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A hybrid prediction model for energy-efficient data collection in wireless sensor networks

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data co...

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Main Authors: Soleymani, Seyed Ahmad, Goudarzi, Shidrokh, Kama, Nazri, Ismail, Saiful Adli, Ali, Mazlan, Zainal, Zaini M.D., Zareei, Mahdi
Format: Article
Language:English
Published: MDPI AG 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/91792/1/MohdNazriKama2020_AHybridPredictionModelforEnergyEfficient.pdf
http://eprints.utm.my/id/eprint/91792/
http://dx.doi.org/10.3390/sym12122024
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spelling my.utm.917922021-07-28T08:42:54Z http://eprints.utm.my/id/eprint/91792/ A hybrid prediction model for energy-efficient data collection in wireless sensor networks Soleymani, Seyed Ahmad Goudarzi, Shidrokh Kama, Nazri Ismail, Saiful Adli Ali, Mazlan Zainal, Zaini M.D. Zareei, Mahdi T Technology (General) Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions. MDPI AG 2020-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/91792/1/MohdNazriKama2020_AHybridPredictionModelforEnergyEfficient.pdf Soleymani, Seyed Ahmad and Goudarzi, Shidrokh and Kama, Nazri and Ismail, Saiful Adli and Ali, Mazlan and Zainal, Zaini M.D. and Zareei, Mahdi (2020) A hybrid prediction model for energy-efficient data collection in wireless sensor networks. Symmetry, 12 (12). pp. 1-16. ISSN 2078994 http://dx.doi.org/10.3390/sym12122024
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Soleymani, Seyed Ahmad
Goudarzi, Shidrokh
Kama, Nazri
Ismail, Saiful Adli
Ali, Mazlan
Zainal, Zaini M.D.
Zareei, Mahdi
A hybrid prediction model for energy-efficient data collection in wireless sensor networks
description Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions.
format Article
author Soleymani, Seyed Ahmad
Goudarzi, Shidrokh
Kama, Nazri
Ismail, Saiful Adli
Ali, Mazlan
Zainal, Zaini M.D.
Zareei, Mahdi
author_facet Soleymani, Seyed Ahmad
Goudarzi, Shidrokh
Kama, Nazri
Ismail, Saiful Adli
Ali, Mazlan
Zainal, Zaini M.D.
Zareei, Mahdi
author_sort Soleymani, Seyed Ahmad
title A hybrid prediction model for energy-efficient data collection in wireless sensor networks
title_short A hybrid prediction model for energy-efficient data collection in wireless sensor networks
title_full A hybrid prediction model for energy-efficient data collection in wireless sensor networks
title_fullStr A hybrid prediction model for energy-efficient data collection in wireless sensor networks
title_full_unstemmed A hybrid prediction model for energy-efficient data collection in wireless sensor networks
title_sort hybrid prediction model for energy-efficient data collection in wireless sensor networks
publisher MDPI AG
publishDate 2020
url http://eprints.utm.my/id/eprint/91792/1/MohdNazriKama2020_AHybridPredictionModelforEnergyEfficient.pdf
http://eprints.utm.my/id/eprint/91792/
http://dx.doi.org/10.3390/sym12122024
_version_ 1706956994753069056
score 13.211869

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