PERSONAF: framework for personalised ontological reasoning in pervasive computing

Pervasive computing creates possibilities for presenting highly personalised information about the people, places and things in a building. One of the challenges for such personalisation is the creation of the system that can support ontological reasoning for several key tasks: reasoning about location; personalisation of information about location at the right level of detail; and personalisation to match each person’s conceptions of the building based on their own use of it and their relationship to other people in the building. From pragmatic perspectives, it should be inexpensive to create the ontology for each new building. It is also critical that users should be able to understand and control pervasive applications. We created the PERSONAF (personalised pervasive scrutable ontological framework) to address these challenges. PERSONAF is a new abstract framework for pervasive ontological reasoning. We report its evaluation at three levels. First, we assessed the power of the ontology for reasoning about noisy and uncertain location information, showing that PERSONAF can improve location modelling. Notably, the best ontological reasoner varies across users. Second, we demonstrate the use of the PERSONAF framework in Adaptive Locator, an application built upon it, using our low cost mechanisms for non-generic layers of the ontology. Finally, we report a user study, which evaluated the PERSONAF approach as seen by users in the Adaptive Locator. We assessed both the personalisation performance and the understandability of explanations of the system reasoning. Together, these three evaluations show that the PERSONAF approach supports building of low cost ontologies, that can achieve flexible ontological reasoning about smart buildings and the people in them, and that this can be used to build applications which give personalised information that can provide understandable explanations of the reasoning underlying the personalisation.     

The ECORA framework: A hybrid architecture for context-oriented pervasive computing

An infrastructure approach to support context-aware pervasive computing is advantageous for rapid prototyping of context-aware distributed applications and beneficial for unifying modelling of context and reasoning in uncertain conditions. This paper presents the ECORA framework for context-aware computing, which is designed with a focus on reasoning about context under uncertainty and addressing issues of heterogeneity, scalability, communication and usability. The framework follows an agent-oriented hybrid approach, combining centralized reasoning services with context-aware, reasoning capable mobile software agents. The use of a centralized reasoning engine provides powerful reasoning capabilities and deploying context-aware mobile agents enables agility and robustness of components in the pervasive system. The design and implementation of the framework at different levels, as well as three case studies, are presented.     

Exploiting smart spaces for interactive TV applications development

The integration of semantic technologies and TV services is a substantial innovation to improve the services to users in an environment that is extended beyond the fixed home environment. But currently, this integration is mainly limited to provide personalized recommendation services and systems by matching user static preferences. Designing and development of interactive TV (iTV) applications using semantic technologies are not realized yet. In this work, we explore the potential of introduction semantic technologies and smart spaces in design and development of iTV applications. We use an example scenario to show how future iTV applications include the mesh-up of information from different sources. We proposed a methodology and show how ontology-driven approach can help to design and develop these iTV applications. We demonstrate the suitability of our ontology-driven application development tools and rule-based approach for the development of highly dynamic context-aware iTV applications.     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

iConAwa - An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.     

Toward an OSGi-based infrastructure for context-aware applications

Applications and services must adapt to changing contexts in dynamic environments. However, building context-aware applications is still complex and time-consuming due to inadequate infrastructure support. We propose a context-aware infrastructure for building and rapidly prototyping such applications in a smart-home environment. This OSGi-based infrastructure manages context-aware services reliably and securely and efficiently supports context acquisition, discovery, and reasoning. A formal, ontology-based context model enables semantic context representation, reasoning, and knowledge sharing. We propose an ontology-based context model that leverages Semantic Web technology and OWL (Web Ontology Language). OWL is an ontology markup language that enables context sharing and context reasoning. Based on our context model, we also propose a service-oriented context-aware middleware (SOCAM) architecture, including a set of independent services that perform context discovery, acquisition, and interpretation.     

普适计算环境下基于构件化中间件的上下文管理技术研究

上下文相关技术已经成为普适计算环境中用于应用服务的不可或缺的核心技术之一。对于基于构件的中间件来说,对高性能的上下文管理的需求更是必不可少,因为它构成了普适计算环境中构件适配和部署的重要基础。因此,在已有构件中间件的基础上,我们针对普适计算环境的特点提出了一种整体的上下文管理方法,将上下文管理集成在已有的构件化中间件中。应用表明,该方法能够更好地支持普适计算环境下上下文相关的构件化应用开发。     

Supporting ubiquitous sensor-cloudlets and context-cloudlets: Programming compositions of context-aware systems for mobile users

Increasing widespread use of sensor and networking technologies are yielding ubiquitous sensors and applications that pervade daily life. At the same time, context-aware pervasive computing has experienced tremendous developments in terms of context modelling and reasoning, and applications. Such developments coupled with a cloud computing model are yielding sensor-cloudlets and context-cloudlets based on sensors and applications deployed as services that can be harnessed in applications on-demand, ad-hoc and on a pay-per-use model. Sensor-cloudlets and context-cloudlets depend on and adapt to the available resources at the time, and involve context-aware systems (including sensors) that need to be dynamically composed as needed. This paper first outlines current trends and key issues and challenges in sensor-cloudlets and context-cloudlets. We then present a key contribution of this paper, which is an application of an abstract model of context-aware systems for specifying compositions of context-aware systems used in sensor-cloudlets and context-cloudlets. We show how expressions in our formalism can be embedded into a programming language (which we show via an example extending the logic programming language Prolog). We then present numerous examples illustrating applications expressed in our extended Prolog language. We also show how compositions specified in our formalism supports estimating the reliability and cost of using such compositions of resources in computations, in a well-defined semantics. Finally, we describe meta-level control operators on evaluation of queries posed to compositions of resources and specify a service-based interface on context-aware systems. We conclude with issues to be tackled in the future.     

基于语境和动作推理的语义Web服务发现

在语义Web服务发现中,服务本身及用户所处的语境是不可忽视的因素.针对现有服务发现方法的不足,给出一种基于语境和动作推理的语义Web服务发现方法.该方法通过建立基于动作的语境模型来刻画静态和动态的语境信息,利用动态描述逻辑中的动作推理实现语境推理,并在此基础上实现语境敏感的语义Web服务发现算法.案例研究及相关工作对比表明,与现有方法相比,文中方法在语境刻画和推理能力方面均有较优表现.同时实验结果也表明,在增加合理逻辑推理的时空开销的前提下,文中方法能为用户提供更符合需求的服务发现结果.     

基于情境上下文感知的温度监测与警报系统

在智能家庭OSGi网关上建立了一个情境上下文感知计算框架,研发了一个单总线测温网络,在框架内实现了智能家庭环境中的情境温度上下文感知计算服务。以温度上下文为中心,通过框架基础服务整合电力总线电器控制服务、GSM网络通信服务等,构成智能家庭环境中基于情境上下文感知的温度监测与警报系统。     

Usability study of ME2.0

Mobile context-aware applications execute in the background of hosts mobile devices. The applications source process and aggregate hosts’ contextual and personal information. This information is disclosed to ubiquitously pervasive services that adapt their offerings to individual preferences. Unfortunately, many developers continue to ignore the user perspective in context-aware application designs as they complicate their overall task and generate exponential requirements. The additional incorporation of privacy mechanisms in context-aware applications to safeguard context and personal information disclosures also complicates users’ tasks resulting to misconfigured or completely abandoned applications. Misconfigured applications give end-users a false assurance of privacy exposing them to comprising services. We present a usability study on Mobile Electronic Personality Version 2 a privacy enhanced context-aware mobile application for personalising ubiquitous services and adapting pervasive smart-spaces. We draw conclusions on key issues related to user needs, based on user interviews, surveys, prototypes and field evaluations. Users’ needs are evaluated against five themes, learn-ability, efficiency, memorability, errors, satisfaction and privacy contention. In addition, design layout preferences, privacy manageability and consensus design comprehension are also evaluated. Clarity of priorities in context-aware mobile applications shaped by usability studies effectively increases the acceptance of levels of potential users.     

基于FDI的用户模型及其在上下文觉察计算中的应用

上下文觉察计算是普适计算的重要内容。当前的上下文觉察计算研究大多没有为用户提供个性化的服务。已有的个性化上下文觉察服务的用户模型,其用户偏好的设置比较简单。本文从心理学的观点出发,提出一个使用基于场依存一独立性（FDI）的用户模型来为用户提供个性化的上下文觉察服务。本文系统分析了这种用户模型对上下文觉察计算的重要性,并给出了使用基于FDI的用户模型在上下文觉察计算中的应用过程。试验表明这种模型提供的个性化服务能够提高上下文觉察计算应用的有效性。     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

基于觉察上下文计算的健康智能家庭研究

利用觉察上下文计算技术来研究实现健康智能家庭,主要研究了健康智能家庭的上下文建模和上下文推理,并构建了一个实验系统AngelHome,分析了健康智能家庭中的各种上下文信息,利用本体技术对其进行建模,并用OWLDL语言表达上下文信息模型,构建了AngelHome本体;在上下文推理部分采用混合推理,对不同的推理任务分别采用本体推理机、自定义规则推理机和贝叶斯神经网络推理.AngelHome采用OSGi框架,具有良好的伸缩性,这里分析了系统的几个主要部分,并进行了测试.实验结果表明,利用觉察上下文计算技术来实现健康智能家庭是可行的.     

Design and validation of a light inference system to support embedded context reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.     

Adaptive personal agents

Personal agents are an important advance in the management of electronic information. Accurate user profiling is critical to the personalisation of agent-based services. In this paper we describe a framework for personal agents, which provides a range of services using a common user profile. The user profile is automatically learned from observation of the electronic documents a user reads, and tracks user interests over time. We report on a large trial of this framework, which has given us important insights into the effectiveness of personal agent applications. In particular we discuss the benefits obtained from the use of a common profile and the interoperation of several personal agent applications.     

Personalization and Context-awareness in Social Local Search: State-of-the-art and Future Research Challenges

Location-based services (LBS) are now the platforms for aggregating relevant information about users and understanding their mobile behavior and preferences based on the location histories. The increasing availability of large amounts of spatio-temporal data brings us opportunities and challenges to automatically discover valuable knowledge. While context-aware properties quickly became the key of the success of these pervasive applications, information related to user preferences and social signals still lack of adequate capitalization. Local search in LBSs is a peculiar service where recent and current interests, the network of explicit and implicit social interactions between users can be combined for effectively performing fine-tuned and personalized recommendations of points of interest. In this article we present the various and peculiar aspects of local search in mobile scenarios. Then we explore the added value of personalization and the benefits of considering social signals, summarizing open challenges and emerging technologies.     

基于语义Web技术的上下文感知的智能移动服务

语义Web技术应用于上下文感知的智能移动服务，通过构建上下文信息本体，使得移动服务的实体之间可以进行上下文信息共享和语义互操作，并进行上下文信息推理，实现智能服务。本文首先介绍了语义Web及本体技术，其次阐述了语义Web技术应用于上下文感知的移动服务，然后详细分析了智能移动服务中的上下文信息本体构建，包括通用的上下文信息本体、用户概况本体、情境本体以及服务本体等，接着介绍了相关的研究项目，最后进行展望和总结。