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Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme
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DOI：10.1016/j.jnca.2024.103841.png)
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Abstract
En 中文
Energy efficiency and security are critical components of Quality of Service (QoS) and remain a challenge in WSN-assisted IoT owing to its open and resource -limited nature. Despite intensive research on WSN-IoT, only a few have achieved significant levels of energy efficiency and load balancing on clustering nodes. This study proposes a novel approach for dynamic cluster -based WSN-IoT networks to enhance the network's resilience using data fusion techniques and eliminate illogical clustering. The Mean Value and Minimum Distance Method identifies the optimal cluster heads within the network by reducing data redundancy, resulting in improved quality of service, energy optimization, and enhanced lifetime. The proposed fused deep learning -based data mining method (RNN-LSTM) mitigates the data fitting and enhances the dynamic routing and balancing load at the WSN fusion center. The novel approach splits the network into layers, assigning sensor nodes to each layer, drastically reducing latency, data transfers, and the fusion center's overhead. Distinct experiments evaluated the suggested approach's efficacy by varying the hidden layer nodes and signaling intervals. The empirical verdicts exhibit that the presented routing algorithms surpass state-of-the-art conventional routing systems in energy depletion, average latency, signaling overhead, cumulative throughput, and route heterogeneity.
Keywords:
Data fusion
Multi-hop clustering
Internet of things
RNN-LSTM
Energy balanced
Inclusive innovation
Journal
IF:
8
Papers:
3.6K
Citations:
1.1W