Current mobile applications treat the end-user device as a "thin client," with all of the heavy computations being offloaded to an infrastructure cloud. However, the computational capabilities of mobile devices are constantly improving, and it is worthwhile considering whether an edge-cloud that consists purely of mobile devices (operating effectively as "thick clients") can perform as well as, or even better than, an infrastructure cloud. In this paper, we study the trade-offs between offloading computation to an infrastructure cloud versus retaining the computation within a mobile edge-cloud. To this end, we develop and run two classes of applications on both types of clouds, and we analyze the performance of the two clouds in terms of the time taken to run the application, along with the total amount of battery power consumed in both cases. Our results indicate that there are indeed classes of applications where an edge-cloud can outperform an infrastructure cloud in terms of both latency and battery power.

PurposeThe aim of this paper is to evaluate the use of blockchain for identity management (IdM) in the context of the Internet of things (IoT) while focusing on privacy-preserving approaches and its applications to healthcare scenarios.Design/methodology/approachThe paper describes the most relevant IdM systems focusing on privacy preserving with or without blockchain and evaluates them against ten selected features grouped into three categories: privacy, usability and IoT. Then, it is important to analyze whether blockchain should be used in all scenarios, according to the importance of each feature for different use cases.FindingsBased on analysis of existing systems, Sovrin is the IdM system that covers more features and is based on blockchain. For each of the evaluated use cases, Sovrin and UniquID were the chosen systems.Research limitations/implicationsThis paper opens new lines of research for IdM systems in IoT, including challenges related to device identity definition, privacy preserving and new security mechanisms.Originality/valueThis paper contributes to the ongoing research in IdM systems for IoT. The adequacy of blockchain is not only analyzed considering the technology; instead the authors analyze its application to real environments considering the required features for each use case.

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The ever-increasing pervasiveness of edge computing is creating challenges for users' privacy. Given this state-of-affairs, we decided to pursuit an overview and future directions for novel approaches for privacy-preserving computation. In this process, we highlight of some most important privacy concepts and their application to both Fog Computing and IoT. While we do not offer a definitive solution for privacy, our work explores several ideas that might lead to significant advances in the area. For this purpose, we explored current literature and discuss the integration of several different approaches. We start by first exploring three major concepts, namely, blockchain, IoT/fog computing and Multi-Party Computation (MPC). These concepts provide the necessary context and background for developing possible research paths and ideas. For blockchain, we describe some practical frameworks and applications and then describe which ones can have an impact in IoT. We then move to investigate and describe current approaches that combine blockchain, IoT and fog computing. Lastly, we explore MPC. Since it is a concept that promotes privacy without a third party, we explore its use in conjunction with the aforementioned concepts. Furthermore, we offer an overview on some potential frameworks for MPC and assess the feasibility of their integration with other privacy concepts. We conclude by discussing current unsolved problems and possible future research directions.

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The overall performance improvement in Byzantine faulttolerant state machine replication algorithms has made them a viable option for critical high-performance systems. However, the construction of the proofs necessary to support these algorithms are complex and often make assumptions that may or may not be true in a particular implementation. Furthermore, the transition from theory to practice is difficult and can lead to the introduction of subtle bugs that may break the assumptions that support these algorithms. To address these issues we have developed Hermes, a fault-injector framework that provides an infrastructure for injecting faults in a Byzantine fault-tolerant state machine. Our main goal with Hermes is to help practitioners in the complex process of debugging their implementations of these algorithms, and at the same time increase the confidence of possible adopters, e.g., systems researchers, industry, by allowing them to test the implementations. In this paper, we discuss our experiences with Hermes to inject faults in BFT-SMaRt, a high-performance Byzantine fault-tolerant state machine replication library.