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http://dx.doi.org/10.3837/tiis.2022.08.009

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks  

Zou, Dongyao (School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Sun, Guohao (School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Li, Zhigang (School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Xi, Guangyong (School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Wang, Liping (School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.8, 2022 , pp. 2627-2647 More about this Journal
Abstract
The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.
Keywords
Wireless Sensor Networks (WSNs); Convolutional Neural Networks (CNN); Data Augmentation; Node Localization; Degree of intersection;
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Times Cited By KSCI : 8  (Citation Analysis)
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