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| Title | Green Laser-Powered Uav Far-Field Wireless Charging And Data Backhauling For A Large-Scale Sensor Network |
|---|---|
| ID_Doc | 28410 |
| Authors | Ma X.; Liu X.; Ansari N. |
| Year | 2024 |
| Published | IEEE Internet of Things Journal, 11, 19 |
| DOI | http://dx.doi.org/10.1109/JIOT.2024.3422252 |
| Abstract | Sixth-generation (6G) wireless communications greatly emphasizes the integration of sensing, communicating, and computing. Unmanned aerial vehicles (UAVs), by leveraging their feasibility and mobility, can naturally facilitate flexible far-field wireless charging and data backhauling for widely implemented wireless rechargeable sensor networks (WRSNs) across diverse domains, such as intelligent agriculture, smart cities, and modern factories. However, the energy constraints inherent to UAVs, coupled with the absence of joint optimization in clustering and trajectory design, present formidable challenges in efficiently leveraging UAVs for large-scale WRSN wireless charging and data backhauling. Therefore, in this work, we empower the green energy-powered base station (GBS) to power a UAV by laser charger to prolong the UAV's uptime. This enables the UAV to effectively perform wireless charging and data backhauling for a WRSN. By considering the GBS's green energy budget, we formulate an optimization problem focused on determining the optimal 3-D hovering points for UAV to maximize the number of sensor nodes (SNs) capable of receiving sufficient energy and uploading data. Given the NP-hard nature of this problem, we propose a two-step solution featuring corresponding heuristic algorithms designed to efficiently address it. Extensive simulations have been conducted to validate the efficacy of our proposed algorithms. © 2014 IEEE. |
| Author Keywords | Data backhauling; green energy far-field wireless charging; sixth-generation (6G) wireless communications; unmanned aerial vehicle (UAV); wireless rechargeable sensor networks (WRSNs) |
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