Datensatz

Zusammenfassung

In wireless sensor networks, the dynamic network topology and the limitation of communication resources may lead to degradation of the estimation performance of distributed algorithms. To solve this problem, we propose an event-triggered adaptive partial diffusion least mean-square algorithm (ET-APDLMS). On the one hand, the adaptive partial diffusion strategy adapts to the dynamic topology of the network while ensuring the estimation performance. On the other hand, the event-triggered mechanism can effectively reduce the data redundancy and save the communication resources of the network. The communication cost analysis of the ET-APDLMS algorithm is given in the performance analysis. The theoretical results prove that the algorithm is asymptotically unbiased, and it converges in the mean sense and the mean-square sense. In the simulation, we compare the mean-square deviation performance of the ET-APDLMS algorithm and other different diffusion algorithms. The simulation results are consistent with the performance analysis, which verifies the effectiveness of the proposed algorithm. In distributed algorithms, nodes cooperate to complete parameter estimation, which plays a vital role in signal processing. The existing traditional distributed algorithms have high estimation performance but often ignore the problem of high communication costs. Therefore, in recent years, there have been many studies on communication cost reduction algorithms (such as partial, data-selective). In this article, we propose the event-triggered adaptive partial diffusion least mean-square (ET-APDLMS) algorithm to reduce the communication costs of the network. Besides, different from the traditional communication cost reduction algorithm, the ET-APDLMS algorithm considers that the actual environment (such as the ocean and atmosphere) on the impact of network topology structure, makes the algorithm adapt to different application environments. The theoretical results prove that the algorithm is asymptotically unbiased, and it converges in the mean sense and the mean-square sense. In the simulation, we compared the mean-square deviation (MSD) performance of the ET-APDLMS algorithm and other different diffusion algorithms