Energy Maximization for Ground Nodes in UAV-Enabled Wireless Power Transfer Systems

Li, Min; Li, Hao; Ma, Pengfei; Wang, Heng

doi:10.1109/jiot.2023.3274549

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Based on the cluster center locations obtained from Algorithm 1, the trajectory planning problem for a UAV is formulated as (11).…”

Section: Uav Trajectory Optimizationmentioning

confidence: 99%

“…Equipping MEC servers can provide additional computational and storage resources to optimize the efficiency of task execution. Finally, they can move close to the ground SNs, making it easy to establish line of sight (LoS) links and reduce the transmit power and packet loss rate [ 11 ]. Currently, data collection in UAV-assisted MEC networks has become a popular research topic, and scholars have carried out a lot of work accordingly.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network

Shen,

Wang,

Huang

et al. 2024

Sensors

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With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV’s flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

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“…Based on the cluster center locations obtained from Algorithm 1, the trajectory planning problem for a UAV is formulated as (11).…”

Section: Uav Trajectory Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%