A Lightweight Web of Things Open Platform to Facilitate Context Data Management and Personalized Healthcare Services Creation
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Abstract:
In the last few years, many health monitoring systems have been designed to fullfil the needs of a large range of scenarios. Although many of those systems provide good ad hoc solutions, most of them lack of mechanisms that allow them to be easily reused. This paper is focused on describing an open platform, the micro Web of Things Open Platform (µWoTOP), which has been conceived to improve the connectivity and reusability of context data to deliver different kinds of health, wellness and ambient home care services. µWoTOP is based on a resource-oriented architecture which may be embedded in mobile and resource constrained devices enabling access to biometric, ambient or activity sensors and actuator resources through uniform interfaces defined according to a RESTful fashion. Additionally, µWoTOP manages two communication modes which allow delivering user context information according to different methods, depending on the requirements of the consumer application. It also generates alert messages based on standards related to health care and risk management, such as the Common Alerting Protocol, in order to make its outputs compatible with existing systems.Keywords:
Reusability
Context management
Context awareness
유비쿼터스 컴퓨팅 환경은 변화하는 상황에 적응함으로써 사람들에게 더 좋은 서비스를 제공할 수 있다. 본 논문에서는 유비쿼터스 프로그램을 정해진 규칙에 따라 변화하는 상황에 적응시키는 상황 적응 시스템을 개발하였다. 본 시스템은 개발자로 하여금 상황 적응 정책만을 간단히 기술하게 함으로써 프로그램을 변화하는 상황에 적응시킬 수 있도록 해준다. 본 시스템의 상황 적응 엔진은 상황 적응 정책에 따라 유비쿼터스 프로그램을 변화하는 상황에 적응시킨다. 또한 본 시스템은 개발자로 하여금 가상으로 자신의 유비쿼터스 프로그램을 변화하는 상황에 적응시켜 볼 수 있도록 하기 위하여 시뮬레이터를 제공한다. 본 시스템은 자바 기반 상황 인식 프로그래밍 프레임워크인 JCAF 위에서 구현되었다. The ubiquitous computing environment could provide better service to users by adapting to changing contexts. In this paper, we developed a context adaptation system, which enables an ubiquitous program to adapt to different contexts, following its adaptation rules. Using this system, programmers can develop ubiquitous programs suitable for changing contexts, by describing the context adaptation policy. The context adaptation engine of this system fits the ubiquitous program to the current context based on the context adaptation rules. This system was implemented using JCAF, context-aware programing framework based on java. A simulator is also provided to simulate ubiquitous programs by changing contexts.
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With the rapid development of the information technology,it is inevitable that the distributed mobile computing will evolve to the pervasive computing gradually whose final goal is fusing the information space composed of computers with the physical space in which the people are working and living in.To achieve this goal,one of the problems is how to continuously monitor/capture and interpret the environment related information efficiently to assure high context awareness.Many attentions have been paid to the research of the context-aware pervasive applications.However,most of them just use the raw context directly or take just some aspects of the Quality of Context(QoC) into account.Therefore,we proposed a middleware based context-aware framework that supports QoC management in various layers.By this framework we can refine raw context,discard duplicate and inconsistent context so as to protect and provide QoS-enriched context information of users to context-aware applications and services.
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Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Now a days, vehicles have become an increasingly important and exciting test bed for ubiquitous computing (UbiComp), however, context-aware vehicle service to enable high adaptation of service to driver, vehicle or even road in ubiquitous environment is still little addressed in the literature. The context management in pervasive computing environments must reflect the specific characteristics of these environments, e.g. mobility, resource-constrained devices, or heterogeneity of context sources. Although a number of context models have been presented in the literature, none of them supports all of these requirements to a sufficient extent at the same time. This paper focuses on building an ontology modeling approach for Context Management in Intelligent Pervasive Middleware for Context-Aware Vehicle Services. To support contextawareness, we embed capabilities of context modeling and context reasoning in an ontology-based context system, which focuses on management of the context and generates a consistent model which promises the common information representation and facilitates a development of context-aware services.
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Context awareness is one of the key aspects of pervasive computing systems. In such systems, a plethora of dynamic context information needs to be constantly retrieved, soundly interpreted, rapidly processed, maintained in various repositories, and securely disseminated. Thus, a flexible, scalable and interoperable context representation scheme needs to be established and solid context management mechanisms need to be adopted, which will perform well in large‐scale distributed pervasive systems. This paper elaborates on the COMPACT context middleware that has been designed to cope with the issues above and saturate pervasive computing environments with context awareness functionality.
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With the rapid development of the information technology, it is inevitable that the distributed mobile computing will evolve to the pervasive computing gradually whose final goal is fusing the information space composed of computers with the physical space in which the people are working and living in. To achieve this goal, one of the problems is how to continuously monitor/capture and interpret the environment related information efficiently to assure high context awareness. Many attentions have been paid to the research of the context-aware pervasive applications. However, most of them just use the raw context directly or take just some aspects of the Quality of Context (QoC) into account. Therefore, we propose a middleware based context-aware framework that support QoC management in various layers. By this framework we can refinery raw context, discard duplicate and inconsistent context so as to protect and provide QoS-enriched context information of users to context-aware applications and services.
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Pervasive computing (also referred to as ubiquitous computing or ambient intelligence) aims to create environments where computers are invisibly and seamlessly integrated and connected into our everyday environment. Pervasive computing applications are often interaction transparent, context aware, and experience capture and reuse capable. Interaction transparency means that the human user is not aware that there is a computer embedded in the tool or device that he or she is using. Context awareness means that applications and services should be aware of their contexts and automatically adapt to their changing contexts-known as context-awareness. An experience capture and reuse capable application can remember when, where, and why something was done and can use that information as input to solve new tasks. Context, as defined by Anind K. Dey in 2001, is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including location, time, activities, and the preferences of each entity. A system is context-aware if it can extract, interpret and use context information and adapt its functionalities to the current context of use. In a pervasive environment, several middleware have been proposed to manage context information such as the Context Toolkit, Aura, Amigo, etc.... In these environments, Context-aware applications adapt their behaviour using Event-Condition-Action (ECA) rules, also referred to as adaptation rules. An ECA rule defines an action to be performed as a reaction to some event under a certain condition. The event part refers to context changes, the condition part to the current context and the action part to an adaptive behaviour. After studying several adaptation works in a pervasive environment we can conclude that the adaptation conducted until now is only supported by static rules in a way that they were specified by developers during the implementation phase and the applications behavior was restricted to the actions specified in that rules. However, in such an environment, some of the adaptation rules change naturally over time, particularly those related to the preferences of a human user. Indeed, the later can decide to perform a particular action in a specific context at a given time and act differently against the same context at a later time. Consequently, the predefined static adaptation rules can't support this frequent change of users' preferences and thus, it is difficult to provide the users with the automatic personalized services. Hence, our work aims to propose an approach for adaptation to the dynamic changing context in pervasive environments. The principle of our approach is to distinguish static and dynamic adaptation rules based on the user preferences. We use the history of users interactions to predict their future behavior and thus to define new adaptation rules or update the existing ones. Our solution combines machine learning techniques, specifically the Case Based Reasoning method (CBR) and data mining techniques to extract and update rules. We are actually at the stage of implementing our approach which we consider to be giving a new impetus to research in the field of adaptation and personalization in pervasive environments.
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본 논문에서 제안한 시스템은 유비쿼터스 컴퓨팅 환경에서 사용자에게 상황 인식 서비스와 주변 환경에 지능적인 상호 작용을 제공한다. 따라서, 유비쿼터스 컴퓨팅의 다양한 상황과 환경에서 시스템은 컨텍스트 정보를 수집과 분석을 하기 위해 상황 인식 능력을 필요 하게 된다. 그러나 현재의 상황 인식 시스템은 다양한 타입에서 컨텍스트의 체계적인 이용에 부족하고, 단지 몇몇 시스템들은 한정된 환경에서 각자 사용자의 선호도를 획득하는 학습 매커니즘을 가진다. 본 논문에서는, 유비쿼터스 컴퓨팅 환경에서 상황 인식을 위한 개괄적인 프레임워크를 제안한다. 본 프레엄워크는 다양한 센서로부터 컨텍스트의 다양한 타입을 쉽게 이용하고 생성할 수 있게 만들어졌다. 프레엄워크는 동적 환경에서 사용자에게 서비스를 제공하기 위하여 센서, 통합, 추론 그리고 학습 컨텍스트 정보를 제공한다. 그리고 사용자의 건강을 실시간으로 관리할 수 있는 u-Health 시스템에 제안한 프레임워크를 적용을 하여 구현을 하였다. The systems in the ubiquitous computing environment need to provide users with context-aware services, intelligently interacting with the surrounding environment. Therefore, the systems in the ubiquitous computing environment require context-awareness ability in order to gather and analyze context information in various situations and environments. However, existing context-aware systems lack the ability to systematically generate and handle various types of context information, and only a few systems have ability learning from environment. In this paper, a general context model is defined to describe various contexts and a context-awareness framework is implemented based in the model, which makes it straightforward to handle and generate various types of context from diverse sensor. The framework is designed to allow a system to sensed, combined, inferred, and learned context information, in order to provide users with services in dynamic environments. We have implemented the proposed framework and applied it to a u-Health management system.
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In this article we highlight and discuss prominent aspects of context and adaptively in pervasive computing environments along with characteristics and categories of context, and context-aware systems. We also briefly refer to use, management and modeling of context in context-aware systems. This paper is not meant to provide an exhaustive review of literature but rather it aims to integrate and extend important theoretical aspects found in the literature. Our perspective reflects context from application and system development point of view, based on the expanded computing setting which is mobile, dynamic,heterogeneous and highly connected (i.e. pervasive computing) while keeping the user in the center. This paper constructs theoretical base of our future research which is enabling anytime and anywhere learning in adaptive learning environments.
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With the rapid development of information technology, it is inevitable that the distributed mobile computing will evolve to pervasive computing gradually whose final goal is fusing the information space composed of computers with the physical space in which the people are working and living in. However, most of WSN contexts coming from may be more and more unstructured, widespread and massive. Therefore, to realize precise location, adaptive reasoning and reliable fusion of these kinds of contexts, we should sense the changes of them efficiently and accurately in real time. One of the problems is how to assure the reliability of context fusion in the complex situations context fluctuating frequently. Many attentions have been paid to the research of the context-aware pervasive applications. However, most of them just use the raw context directly or take just some aspects of the Quality of Context (QoC) into account. Therefore, we propose an uncertain context fusion framework that supports QoC management in various layers. By this framework, we can use threshold management, quality factor management and inconsistent context management to protect and provide QoS-enriched context fusion efficiently for context-aware applications and services such as complex Internet of Vehicles.
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