Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced.

中文翻译：

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水声传感器网络中基于Q-Learning的多AUV协同数据采集算法

智能数据采集是水声传感器网络的关键组成部分，在海底环境监测、海洋资源探测和海洋灾害预警等方面发挥着重要作用。由于水下环境的特殊性，例如减少的基础设施和嘈杂的通信信道，水下节点收集的数据通过自主水下航行器（AUV）更有效地传输到水面控制中心。然而，随着水下任务的日益复杂，使用单个AUV进行数据采集已不能满足低延迟和低功耗的要求。针对这一问题，提出了一种多AUV协同数据采集算法，减少了单个AUV上数据采集任务的负载。算法分为多AUV任务分配和基于Q-learning的AUV路径规划两个阶段。集群的数据传输被视为一组不同的任务，这些任务被分配给 AUV 完成。随后，执行路径规划以引导 AUV，以便快速完成任务并降低成本。仿真结果表明，所提出的算法可以充分利用网络的能量消耗并延长其生命周期。所提算法在能耗方面的性能提高了10%左右，数据采集的延迟也显着降低。执行路径规划以引导 AUV，以便快速完成任务并降低成本。仿真结果表明，所提出的算法可以充分利用网络的能量消耗并延长其生命周期。所提算法在能耗方面的性能提高了10%左右，数据采集的延迟也显着降低。执行路径规划以引导 AUV，以便快速完成任务并降低成本。仿真结果表明，所提出的算法可以充分利用网络的能量消耗并延长其生命周期。所提算法在能耗方面的性能提高了10%左右，数据采集的延迟也显着降低。