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An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)

Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for p...

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Autores principales: Ismael, Waleed M., Gao, Mingsheng, Al-Shargabi, Asma A., Zahary, Ammar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412591/
https://www.ncbi.nlm.nih.gov/pubmed/30781406
http://dx.doi.org/10.3390/s19040795
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author Ismael, Waleed M.
Gao, Mingsheng
Al-Shargabi, Asma A.
Zahary, Ammar
author_facet Ismael, Waleed M.
Gao, Mingsheng
Al-Shargabi, Asma A.
Zahary, Ammar
author_sort Ismael, Waleed M.
collection PubMed
description Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou’s (CLONE) method.
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spelling pubmed-64125912019-04-03 An In-Networking Double-Layered Data Reduction for Internet of Things (IoT) Ismael, Waleed M. Gao, Mingsheng Al-Shargabi, Asma A. Zahary, Ammar Sensors (Basel) Article Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou’s (CLONE) method. MDPI 2019-02-15 /pmc/articles/PMC6412591/ /pubmed/30781406 http://dx.doi.org/10.3390/s19040795 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
Ismael, Waleed M.
Gao, Mingsheng
Al-Shargabi, Asma A.
Zahary, Ammar
An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title_full An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title_fullStr An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title_full_unstemmed An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title_short An In-Networking Double-Layered Data Reduction for Internet of Things (IoT)
title_sort in-networking double-layered data reduction for internet of things (iot)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412591/
https://www.ncbi.nlm.nih.gov/pubmed/30781406
http://dx.doi.org/10.3390/s19040795
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