Universiti Teknologi Malaysia Institutional Repository

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

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


Official URL: http://dx.doi.org/10.3390/sym12122024


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.

Item Type:Article
Uncontrolled Keywords:Clustering, Energy consumption
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:91792
Deposited By: Widya Wahid
Deposited On:28 Jul 2021 16:42
Last Modified:28 Jul 2021 16:42

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