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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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Part of the location taxonomy used on our system. The taxonomy depicts the “contains” relations of the used ontology.
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f3-sensors-12-10208: Part of the location taxonomy used on our system. The taxonomy depicts the “contains” relations of the used ontology.

Mentions: The location where the measures originate from. As with the ontological concepts we have a taxonomy of the locations extracted from our ontology. This taxonomy models the “contains” relations between the different rooms, floors and buildings (see Figure 3). The context consumer can search for an specific location (the Smartlab laboratory in our example) or for a set of related locations (for example all the rooms in the first floor of the engineering building).


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

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

Part of the location taxonomy used on our system. The taxonomy depicts the “contains” relations of the used ontology.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-10208: Part of the location taxonomy used on our system. The taxonomy depicts the “contains” relations of the used ontology.
Mentions: The location where the measures originate from. As with the ontological concepts we have a taxonomy of the locations extracted from our ontology. This taxonomy models the “contains” relations between the different rooms, floors and buildings (see Figure 3). The context consumer can search for an specific location (the Smartlab laboratory in our example) or for a set of related locations (for example all the rooms in the first floor of the engineering building).

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