Vol.69, No.1, 2021, pp.1097-1108, doi:10.32604/cmc.2021.018187
An Optimized Data Fusion Paradigm for WSN Based on Neural Networks
  • Moath Alsafasfeh1,*, Zaid A. Arida2, Omar A. Saraereh3, Qais Alsafasfeh4, Salem Alemaishat5
1 Department of Computer Engineering, Al-Hussein Bin Talal University, Ma’an, Jordan
2 School of Computing and Informatics, Al Hussein Technical University, Amman, 11831, Jordan
3 Department of Electrical Engineering, Hashemite University, Zarqa, 13133, Jordan
4 Department of Electrical Power and Mechatronics, Tafila Technical University, Tafila, Jordan
5 School of Computing and Informatics, Al-Hussein Technical University KHBP, Amman, 11855, Jordan
* Corresponding Author: Moath Alsafasfeh. Email:
Received 28 February 2021; Accepted 31 March 2021; Issue published 04 June 2021
Wireless sensor networks (WSNs) have gotten a lot of attention as useful tools for gathering data. The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes. This paper proposed a data fusion model based on the back propagation neural network (BPNN) model to address the problem of a large number of invalid or redundant data. Using three layered-based BPNNs and a TEEN threshold, the proposed model describes the cluster structure and filters out unnecessary details. During the information transmission process, the neural network’s output function is used to deal with a large amount of sensing data, where the feature value of sensing data is extracted and transmitted to the sink node. In terms of life cycle, data traffic, and network use, simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol. As a result, the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.
WSN; clustering; data collection; neural networks
Cite This Article
M. Alsafasfeh, Z. A. Arida, O. A. Saraereh, Q. Alsafasfeh and S. Alemaishat, "An optimized data fusion paradigm for wsn based on neural networks," Computers, Materials & Continua, vol. 69, no.1, pp. 1097–1108, 2021.
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