Distributed Ontology-Based Monitoring on the IBBT WiLab.t Infrastructure

Verstichel, Stijn; Poorter, Eli De; Pauw, Tim De; Becue, Pieter; Volckaert, Bruno; Turck, Filip De; Moerman, Ingrid; Demeester, Piet

doi:10.1007/978-3-642-17851-1_39

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The Sensor & Observation ontology is one of the most important ontologies for filtering data. The concepts preceded by the wsn namespace prefix are imported from the Wireless Sensor Network ontology (WSN) [ 65 , 66 ], which was developed by the co-authors and allows giving meaning to data values monitored by sensors. It contains a System concept, which models a system and its components.…”

Section: Methodsmentioning

confidence: 99%

Ambient-aware continuous care through semantic context dissemination

Ongenae

Famaey

Verstichel

et al. 2014

BMC Med Inform Decis Mak

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BackgroundThe ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate.The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data.MethodsThe developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability.ResultsA prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered.ConclusionsThe OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.

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Section: Methodsmentioning

confidence: 99%

Ambient-aware continuous care through semantic context dissemination

Ongenae

Famaey

Verstichel

et al. 2014

BMC Med Inform Decis Mak

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“…A comprehensive list of existing ontologies to describe sensors and sensor networks can be found in the W3C Semantic Sensor Network XG Final Report (Barnaghi et al, 2011). Within the iMinds DEUS Project (2010) the Wireless Sensor Network (WSN) (Verstichel et al, 2010) ontology was developed. The most important concepts of this ontology are visualized in Fig.…”

Section: The Sensor High-level Ontologymentioning

confidence: 99%

An ontology co-design method for the co-creation of a continuous care ontology

et al. 2014

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Ontology engineering methodologies tend to emphasize the role of the knowledge engineer or require a very active role of domain experts. In this paper, a participatory ontology engineering method is described that holds the middle ground between these two 'extremes'. After thorough ethnographic research, an interdisciplinary group of domain experts closely interacted with ontology engineers and social scientists in a series of workshops. Once a preliminary ontology was developed, a dynamic care request system was built using the ontology. Additional workshops were organized involving a broader group of domain experts to ensure the applicability of the ontology across continuous care settings. The proposed method successfully actively engaged domain experts in constructing the ontology, without overburdening them. Its applicability is illustrated by presenting the co-created continuous care ontology. The lessons learned during the design and execution of the approach are also presented. F. Ongenae et al. / Ontology co-design methodgeneric when it comes to issues of knowledge elicitation from domain experts. The methodologies acknowledge there exists an imperative need for a close interaction between domain experts and ontology engineers, but extensive studies on which techniques should be used for this are largely missing.In this paper, an ontology engineering approach is described that aims at involving the domain experts in each step of the ontology life cycle without asking them to construct the ontology themselves or attribute a large amount of their time. The approach acknowledges that domain experts are not ontology engineers and vice versa. To reach this goal, user-driven and participatory methods and tools are employed. The rationale behind this approach is that it increases the acceptance of ontology-driven technologies and facilitates their appropriation by the domain experts. It encourages users to feel in control of the ontology, continue to adapt it and to thus increase its accuracy.The objective of this paper is twofold. On one hand, the process we have designed to realize a participatory ontology engineering method is discussed. A series of workshops is described that were organized to actively involve domain experts. The instruments that were used to unite all the insights gained during these workshops and how these were translated into the ontology are presented. On the other hand, the continuous care ontology, which resulted from applying the method, is presented.The following specific research questions are addressed:• RQ1: How to involve users in the creation of an ontology without overburdening them?• RQ2: How to reach a cross-institutional validity of an ontology?This paper elaborates on previous publications about this research (Bleumers et al., 2011;Ongenae et al., 2011a) by concentrating on the whole ontology development process by including later stages of the ontology construction, i.e., the workshops that have addressed the cross-institutional validity of the ontology. As such, a reflec...

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“…In [8], we have explored the adoption of an ontologybased methodology for building and wireless sensor network (WSN) monitoring, starting from an existing WSN ontology. One example definition modelled in the extension of this existing ontology specifies that a TMoteSky sensor node which has a sensor part that outputs at the same time zero as value for temperature, humidity and light intensity, or which does not produce anything at all is to be classified as faulty.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Scheduling of Distributed DL-Reasoning Tasks in Wireless Sensor Networks

Verstichel

Volckaert

Dhoedt

et al. 2011

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) provide a means to acquire lots of raw data from vast amounts of easy-to-deploy sensors.Ontologies facilitate structuring data into information and support automatic inference mechanisms. The combination of wireless sensor networks and ontologies can bring significant added value to intelligently process the raw data into meaningful information. In an ontology-based system, this process is referred to as description logics (DL) reasoning. However, the sensors might not be able to execute the reasoning process locally because of resource constraints. Additionally, the usage of the radio interface consumes a lot of power. Therefore, a balance has to be found between local processing and transmission towards the more powerful nodes. In this paper, we present our collaboration platform to bring together wireless sensor networks and distributed ontology A-Box reasoning. This platform should support the adoption of ontology-based methodologies and DL-Reasoning in a distributed setting. We detail a number of algorithms to optimise both bandwidth utilisation and power consumption. These algorithms have been evaluated on a real-life wireless sensor and mesh network test bed, namely WiLab.t. The results show that significant savings of up to 92% in terms of bandwidth utilisation can result from our approach.

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Distributed Ontology-Based Monitoring on the IBBT WiLab.t Infrastructure

Cited by 6 publications

References 8 publications

Ambient-aware continuous care through semantic context dissemination

Ambient-aware continuous care through semantic context dissemination

An ontology co-design method for the co-creation of a continuous care ontology

Context-Aware Scheduling of Distributed DL-Reasoning Tasks in Wireless Sensor Networks

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