1
Master student of computer engineering Qom University of Technology
2
assistance professor of Qom university of technology
Abstract
In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not Arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. In order to predict the missing values, a model for estimating lost data based on LSTM (short-term memory) neural network is presented in this paper. This model combines five vital signs as input to predict the amount lost. The results show that sgdm-LSTM is a good way to estimate the amount lost. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 4.1495 with the best network parameters.
Ebrahimi, A., Shamsi, M., & mohajjel, M. (2024). Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks. Engineering Management and Soft Computing, (), 253-287.
MLA
Aboulfazl Ebrahimi; Mahboubeh Shamsi; morteza mohajjel. "Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks". Engineering Management and Soft Computing, , , 2024, 253-287.
HARVARD
Ebrahimi, A., Shamsi, M., mohajjel, M. (2024). 'Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks', Engineering Management and Soft Computing, (), pp. 253-287.
VANCOUVER
Ebrahimi, A., Shamsi, M., mohajjel, M. Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks. Engineering Management and Soft Computing, 2024; (): 253-287.
)
Send comment about this article