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- 저자 : Guangyong Xi (검색결과 2 건)
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A hybrid model of regional path loss of wireless signals through the wall
Guangyong Xi,Shizhen Lin,Dongyao Zou 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.9
Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.
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Dongyao Zou,Guohao Sun,Zhigang Li,Guangyong Xi,Liping Wang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.8
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.
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