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A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments...

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Autores principales: Zeshan, Furkh, Ahmad, Adnan, Abdel-Aty, Abdel-Haleem, Algarni, Fahad, Mahmoud, Emad E., Ahmad, Ashfaq
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590014/
https://www.ncbi.nlm.nih.gov/pubmed/33092118
http://dx.doi.org/10.3390/s20205918
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author Zeshan, Furkh
Ahmad, Adnan
Abdel-Aty, Abdel-Haleem
Algarni, Fahad
Mahmoud, Emad E.
Ahmad, Ashfaq
author_facet Zeshan, Furkh
Ahmad, Adnan
Abdel-Aty, Abdel-Haleem
Algarni, Fahad
Mahmoud, Emad E.
Ahmad, Ashfaq
author_sort Zeshan, Furkh
collection PubMed
description Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.
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spelling pubmed-75900142020-10-29 A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments Zeshan, Furkh Ahmad, Adnan Abdel-Aty, Abdel-Haleem Algarni, Fahad Mahmoud, Emad E. Ahmad, Ashfaq Sensors (Basel) Article Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results. MDPI 2020-10-20 /pmc/articles/PMC7590014/ /pubmed/33092118 http://dx.doi.org/10.3390/s20205918 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zeshan, Furkh
Ahmad, Adnan
Abdel-Aty, Abdel-Haleem
Algarni, Fahad
Mahmoud, Emad E.
Ahmad, Ashfaq
A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title_full A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title_fullStr A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title_full_unstemmed A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title_short A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments
title_sort hybrid semantic knowledge integration and sharing approach for distributed smart environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590014/
https://www.ncbi.nlm.nih.gov/pubmed/33092118
http://dx.doi.org/10.3390/s20205918
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