Dynamic discovery and semantic reasoning for next generation intelligent environments

López‐de‐Ipiña, Diego; Almeida, Aitor; Aguilera, Unai; Larizgoitia, Iker; Laiseca, Xabier; Orduña, Pablo; Barbier, Ander; Vázquez, Juan Ignacio

doi:10.1049/cp:20081119

Cited by 13 publications

(14 citation statements)

References 8 publications

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“…In [4] we analyzed the performance of different semantic reasoning engines.The architecture of the reasoning engine ecosystem: The final architecture of the distributed reasoning ecosystem will depend on the specific domain of the problem. The architecture will dictate how much of the reasoning process can be parallelized.…”

Section: Discussionmentioning

confidence: 99%

A Distributed Reasoning Engine Ecosystem for Semantic Context-Management in Smart Environments

Almeida

López-de-Ipiña

2012

Sensors

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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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Section: Discussionmentioning

confidence: 99%

A Distributed Reasoning Engine Ecosystem for Semantic Context-Management in Smart Environments

Almeida

López-de-Ipiña

2012

Sensors

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“…Several interesting architectures and middleware systems for smart spaces have been developed in the last years. Several of them were layered architectures most of which have used agent technologies like the ones proposed by [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The Smart-M3 was based on a blackboard architecture model [19].…”

Section: Related Workmentioning

confidence: 99%

Agent-Based Context-Aware Architecture for a Smart Living Room

Miraoui

El-etriby

Abid

et al. 2016

IJSH

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“…Previously created context-aware enabling frameworks and architectures have used different approaches in order to promote personal autonomy [3]- [6]. Their analysis allows to highlight features like resource discovery, capability to reason over context, or the context model used in different scenarios.…”

Section: Related Workmentioning

confidence: 99%