Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Content-aware power saving multimedia adaptation for mobile learning

Due to the tremendous enhancements in the capabilities of mobile devices in recent years and accessibility to higher bandwidth mobile internet, the use of online multimedia learning resources on mobile devices is increasingly becoming popular. Improvements in battery capacity have not matched the same advancements compared to other features of mobile devices. Limited Battery power is introducing a significant challenge in making better use of online educational multimedia resources. Online Multimedia Resources drains more battery power as a result of higher amount of wireless data transfer and therefore limiting learning opportunities on the move. Many power saving multimedia adaptation techniques have been suggested. Majority of these techniques achieve battery efficiency while reducing multimedia quality. So far, however, to the best of our knowledge no previous effort has considered the factor of learning efficacy in multimedia adaptation process. Existing adaptation techniques are susceptible to information loss as a result of quality of reduction. Such loss affects the learning content efficacy and jeopardizes the learning process. In this paper, we recommend a novel power save educational multimedia adaptation approach that considers the learning aspect of multimedia in the adaptation process. Our technique enables learning for extended duration by battery power saving without putting the learning process at risk. Efficacy of entire learning resources is managed by not allowing any part of the learning multimedia to be delivered in a quality that will negatively affect the learning outcome. We also present a framework that guides the implementation of our approach followed by description of our prototype application that uses educational multimedia metadata implemented in semantic web technologies

Policy-based management for body-sensor networks

Accepted versio

A Framework for Quality-Driven Delivery in Distributed Multimedia Systems

In this paper, we propose a framework for Quality-Driven Delivery (QDD) in distributed multimedia environments. Quality-driven delivery refers to the capacity of a system to deliver documents, or more generally objects, while considering the users expectations in terms of non-functional requirements. For this QDD framework, we propose a model-driven approach where we focus on QoS information modeling and transformation. QoS information models and meta-models are used during different QoS activities for mapping requirements to system constraints, for exchanging QoS information, for checking compatibility between QoS information and more generally for making QoS decisions. We also investigate which model transformation operators have to be implemented in order to support some QoS activities such as QoS mapping