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- W4309156430 abstract "With the development of Internet of Things (IoT), wireless rechargeable sensor networks (WRSNs) have been widely applied in modern society. There has a greater demand for green and efficient remote wireless charging to power the wireless sensor nodes (SNs) in WRSNs. However, few has tackled the wireless charging efficiency problem that can achieve the low dead SNs percentage (DSP) in far-field wireless charging. Existing far-field wireless charging schemes cannot meet the energy supply requirement of large-scale SNs. Therefore, this work proposes the multiple mobile chargers (MCs)-assisted efficient green energy wireless charging for WRSNs. Firstly, according to the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the area not covered by the wireless charging base stations (BSs) is divided into multiple irregular-shape sub-regions. Then, we leverage multiple MCs to wirelessly charge the SNs located in these sub-regions. Finally, we propose the Minimal chArging grouPings (MAP) algorithm to minimize the number of the MCs’ anchor points (APs) and efficiently charge the SNs. The simulation results validate that our proposed algorithm can effectively improve the wireless charging energy efficiency and reduce the DSP." @default.
- W4309156430 created "2022-11-24" @default.
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- W4309156430 date "2022-10-19" @default.
- W4309156430 modified "2023-10-02" @default.
- W4309156430 title "Multiple Mobile Chargers-assisted Efficient Green Energy Wireless Charging for WRSNs" @default.
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- W4309156430 doi "https://doi.org/10.1109/apcc55198.2022.9943772" @default.
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