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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 wireless sensor networks, dynamic environmental changes may lead to the distribution of some sensors becoming very dense, which leads to the waste of communication resources in distributed estimation. In this paper, it is proposed that Event-triggered distributed dynamic adaptive-neighbour selection LMS (ET-dyANDLMS) algorithm can effectively solve the problems mentioned above. On the one hand, the network communication resources are reduced by the adaptive-neighbour selection. On the other hand, the network data redundancy is reduced by the event-trigger mechanism. From a theoretical point of view, the algorithm converges stably in mean meaning and mean square meaning, and the communication cost is reduced compared with other distributed algorithms. The theoretical analysis results verified by experimental simulation demonstrate the effectiveness of the proposed algorithm.