普适计算环境下上下文信息推理的研究

从各种低层上下文信息得到对人们更加有用的高层上下文信息即上下文推理是当前研究的热点.针对该问题,采用描述逻辑,研究基于本体模型的上下文推理方法.首先简要介绍基于本体的上下文模型,该模型增加了对上下文特性的建模,然后分别研究基于本体的推理、基于规则的推理及不一致性验证3种推理方式,借助Jena框架的推理接口实现,推理功能全面,通用性强,基本满足了普适计算系统中上下文推理的需求,最后给出了推理的可用性.     

Affective e-Learning in residential and pervasive computing environments

This article examines how emerging pervasive computing and affective computing technologies might enhance the adoption of ICT in e-Learning which takes place in the home and wider city environment. In support of this vision we describe two cutting edge ICT environments which combine to form a holistic connected future learning environment. The first is the iSpace, a specialized digital-home test-bed that represents the kind of high-tech, context aware home-based learning environment we envisage future learners using, the second a sophisticated pervasive e-Learning platform that typifies the educational delivery platform our research is targeting. After describing these environments we then present our research that explores how emotion evolves during the learning process and how to leverage emotion feedback to provide adaptive e-Learning system. The motivation driving this work is our desire to improve the performance of the educational experience by developing learning systems that recognize and respond appropriately to emotions exhibited by learners. Finally we report on the results about the emotion recognition from physiological signals which achieved a best-case accuracy rate of 86.5% for four types of learning emotion. To the best of our knowledge, this is the first report on emotion detection by data collected from close-to-real-world learning sessions. We also report some finding about emotion evolution during learning, which are still not enough to validate Kort’s learning spiral model.

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

Situational computing: An innovative architecture with imprecise reasoning

Situation awareness is introduced as a more holistic variant of context awareness where situations are regarded as logically aggregated pieces of context. Situational computing can be viewed as the pervasive computing paradigm that deals with situational context representation and reasoning. One important problem that arises in such paradigm is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current user situation. Hence, the knowledge upon which the context/situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed situation ontologies in order to approximately infer the current user context. Finally, we present a series of experimental results that provide evidence on the flexible, efficient nature of the proposed situational computing.     

Data-centric middleware for context-aware pervasive computing

The complexity of developing and deploying context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggregate, and disseminate contextual data. In this paper, we motivate a data-centric design for such an infrastructure to support context-aware applications. Our middleware system, Solar, treats contextual data sources as stream publishers. The core of Solar is a scalable and self-organizing peer-to-peer overlay to support data-driven services. We describe how different services can be systematically integrated on top of the Solar overlay and evaluate the resource discovery and data-dissemination services. We also discuss our experience and lessons learned when using Solar to support several implemented scenarios. We conclude that a data-centric infrastructure is necessary to facilitate both the development and deployment of context-aware pervasive-computing applications.     

Immediate detection of predicates in pervasive environments

An important problem in pervasive environments is detecting predicates on sensed variables in an asynchronous distributed setting to determine context and to respond. We do not assume the availability of synchronized physical clocks because they may not be available or may be too expensive for predicate detection in such environments with a (relatively) low event occurrence rate. We address the problem of detecting each occurrence of a global predicate, at the earliest possible instant, by proposing a suite of three on-line middleware protocols having varying degrees of accuracy. We analyze the degree of accuracy for the proposed protocols. The extent of false negatives and false positives is determined by the run-time message processing latencies.     

Granular best match algorithm for context-aware computing systems

In order to be context-aware, a system or application should adapt its behaviour according to current context, acquired by various context provision mechanisms. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA – Context-Aware Personal Reminder Agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the user’s intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method.     

Ubiquitous computing: connecting Pervasive computing through Semantic Web

Ubiquitous computing refers to building a global computing environment where seamless and invisible access to computing resources is provided to the user. Pervasive computing deals with acquiring context knowledge from the environment and providing dynamic, proactive and context-aware services to the user. A Ubiquitous computing environment is created by sharing knowledge and information between Pervasive computing environments. In this paper we propose a framework that uses the potential of the Semantic Web to weave Pervasive computing environments into a Ubiquitous computing environment. We discuss how the collaboration of these Pervasive environments can create an effective Ubiquitous computing environment referred herein as the Integrated Global Pervasive Computing Framework (IGPF). We test the effectiveness of the Ubiquitous environment through a small scenario from a prototype system that we have implemented over this framework to handle medical emergency scenario.     

Collaborative joins in a pervasive computing environment

We present a collaborative query processing protocol based on the principles of Contract Nets. The protocol is designed for pervasive computing environments where, in addition to operating on limited computing and battery resources, mobile devices cannot always rely on being able to access the wired infrastructure. Devices, therefore, need to collaborate with each other in order to obtain data otherwise inaccessible due to the nature of the environment. Furthermore, by intelligently using answers cached by peers, devices can reduce their computation cost. We show the effectiveness of our approach by evaluating performance of devices querying for data while moving in a citylike environment.Received: 28 July 2003, Accepted: 26 March 2004, Published online: 8 July 2004Edited by: K. Ramamritham     

觉察上下文计算环境中上下文服务器的设计

介绍了觉察上下文计算的基本概念和上下文以及上下文服务器的定义,阐述了实现上下文服务器所需要解决的问题,介绍一个上下文服务原型系统(Context Server for Web-based Mobile Service,CS4WMS)的系统架构、设计及实现。     

EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support

Pervasive computing environments are populated with networked software and hardware resources providing various functionalities that are abstracted, thanks to the Service Oriented Architecture paradigm, as services. Within these environments, service discovery enabled by service discovery protocols (SDPs) is a critical functionality for establishing ad hoc associations between service providers and service requesters. Furthermore, the dynamics, the openness and the user-centric vision aimed at by the pervasive computing paradigm call for solutions that enable rich, semantic, context- and QoS-aware service discovery. Although the semantic Web paradigm envisions to achieve such support, current solutions are hardly deployable in the pervasive environment due to the costly underlying semantic reasoning with ontologies. In this article, we present EASY to support efficient, semantic, context- and QoS-aware service discovery on top of existing SDPs. EASY provides EASY-L, a language for semantic specification of functional and non-functional service properties, as well as EASY-M, a corresponding set of conformance relations. Furthermore, EASY provides solutions to efficiently assess conformance between service capabilities. These solutions are based on an efficient encoding technique, as well as on an efficient organization of service repositories (caches), which enables both fast service advertising and discovery. Experimental results show that the deployment of EASY on top of an existing SDP, namely Ariadne, enhancing it only with slight changes to EASY-Ariadne, enables rich semantic, context- and QoS-aware service discovery, which furthermore performs better than the classical, rigid, syntactic matching, and improves the scalability of Ariadne.     

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.     

Composing software services in the pervasive computing environment: Languages or APIs?

The pervasive computing environment will be composed of heterogeneous services. In this work, we have explored how a domain specific language for service composition can be implemented to capture the common design patterns for service composition, yet still retain a comparable performance to other systems written in mainstream languages such as Java. In particular, we have proposed the use of the method delegation design pattern, the resolution of service bindings through the use of dynamically adjustable characteristics and the late binding of services as key features in simplifying the service composition task. These are realised through the Scooby language, and the approach is compared to the use of APIs to define adaptable services.     

Trust force-based service selection in pervasive computing environments

Trust is an important aspect of decision making for pervasive applications and it is important to choose and use services efficiently in pervasive computing environments. Trust force is presented to specify trust relationships among interactive entities in a pervasive computing environment by using experience and knowledge in a social network and the coulomb＇s law in real word. Based on trust force, a Trust Management and Service Selection model are presented, named TMSS. TMSS was tested and the experimental results show that our method for selecting service is not only more efficient than traditional and heuristic methods, but also can identify good services from bad ones.     

面向普适计算环境的资源描述方法

普适计算环境下的资源描述是支持各项普适计算资源管理活动的基础,普适计算环境的开放性、面向用户特点和固有的不确定性因素对资源描述方法提出了新的要求。现有研究大多针对资源描述的服务化、语义化和用户化需求部分,对如何描述资源的不确定性信息支持不足。在语义Web服务描述OWL-S的基础上,增加对随机性信息表示的支持,扩展为新的OWL-SP。新方法较为全面地支持了普适资源描述的关键需求（服务化、语义化、用户化和随机化需求）,构成了一个较为完整的普适资源描述,可作为普适资源管理活动的基础。     

状态自动机支持的环境感知应用框架研究

环境感知是普及计算中一个重要的研究领域。为了便于开发环境感知应用程序,许多研究者已经提出了一些环境感知应用框架。然而,目前的一些环境感知应用框架侧重于让应用程序直接订阅或查询相关的环境信息,使得开发者需要关注众多的环境信息且要定义许多相似的规则来区分众多不同的操作,造成了开发和修改环境感知的应用程序仍存在着较多的困难。为解决这些问题,本文在研究环境感知应用框架的基础上,引入了信息空间环境状态的概念,提出了一种基于状态自动机的环境感知改进框架BS-CTK,并设计和实现了相应的组件以支持基于状态自动机的环境感知应用开发。     

一种普适计算环境下基于语义的服务匹配算法

在普适计算环境中通过语义本体来表示环境中的概念进行服务发现可以提高服务发现的鲁棒性。给出了一种基于语义的服务匹配算法,根据不同的服务请求可以通过服务类型匹配、服务输入输出参数匹配和QoS参数匹配,实现了分层次、逐步过滤不匹配的服务。同时通过服务相似度的计算为服务请求者提供最满足需求的服务。实验结果证明该算法具有较高的匹配率和较好的匹配速度,完全适用于普适计算环境。     

普适计算的上下文访问控制模型*

上下文访问控制是普适计算安全中极具挑战性的重要问题。基于使用控制模型（UCONABC）,提出了普适计算的上下文访问控制模型（CACM）。CACM不仅关心授权,而且引入了职责和条件。针对普适计算医疗保健环境中的三种典型的上下文访问控制应用,灵活扩展了组合子模型。通过实例分析,证明了模型的安全性、灵活性和有效性。     

普适环境下基于生物加密的认证机制

普适计算的出现对网络通信中的安全和隐私提出了新的挑战,传统的认证技术已经不能满足普适环境的安全需求。提出了一种普适环境中用于完成服务使用者与提供者之间双向认证及密钥建立的机制。该机制高度融合了生物加密技术和Diffie-Hellman密钥交换技术,在不泄露用户隐私的情况完成双向认证。该机制提供了安全的建立密钥的算法,并且通过使用生物加密技术实现了访问控制策略的区别对待。经分析证明,该协议能很好地抵抗各种攻击,尤其是拒绝服务（DoS）攻击。     

证据理论在不确定性推理中的应用研究*

利用证据理论中的基本概率分配函数、信任函数和似然函数来描述和处理知识的不确定性。提出一个特殊的概率分配函数和新的组合规则,并以其为基础建立一个不确定性推理模型。实例证明该模型能有效地度量最终结论的不确定性。