FogChain can achieve a 62.6% faster response time when compared to Cloud-like Blockchain infrastructures. The results obtained from the evaluation endorses the capacity of our model in achieving its goals while retaining application performance.

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.

Activities of a clinical staff in healthcare environments must regularly be adapted to new treatment methods, medications, and technologies. This constant evolution requires the monitoring of the workflow, or the sequence of actions from actors involved in a procedure, to ensure quality of medical services. In this context, recent advances in sensing technologies, including Real-time Location Systems and Computer Vision, enable high-precision tracking of actors and equipment. The current state-of-the-art about healthcare workflow monitoring typically focuses on a single technology and does not discuss its integration with others. Such an integration can lead to better solutions to evaluate medical workflows. This study aims to fill the gap regarding the analysis of monitoring technologies with a systematic literature review about sensors for capturing the workflow of healthcare environments. Its main scientific contribution is to identify both current technologies used to track activities in a clinical environment and gaps on their combination to achieve better results. It also proposes a taxonomy to classify work regarding sensing technologies and methods. The literature review does not present proposals that combine data obtained from Real-time Location Systems and Computer Vision sensors. Further analysis shows that a multimodal analysis is more flexible and could yield better results.

Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.