Task-driven automated component deployment for ambient intelligence environments

Rigole, Peter; Clerckx, Tim; Berbers, Yolande; Coninx, Karin

doi:10.1016/j.pmcj.2007.01.001

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…For instance, Chantzara et al [13] and Takemoto et al [63] studied service provisioning issues, Bertolino et al [5] and Butford et al [10] tackled service validation and trust issues, Kalasapur et al [26] targeted optimization of service communication paths, Nakazawa et al [39] focused on automatic generation of application code, Rigole et al [51,52] addressed distributed user interface deployment, Mungellini et al [36] applied composition to fast-prototype multimodal applications, Sousa et al, Ben Mokhtar et al, Cang et al and Lindenberg et al [4,12,30,61] introduced specification languages for querying and describing application composition, and finally, Sousa et al [62] and Paluska et al [43] introduced design styles for developing adaptive applications through composition. A number of authors surveyed application composition approaches in ubiquitous computing.…”

Section: Related Workmentioning

confidence: 99%

iCompose: context-aware physical user interface for application composition

Davidyuk

Gilman

Milara

et al. 2011

Open Computer Science

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Ubiquitous computing environments advocate creating applications by plugging together various resources (mobile devices, displays, augmented everyday objects, and so on) and Web Services to support the user's everyday activities and needs. This approach is referred to as application composition, and such applications are called composite. Due to the dynamic nature of ubiquitous environments, application composition has to be supported at runtime, so that the applications are able to adapt to the situation within the environment and other contexts. Application composition is usually performed by autonomous context-aware mechanisms that provide limited or no control for users. Still, users need to be aware of their environments and be able to control and configure applications when they are composed and executed. Towards this goal we present in this article a context-aware application composition system based on the iCompose interface for composing and controlling applications at runtime. Users compose applications by simply touching the resources in the environment with their mobile devices, while the iCompose interface provides feedback and assists users by suggesting them possible further actions. The interface relies on a rule-based reasoner and utilizes various context sources to support users dynamically, according to the situation in which they compose applications. We present a complete implementation of the system and report the results of a user evaluation study conducted with 21 participants. This study assesses the issues of control, usability, feasibility and user acceptance of the iCompose interface for context-aware application composition and the prototype as a whole.Keywords: ubiquitous computing • interaction design • physical user interface design • application composition © Versita Sp. z o.o.

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Section: Related Workmentioning

confidence: 99%

iCompose: context-aware physical user interface for application composition

Davidyuk

Gilman

Milara

et al. 2011

Open Computer Science

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“…The CoDAMos project proposed a model-based approach that enables architecture-based dynamic component deployment [10,11]. In this project, the deployment strategy was established by linking component composition models with task models in order to deduce a run-time deployment plan.…”

Section: Task-driven Computingmentioning

confidence: 99%

A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Park

Koo

2011

Softw Pract Exp

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SUMMARY Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.

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“…An Ambient Intelligence (AmI) environment is a surrounding computing space that is spontaneously and proactively supporting the user's needs (Rigole et al (2007)). The principle (Vasilakos and Pedrycz (2006)) is to integrate different computer science backgrounds together with psychology and social sciences in order to create a network of intelligent devices (with sensors and actuators) able to enhance the quality of people life.…”

Section: Ambient Intelligence In Work Environmentsmentioning

confidence: 99%