A Survey on Service Composition Middleware in Pervasive Environments

The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments

Interoperating Context Discovery Mechanisms

Context-Aware applications adapt their behaviour to the current situation of the user. This information, for instance user location and user availability, is called context information. Context is delivered by distributed context sources that need to be discovered before they can be used to retrieve context. Currently, multiple context discovery mechanisms exist, exhibiting heterogeneous capabilities (e.g. communication mechanisms, and data formats), which can be available to context-aware applications at arbitrary moments during the ap-plication’s lifespan. In this paper, we discuss a middleware mechanism that en-ables a (mobile) context-aware application to interoperate transparently with different context discovery mechanisms available at run-time. The goal of the proposed mechanism is to hide the heterogeneity and availability of context discovery mechanisms for context-aware applications, thereby facilitating their development

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Towards runtime discovery, selection and composition of semantic services

Service-orientation is gaining momentum in distributed software applications, mainly because it facilitates interoperability and allows application designers to abstract from underlying implementation technologies. Service composition has been acknowledged as a promising approach to create composite services that are capable of supporting service user needs, possibly by personalising the service delivery through the use of context information or user preferences. In this paper we discuss the challenges of automatic service composition, and present DynamiCoS, which is a novel framework that aims at supporting service composition on demand and at runtime for the benefit of service end-users. We define the DynamiCoS framework based on a service composition life-cycle. Framework mechanisms are introduced to tackle each of the phases and requirements of this life-cycle. Semantic services are used in our framework to enable reasoning on the service requests issued by end users, making it possible to automate service discovery, selection and composition. We validate our framework with a prototype that we have built in order to experiment with the mechanisms we have designed. The prototype was evaluated in a testing environment using some use case scenarios. The results of our evaluation give evidences of the feasibility of our approach to support runtime service composition. We also show the benefits of semantic-based frameworks for service composition, particularly for end-users who will be able to have more control on the service composition process

Intention Prediction Mechanism In An Intentional Pervasive Information System

International audienceNowadays, the development of pervasive technologies has allowed the improvement of services availability. These services, offered by information systems (IS), are becoming more pervasive, i.e., accessed anytime, anywhere. However, those pervasive information systems (PIS) remain too complex for the user, who just wants a service satisfying his needs. This complexity requires considerable efforts from the user in order to select the most appropriate service. Thus, an important challenge in PIS is to reduce user's understanding effort. In this chapter, we propose to enhance PIS transparency and productivity through a user-centred vision based on an intentional approach. We propose an intention prediction approach. This approach allows anticipating user's future requirements, offering the most suitable service in a transparent and discrete way. This intention prediction approach is guided by the user's context. It is based on the analysis of the user's previous situations in order to learn user's behaviour in a dynamic environment

Configuration of smart environments made simple combining visual modeling with semantic metadata and reasoning

We present an approach that combines semantic metadata and reasoning with a visual modeling tool to enable the goal-driven configuration of smart environments for end users. In contrast to process-driven systems where service mashups are statically defined, this approach makes use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of the presented system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support end users in expressing their goals, we integrated a visual programming tool with our system. This tool enables users to model the desired state of their smart environment graphically and thus hides the technicalities of the underlying semantics and the reasoning. Possible applications of the presented system include the configuration of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains