Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks
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...
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University of Qom
2023-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_2346_9e41dc69acba12012a1e8d76198a2774.pdf |
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| _version_ | 1832577525817016320 |
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| author | Aboulfazl Ebrahimi Mahboubeh Shamsi Morteza Mohajjel |
| author_facet | Aboulfazl Ebrahimi Mahboubeh Shamsi Morteza Mohajjel |
| author_sort | Aboulfazl Ebrahimi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4535356a784646f5a852554a361c9a12 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2023-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-4535356a784646f5a852554a361c9a122025-01-30T20:18:53ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752023-09-019116218810.22091/JEMSC.2022.7422.11622346Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networksAboulfazl Ebrahimi0Mahboubeh Shamsi1Morteza Mohajjel2MSC. Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: ebrahimi.a@qut.ac.irAssistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: shamsi@qut.ac.irAssistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: mohajjel@qut.ac.irIn 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.https://jemsc.qom.ac.ir/article_2346_9e41dc69acba12012a1e8d76198a2774.pdfwbandeep learningartificial neural networkmissing dataestimation |
| spellingShingle | Aboulfazl Ebrahimi Mahboubeh Shamsi Morteza Mohajjel Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks مدیریت مهندسی و رایانش نرم wban deep learning artificial neural network missing data estimation |
| title | Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| title_full | Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| title_fullStr | Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| title_full_unstemmed | Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| title_short | Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| title_sort | optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks |
| topic | wban deep learning artificial neural network missing data estimation |
| url | https://jemsc.qom.ac.ir/article_2346_9e41dc69acba12012a1e8d76198a2774.pdf |
| work_keys_str_mv | AT aboulfazlebrahimi optimaladjustmentofdeepneuralnetworkparametersinestimatinglostvitalsigndatainbodywirelesssensornetworks AT mahboubehshamsi optimaladjustmentofdeepneuralnetworkparametersinestimatinglostvitalsigndatainbodywirelesssensornetworks AT mortezamohajjel optimaladjustmentofdeepneuralnetworkparametersinestimatinglostvitalsigndatainbodywirelesssensornetworks |