Abstract
Since many information fusion algorithms fail to effectively identify the boundary threshold value, this paper integrates D–S evidence theory with stacked auto-encoder, and proposes an information fusion algorithm for wireless sensor network based on mass deep auto-encoder learning and adaptive weighted D–S evidence synthesis. Firstly, feature extraction and classification model is designed in our proposed algorithm to extract and classify the data features of nodes in each cluster, and then these features in the same class are sent to sink node. Finally, the fused feature information from different sensor nodes can be obtained through adaptive weighted D–S evidence theory. Experimental results show that the performance of our proposed method is better than those of the popular comparison methods in both objective and subjective quality, which has relative advantages for engineering applications.
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Luo, Lx. Information fusion for wireless sensor network based on mass deep auto-encoder learning and adaptive weighted D–S evidence synthesis. J Ambient Intell Human Comput 11, 519–526 (2020). https://doi.org/10.1007/s12652-018-0999-5
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DOI: https://doi.org/10.1007/s12652-018-0999-5