Context-awareness refers to computing systems that are able to sense and to comprehend their environment in order to adapt themselves in dependence to the available and relevant contextual information they depend upon. Gathering such contextual information involves real world entities such as sensors, which, for various reasons, are often prone to certain degrees of uncertainty and inaccuracy. Nevertheless, high quality context information plays a vital role in ensuring correct system behavior as well as dynamic system and service adaptation. Thus, a set of indicators is required which allows determining the quality of contextual information, which is commonly known as Quality of Context (QoC). One of the most relevant parameter of the QoC is the Probability of Correctness (PoC), which expresses the level of confidence, that the contextual information sensed, are in fact correct or not. In this paper, we propose an approach for measuring the PoC of context information by firstly analyzing the nature of context information and, secondly, revisiting the concept of Quality of Context also discussing other QoC parameters. Finally, we present a novel approach for quantifying the PoC for specific context information and evaluate the proposed method on a concrete case study.

Pervasive computing services exploit information about the physical world both to adapt their own behavior in a context-aware way and to deliver to users enhanced means of interaction with their surrounding environment. The technology to acquire digital information about the physical world is becoming more available, making services at risk of being overwhelmed by such growing amounts of data. This calls for novel approaches to represent and automatically organize, aggregate, and prune such data before delivering them to services. In particular, individual data items should form a sort of self-organized ecology in which, by linking and combining with each other into sorts of "knowledge networks" (KNs), they are able to provide compact and easyto-be-managed higher level knowledge about situations occurring in the environment. In this context, the contribution of this paper is twofold. First, with the help of a simple case study, we motivate the need to evolve from models of "context awareness" toward models of "situation awareness" via proper self-organized "KN" tools, and we introduce a general reference architecture for KNs. Second, we describe the design and implementation of a KN toolkit that we have developed, and we exemplify and evaluate algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed.

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