| Autor: |
Wang, Tiantian, Li, Yuhan, Chen, Feng, Duan, Shukai |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Aerospace & Electronic Systems; Oct2022, Vol. 58 Issue 5, p4004-4019, 16p |
| Abstrakt: |
Recently, the problem of security distributed estimation in adversarial multitask wireless sensor networks has attracted extensive attention. For example, malicious attackers always affect signal processing and reduce network estimation performance by destroying data information. To address this issue, by using task similarity, this article proposes a diffusion least-mean square algorithm based on Bayes (BDLMS) in multitask adversarial network environment. The BDLMS algorithm can divide the wireless sensor system into two subsystems: Noncooperative LMS (NCLMS) subsystem and distributed diffusion LMS (DLMS) subsystem. Among them, the NCLMS subsystem ensures the state consistency of neighboring nodes by task similarity and calculates the posterior probability of node being attacked by Bayes rule to achieve attack detection. The DLMS subsystem performs parameter estimation by combining safe and reliable information. In addition, the mean and mean-square stability of the BDLMS algorithm are analyzed. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments in different network environments. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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