Cargando…
Clustered Data Muling in the Internet of Things in Motion†
This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sens...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387288/ https://www.ncbi.nlm.nih.gov/pubmed/30682867 http://dx.doi.org/10.3390/s19030484 |
_version_ | 1783397547206770688 |
---|---|
author | Tuyishimire, Emmanuel Bagula, Antoine Ismail, Adiel |
author_facet | Tuyishimire, Emmanuel Bagula, Antoine Ismail, Adiel |
author_sort | Tuyishimire, Emmanuel |
collection | PubMed |
description | This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case. |
format | Online Article Text |
id | pubmed-6387288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63872882019-02-26 Clustered Data Muling in the Internet of Things in Motion† Tuyishimire, Emmanuel Bagula, Antoine Ismail, Adiel Sensors (Basel) Article This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case. MDPI 2019-01-24 /pmc/articles/PMC6387288/ /pubmed/30682867 http://dx.doi.org/10.3390/s19030484 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 Tuyishimire, Emmanuel Bagula, Antoine Ismail, Adiel Clustered Data Muling in the Internet of Things in Motion† |
title | Clustered Data Muling in the Internet of Things in Motion† |
title_full | Clustered Data Muling in the Internet of Things in Motion† |
title_fullStr | Clustered Data Muling in the Internet of Things in Motion† |
title_full_unstemmed | Clustered Data Muling in the Internet of Things in Motion† |
title_short | Clustered Data Muling in the Internet of Things in Motion† |
title_sort | clustered data muling in the internet of things in motion† |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387288/ https://www.ncbi.nlm.nih.gov/pubmed/30682867 http://dx.doi.org/10.3390/s19030484 |
work_keys_str_mv | AT tuyishimireemmanuel clustereddatamulingintheinternetofthingsinmotion AT bagulaantoine clustereddatamulingintheinternetofthingsinmotion AT ismailadiel clustereddatamulingintheinternetofthingsinmotion |