Peter Rigole

Towards an Extensible Context Ontology for Ambient Intelligence

To realise an Ambient Intelligence environment, it is paramount that applications can dispose of information about the context in which they operate, preferably in a very general manner. For this purpose various types of information should be assembled to form a representation of the context of the device on which aforementioned applications run. To allow interoperability in an Ambient Intelligence environment, it is necessary that the context terminology is commonly understood by all participating devices. In this paper we propose an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms. The ontology has been designed to solve several key challenges in Ambient Intelligence, such as application adaptation, automatic code generation and code mobility, and generation of device specific user interfaces.

Genetic algorithm-based optimization of service composition and deployment

Services running on mobile systems must be able to adapt themselves to changing user needs and availability of resources. We propose to use Genetic Algorithms to search for the best service variant in the current context. The chosen service composition is then deployed on a set of available nodes in an optimal way. We illustrate that Genetic Algorithms provide a scalable and self-organizing solution to service composition and deployment. We argue that the approach meets some main requirements demanded by services running on mobile systems. A motivating scenario is presented in which a distributed server allows users to share content and run applications in mobile ad-hoc networks.

Using Jini to Integrate Home Automation in a Distributed Software-System

The last few years, a tendency arose to integrate various programmable devices through ever expanding computer networks. One particular domain in which this evolution stood out clearly is home automation. The EIB standard defines a home automation solution that consists of a network of cooperating components with little computational power. Bridging the EIB network with a computer network allows software to interact with these EIB components. However, past attempts to link EIB components with computer networks fell short in dynamism, automatism or user friendliness. In this paper we present a distributed software framework that overcomes these issues, and is capable of automatically generating a software model based on the configuration of the components on the EIB fieldbus. A framework that can be used in this perspective is Jini. Its excellent capabilities for dynamic reconfiguration and its proven deployment in domestic and office environments make it an appropriate candidate for supporting home automation systems.

Task-driven automated component deployment for ambient intelligence environments

This article presents a strategy for deploying component-based applications gradually in order to match the functionality of pervasive computing applications onto the current needs of the user. We establish this deployment strategy by linking component composition models with task models at design-time, from which a run-time deployment plan is deduced. Enhanced with a Markov model, this deployment plan is able to drive a component life cycle manager to anticipate future deployments. The result is a seamless integration of pervasive computing applications with the users tasks, guaranteeing the availability of the required functionality without wasting computing resources on components that are not currently needed.

Mobile adaptive tasks guided by resource contracts

This paper proposes a way to realize the idea of calm computing by adding a dynamic task model into the pervasive computing environment. This task model contains information about the actions to undertake to help a user realize his daily tasks. The task models mapping onto a deployment plan guides an internal adaptation mechanism, which helps applications to evolve without causing user distraction. In addition, a foraging technique (relocation) is proposed that allows for expanding an applications computing space automatically whenever possible. This technique involves external adaptation mechanisms. Both adaptation mechanisms are driven by resource information and resource contracts that are negotiated between the middleware and the application components. This allows the middleware to do the adaptations automatically, realizing the idea of calm computing.

Bluetooth enabled interaction in a distributed camera surveillance system

This paper reports on the challenge to extend an existing distributed camera surveillance security application with Bluetooth driven wireless communication using handheld PCs. The main focus is on the engineering aspect of the development of BlueGuard. The paper gives an in-depth assessment of the chosen open software used and the choices made, on how Bluetooth support was designed in the BlueGuard application, and on how was coped with the technical difficulties encountered while implementing spontaneous ad hoc connections between handheld and PC by wireless PANs. Several new software components of BlueGuard are designed in such a way that they can be re-used in other applications for setting up ad hoc connections over IP. The distributed camera surveillance security application was built using the SEESCOA (Software Engineering for Embedded Systems using a Component Oriented Approach) component-oriented methodology and is an example of re-use and modularity.

COCONES: an approach for components and contracts in embedded systems

This chapter presents CoConES (Components and Contracts for Embedded Software), a methodology for the development of embedded software, supported by a tool chain. The methodology is based on the composition of reusable components with the addition of a contract principle for modeling non-functional constraints. Non-functional constraints are an important aspect of embedded systems, and need to be modeled explicitly. The tool chain contains CCOM, a tool used for the design phase of software development, coupled with Draco, a middleware layer that supports the component-based architecture at run-time.

Resource-Driven Collaborative Component Deployment in Mobile Environments

Two techniques are presented in this paper that address the issue of hosting applications in a ubiquitous computing space: (1) context-aware component deployment and (2) self-adaptive component deployment configurations. The former focuses on deploying an application, which is composed of fine-grained components, in a mobile distributed computing environment based on resource information that is spread through context-data. The latter focuses on letting the component deployment configuration adapt itself when the resources in the environment change due to device mobility. Both techniques are solved using a single mechanism that is based on an extended version of collaborative reinforcement learning (CRL) in the design of our component deployment framework. In order to find local optimal solutions for deploying a component-based application in a distributed resource constrained environment, the CRL method balances the resource consumption of components on several nodes that are in each others proximity

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.