Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

The serializable and incremental semantic reasoner fuzzyDL

Serializable and incremental semantic reasoners make it easier to reason on a mobile device with limited resources, as they allow the reuse of previous inferences computed by another device without starting from scratch. This paper describes an extension of the fuzzy ontology reasoner fuzzyDL to make it the first serializable and incremental semantic reasoner. We empirically show that the size of the serialized files is smaller than in another serializable semantic reasoner (JFact), and that there is a significant decrease in the reasoning time

A Description Logic of Typicality for Conceptual Combination

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of combining prototypical concepts, an open problem in the fields of AI and cognitive modelling. Our logic extends the logic of typicality ALC + TR, based on the notion of rational closure, by inclusions p :: T(C) v D (“we have probability p that typical Cs are Ds”), coming from the distributed semantics of probabilistic Description Logics. Additionally, it embeds a set of cognitive heuristics for concept combination. We show that the complexity of reasoning in our logic is EXPTIME-complete as in ALC

ODDIN: ontology-driven differential diagnosis based on logical inference and probabilistic refinements

Medical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge based technologies which avoid ambiguity, such as ontologies rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which applies the aforementioned strat egies. The architecture and proof of concept implementation is described, and results of the evaluation are discussed.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI-020100-2008-564) and SONAR2 (TSI-020100-2008-665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02).Publicad

Talking about Forests: an Example of Sharing Information Expressed with Vague Terms

Most natural language terms do not have precise universally agreed definitions that fix their meanings. Even when conversation participants share the same vocabulary and agree on taxonomic relationships (such as subsumption and mutual exclusivity, which might be encoded in an ontology), they may differ greatly in the specific semantics they give to the terms. We illustrate this with the example of `forest', for which the problematic arising of the assignation of different meanings is repeatedly reported in the literature. This is especially the case in the context of an unprecedented scale of publicly available geographic data, where information and databases, even when tagged to ontologies, may present a substantial semantic variation, which challenges interoperability and knowledge exchange. Our research addresses the issue of conceptual vagueness in ontology by providing a framework based on supervaluation semantics that explicitly represents the semantic variability of a concept as a set of admissible precise interpretations. Moreover, we describe the tools that support the conceptual negotiation between an agent and the system, and the specification and reasoning within standpoints

Learning in Description Logics with Fuzzy Concrete Domains

Description Logics (DLs) are a family of logic-based Knowledge Representation (KR) formalisms, which are particularly suitable for representing incomplete yet precise structured knowledge. Several fuzzy extensions of DLs have been proposed in the KR field in order to handle imprecise knowledge which is particularly pervading in those domains where entities could be better described in natural language. Among the many approaches to fuzzification in DLs, a simple yet interesting one involves the use of fuzzy concrete domains. In this paper, we present a method for learning within the KR framework of fuzzy DLs. The method induces fuzzy DL inclusion axioms from any crisp DL knowledge base. Notably, the induced axioms may contain fuzzy concepts automatically generated from numerical concrete domains during the learning process. We discuss the results obtained on a popular learning problem in comparison with state-of-the-art DL learning algorithms, and on a test bed in order to evaluate the classification performance