A Node Location Algorithm Based on Node Movement Prediction in Underwater Acoustic Sensor Networks

Aiming at the problems of the low mobility, low location accuracy, high communication overhead, and high energy consumption of sensor nodes in underwater acoustic sensor networks, the MPL (movement prediction location) algorithm is proposed in this article. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, a TOA (time of arrival)-based ranging strategy is first proposed to reduce communication overhead and energy consumption. Then, after dimension reduction processing, the grey wolf optimizer (GWO) is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In coastal areas, the tidal phenomenon is the main factor leading to node movement; thus, a more practical node movement model is constructed by combining the tidal model with node stress. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and underwater location is then completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance compared with the LSLS, SLMP, and GA-SLMP algorithms. Additionally, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.