Byzantine Fault Tolerant state machine replication (BFT) protocols are replication protocols that tolerate arbitrary faults of a fraction of the replicas. Although significant efforts have been recently made, existing BFT protocols do not provide acceptable performance when faults occur. As we show in this paper, this comes from the fact that all existing BFT protocols targeting high throughput use a special replica, called the primary, which indicates to other replicas the order in which requests should be processed. This primary can be smartly malicious and degrade the performance of the system without being detected by correct replicas. In this paper, we propose a new approach, called RBFT for Redundant-BFT: we execute multiple instances of the same BFT protocol, each with a primary replica executing on a different machine. All the instances order the requests, but only the requests ordered by one of the instances, called the master instance, are actually executed. The performance of the different instances is closely monitored, in order to check that the master instance provides adequate performance. If that is not the case, the primary replica of the master instance is considered malicious and replaced. We implemented RBFT and compared its performance to that of other existing robust protocols. Our evaluation shows that RBFT achieves similar performance as the most robust protocols when there is no failure and that, under faults, its maximum performance degradation is about 3%, whereas it is at least equal to 78% for existing protocols.

Pervasive computing environments are populated with networked software and hardware resources providing various functionalities that are abstracted, thanks to the Service Oriented Architecture paradigm, as services. Within these environments, service discovery enabled by service discovery protocols (SDPs) is a critical functionality for establishing ad hoc associations between service providers and service requesters. Furthermore, the dynamics, the openness and the usercentric vision aimed at by the pervasive computing paradigm call for solutions that enable rich, semantic, context-and QoS-aware service discovery. Although the Semantic Web paradigm envisions to achieve such support, current solutions are hardly deployable in the pervasive environment due to the costly underlying semantic reasoning with ontologies. In this article, we present EASY to support efficient, semantic, context-and QoS-aware service discovery on top of existing SDPs. EASY provides EASY-L, a language for semantic specification of functional and non-functional service properties, as well as EASY-M, a corresponding set of conformance relations. Furthermore, EASY provides solutions to efficiently assess conformance between service capabilities. These solutions are based on an efficient encoding technique, as well as on an efficient organization of service repositories (caches), which enables both fast service advertising and discovery. Experimental results show that the deployment of EASY on top of an existing SDP, namely Ariadne, enhancing it only with slight changes to EASY-Ariadne, enables rich semantic, context-and QoS-aware service discovery, which furthermore performs better than the classical, rigid, syntactic matching, and improves the scalability of Ariadne.

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The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.