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Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actua...

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Detalles Bibliográficos
Autores principales: Hassani, Alireza, Medvedev, Alexey, Zaslavsky, Arkady, Delir Haghighi, Pari, Jayaraman, Prem Prakash, Ling, Sea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960719/
https://www.ncbi.nlm.nih.gov/pubmed/31835743
http://dx.doi.org/10.3390/s19245457
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author Hassani, Alireza
Medvedev, Alexey
Zaslavsky, Arkady
Delir Haghighi, Pari
Jayaraman, Prem Prakash
Ling, Sea
author_facet Hassani, Alireza
Medvedev, Alexey
Zaslavsky, Arkady
Delir Haghighi, Pari
Jayaraman, Prem Prakash
Ling, Sea
author_sort Hassani, Alireza
collection PubMed
description As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.
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spelling pubmed-69607192020-01-23 Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications Hassani, Alireza Medvedev, Alexey Zaslavsky, Arkady Delir Haghighi, Pari Jayaraman, Prem Prakash Ling, Sea Sensors (Basel) Article As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments. MDPI 2019-12-11 /pmc/articles/PMC6960719/ /pubmed/31835743 http://dx.doi.org/10.3390/s19245457 Text en © 2019 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
Hassani, Alireza
Medvedev, Alexey
Zaslavsky, Arkady
Delir Haghighi, Pari
Jayaraman, Prem Prakash
Ling, Sea
Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title_full Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title_fullStr Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title_full_unstemmed Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title_short Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
title_sort efficient execution of complex context queries to enable near real-time smart iot applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960719/
https://www.ncbi.nlm.nih.gov/pubmed/31835743
http://dx.doi.org/10.3390/s19245457
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