Joint Wireless Charging and Data Collection for UAV-Enabled Internet of Things Network

Article Engineering, Electrical & Electronic

On Tethered UAV-Assisted Heterogeneous Network

Shuai Zhang et al.

Summary: This paper proposes an algorithm to optimize user association, resource allocation, and placement of UAVs and ground charging stations in order to maximize the sum rate of all users in a UAV-assisted heterogeneous network.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2022)

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Article Computer Science, Information Systems

Multiagent Deep Reinforcement Learning for Wireless-Powered UAV Networks

Omar Sami Oubbati et al.

Summary: Unmanned aerial vehicles (UAVs) face challenges in terms of energy limitation, but wireless power transfer (WPT) technology can solve this issue. This study proposes a multiagent deep reinforcement learning (MADRL) method to optimize the energy transfer task and uses directional energy transfer for higher reliability. Simulation results demonstrate significant improvement in energy transfer efficiency.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Computer Science, Information Systems

On Optimizing the Divergence Angle of an FSO-Based Fronthaul Link in Drone-Assisted Mobile Networks

Tianrun Zhang et al.

Summary: This article discusses the issue of determining the divergence angle in a drone-assisted mobile network to optimize the tradeoff between capacity and outage probability of the fronthaul link. An algorithm called LEARN is proposed to solve this problem, and its performance is demonstrated through extensive simulations.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Telecommunications

Time Allocation and Optimization in UAV-Enabled Wireless Powered Communication Networks

Hua Yan et al.

Summary: This paper investigates the use of UAVs in wireless sensor networks for energy transmission and data collection. By optimizing the time allocation for different tasks, the amount of data uploaded to the base station can be maximized.

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING (2022)

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Article Engineering, Electrical & Electronic

Latency Aware 3D Placement and User Association in Drone-Assisted Heterogeneous Networks With FSO-Based Backhaul

Shuai Zhang et al.

Summary: This paper focuses on determining the 3D location of the DBS, user association, and bandwidth allocation policy between the MBS and the DBS to minimize the total average latency ratio of all users. The proposed algorithm efficiently solves the problem by iteratively optimizing two sub-problems, resulting in a significant reduction in latency ratio compared to other baseline schemes.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Computer Science, Information Systems

Energy-Efficient UAV-Enabled Data Collection via Wireless Charging: A Reinforcement Learning Approach

Shu Fu et al.

Summary: The article examines the use of unmanned aerial vehicles (UAVs) for data collection with wireless charging in next-generation wireless networks. A reinforcement learning approach is proposed to plan UAV routes for collecting sensor data from scattered devices in the physical environment. By dividing the physical environment into grids with UAV hovering spots and wireless chargers, the energy efficiency and data collection performance are improved using Q-learning.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Information Systems

Covertness and Timeliness of Data Collection in UAV-Aided Wireless-Powered IoT

Xingbo Lu et al.

Summary: This work focuses on maximizing the timeliness of data collection in UAV-aided IoT networks under a covertness constraint. By deriving analytical expressions for covertness constraint and Age of Information, and optimizing the UAV's transmit power for WPT, WPT duration, and data transmission duration, the study reveals trade-offs between timeliness and covertness in data collection. The numerical results demonstrate the impact of different scenarios on the optimal strategies for data transmission.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Proceedings Paper Computer Science, Theory & Methods

Multi-UAV-enabled AoI-aware WPCN: A Multi-agent Reinforcement Learning Strategy

Omar Sami Oubbati et al.

Summary: This study proposed a separated UAV-assisted WPCN system and optimized UAV trajectories to minimize the expected age of information, enhance energy transfer to devices, and reduce energy consumption. Simulation results showed significant improvement in performance compared to baseline methods.

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021) (2021)

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Article Engineering, Electrical & Electronic