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On Connectivity of Wireless Sensor Networks with Directional Antennas
In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna mo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298707/ https://www.ncbi.nlm.nih.gov/pubmed/28085081 http://dx.doi.org/10.3390/s17010134 |
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author | Wang, Qiu Dai, Hong-Ning Zheng, Zibin Imran, Muhammad Vasilakos, Athanasios V. |
author_facet | Wang, Qiu Dai, Hong-Ning Zheng, Zibin Imran, Muhammad Vasilakos, Athanasios V. |
author_sort | Wang, Qiu |
collection | PubMed |
description | In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models. |
format | Online Article Text |
id | pubmed-5298707 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-52987072017-02-10 On Connectivity of Wireless Sensor Networks with Directional Antennas Wang, Qiu Dai, Hong-Ning Zheng, Zibin Imran, Muhammad Vasilakos, Athanasios V. Sensors (Basel) Article In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models. MDPI 2017-01-12 /pmc/articles/PMC5298707/ /pubmed/28085081 http://dx.doi.org/10.3390/s17010134 Text en © 2017 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 Wang, Qiu Dai, Hong-Ning Zheng, Zibin Imran, Muhammad Vasilakos, Athanasios V. On Connectivity of Wireless Sensor Networks with Directional Antennas |
title | On Connectivity of Wireless Sensor Networks with Directional Antennas |
title_full | On Connectivity of Wireless Sensor Networks with Directional Antennas |
title_fullStr | On Connectivity of Wireless Sensor Networks with Directional Antennas |
title_full_unstemmed | On Connectivity of Wireless Sensor Networks with Directional Antennas |
title_short | On Connectivity of Wireless Sensor Networks with Directional Antennas |
title_sort | on connectivity of wireless sensor networks with directional antennas |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298707/ https://www.ncbi.nlm.nih.gov/pubmed/28085081 http://dx.doi.org/10.3390/s17010134 |
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