In maritime data collection scenarios, due to the constraints of wireless communication and environmental factors such as wave motion, sea surface ducting effects, and sea surface curvature, floating sensor nodes are unable to establish direct data transmission links with the base station. The advent of unmanned aerial vehicle (UAV)-assisted Maritime Internet of Things (MIoT) provides a feasible solution to this challenge. However, in existing maritime environments, floating sensor nodes drift due to ocean currents, posing significant challenges for long-distance data transmission while maintaining a low age of information (AoI). Consequently, we introduce a prediction-based UAV-assisted data collection mechanism for MIoT. In this scheme, we first select convergence nodes responsible for gathering data from floating sensor nodes and forwarding it to passing UAVs. We then propose a dynamic clustering algorithm to allocate task areas to UAVs, with each area assigned to a single UAV for data collection from floating sensor nodes. To ensure stable data offloading by UAVs, we develop a UAV relay pairing algorithm to establish reliable air-to-air relay paths and provide two data offloading modes: distal UAV and proximate UAV. Owing to the drift of floating sensor nodes influenced by ocean currents, we employ a deep echo state network to predict the positions of floating sensor nodes and utilize a multi-agent deep deterministic policy gradient to solve the UAVs trajectory planning problem. Under this mechanism, the UAVs can adaptively adjust its flight path while exploring floating sensor nodes in dynamically changing ocean sensor node scenarios. Extensive experiments demonstrate that the proposed scheme can adapt to dynamic ocean environments, achieving low-AoI data collection from floating sensor nodes.

中文翻译：

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无人机辅助海上物联网基于预测的数据采集


在海事数据采集场景中，由于无线通信的限制以及波浪运动、海面导管效应、海面曲率等环境因素，浮动传感器节点无法与基站建立直接的数据传输链路。无人机 （UAV） 辅助海上物联网 （MIoT） 的出现为这一挑战提供了可行的解决方案。然而，在现有的海洋环境中，浮动传感器节点会因洋流而漂移，在保持低信息年龄 （AoI） 的同时，对长距离数据传输构成重大挑战。因此，我们为 MIoT 引入了一种基于预测的无人机辅助数据收集机制。在这个方案中，我们首先选择汇聚节点，负责从浮动传感器节点收集数据，并将其转发给经过的无人机。然后，我们提出了一种动态聚类算法，将任务区域分配给 UAV，每个区域分配给单个 UAV，以便从浮动传感器节点收集数据。为保证无人机稳定地卸载数据，我们开发了无人机中继配对算法，建立可靠的空对空中继路径，并提供远端无人机和近端无人机两种数据卸载模式。由于浮动传感器节点受洋流影响的漂移，该文采用深度回波状态网络来预测浮动传感器节点的位置，并利用多智能体深度确定性策略梯度来解决无人机的轨迹规划问题。在此机制下，无人机可以在动态变化的海洋传感器节点场景中探索浮动传感器节点的同时，自适应地调整飞行路径。 大量实验表明，所提出的方案能够适应动态海洋环境，实现从浮动传感器节点的低 AoI 数据收集。