Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms

CHAI Rong; LI Peixin; LIANG Chengchao; CHEN Qianbin

doi:10.11999/JEIT240048

Volume 46 Issue 10

Oct. 2024

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CHAI Rong, LI Peixin, LIANG Chengchao, CHEN Qianbin. Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4009-4016. doi: 10.11999/JEIT240048

Citation:

CHAI Rong, LI Peixin, LIANG Chengchao, CHEN Qianbin. Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4009-4016. doi: 10.11999/JEIT240048

Citation:

CHAI Rong, LI Peixin, LIANG Chengchao, CHEN Qianbin. Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4009-4016. doi: 10.11999/JEIT240048

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Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms

doi: 10.11999/JEIT240048

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China(62271097)

Received Date: 2024-01-24
Rev Recd Date: 2024-08-27

Available Online: 2024-09-01

Publish Date: 2024-10-30

Abstract

Abstract

Data collection problem in an Unmanned Aerial Vehicle (UAV)-assisted wireless sensor network is addressed. Firstly, an initial Sensor Node (SN) clustering strategy is proposed based on the mean drift algorithm, then an SN switching algorithm is designed to achieve load balancing between clusters. Based on the obtained clustering strategy, the UAV data collection and trajectory planning problem is formulated as a system energy consumption minimization problem. Since the formulated problem is a non-convex problem and is difficult to solve directly, it is decoupled into two subproblems, namely data scheduling subproblem and UAV trajectory planning subproblem. To tackle the data scheduling subproblem, a multi-slot Kuhn-Munkres algorithm-based time-frequency resource scheduling strategy is proposed. To solve the UAV trajectory planning subproblem, the problem is modeled as a Markov decision-making process, and a deep Q-network-based algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.
- Unmanned aerial vehicle,
- Data collection,
- Trajectory planning,
- Markov decision process

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References(15)

References

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