Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

Operation Prediction for Context-Aware User Interfaces of Mobile Phones

Abstract: Nowadays mobile phones are multifunctional devices that provide us with various useful applications and services anytime and anywhere. However, people are sometimes unable to access an appropriate application due to the complexity and depth of the menu structure. This paper focuses on a feasibility study of operation prediction using observable attributes to realize self-optimization functionality in the mobile phones that can automatically and adaptively change their user interface (UI) according to user characteristics and circumstances. Machine learning (ML) is a promising technology for enhancing UI. However, few studies have been conducted for the operation prediction using the ML framework. We analyzed the real usage data collected by practical mobile phones and found that ML-based prediction methods were feasible to estimate future operations, and to provide context-aware UI.

Trends and challenges in smart healthcare research: A journey from data to wisdom

Abstract: Smart Healthcare is a relatively new context-aware healthcare paradigm influenced by several fields of knowledge, namely medical informatics, communications and electronics, bioengineering, ethics and so on. Thus, many challenging problems are related to smart healthcare but in many cases they are explored individually in their respective fields and, as a result, they are not always known by the smart healthcare research community working in more specific domains. The aim of this article is to identify some of the most relevant trends and research lines that are going to affect the smart healthcare field in the years to come. To do so, the article considers a systematic approach that classifies the identified research trends and problems according to their appearance within the data life cycle, this is, from the data gathering in the physical layer (lowest level) until their final use in the application layer (highest level). By identifying and classifying those research trends and challenges, we help to pose questions that the smart healthcare community will need to address. Consequently, we set a common ground to explore important problems in the field, which will have significant impact in the years to come.

A Knowledge Fusion Approach for Context Awareness in Vehicular Networks

Abstract: Vehicular ad-hoc networks (VANETs) are a challenging Internet of Things scenario. While research is proposing increasingly sophisticated hardware and software solutions for on-board context detection, probably high-level context information sharing has not been adequately addressed so far. This paper proposes a novel logic-based framework enabling a contextual data management and mining in VANETs. It grounds on a knowledge fusion algorithm based on nonstandard, nonmonotonic inference services in Description Logics, adopting standard Semantic Web languages. Ontology-referred context annotations produced by individual VANET nodes are merged with automatic reconciliation of inconsistencies. An efficient information dissemination protocol complements the proposal. The approach has been implemented in a vehicular network simulator and early experimental results proved its effectiveness and feasibility.

Distributed context management in a mobility and adaptation enabling middleware (MADAM)

Abstract: As computing devices are getting smaller, we tend to bring them everywhere. Consequently the operating conditions of the devices are constantly changing (e.g. changing user requirements, change in the system context and environment context). In order to be usable and dependable, applications and services need to self-adapt to changes in context. This work describes a context management approach for reducing the complexity of context aggregation and utilisation. The context manager is a core component in the MADAM (Mobility and ADaptation enAbling Middleware) project.

Towards context-aware and resource-driven self-adaptation for mobile handheld applications

Abstract: Mobile handheld computing is gaining momentum as more and more wireless handheld devices are being used to accomplish various computing tasks. Context-awareness can help to adapt and personalize applications or to optimize resource usage. However, many existing context infrastructures by themselves impose heavy resource requirements for deployment or highly affect the limited autonomy of the mobile device. In this paper we describe how resource-driven self-adaptation is used in our layered context-driven application middleware in order to enable deployment on a mobile device and optimize resource usage. Our evaluation shows that the introspection and intercession capabilities of the self-adapting middleware provide the necessary flexibility to achieve a balanced resource usage between the context-driven application middleware and the context-aware applications, and that the resource-driven self-adaptation is able to more than double the battery lifetime in real life scenarios.