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Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks

We propose a new harvesting approach for Vehicular Sensor Networks based on compressed sensing (CS) technology called Compressed Sensing-based Vehicular Data Harvesting (CS-VDH). This compression technology allows for the reduction of the information volume that nodes must send back to the fusion ce...

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Detalles Bibliográficos
Autores principales: Martinez, Juan Antonio, Ruiz, Pedro Miguel, Skarmeta, Antonio F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085759/
https://www.ncbi.nlm.nih.gov/pubmed/32155718
http://dx.doi.org/10.3390/s20051434
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author Martinez, Juan Antonio
Ruiz, Pedro Miguel
Skarmeta, Antonio F.
author_facet Martinez, Juan Antonio
Ruiz, Pedro Miguel
Skarmeta, Antonio F.
author_sort Martinez, Juan Antonio
collection PubMed
description We propose a new harvesting approach for Vehicular Sensor Networks based on compressed sensing (CS) technology called Compressed Sensing-based Vehicular Data Harvesting (CS-VDH). This compression technology allows for the reduction of the information volume that nodes must send back to the fusion center and also an accurate recovery of the original data, even in absence of several original measurements. Our proposed method, thanks to a proper design of a delay function, orders the transmission of these measurements, being the nodes farther from the fusion center, the ones starting this transmission. This way, intermediate nodes are more likely to introduce their measurements in a packet traversing the network and to apply the CS technology. This way the contribution is twofold, adding different measurements to traversing packets, we reduce the total overload of the network, and also reducing the size of the packets thanks to the applied compression technology. We evaluate our solution by using ns-2 simulations in a realistic vehicular environment generated by SUMO, a well-known traffic simulator tool in the Vehicular Network domain. Our simulations show that CS-VDH outperforms Delay-Bounded Vehicular Data Gathering (DB-VDG), a well-known protocol for data gathering in vehicular sensor networks which considers a specific delay bound. We also evaluated the proper design of our delay function, as well as the accuracy in the reconstruction of the original data. Regarding this latter topic, our experiments proved that our proposed solution can recover sampled data with little error while still reducing the amount of information traveling through the vehicular network.
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spelling pubmed-70857592020-03-25 Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks Martinez, Juan Antonio Ruiz, Pedro Miguel Skarmeta, Antonio F. Sensors (Basel) Article We propose a new harvesting approach for Vehicular Sensor Networks based on compressed sensing (CS) technology called Compressed Sensing-based Vehicular Data Harvesting (CS-VDH). This compression technology allows for the reduction of the information volume that nodes must send back to the fusion center and also an accurate recovery of the original data, even in absence of several original measurements. Our proposed method, thanks to a proper design of a delay function, orders the transmission of these measurements, being the nodes farther from the fusion center, the ones starting this transmission. This way, intermediate nodes are more likely to introduce their measurements in a packet traversing the network and to apply the CS technology. This way the contribution is twofold, adding different measurements to traversing packets, we reduce the total overload of the network, and also reducing the size of the packets thanks to the applied compression technology. We evaluate our solution by using ns-2 simulations in a realistic vehicular environment generated by SUMO, a well-known traffic simulator tool in the Vehicular Network domain. Our simulations show that CS-VDH outperforms Delay-Bounded Vehicular Data Gathering (DB-VDG), a well-known protocol for data gathering in vehicular sensor networks which considers a specific delay bound. We also evaluated the proper design of our delay function, as well as the accuracy in the reconstruction of the original data. Regarding this latter topic, our experiments proved that our proposed solution can recover sampled data with little error while still reducing the amount of information traveling through the vehicular network. MDPI 2020-03-06 /pmc/articles/PMC7085759/ /pubmed/32155718 http://dx.doi.org/10.3390/s20051434 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 Article
Martinez, Juan Antonio
Ruiz, Pedro Miguel
Skarmeta, Antonio F.
Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title_full Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title_fullStr Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title_full_unstemmed Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title_short Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
title_sort evaluation of the use of compressed sensing in data harvesting for vehicular sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085759/
https://www.ncbi.nlm.nih.gov/pubmed/32155718
http://dx.doi.org/10.3390/s20051434
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