This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

An Ontology for Context-Aware Pervasive Computing Environments

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An ontology for context-aware pervasive computing environments

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.

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The SOUPA Ontology for Pervasive Computing

In recent years, RFID (radio frequency identification) system has become very popular in service industries, logistics and manufacturing, as it is an inexpensive and reliable device for automatic identification. Therefore, RFID system would be useful in a problem of mobile robot self-localization, if tags are distributed in the environment, and if the robot is equipped with a RFID reader to communicate with the tags. In this paper, we propose a novel method for learning-based localization with a RFID system by using support vector machine (SVM). In order to obtain various training data for SVM learning, a number of synthesized sensor data are generated from limited amount of real sensor data. We also propose a method that enables a user to easily place tags in effective locations. In experiments with a mobile robot, the performance of the proposed method is demonstrated.

Self-localization of mobile robots with RFID system by using support vector machine

Context-aware pervasive systems are emerging as an important class of applications. Such systems can respond intelligently to contextual information about the physical world acquired via sensors and information about the computational environment. A declarative approach to building context-aware pervasive systems is presented, and the notion of the situation program is introduced, which highlights the primacy of the situation abstraction for building context-aware pervasive systems. There is also a demonstration of how to manipulate situation programs using meta-programming within an extension of the Prolog logic programming language which is called LogicCAP. Such meta-reasoning enables complex situations to be described in terms of other situations. Furthermore, a discussion is given on how the design of situation programs can affect the properties of a context-aware system. The approach encourages a high-level of abstraction for representing and reasoning with situations, and supports building context-aware systems incrementally by providing modularity and separation of concerns.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888905000263

Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective

When building intelligent spaces, the knowledge representation for encapsulating rooms, users, groups, roles, and other information is a fundamental design question. We present a semantic network as such a representation, and demonstrate its utility as a basis for ongoing work.

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H.E. Shrobe

Papers

Using semantic networks for knowledge representation in an intelligent environment