With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

中文翻译：

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一种应用于任务分配的新型分类自组织图

随着传感器技术的发展，传感器节点越来越多地用于水下环境。本文提出的策略旨在解决当 AUV 的数量少于目标传感器的数量时，使用有限数量的自主水下航行器 (AUV) 完成诸如从传感器节点收集数据等任务的问题。针对该问题，提出了一种新的分类自组织映射算法。首先，根据 K-means 算法，目标被分类为由 AUV 数量决定的组。其次，根据自组织地图算法，将AUV与组匹配。第三，为每个 AUV 提供组中目标的可访问顺序。新颖的分类自组织地图算法不仅可用于减少多 AUV 系统中的总能耗，还可以为 AUV 提供最有效的目标可访问顺序。给出了证明算法适用性的仿真结果。