Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms
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摘要: 该文研究了无人机(UAV)辅助无线传感器网络的数据收集问题。首先提出基于均值漂移算法的传感器节点(SN)初始分簇策略，进而以簇间负载均衡为目标，设计SN切换算法。基于所得成簇策略，将UAV数据收集及轨迹规划问题建模为系统能耗最小化问题。由于该问题是一个非凸问题，难以直接求解，将其分为两个子问题，即数据调度子问题及UAV轨迹规划子问题。针对数据调度子问题，提出一种基于多时隙库恩-蒙克雷斯算法的时频资源调度策略。针对UAV轨迹规划子问题，将其建模为马尔可夫决策过程，并提出一种基于深度Q网络的UAV轨迹规划算法。仿真结果验证了所提算法的有效性。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.
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Key words:
- Unmanned aerial vehicle /
- Data collection /
- Trajectory planning /
- Markov decision process
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表 1 仿真参数设置
仿真参数 数值 SN数据量$ {\varphi _k} $ [0, 1024 ] MB载波频率Cf [1, 3] GHz 节点可用带宽B 1 MHz SN发射功率pc 0.1 W UAV飞行高度H 70 m UAV飞行速度v 10 m/s UAV平均转子诱导速度v0 4.03 m/s 空气密度ρ 1.225 km/m3 转子盘面积Sr 0.503 m2 
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