Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, Quality of Service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: 1) a middleware for operating rooms with automatic QoS support for real-time data transmission and 2) a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultrawideband (UWB) real-time location systems (RTLSs) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.

The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

Virtual Reality (VR) content is gaining popularity and allowing users to immerse themselves in a new world over the Internet. However, the high-demand for resources and the low latency requirements of VR services require changes in the current 5G networks to deliver VR with quality assurance. Microservices present a suitable model for deploying services at different levels of a 5G fog computing architecture for managing traffic and providing Quality of Experience (QoE) guarantees to VR clients. However, finding the most suitable fog node to allocate microservices for VR clients in QoE-aware 5G scenarios is a difficult task. This article proposes a QoE VR-based mechanism for allocating microservice dynamically in 5G architectures, called Fog4VR. Fog4VR determines the optimal fog node to allocate the VR microservice based on delay, migration time, and resource utilization rate. This article also presents the INFORMER, an integer linear programming model aiming to find the optimal global solution for microservice allocation. Results obtained with INFORMER serve as a baseline to evaluate Fog4VR in different scenarios using a simulation environment. Results demonstrate the efficiency of Fog4VR compared to existing mechanisms in terms of cost, migration time, fairness index, and QoE.

Content-Centric Networking (CCN) is a promising architectural approach that focuses on the efficient distribution of uniquely named data objects. A piece of content is represented by a single object in the network and is divided into multiple chunks which can be uniquely named and cached by network nodes. However, in its current form, the potential of CCN is not fully exploited due to the lack of common means to express and take advantage from possible relations that may exist among different objects. Our work explores the simple yet effective idea of supporting and exploiting such relations in CCN. In this paper, we present CCNrel as a backward-compatible mechanism for CCN that enables publishers to distribute contents as related objects. Differently from existing relation mechanisms, which focus on one type of content and are application-specific, CCNrel is generic and enables the use of relations in both current and novel application domains. First, we discuss CCNrel fundamental concepts and main design aspects. Next, we use CCNrel as foundation for a case study of data redundancy elimination in multimedia content distribution. Through extensive simulation work we evaluate the potential benefits of leveraging relations measured by the clients experience and overall network efficiency. Results of the presented use case show that, on average and when compared to default CCN operations, content download times are improved in 34%, publishers load in 56%, and the network bandwidth usage in 43%.

The swarm-based P2P networks research area evolved to a mature state due to studies related to algorithms for efficient data exchange. However, comparatively less attention has been given to the overlay network topological characteristics, which result from connections between peers. In swarm-based networks, there are three known connection management strategies: inactive, preemptive and pro-active. So far, there has been no previous work comparing these three strategies under the same scenarios. This paper presents a model based on evolving graphs to describe the resulting system topology according to network configuration and connection management strategies. Base on this model, we analyze the robustness and performance potential of topologies under different peer arrival patterns and network reachability ratios in two distinct moments: at the end of the arrival period and in the steady-state period. Our findings show that, in all scenarios, the preemptive and pro-active strategies generate topologies with higher robustness, because they distribute more uniformly connections among all peers, and better performance due to greater centralization of the content source in the overlay topology.

BitTorrent networks are nowadays the most employed method of Peer-to-Peer (P2P) file sharing in the Internet. Recent monitoring reports reveal that content copies being shared are mostly illegal and movies are the most popular media type. Research efforts carried out to understand the dynamics of content production and sharing in BT networks have been unable to provide precise information regarding the dissemination of illegal copies. In this paper we perform an extensive experimental study in order to characterize the behavior of producers, publishers and providers of copyright-infringing files. The study is based on four months of traces obtained by monitoring swarms sharing movies via one of the most popular BT public communities. Traces were obtained with an extension of a BitTorrent “universe” observation architecture, which allowed the collection of a database with information about more than 40,000 torrents, 900 trackers and 1.3 million IPs. Our analysis not only shows that a small group of active users is responsible for the majority of disseminated illegal copies, as well as unravels existing relationships among these actors.

The significance of BitTorrent motivated various studies focused on modeling and evaluating the protocol characteristics and its current implementations in the Internet. So far, however, no work has investigated Optimistic Disconnect (OD), an ad hoc connection management mechanism widely employed in BitTorrent agents. OD allows a peer to search for “better” neighbors in the swarm by disconnecting peers from the current neighborhood and connecting to others. This paper presents an extensive experimental evaluation to study and quantify potential benefits of OD, such as average download time and topology robustness. We evaluate different scenarios and the impact of factors such as average peer reachability and arrival pattern. We found that OD generally improves the overall performance of the swarm (in up to 30% in the evaluated scenarios), while improving the robustness of its topology.

Abstract Despite being currently one of the main Internet applications, P2P file sharing has been hampered by content pollution attacks. To tackle this problem, we introduce a novel pollution control strategy that consists in adjusting the rate in which content is disseminated, according to content version reputation. The proposed strategy is modeled and evaluated using simplifying assumptions. Then, inspired by classic distributed designs, we propose a pollution control mechanism that implements such a strategy. The mechanism is evaluated in terms of the delays imposed on non‐polluted version dissemination, the effectiveness of reducing dissemination when the version is polluted, and the negative impact that collusion attacks can impose on the reputation system upon which our mechanism is built. Simulation results looking at scenarios with several hundred peers indicate that the pollution control mechanism can effectively reduce pollution without substantially affecting the dissemination of non‐polluted content. Copyright © 2010 John Wiley & Sons, Ltd.

The latest technological advances, which introduced innovative and more affordable devices, have contributed to boost the practical application of research in the field of ubiquitous computing (ubicomp). For the development of applications in this area, we need an adequate software infrastructure. In order to do so, we have proposed Continuum, an infrastructure based on service-oriented architecture (SOA), making use of framework and middleware, and employing a redefinition of follow-me semantics. In this redefined vision, users can go anywhere carrying the data and application they want, which they can use in a seamlessly integrated fashion with the real world. In this article, we focus on the description of the service-based architecture proposed for Continuum. The proposal widens the Web services standards to support the mobility of services, allowing them to be deployed, copied, or moved. Besides, the abstraction provided enables the adaptation of legacy applications as Continuum pluggable services.