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A Survey of Using Swarm Intelligence Algorithms in IoT
With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provide...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085620/ https://www.ncbi.nlm.nih.gov/pubmed/32150912 http://dx.doi.org/10.3390/s20051420 |
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author | Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei |
author_facet | Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei |
author_sort | Sun, Weifeng |
collection | PubMed |
description | With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn. |
format | Online Article Text |
id | pubmed-7085620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70856202020-04-21 A Survey of Using Swarm Intelligence Algorithms in IoT Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei Sensors (Basel) Review With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn. MDPI 2020-03-05 /pmc/articles/PMC7085620/ /pubmed/32150912 http://dx.doi.org/10.3390/s20051420 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 | Review Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei A Survey of Using Swarm Intelligence Algorithms in IoT |
title | A Survey of Using Swarm Intelligence Algorithms in IoT |
title_full | A Survey of Using Swarm Intelligence Algorithms in IoT |
title_fullStr | A Survey of Using Swarm Intelligence Algorithms in IoT |
title_full_unstemmed | A Survey of Using Swarm Intelligence Algorithms in IoT |
title_short | A Survey of Using Swarm Intelligence Algorithms in IoT |
title_sort | survey of using swarm intelligence algorithms in iot |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085620/ https://www.ncbi.nlm.nih.gov/pubmed/32150912 http://dx.doi.org/10.3390/s20051420 |
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