Article
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Preserved in Portico This version is not peer-reviewed
Multi-DBN Weighted Voting Algorithm for Multi-Targets Classification in WSN
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Version 1
: Received: 1 November 2023 / Approved: 1 November 2023 / Online: 1 November 2023 (09:35:38 CET)
A peer-reviewed article of this Preprint also exists.
Zhang, H.; Zhou, Y. A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN. Sensors 2024, 24, 123. Zhang, H.; Zhou, Y. A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN. Sensors 2024, 24, 123.
Abstract
One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.
Keywords
WSN; multi-targets classification; DBN classifier; multi-DBN weighted voting algorithm
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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