Many types of information available in a pervasive computing environment, such as people location information, should be accessible only by a limited set of people. Some properties of the information raise unique challenges for the design of an access control mechanism: Information can emanate from more than one source, it might change its nature or granularity before reaching its final receiver, and it can flow through nodes administrated by different entities. We propose three design principles for the architecture of an access control mechanism: (1) extract pieces of information in raw data streams early, (2) define policies controlling access at the information level, and (3) exploit information relationships for access control. We describe an example architecture in which we apply these principles. We also report how our earlier work about adding access control to a people location service contributed to the more general access control architecture proposed here.

An expressive representation for location is an important component in many applications. However, while many location-aware applications can reason about space at the level of coordinates and containment relationships, they have no way to express the semantics that define how a particular space is used. We present lair, an ontology that addresses this problem by modeling both the geographical relationships between spaces as well as the functional purpose of a given space. We describe how lair was used to create an application that produces walking directions comparable to those given by a person, and a pilot study that evaluated the quality of these directions. We also describe how lair can be used to evaluate other intelligent user interfaces.

Abstract. This paper describes SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications) and the use of this ontology in building the Context Broker Architecture (CoBrA). CoBrA is a new agent architecture for supporting pervasive context-aware systems in a smart space environment. The SOUPA ontology is expressed using the Web Ontology Language OWL and includes modular component vocabularies to represent intelligent agents with associated beliefs, desire, and intentions, time, space, events, user profiles, actions, and policies for security and privacy. Central to CoBrA is an intelligent broker agent that exploits ontologies to support knowledge sharing, context reasoning, and user privacy protection. We also describe two prototype systems that we have developed to demonstrate the feasibility and the use of CoBrA. 1.

Abstract—When we connect smart devices to one another we open up many new possibilities. One interesting possibility is to support high-level semantic interaction without requiring multiple steps on multiple devices. In this paper we investigate how ontologies, runtime task models, Belief-Desire-Intention (BDI) models, and the blackboard architectural pattern may be used to enable semantic interaction for pervasive computing. An initial demonstrator was developed to visualize and manipulate semantic connections between devices in a smart home environment. The demonstrator provides a way for users to physically interact with devices on a high level of semantic abstraction without being bothered with the low-level details. Keywords-Semantic Web; user interaction; smart home; ontologies; blackboard architectural pattern; task model; BDI model I.

Abstract. A major challenge in pervasive computing environments is to provide users with complex, context-sensitive applications, dynamically composed from networked services. In this paper, we present an approach to the dynamic, context-aware composition of services to perform user tasks, i.e., software applications abstractly described on the user’s handheld device. Both networked services and user tasks are modeled as semantic Web services in OWL-S extended with context information. The distinctive feature of our solution is the ability to compose Web services that expose complex behaviors (conversations) to realize a user task that itself has a complex behavior. Furthermore, the context-related requirements of the task are met by aggregating the context-sensitive behaviors of the individual services. 1

Context-awareness in mobile and ubiquitous computing requires the acquisition, representation and processing of information which goes beyond the device features, network status, and user location, to include semantically rich data, like user interests and user current activity. On the other hand, when services have to be provided on-the-fly to many mobile users, the efficiency of reasoning with these data becomes a relevant issue. Experimental evidence has lead us to consider currently impractical a tight integration of ontological reasoning with rule based reasoning at the time of request. This paper illustrates a hybrid approach where ontological reasoning is loosely coupled with the efficient rule-based reasoning of a middleware architecture for service adaptation. While rule-based reasoning is performed at the time of service request to evaluate adaptation policies and reconcile possibly conflicting context information, ontological reasoning is mostly performed asynchronously by local context providers to derive non-shallow context information. A limited form of ontological reasoning is activated at the time of request only when essential for service provisioning. 1

There is a commonly accepted need for contexts and ontologies to describe the vast amounts of data that are available to pervasive computing applications. Existing contexts and ontologies are either much generalised, very application specific, or inflexible. An integrated approach is required in which new concepts can be added and related to existing ones transparently. This paper describes a novel approach to the design of a set of contexts and ontologies for context-aware pervasive computing systems. It describes a Query Service, that lies between applications and contextual information, which complemented by the contexts and ontologies, offers a more powerful query answering service to application developers than is currently available. 1

Services for mobile and pervasive computing should extensively exploit contextual information both to adapt to user needs and to enable autonomic behavior. This raises the problem of how to represent, organize, aggregate, and make available such data to services so as to have it become meaningful and usable knowledge. In this paper, we identify the key software engineering challenges introduced by the need of accessing and exploiting huge amount of heterogeneous contextual information. Following, we survey the relevant proposals in the area of context-aware pervasive computing, data mining and granular computing discussing their potentials and limitations with regard to their adoption in the development of context-aware pervasive services. On these bases, we propose the W4 model for contextual data and show how it can represent a simple yet effective model to enable flexible general-purpose management of contextual knowledge by pervasive services. A summarizing discussion and the identification of current limitations and open research directions conclude the paper.

Abstract. Developing applications that make use of machine-readable knowledge sources as promised by the Semantic Web vision is attracting much of current research interest; this vision is also affecting important trends in Computer Science such as Grid and Mobile computing. In this paper we propose a version of the BDI agent-oriented programming language AgentSpeak that is based on Description Logic (DL). In this approach, the belief base of an agent contains the definition of complex concepts, besides specific factual knowledge. The advantages of combining AgentSpeak with DL are: (i) queries to the belief base are more expressive as their results do not rely only on explicit knowledge but can be inferred from the ontology; (ii) the notion of belief update is refined since the (ontological) consistency of a belief addition can be checked; (iii) the search for a plan for handling an event is more flexible as it is not based solely on unification but on the subsumption relation between concepts; and (iv) agents may share knowledge by using ontology languages such as the Ontology Web Language (OWL). Extending agent programming languages with DL can have a significant impact on the development of multi-agent systems for the Semantic Web. 1

Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of situations as abstractions of context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques.