Context for Ubiquitous Data Management

In response to the advance of ubiquitous computing technologies, we believe that for computer systems to be ubiquitous, they must be context-aware. In this paper, we address the impact of context-awareness on ubiquitous data management. To do this, we overview different characteristics of context in order to develop a clear understanding of context, as well as its implications and requirements for context-aware data management. References to recent research activities and applicable techniques are also provided

HiCAS (Highway Context Awareness System): Information Delivery On Highway Facilities –A Preliminary Study

Context information is the information that documents the relationships of the content Information to its environment. Storage of such information can define how effective the deliverance of retrieving information. This research tries to create an on-time alert information for highway user comfort and safety measures in supporting their pleasure journey. Computer application basic inputs, such as keyboard strokes or pointing devices, supply only limited information about the surrounding highway environment, and it is not suitable for highway user to attempt the same technology. HiCAS is an attempt to reduce illiterate highway users and produce a core ideas on the approach of educating the highway user through context information technolog

A new framework for ubiquitous context-aware healthcare applications

Nowadays, there is a significant lack of generic application frameworks providing third party developers with the appropriate mechanisms for building ubiquitous contextaware healthcare applications over medical heterogeneous networks. In this paper, we propose a new framework integrating context-aware and security mechanisms with mechanisms that allow the ease exploitation of the core networks’ functionality to enable third party developers to build reliable and secure ubiquitous context-aware healthcare applications over medical heterogeneous networks

Moving forward on u-healthcare: A framework for patient-centric

Delivering remote healthcare services without deteriorating the ‘patient experience’ requires building highly usable and adaptive applications. Efficient context data collection and management make possible to infer extra knowledge on the user’s situation, making easier the design of these advanced ubiquitous applications. This contribution, part of a work in progress which aims at building an operative AmI middleware, presents a generic architecture to provide u-healthcare services, to be delivered both in mobile and home environments. In particular, we address the design of the Context Management Component (CMC), the module that takes context data from the sensing layer and performs data fusion and reasoning to build an aggregated ‘context image’. We especially explain the requirements on data modelling and the functional features that are imposed to the CMC. The resulting logical multilayered architecture -composed by acquisition and fusion, inference and reasoning levels- is detailed, and the technologies needed to develop the Context Management Component are finally specifie

Context modelling for just-in-time mobile information retrieval (JIT-MobIR)

Mobile users have the capability of accessing information anywhere at any time with the introduction of mobile browsers and mobile web search. However, the current mobile browsers are implemented without considering the characteristics of mobile searches. As a result, mobile users need to devote time and effort in order to retrieve relevant information from the web in mobile devices. On the other hand, mobile users often request information related to their surroundings, which is also known as context. This recognizes the importance of including context in information retrieval. Besides, the availability of the embedded sensors in mobile devices has supported the recognition of context. In this study, the context acquisition and utilization for mobile information retrieval are proposed. The "just-in-time" approach is exploited in which the information that is relevant to a user is retrieved without the user requesting it. This will reduce the mobile user's effort, time and interaction when retrieving information in mobile devices. In this paper, the context dimensions and context model are presented. Simple experiments are shown where user context is predicted using the context model

Energy conservation in mobile devices and applications: A case for context parsing, processing and distribution in clouds

Context information consumed and produced by the applications on mobile devices needs to be represented, disseminated, processed and consumed by numerous components in a context-aware system. Significant amounts of context consumption, production and processing takes place on mobile devices and there is limited or no support for collaborative modelling, persistence and processing between device-Cloud ecosystems. In this paper we propose an environment for context processing in a Cloud-based distributed infrastructure that offloads complex context processing from the applications on mobile devices. An experimental analysis of complexity based context-processing categories has been carried out to establish the processing-load boundary. The results demonstrate that the proposed collaborative infrastructure provides significant performance and energy conservation benefits for mobile devices and applications

High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /