LAUSR

This paper investigates the underwater node dynamic localization and prediction problems in a dynamic sensor network. Node localization in the internet of underwater things is the basis of target tracking and ocean monitoring. At present, most of the node location algorithms assume calm sea and fixed node location. However, the current velocity is uncertain in space and time. The nodes are drifted with the current motion. Therefore, most of the localization algorithms lose efficacy in the actual marine environment. In order to solve the above problems, a node dynamic prediction algorithm is proposed. First, the node mobility model is improved, which is more suitable for the actual marine environment. Second, a frequency based anchor node prediction algorithm is designed to improve anchor node location accuracy. Third, when the ordinary node receives the signals sent by anchor nodes of different depths, a deep information based weighted fusion method is designed for the ordinary node localization in order to mine more information in each direction. Finally, location and prediction simulation in sensor networks is carried out. The results show that the proposed node localization and prediction algorithm is more accurate than SMLP and HLMP algorithms and prove the enhanced effect of our method in dynamic marine.