A Lossless Convergence Method for Reducing Data Fragments on WSN
This article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8863893/ |
| _version_ | 1818877628768059392 |
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| author | Dongchao Ma Xinlu Du Ailing Xiao Rong Xiao Li Ma Qipeng Hu |
| author_facet | Dongchao Ma Xinlu Du Ailing Xiao Rong Xiao Li Ma Qipeng Hu |
| author_sort | Dongchao Ma |
| collection | DOAJ |
| description | This article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming) model called FAT-WSN (fragmentation aggregation transmission WSN) is proposed to minimize the number of data fragments. Moreover, we propose an iterative solution for this MIP problem with elasticity and low complexity. The main optimization method for this model is to adjust topology and traffic distribution. It focuses on optimizing the number of data transfers without modifying any data and without introducing a compression calculation burden. Finally, simulation and small-scale real node verifications are performed for the FAT-WSN scheme. The experimental results show that FAT-WSN can effectively reduce the number of data transmission and reception, thereby reducing energy consumption and improving network life. Compared with the MinST model, JGDC (Jointly Gaussian Distributed Compress) model and AMREST (Approximately Maximum min-Residual Energy Steiner Tree) model, the network life can be increased by 10%-30% without extending the calculation time. |
| first_indexed | 2024-12-19T14:01:19Z |
| format | Article |
| id | doaj.art-e158bffa86144fa081c2ca2dac82e5cb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:01:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e158bffa86144fa081c2ca2dac82e5cb2022-12-21T20:18:26ZengIEEEIEEE Access2169-35362019-01-01714615814616910.1109/ACCESS.2019.29448368863893A Lossless Convergence Method for Reducing Data Fragments on WSNDongchao Ma0Xinlu Du1https://orcid.org/0000-0002-0945-854XAiling Xiao2Rong Xiao3Li Ma4Qipeng Hu5School of Computer Science, North China University of Technology, Beijing, ChinaSchool of Computer Science, North China University of Technology, Beijing, ChinaSchool of Computer Science, North China University of Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing Normal University, Beijing, ChinaSchool of Computer Science, North China University of Technology, Beijing, ChinaBeijing-Dublin International College, Beijing University of Technology, Beijing, ChinaThis article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming) model called FAT-WSN (fragmentation aggregation transmission WSN) is proposed to minimize the number of data fragments. Moreover, we propose an iterative solution for this MIP problem with elasticity and low complexity. The main optimization method for this model is to adjust topology and traffic distribution. It focuses on optimizing the number of data transfers without modifying any data and without introducing a compression calculation burden. Finally, simulation and small-scale real node verifications are performed for the FAT-WSN scheme. The experimental results show that FAT-WSN can effectively reduce the number of data transmission and reception, thereby reducing energy consumption and improving network life. Compared with the MinST model, JGDC (Jointly Gaussian Distributed Compress) model and AMREST (Approximately Maximum min-Residual Energy Steiner Tree) model, the network life can be increased by 10%-30% without extending the calculation time.https://ieeexplore.ieee.org/document/8863893/Wireless sensor networkdata convergencemaximum lifetimeenergy efficient routing protocol |
| spellingShingle | Dongchao Ma Xinlu Du Ailing Xiao Rong Xiao Li Ma Qipeng Hu A Lossless Convergence Method for Reducing Data Fragments on WSN IEEE Access Wireless sensor network data convergence maximum lifetime energy efficient routing protocol |
| title | A Lossless Convergence Method for Reducing Data Fragments on WSN |
| title_full | A Lossless Convergence Method for Reducing Data Fragments on WSN |
| title_fullStr | A Lossless Convergence Method for Reducing Data Fragments on WSN |
| title_full_unstemmed | A Lossless Convergence Method for Reducing Data Fragments on WSN |
| title_short | A Lossless Convergence Method for Reducing Data Fragments on WSN |
| title_sort | lossless convergence method for reducing data fragments on wsn |
| topic | Wireless sensor network data convergence maximum lifetime energy efficient routing protocol |
| url | https://ieeexplore.ieee.org/document/8863893/ |
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