Ontologies for the Internet of Things

International audienceChallenges the Internet of Things (IoT) is facing are directly inherited from today's Internet. However, they are amplified by the anticipated large scale deployments of devices and ser- vices, information flow and involved users in the IoT. Chal- lenges are many and we focus on addressing those related to scalability, heterogeneity of IoT components, and the highly dynamic and unknown nature of the network topology. In this paper, we give an overview of a service-oriented middle- ware solution that addresses those challenges using semantic technologies to provide interoperability and flexibility. We especially focus on modeling a set of ontologies that describe devices and their functionalities and thoroughly model the domain of physics. The physics domain is indeed at the core of the IoT, as it allows the approximation and estimation of functionalities usually provided by things. Those function- alities will be deployed as services on appropriate devices through our middleware

Service-Oriented Middleware for the Mobile Internet of Things: A Scalable Solution

International audienceThe Internet of Things (IoT) is characterized by a wide penetration in the regular user's life through an increasing number of mobile Things, such as mobile phones hosting sensors and actuators. However, the shift to the mobile IoT does not come without challenges, as many already existing issues remain unresolved and are amplified by the IoT scale and the mobility of its Things. The most challenging issues are handling the abundance of users and Things, providing interoperability across the heterogeneous Things, and overcoming the unknown dynamic environment due to the mobility of Things. This paper addresses the above challenges as we revisit the commonly used Service-Oriented Architecture (SOA). This leads to the design, implementation and evaluation of MobIoT, a new service-oriented middleware. MobIoT modifies standard SOA functionalities, namely service discovery, composition and access, to better address the challenges posed by the IoT, especially its scale. Specifically, MobIoT adopts probabilistic methods to decrease the number of involved devices, while building on semantic knowledge to support interoperability and fulfill users' queries for Thing-based measurements/actions

Runtime Quantitative Verification of Self-Adaptive Systems

Software systems used in mission- and business-critical applications in domains including defence, healthcare, and finance must comply with strict dependability, performance, and other Quality-of-Service (QoS) requirements. Self-adaptive systems achieve this compliance under changing environmental conditions, evolving requirements and system failures by using closed-loop control to modify their behaviour and structure in response to these events. Runtime quantitative verification (RQV) is a mathematically-based approach that implements the closed-loop control of self-adaptive systems. Using runtime observations of a system and its environment, RQV updates stochastic models whose formal analysis underpins the adaptation decisions made within the control loop. The approach can identify and, under certain conditions, predict violation of QoS requirements, and can drive self-adaptation in ways guaranteed to restore or maintain compliance with these requirements. Despite its merits, RQV has significant computation and memory overheads, which restrict its applicability to small systems and to adaptations affecting only the configuration parameters of the system. In this thesis, we introduce RQV variants that improve the efficiency and scalability of the approach and extend its applicability to larger and more complex self-adaptive software systems, and to adaptations that modify the structure of a system. First, we integrate RQV with established efficiency improvement techniques from other software engineering areas. We use caching of recent analysis results, limited lookahead to precompute suitable adaptations for potential future changes, and nearly-optimal reconfiguration to eliminate the need for an exhaustive analysis of the entire reconfiguration space. Second, we introduce an RQV variant that incorporates evolutionary algorithms into the RQV process facilitating the efficient search through large reconfiguration spaces and enabling adaptations that include structural changes. Third, we propose an RQV-driven approach that decentralises the control loops in distributed self-adaptive systems. Finally, we devise an RQV-based methodology for the engineering of trustworthy self-adaptive systems. We evaluate the proposed RQV variants using prototype self-adaptive systems from several application domains, including an embedded system for unmanned underwater vehicles and a foreign exchange service-based system. Our results, subject to the adaptation scenarios used in the evaluation, demonstrate the effectiveness and generality of the new RQV variants

CHOReOS Middleware Specification (D3.1)

This deliverable specifies the main concepts of the CHOReOS middleware architecture. Starting from the Future Internet (FI) challenges for scalability, heterogeneity, mobility, awareness, and adaptation that have been investigated in prior work done in WP1, we introduce the aforementioned concepts to deal with the requirements derived from the FI challenges. In particular, we propose an extensible and scalable service discovery approach for the organization and discovery of services that relies on multiple service discovery protocols. Moreover, we introduce an extensible and scalable approach, based on the service bus paradigm, for service access that features the integration and adaptation of multiple interaction protocols. Furthermore, we propose solutions that enable the execution of FI service compositions that range from compositions of choreographed services, developed according to the CHOReOS development process, to massive compositions of things. Finally, we detail the Cloud & Grid middleware facilities that support the overall middleware and the choreographies that are built on it, via a unified API that provides access to multiple cloud infrastructures (e.g., Amazon EC2, HP Open Cirrus, private clouds)