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2022 | OriginalPaper | Buchkapitel

31. A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network

verfasst von : Thi-Kien Dao, Trong-The Nguyen, Van-Dinh Vu, Truong-Giang Ngo

Erschienen in: Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Verlag: Springer Singapore

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Abstract

This study suggests a scheme of data fusion strategy in wireless sensor networks (WSNs) based on optimizing the neural network (NN) to decrease data redundancy, increase data transmission, and save communication energy consumption in WSN. The optimal parameters are optimized by applying the bat algorithm (BA). The optimized neural network (NNBA) is used to fuse captured data in cluster head (CH) and then forwards the combined data to the base station (BS) of a WSN. The simulation experiment is implemented in several scenarios to test the proposed scheme performance. The proposed scheme's results show that the proposed algorithm can save sensor node energy consumption, extend the lifetime, and increase the data fusion accuracy of WSN.

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Metadaten
Titel
A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network
verfasst von
Thi-Kien Dao
Trong-The Nguyen
Van-Dinh Vu
Truong-Giang Ngo
Copyright-Jahr
2022
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-16-4039-1_31

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