Demand‐based charging strategy for wireless rechargeable sensor networks
A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2019-01-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2018-0126 |
| _version_ | 1819013703975043072 |
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| author | Ying Dong Yuhou Wang Shiyuan Li Mengyao Cui Hao Wu |
| author_facet | Ying Dong Yuhou Wang Shiyuan Li Mengyao Cui Hao Wu |
| author_sort | Ying Dong |
| collection | DOAJ |
| description | A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility. |
| first_indexed | 2024-12-21T02:04:10Z |
| format | Article |
| id | doaj.art-d948e03023794c6d8dcf7b5c4b3347e8 |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-12-21T02:04:10Z |
| publishDate | 2019-01-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-d948e03023794c6d8dcf7b5c4b3347e82022-12-21T19:19:34ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632019-01-0141332633610.4218/etrij.2018-012610.4218/etrij.2018-0126Demand‐based charging strategy for wireless rechargeable sensor networksYing DongYuhou WangShiyuan LiMengyao CuiHao WuA wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.https://doi.org/10.4218/etrij.2018-0126chargingcharging programmingenergy balancewireless rechargeable sensor network |
| spellingShingle | Ying Dong Yuhou Wang Shiyuan Li Mengyao Cui Hao Wu Demand‐based charging strategy for wireless rechargeable sensor networks ETRI Journal charging charging programming energy balance wireless rechargeable sensor network |
| title | Demand‐based charging strategy for wireless rechargeable sensor networks |
| title_full | Demand‐based charging strategy for wireless rechargeable sensor networks |
| title_fullStr | Demand‐based charging strategy for wireless rechargeable sensor networks |
| title_full_unstemmed | Demand‐based charging strategy for wireless rechargeable sensor networks |
| title_short | Demand‐based charging strategy for wireless rechargeable sensor networks |
| title_sort | demand based charging strategy for wireless rechargeable sensor networks |
| topic | charging charging programming energy balance wireless rechargeable sensor network |
| url | https://doi.org/10.4218/etrij.2018-0126 |
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