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Delay Analysis in IoT Sensor Networks †
Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other...
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/PMC8200081/ https://www.ncbi.nlm.nih.gov/pubmed/34199784 http://dx.doi.org/10.3390/s21113876 |
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author | Althoubi, Asaad Alshahrani, Reem Peyravi, Hassan |
author_facet | Althoubi, Asaad Alshahrani, Reem Peyravi, Hassan |
author_sort | Althoubi, Asaad |
collection | PubMed |
description | Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model. |
format | Online Article Text |
id | pubmed-8200081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82000812021-06-14 Delay Analysis in IoT Sensor Networks † Althoubi, Asaad Alshahrani, Reem Peyravi, Hassan Sensors (Basel) Article Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model. MDPI 2021-06-04 /pmc/articles/PMC8200081/ /pubmed/34199784 http://dx.doi.org/10.3390/s21113876 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 Althoubi, Asaad Alshahrani, Reem Peyravi, Hassan Delay Analysis in IoT Sensor Networks † |
title | Delay Analysis in IoT Sensor Networks † |
title_full | Delay Analysis in IoT Sensor Networks † |
title_fullStr | Delay Analysis in IoT Sensor Networks † |
title_full_unstemmed | Delay Analysis in IoT Sensor Networks † |
title_short | Delay Analysis in IoT Sensor Networks † |
title_sort | delay analysis in iot sensor networks † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200081/ https://www.ncbi.nlm.nih.gov/pubmed/34199784 http://dx.doi.org/10.3390/s21113876 |
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