Abstract. QoS-aware service composition is a key requirement in Service Oriented Computing (SOC) since it enables fulfilling complex user tasks while meeting Quality of Service (QoS) constraints. A challenging issue towards this purpose is the selection of the best set of services to compose, meeting global QoS constraints imposed by the user, which is known to be a NP-hard problem. This challenge becomes even more relevant when it is considered in the context of dynamic service environments. Indeed, two specific issues arise. First, required tasks are fulfilled on the fly, thus the time available for services' selection and composition is limited. Second, service compositions have to be adaptive so that they can cope with changing conditions of the environment. In this paper, we present an efficient service selection algorithm that provides the appropriate ground for QoS-aware composition in dynamic service environments. Our algorithm is formed as a guided heuristic. The paper also presents a set of experiments conducted to evaluate the efficiency of our algorithm, which shows its timeliness and optimality.

After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology

Abstract.Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion.

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.

Mobile Ad hoc NETworks (MANETs)