A k-means based multi-AUV hydroacoustic sensor network data acquisition algorithm

With the development of science and technology, people have made new progress in the exploration of the ocean. In order to study the ocean in greater depth, implementation monitoring of the ocean is required. Due to the complex underwater communication environment, underwater information data collection is more difficult compared to land, and autonomous underwater vehicles (AUVs) are often needed to assist in the collection. How to plan the cruise path of AUVs is a major problem in data collection. To address this problem, a hybrid optimization algorithm based on k-means clustering algorithm is proposed in this paper, which can plan multiple AUVs for data collection. After simulation experiments, the effectiveness of the algorithm is determined. Compared with the SOM-based (Self-Organizing Map) algorithm, the length of the planned path and the algorithm response time of this algorithm are better than the SOM-based algorithm, which can save energy for AUVs and extend the life of sensors.