TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks |
| 영문제목(English Title) |
Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks |
| 저자(Author) |
Dongyao Zou
Guohao Sun
Zhigang Li
Guangyong Xi
Liping Wang
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| 원문수록처(Citation) |
VOL 16 NO. 8 PP. 2627 ~ 2647 (2022. 8) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
Wireless Sensor Networks (WSNs)
Convolutional Neural Networks (CNN)
Data Augmentation
Node Localization
Degree of intersection
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| 파일첨부 |
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