2,833 research outputs found

    Context Aware Computing for The Internet of Things: A Survey

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    As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

    Service adaptation using fuzzy theory in context-aware mobile computing middleware

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    2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

    A QoS-Control Architecture for Object Middleware

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    This paper presents an architecture for QoS-aware middleware platforms. We present a general framework for control, and specialise this framework for QoS provisioning in the middleware context. We identify different alternatives for control, and we elaborate the technical issues related to controlling the internal characteristics of object middleware. We illustrate our QoS control approach by means of a scenario based on CORBA

    Integrated Support for Handoff Management and Context-Awareness in Heterogeneous Wireless Networks

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    The overwhelming success of mobile devices and wireless communications is stressing the need for the development of mobility-aware services. Device mobility requires services adapting their behavior to sudden context changes and being aware of handoffs, which introduce unpredictable delays and intermittent discontinuities. Heterogeneity of wireless technologies (Wi-Fi, Bluetooth, 3G) complicates the situation, since a different treatment of context-awareness and handoffs is required for each solution. This paper presents a middleware architecture designed to ease mobility-aware service development. The architecture hides technology-specific mechanisms and offers a set of facilities for context awareness and handoff management. The architecture prototype works with Bluetooth and Wi-Fi, which today represent two of the most widespread wireless technologies. In addition, the paper discusses motivations and design details in the challenging context of mobile multimedia streaming applications

    Robotic ubiquitous cognitive ecology for smart homes

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    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
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