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A distributed reasoning engine ecosystem for semantic context-management in smart environments.

Almeida A, López-de-Ipiña D - Sensors (Basel) (2012)

Bottom Line: Ontologies have proven themselves to be one of the best tools to do it.In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time.Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.

View Article: PubMed Central - PubMed

Affiliation: Deusto Institute of Technology (DeustoTech), University of Deusto, Bilbao 48007, Spain. aitor.almeida@deusto.es

ABSTRACT
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method is needed. Ontologies have proven themselves to be one of the best tools to do it. Semantic inference provides a powerful framework to reason over the context data. But there are some problems with this approach. The inference over semantic context information can be cumbersome when working with a large amount of data. This situation has become more common in modern smart environments where there are a lot sensors and devices available. In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time. In this paper we describe a distributed peer-to-peer agent architecture of context consumers and context providers. We explain how this inference sharing process works, partitioning the context information according to the interests of the agents, location and a certainty factor. We also discuss the system architecture, analyzing the negotiation process between the agents. Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.

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Subset of the main ontology concepts. Image extracted from [27].
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f4-sensors-12-10208: Subset of the main ontology concepts. Image extracted from [27].

Mentions: LocableThing: The subclasses of this class represent the elements of the system that have a physical location. It contains three subclasses: the Person subclass represents the users, the Device subclass models the different devices of the environment and the ContextData subclass models the measures taken by the sensors. As we will explain in the next section there are two types of measures, those taken by the devices and the global measures for each room calculated by our data fusion mechanism. Figure 4 shows a subset of the type of context data taken into account in the ontology.


A distributed reasoning engine ecosystem for semantic context-management in smart environments.

Almeida A, López-de-Ipiña D - Sensors (Basel) (2012)

Subset of the main ontology concepts. Image extracted from [27].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472824&req=5

f4-sensors-12-10208: Subset of the main ontology concepts. Image extracted from [27].
Mentions: LocableThing: The subclasses of this class represent the elements of the system that have a physical location. It contains three subclasses: the Person subclass represents the users, the Device subclass models the different devices of the environment and the ContextData subclass models the measures taken by the sensors. As we will explain in the next section there are two types of measures, those taken by the devices and the global measures for each room calculated by our data fusion mechanism. Figure 4 shows a subset of the type of context data taken into account in the ontology.

Bottom Line: Ontologies have proven themselves to be one of the best tools to do it.In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time.Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.

View Article: PubMed Central - PubMed

Affiliation: Deusto Institute of Technology (DeustoTech), University of Deusto, Bilbao 48007, Spain. aitor.almeida@deusto.es

ABSTRACT
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method is needed. Ontologies have proven themselves to be one of the best tools to do it. Semantic inference provides a powerful framework to reason over the context data. But there are some problems with this approach. The inference over semantic context information can be cumbersome when working with a large amount of data. This situation has become more common in modern smart environments where there are a lot sensors and devices available. In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time. In this paper we describe a distributed peer-to-peer agent architecture of context consumers and context providers. We explain how this inference sharing process works, partitioning the context information according to the interests of the agents, location and a certainty factor. We also discuss the system architecture, analyzing the negotiation process between the agents. Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.

Show MeSH