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首页> 外文期刊>IEEE transactions on industrial informatics >District Partition-Based Data Collection Algorithm With Event Dynamic Competition in Underwater Acoustic Sensor Networks
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District Partition-Based Data Collection Algorithm With Event Dynamic Competition in Underwater Acoustic Sensor Networks

机译：水下声传感器网络中具有事件动态竞争的基于分区的数据收集算法

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摘要

The advent of underwater acoustic sensor networks (UASNs) has enhanced marine environmental monitoring, auxiliary navigation, and marine military defense. One of the core functions of UASNs is data collection. However, current underwater data collection schemes generally encounter problems such as high energy consumption and high latency. Furthermore, the application of multiple autonomous underwater vehicles (AUVs) has contributed to more problems of task assignment and load balancing. This leads to significant failure in data collections and controlling of spontaneous emergencies. To address these problems, a district partition-based data collection algorithm with event dynamic competition in UASNs has been proposed. In this algorithm, the value of information of the packet determines the priority of its transmission to the cluster head. The navigation position of the mobile sink and the area under the responsibility of each AUV are determined by the spatial region division. The path of the AUV in the subregion is then planned using reinforcement learning. Subsequently, the dynamic competition of multiple AUVs is used to handle emergency tasks. The simulation demonstrates that our proposed algorithm significantly reduces energy consumption to guarantee load balancing while reducing end-to-end transmission delay.
机译：水下声传感器网络（UASN）的出现增强了海洋环境监测，辅助导航和海洋军事防御能力。 UASN的核心功能之一是数据收集。然而，当前的水下数据收集方案通常遇到诸如高能耗和高等待时间的问题。此外，多种自主水下航行器（AUV）的应用导致更多的任务分配和负载平衡问题。这导致数据收集和自发紧急情况的控制严重失败。为了解决这些问题，已经提出了一种具有事件动态竞争的UASN中基于分区的数据收集算法。在此算法中，数据包的信息值决定了其向群集头传输的优先级。移动水槽的导航位置和每个AUV负责的区域由空间区域划分确定。然后，通过强化学习计划AUV在该次区域的路径。随后，多个AUV的动态竞争被用于处理紧急任务。仿真表明，我们提出的算法可显着降低能耗，以确保负载平衡，同时减少端到端的传输延迟。

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