Improved AP-Clustering-Based AUV-Aided Data Collection Method for UWSNs

Abstract

In underwater sensor networks, nodes are usually sparsely arranged, and autonomous underwater vehicles (AUVs) are often used to collect data by moving. However, it is a key problem for AUVs to shorten the path as much as possible to guarantee whole-network data acquisition. Aiming to solve this problem, this paper proposes an AUV underwater data collection method based on affinity propagation clustering. First, this clustering theory is used to select the nodes with large residual energy as cluster head nodes, and the clustering results are dynamically adjusted according to the communication radius of nodes to obtain the final clustering results. Finally, the cluster head nodes of the clusters are sequentially traversed using the traveling salesman problem, and an optimal path of AUV is planned to complete data collection for the whole network. A large number of simulation experiments verify that this method can effectively plan the mobile path of AUVs.