Accurate localization of underwater acoustic sensor networks is one of the essential operations. The range-free DV-Hop technique is more acceptable due to its low hardware cost and simplicity than the range-based approach. But the main drawback of this basic DV-Hop method is low accuracy and link failure due to maintenance or short battery life during the continuous localization calculation in the underwater scenario. Most algorithms in this field do not pay enough attention to the mobility of the nodes. By examining the movement patterns of water, this manuscript uses a technique for UWSN localization based on a mobility prediction algorithm. This manuscript proposed a reference node-based multi-hop improved DV-Hop​ technique using School Topper Optimization (STO) and mobility prediction algorithms. This proposed scheme’s highlighted advantage is to reduce the localization error to identify the unknown target nodes using a reference node-based localization scheme using a mobility prediction algorithm without increasing the beacon nodes, and the multi-hop improved DV-Hop​ algorithm using STO significantly improved the link failure problem by iteratively selecting the different sets of the beacon nodes in the same scenario The simulation report has proved that our framework takes better computational time, has a better convergence rate by introducing the STO technique, provides 23.76% and 37.18% better avg. localization error and localization error variance, and 16.12% better coverage from PSODV-Hop and GSODV-Hop techniques.

水声传感器网络的准确定位是必不可少的操作之一。与基于范围的方法相比，无范围的 DV-Hop 技术由于其低硬件成本和简单性而更容易被接受。但这种基本 DV-Hop 方法的主要缺点是在水下场景中的连续定位计算过程中，由于维护或电池寿命短，精度低和链路故障。该领域的大多数算法都没有对节点的移动性给予足够的重视。通过检查水的运动模式，这份手稿使用了一种基于移动性预测算法的 UWSN 定位技术。该手稿提出了一种基于参考节点的多跳改进 DV-Hop 技术，该技术使用 School Topper 优化 (STO) 和移动性预测算法。该方案的突出优点是在不增加信标节点的情况下，使用基于参考节点的定位方案使用移动性预测算法来减少定位误差以识别未知目标节点，并且使用 STO 的多跳改进 DV-Hop 算法显着通过在同一场景中迭代选择不同的信标节点集来改善链路故障问题仿真报告证明，我们的框架通过引入STO技术需要更好的计算时间，具有更好的收敛速度，提供23.76％和37.18％的更好平均 定位误差和定位误差方差，以及 PSODV-Hop 和 GSODV-Hop 技术的 16.12% 更好的覆盖率。