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Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering
Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud pu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704366/ https://www.ncbi.nlm.nih.gov/pubmed/34960325 http://dx.doi.org/10.3390/s21248232 |
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author | Pham, Van-Nam Lee, Ga-Won Nguyen, VanDung Huh, Eui-Nam |
author_facet | Pham, Van-Nam Lee, Ga-Won Nguyen, VanDung Huh, Eui-Nam |
author_sort | Pham, Van-Nam |
collection | PubMed |
description | Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one. |
format | Online Article Text |
id | pubmed-8704366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87043662021-12-25 Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering Pham, Van-Nam Lee, Ga-Won Nguyen, VanDung Huh, Eui-Nam Sensors (Basel) Article Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one. MDPI 2021-12-09 /pmc/articles/PMC8704366/ /pubmed/34960325 http://dx.doi.org/10.3390/s21248232 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pham, Van-Nam Lee, Ga-Won Nguyen, VanDung Huh, Eui-Nam Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title | Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title_full | Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title_fullStr | Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title_full_unstemmed | Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title_short | Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering |
title_sort | efficient solution for large-scale iot applications with proactive edge-cloud publish/subscribe brokers clustering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704366/ https://www.ncbi.nlm.nih.gov/pubmed/34960325 http://dx.doi.org/10.3390/s21248232 |
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