Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network
Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network
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DOI：
10.1109/jsen.2023.3305348
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发表时间：
2023-08
影响因子：
4.3
通讯作者：
Mingyue Zhu;Zhiqing Wei;Chen Qiu;Wangjun Jiang;Huici Wu;Zhiyong Feng
中科院分区：
文献类型：
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作者：
Mingyue Zhu;Zhiqing Wei;Chen Qiu;Wangjun Jiang;Huici Wu;Zhiyong Feng
由于无人机具有高机动性和易于部署的特点，在无线通信和定位领域受到了广泛的关注。针对无线传感器网络（WSN）中基础设施覆盖不足、传感器位置不确定、传感数据采集量大等问题，提出了一种多无人机支持的无线传感器网络数据采集与传感器定位高效联合方案。具体而言，设置一架无人机作为采集数据的主无人机，其他无人机作为辅助无人机，利用到达时差（TDoA）进行传感器定位。建立了一个传感器位置不确定的混合整数非凸优化问题。目标是通过联合优化无人机轨迹、传感器传输计划和定位观测点（pop）来最小化所有传感器的平均定位误差。为了求解该优化模型，基于路径离散方法将原问题分解为两个子问题。首先，采用块坐标下降法（BCD）和逐次凸逼近法（SCA）迭代优化主无人机飞行轨迹和传感器传输调度，实现传感器上传数据量最小化；然后，基于主无人机的飞行轨迹，设计了一种基于粒子群算法（PSO）的无人机POPs优化算法；最后，利用样条曲线生成辅助无人机的飞行轨迹。仿真结果表明，该方案能够满足数据采集的要求，具有良好的定位性能。
Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs) have attracted much attention in the field of wireless communication and positioning. To meet the challenges of lack of infrastructure coverage, uncertain sensor position and large amount of sensing data collection in the wireless sensor network (WSN), this article presents an efficient joint data collection and sensor positioning scheme for WSN supported by multiple UAVs. Specifically, a UAV is set as the main UAV to collect data, and other UAVs are used as auxiliary UAVs for sensor positioning using time difference of arrival (TDoA). A mixed-integer nonconvex optimization problem with uncertain sensor position is established. The goal is to minimize the average positioning error of all sensors by jointly optimizing the UAV trajectories, sensor transmission schedule, and positioning observation points (POPs). To solve this optimization model, the original problem is decomposed into two subproblems based on the path discrete method. First, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, to maximize the minimum amount of data uploaded by the sensor. Then, based on the trajectory of the main UAV, a particle swarm optimization (PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the spline curve is applied to generate the trajectories of auxiliary UAVs. The simulation results show that the proposed scheme can meet the requirements of data collection and has a good positioning performance.