A Hybrid Context Model for Pervasive Healthcare System

2011 ◽  
Vol 474-476 ◽  
pp. 531-536
Author(s):  
Shou Ming Ma ◽  
Ru Chuan Wang ◽  
Ning Ye
Keyword(s):  
Pervasive Computing ◽  
The Elderly ◽  
Application Framework ◽  
Context Model ◽  
Context Management ◽  
Pervasive Healthcare ◽  
Context Reasoning ◽  
Context Modelling ◽  
Context Data ◽  
Modelling Approach

Pervasive computing applications aim to provide appropriate services that respond directly to their users and environments, with greatly reduced explicit human guidance. These applications usually adapt to changing failure-prone context information which was acquired from various sources that differ in quality and format. To efficiently acquire, share, correlate, and reason over raw existing context data, they must be modeled in a homogenous fashion. In this paper, we propose a hybrid context modelling approach, which attempt to integrate the advantages of object-oriented model and ontology-based model for their distributed context handling and efficient context reasoning respectively. We have applied this model to the development of a context management middleware which providing an extensible application framework for monitoring and assisting the elderly at home environments.

Medical Data Visualization via a Pervasive Multi-Agent Platform

2009 ◽  
Vol 1 (4) ◽  
pp. 68-81
Author(s):  
Antonio Coronato ◽  
Luigi Gallo ◽  
Giuseppe De Pietro
Keyword(s):  
Pervasive Computing ◽  
Medical Data ◽  
Volume Data ◽  
Software Infrastructure ◽  
Pervasive Healthcare ◽  
Medical Practitioners ◽  
Agent Platform ◽  
Computing Paradigm ◽  
Multi Agent ◽  
Pervasive Access

Pervasive healthcare is the field of application emerging from the combination of healthcare with pervasive computing, which is the computing paradigm that provides users with access to services in a transparent way, wherever they are and whichever their interacting device is. In this paper, a software infrastructure for pervasive healthcare is presented. Such an infrastructure aims at supporting medical practitioners with advanced pervasive access to medical data, which is also context-aware in the sense that the modality to fruit data depends on the device used by the operator and on his or her physical position within the environment. The paper also describes a service for high quality 3D rendering of medical volume data, which takes advantage of the software infrastructure to distribute the computational load upon the devices available in the environment in a completely transparent way to users.

Context Modelling Approaches for Mobile Systems

2011 ◽  
pp. 364-378
Author(s):  
Danilo Avola
Keyword(s):  
Mobile Devices ◽  
Human Life ◽  
Mobile Systems ◽  
Suitable Model ◽  
Context Modeling ◽  
Web Resources ◽  
Context Modelling ◽  
Context Data ◽  
Complex Activities ◽  
The Web

The actual mobile technology and the increasing need to obtain rich multimedia content about each and every aspect of the human life are changing the approach of the users to the World Wide Web. Indeed, the pervasive use of mobile devices and the heterogeneity of the provided services and information make the accessibility and usability of the Web resources a hard assignment. In particular two main tasks have been identified as focal issues, the first one regards the choose of a suitable model to express the complex activities of the Web (context modeling approaches), and the second one regards the translation of the different schemas, representing these Web activities, in a more suitable, manageable and standardizing schema. In this chapter we will present the problems related to the modeling of context data, and we will describe the actual and future approaches of Context Modeling according to the mobile devices world.

Spatiotemporal Traffic Analysis using Big Data

2020 ◽  
pp. 37-40
Author(s):  
Anandakumar H ◽  
Abishek Sailesh ◽  
Muthumeenal C ◽  
Visalakshi S ◽  
Muthumani K
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Classifier ◽  
Convergence Time ◽  
Traffic Patterns ◽  
Context Model ◽  
Real Time Traffic ◽  
Context Data

In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different traffic and context model form distributed traffic patterns. One major challenge in predicting traffic is how much to rely on the prediction model constructed using historical data in the real-time traffic situation, which may differ from that of the historical data due to the fact that traffic situations are numerous and changing over time. The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together to improve the learning accuracy of classifier. The conducted experimental results on prediction of traffic dataset prove that the proposed algorithm significantly outperforms the existing algorithm.

scholarly journals ON THE CHALLENGE OF SERVICE RECOMMENDATION TO MOBILE USERS IN SMART CITIES: CONTEXT AND ARCHITECTURE

2020 ◽  
Vol XLIV-4/W2-2020 ◽  
pp. 1-8
Author(s):  
A. Ameur ◽  
S. Ichou ◽  
S. Hammoudi ◽  
A. Benna ◽  
A. Meziane
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Mobile Services ◽  
Mobile User ◽  
Mobile Users ◽  
Service Recommendation ◽  
Context Model ◽  
Wide Range ◽  
Context Data ◽  
The Right

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.

scholarly journals An Adaptable Context Management Framework for Pervasive Computing

2010 ◽  
pp. 276-308 ◽  
Author(s):  
Jared Zebedee ◽  
Patrick Martin ◽  
Kirk Wilson ◽  
Wendy Powley
Keyword(s):  
Web Services ◽  
Pervasive Computing ◽  
Context Awareness ◽  
Management System ◽  
Autonomic Computing ◽  
Context Management ◽  
Management Framework ◽  
Intermittent Connectivity ◽  
Computing Paradigm ◽  
Pervasive Environment

Pervasive computing presents an exciting realm where intelligent devices interact within the background of our environments to create a more intuitive experience for their human users. Context-awareness is a key requirement in a pervasive environment because it enables an application to adapt to the current situation. Context-awareness is best facilitated by a context management system that supports the automatic discovery, retrieval and exchange of context information by devices. Such a system must perform its functions in a pervasive computing environment that involves heterogeneous mobile devices which may experience intermittent connectivity and resource and power constraints. The objective of the chapter is to describe a robust and adaptable context management system. We achieve an adaptable context management system by adopting the autonomic computing paradigm, which supports systems that are aware of their surroundings and that can automatically react to changes in them. A robust context management system is achieved with an implementation based on widely accepted standards, specifically Web services and the Web Services Distributed Management (WSDM) standard.

Spontaneous Service-Providing using WS4D in Smart Environments

2014 ◽  
Vol 6 (4) ◽  
pp. 71-87
Author(s):  
Mohammed Fethi Khalfi ◽  
Sidi Mohamed Benslimane
Keyword(s):  
Pervasive Computing ◽  
Service Discovery ◽  
User Preferences ◽  
Formal Context ◽  
Context Model ◽  
Embedded Devices ◽  
Generic Level ◽  
Specific Level ◽  
Domain Specific ◽  
Service Discovery And Selection

Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. Spontaneous service discovery and selection is one of the most important fields of research in pervasive computing. In this paper the authors will present an enhancement of ubiquitous computing discovery mechanisms adding context handling capabilities to Web Services for Devices in Pervasive Computing using UPnP as an infrastructure to address these implicit requests. User preferences, network and location are described by a formal context model ontology that is based on two levels: a generic level and a domain specific level. As compared with previous research, the authors' method uses location aware, UPnP infrastructure, web service for devices and the notion of proactivity in pervasive computing to continuously present the Spontaneous most relevant services to the user or device in response to changes of context, services or user preferences.

Sign in / Sign up

Export Citation Format

Share Document