In our proposed algorithm, we use the curved face recovery to utilize the spatial correlation in the sensory data, then we explore the temporal correlation ...
A novel algorithm based on the Markov Random Field is proposed to reduce the data error ratio during the data recovery process and can reduce the error rate ...
In this paper, we propose a algorithm to recover the lost data in the wireless sensor network, taking into account the space-time correlations under the Markov ...
This method predicts the probability that a node becomes failure due to lack of energy. The authors in [30] formulated the data recovery problem in the ...
Various data recovery approaches such as spatial correlation, Markov random field model and compressed sensing are available in the wireless sensors networks ( ...
In this paper, we investigate how to solve a challenging problem in the WSNs: how to omit a considerable number of sensor nodes from the monitoring field and ...
Missing: Markov | Show results with:Markov
Jul 31, 2024 · Our proposed system integrates advanced data recovery techniques, employing bi-directional long short-term memory (Bi-LSTM) networks with an ...
Data recovery in wireless sensor networks using Markov random field model. Hongju Cheng, Leihuo Wu, Yayun Zhang, N. Xiong. 2018, International Conference on ...
This paper explains how the principles underlying MRF theory naturally fit design requirements in sensor networks in particular the need to rely on ...
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The analysis and experimental results demonstrate that the proposed algorithm can significantly reduce the complexity and ensure the accuracy of recovery,thus ...