Learning-based Coordination of Distributed Component Deployment

Self-organizing and resource-aware component deployment is an important feature of mobile pervasive systems. Distributed resources must be dynamically allocated to software components to ensure QoS demands and not distracting the user. In this paper, we propose a Reinforcement Learning technique to optimize distributed component deployment and migration. We argue that the approach meets some main requirements demanded by applications running on mobile systems. A motivating scenario is presented in which a distributed application server allows users to share content and run applications in mobile ad-hoc networks

Context-Aware Service Selection with Uncertain Context Information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. These are services whose description is enriched with context information related to the service execution environment and adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems context information is naturally dynamic, uncertain and incomplete, which represents an important issue when comparing service description and user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In order to handle uncertain and incomplete context information, we propose a mechanism inspired by graph-comparison for matching contextual service descriptions using similarity measures that allow inexact matching. Service description and requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole. We show how the proposed mechanism is integrated in MUSIC, an existing adaptation middleware, and how it enables more optimal adaptation decision making

Context-based Grouping and Recommendation in MANETs

International audienceWe propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user

Middleware for the Internet of Things, Design Goals and Challenges

As the number of wireless devices increases and their size becomes smaller, there can be more interaction between everyday objects of our life. With advances in RFID chips and the introduction of new generations of these devices that are smaller and cheaper, it is possible to put a wireless interface on almost all everyday objects: vehicles, clothes, foodstuffs, etc. This concept is called the \textit{Internet of Things}. Interaction with thousands of wireless devices leads to a continuous and massive flow of events which are generated spontaneously. The question of how to deal with this enormous number of events is challenging and introduces new design goals for a communication mechanism. In this paper we argue that a middleware together with suitable linguistic abstractions is a proper solution. We also point out the challenges in developing this middleware. Moreover, we give an overview of recent related work and describe why they fail to address these challenges

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

Efficient Prediction of Future Context for Proactive Smart Systems (Efficiënte voorspelling van toekomstige context voor proactieve intelligente systemen)

Many current context-aware adaptive systems only react to the current situations and context changes as they occur. A major concern is that systems may respond too late. Sensing of and reasoning with context information takes time, leading to applications reacting on outdated context information. Therefore, in order to anticipate and adapt proactively and timely, these systems should be aware of the most likely future situations so that the context processing delay is mitigated. Since a prediction can be wrong and the cost of adapting based on a wrong prediction can be high in terms of wasted resources and user annoyance, quality metrics for predicted context information are needed. Being able to assess the quality of the predicted context is a key requirement for applications to effectively use future context.In this dissertation, an overall approach to context prediction is proposed which stresses the importance of incorporating domain knowledge to achieve efficient context prediction. We present context predictor components and prediction quality metrics to evaluate the probability of future situations. These components are integrated in a generic context middleware, providing a methodology, an interface and appropriate runtime mechanisms to support the developer in realizing anticipatory context-aware distributed applications. A selection mechanism inspired by context-aware service selection automatically decides which predictor in the network is the most suited according to application requirements and the quality attributes of the different context predictors. To further improve anticipatory behavior, a context-based grouping mechanism allows for efficiently distributing the context information among the subscribers. All together, this research presents a framework that addresses the needs of the developer aiming to build context-aware applications that realize proactive behavior with regard to past, present and future context.nrpages: 146status: publishe

Opinion nets for reasoning with uncertain context information

Context-aware systems must be able to deal with uncertain context information. We propose a generic context architecture and representation that incorporates the uncertainty of context elements in terms of upper and lower bounds of probabilities. It is shown how opinion nets can be used to reason with these upper and lower bound prob- abilities. In this way it is possible to combine ambiguous or conflicting context information that comes from different sources. Moreover, information coming from different sources can be combined with experience learned from the past in a clean way.status: publishe

Efficient context prediction for decision making in pervasive health care environments: a case study

Mobile real-time Decision Support Systems (DSSs) find themselves deployed in a highly dynamic environment. Decision makers must be assisted taking into account various time-critical requirements. Perhaps even more important, the quality of the support given by the system depends heavily on the knowledge of the current and future context of the system. A DSS should exhibit inherent proactive behavior and automatically derive the decision making person's needs for specific information from the context that surrounds him. We propose to run a DSS on top of a middleware that helps the decision maker to contextualize information. Moreover, we give a set of requirements the middleware should fulfill to learn, detect and predict patterns in context to optimize the information flow to the decision making person. The approach is made concrete and validated in a case study in the domain of medical health care. Representative location prediction algorithms are evaluated using an existing dataset.edition: 1status: publishe

Context grouping and distribution in the MUSIC middleware

Context distribution is a key aspect in mobile and ubiquitous computing environments. The successful adaptation of applications depends on the availability of context information, which is disseminated over the network. Only a fraction of all available context information is required by the adaptation mechanisms. Moreover, for privacy reasons, it is important to limit the scope of context dissemination. We propose a context grouping mechanism which allows the definition of groups based on context information and which provides a low-level privacy mechanism.status: publishe