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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Dao, TK., Nguyen, TT., Vu, VD., Ngo, TG. (2022). A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network. In: Wu, TY., Ni, S., Chu, SC., Chen, CH., Favorskaya, M. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 250. Springer, Singapore. https://doi.org/10.1007/978-981-16-4039-1_31
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DOI: https://doi.org/10.1007/978-981-16-4039-1_31
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