Enhancing intelligence and dependability of a product line enabled pervasive middleware

To provide good support for user-centered application scenarios in pervasive computing environments, pervasive middleware must react to context changes and prepare services accordingly. At the same time, pervasive middleware should provide extended dependability via self-management capabilities, to conduct self-diagnosis of possible malfunctions using the current runtime context, and self-configuration and self-adaptation when there are service mismatches. In this article, we present an approach to combine the power of BDI practical reasoning and OWL/SWRL ontologies theoretical reasoning in order to improve the intelligence of pervasive middleware, supported by a set of Self-Management Pervasive Service (SeMaPS) ontologies featuring dynamic context, complex context, and self-management rules modeling. In this approach, belief sets are enriched with the results of OWL/SWRL theoretical reasoning to derive beliefs that cannot be obtained directly or explicitly. This is demonstrated with agents negotiating sports appointments. To cope with self-management, the corresponding monitoring, configuration, adaptation and diagnosis rules are developed based on OWL and SWRL utilizing SeMaPS ontologies. Evaluations show this combined reasoning approach can perform well, and that Semantic Web-based self-management is promising for pervasive computing environments.     

Decentralized checking of context inconsistency in pervasive computing environments

Contexts are often inconsistent in pervasive computing environments, owing to many heterogeneous devices with limited processing capabilities, imperfect measurement techniques, and user movement. A variety of schemes have been proposed to check context inconsistency. However, they implicitly require central control. This requirement inhibits their effectiveness in some pervasive computing environments (e.g., transport systems) where all nodes are resource-constrained and cannot act as a centralized node. To this end, we propose in this paper DCCI—a scheme of Decentralized Checking of Context Inconsistency in pervasive computing environments. DCCI exploits a simple, yet efficient, preference-based locality that denotes nodes requiring that the same context can check the inconsistency on this type of contexts. According to this locality, DCCI constructs a preference-based shortcut structure such that it checks context inconsistency within the shortcut structure. Extensive experiments show that DCCI can accurately and efficiently check context inconsistency in the presence of node churns and heterogeneity.     

Multiple-Agent Perspectives in Reasoning About Situations for Context-Aware Pervasive Computing Systems

In open heterogeneous context-aware pervasive computing systems, suitable context models and reasoning approaches are necessary to enable collaboration and distributed reasoning among agents. This paper proposes, develops, and demonstrates the following: 1) a novel context model and reasoning approach developed with concepts from the state-space model, which describes context and situations as geometrical structures in a multidimensional space; and 2) a context algebra based on the model, which enables distributed reasoning by merging and partitioning context models that represent different perspectives of computing entities over the object of reasoning. We show how merging and reconciling different points of view over context enhances the outcomes of reasoning about the context. We develop and evaluate our proposed algebraic operators and reasoning approaches with cases using real sensors and with simulations. We embed agents and mobile agents with these modeling and reasoning capabilities, thus facilitating context-aware and adaptive mobile agents operating in open pervasive environments.     

A goal-based framework for contextual requirements modeling and analysis

Requirements engineering (RE) research often ignores or presumes a uniform nature of the context in which the system operates. This assumption is no longer valid in emerging computing paradigms, such as ambient, pervasive and ubiquitous computing, where it is essential to monitor and adapt to an inherently varying context. Besides influencing the software, context may influence stakeholders’ goals and their choices to meet them. In this paper, we propose a goal-oriented RE modeling and reasoning framework for systems operating in varying contexts. We introduce contextual goal models to relate goals and contexts; context analysis to refine contexts and identify ways to verify them; reasoning techniques to derive requirements reflecting the context and users priorities at runtime; and finally, design time reasoning techniques to derive requirements for a system to be developed at minimum cost and valid in all considered contexts. We illustrate and evaluate our approach through a case study about a museum-guide mobile information system.     

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.     

面向普适计算的自适应模糊访问控制方法

普适环境中的上下文信息是普适访问控制的关键因素,对主体的授权和对主体使用权限过程的控制具有决定性影响。系统安全强度和安全策略应随上下文的变化而动态改变。传统访问控制模型均未考虑上下文对安全强度和安全策略的动态影响,不适合普适计算环境。提出了普适环境下安全强度和安全策略随上下文动态变化的思想,基于区间值模糊集合理论建立了上下文信息相关的产生式规则,设计了一种简单高效的区间值模糊访问控制方法,以提高普适计算系统中安全强度和安全策略的自适应性,更符合普适环境。     

Semantic Space: an infrastructure for smart spaces

Semantic Space is a pervasive computing infrastructure that exploits semantic Web technologies to support explicit representation, expressive querying, and flexible reasoning of contexts in smart spaces.     

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.     

基于区间值加权模糊推理的访问控制模型

普适计算已被国内外学术界和工业界公认为未来计算的主流模式,其安全问题是信息安全的一个基础问题。传统的访问控制模型从系统的角度出发保护资源,在进行权限的控制时没有考虑上下文信息等因素。为了满足普适计算环境下访问控制策略的动态自适应性以及上下文信息的模糊不确定性,应用模糊集合理论与模糊推理方法,提出一种基于区间值加权模糊推理的访问控制模型;给出加权模糊匹配函数公式与区间值加权模糊推理方法,实现访问控制策略与实际安全需求的一致性。     

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.     

普适计算环境下基于信任度的模糊自适应访问控制模型*

在信任模型基础上,提出一种基于信任度的模糊自适应访问控制模型。该模型扩展信任度的概念,建立权限的区间值模糊策略规则,通过对与主体相关的上下文信息的模糊推理实现授权的有效控制。描述模型的构成要素,研究模型的区间值模糊推理算法,为解决普适计算环境下动态访问控制授权问题提供了一定的技术手段。     

Logical foundations for representing object-oriented systems

Abstract

Object-oriented programming languages are designed for computing or simulating the behaviour of interacting objects, but their encapsulated contexts and procedural methods are not well suited to non-procedural techniques in theorem provers, optimizers, and automated design and analysis tools. Logic is the non-procedural system par excellence, but the predicate calculus notation for logic is awkward for representing and reasoning about encapsulated contexts. Conceptual graphs are a graphic system of logic that is better suited to O-O systems. First, they explicitly represent the contexts that are ignored or obscured in predicate calculus. Second, Peirce's rules of inference for reasoning with graphs are explicitly formulated in terms of contexts and the conditions for importing and exporting information from contexts. This article describes the context mechanisms of conceptual graphs, the rules of inference for reasoning with the graphs, and their use as a design language for object-oriented systems.     

Trust-based security in pervasive computing environments

Traditionally, stand-alone computers and small networks rely on user authentication and access control to provide security. These physical methods use system-based controls to verify the identity of a person or process, explicitly enabling or restricting the ability to use, change, or view a computer resource. However, these strategies are inadequate for the increased flexibility that distributed networks such as the Internet and pervasive computing environments require because such systems lack central control and their users are not all predetermined. Mobile users expect to access locally hosted resources and services anytime and anywhere, leading to serious security risks and access control problems. We propose a solution based on trust management that involves developing a security policy, assigning credentials to entities, verifying that the credentials fulfill the policy, delegating trust to third parties, and reasoning about users' access rights. This architecture is generally applicable to distributed systems but geared toward pervasive computing environments     

An approach to domain-based scalable context management architecture in pervasive environments

In pervasive environments, context management systems are expected to administrate large volume of contextual information that is captured from spatial to nonspatial elements. Research in context-aware computing produced a number of middleware systems for context management to intermediate the communications between applications and context providers. In particular, in pervasive environments, the design of distributed storage, retrieval and propagation mechanisms of context information across domains is vital. In this paper, we propose a domain-based approach to address the requirements of scalable distributed context management, cross-domain efficient context information dissemination and domain-based privacy policy enforcement. We propose infinitum, a middleware architecture that incorporates the management and communication benefits of the Google Wave Federation Protocol, while also taking advantage of the semantic and inference benefits of ontology-based context models. This architecture establishes a robust cross-domain scalable context management and collaboration framework, which has been implemented and evaluated in a real-life application of “SMART University” to support virtual team collaboration.     

iCROSS: toward a scalable infrastructure for cross-domain context management

With the development of pervasive computing techniques, the world will be filled with interconnected context-aware domains (e.g., homes, offices, hospitals, etc.). While the previous studies focused solely on the management of contexts produced in a single domain, in this paper we discuss the challenges to be addressed for cross-domain context management. By analyzing the requirements from several scenarios, we identify two context producer–consumer patterns in multi-domain environments. Furthermore, to deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed. Two prototype applications—smart home and community services in a smart campus—have been developed to demonstrate the key features and usefulness of cross-domain context management. Initial experiments have also been conducted to evaluate the performance of our system.     

一种CBR与RBR相结合的智能家庭推理系统*

介绍了一种CBR与RBR相结合的智能家庭推理系统。将CBR与RBR两种人工智能技术相结合,运用于普适计算的典型应用智能家庭中,首先通过RBR推理出当前用户的活动以及心情等较高级上下文;然后再用CBR进行上下文的再处理,融合多类型或历史的上下文,自动预测相似度最大的上下文,并基于该上下文为用户提供个性化服务。     

Semantic management of multiple contexts in a pervasive computing framework

Mobile devices can perceive greater details of user states with the increasing integration of mobile sensors into a pervasive computing framework, yet they consume large amounts of batteries and computational resources. This paper proposes a semantic management method which efficiently integrates multiple contexts into the mobile system by analyzing the semantic hierarchy and temporal relations. The proposed method semantically decides the recognition order of the contexts and identifies each context using a corresponding dynamic Bayesian network (DBN). To sort out the contexts, we designed a semantic network using a knowledge-driven approach, whereas DBNs are constructed with a data-driven approach. The proposed method was validated on a pervasive computing framework, which included multiple mobile sensors (such as motion sensors, data-gloves, and bio-signal sensors). Experimental results showed that the semantic management of multiple contexts dramatically reduced the recognition cost.     

Context-adaptive and energy-efficient mobile transaction management in pervasive environments

Pervasive computing is a user-centric, scalable, parallel, and distributed computing paradigm, allowing users to access to their preferred services even while moving around. Transaction management for pervasive environments has to provide mobile users with reliable and transparent services anytime anywhere. To make such a vision a reality, the communication of pervasive transaction processing should be context-aware for adapting to dynamically changing execution environments, and energy-efficient for prolonging the lifetime of battery-powered mobile devices. In this paper, we propose a context model and a context-aware transaction model for pervasive transactions, and present a c\underline{\mathrm{c}} ontext-adaptive and e\underline{\mathrm{e}} nergy-efficient t\underline{\mathrm{t}} ransaction m\underline{\mathrm{m}} anagement mechanism (CETM) that can dynamically adjust transaction execution behaviors in terms of current context information. Moreover, we model and verify the correctness of the CETM through Petri nets. The simulation results have demonstrated that our transaction management mechanism CETM can significantly reduce the failed probability of concurrent pervasive transactions.