基于多目标沙蚕群算法优化的 DV-Hop 算法。

DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization.

机构信息

College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China.

HUIDA Sanitary Ware Co., Ltd., Tangshan 063000, China.

出版信息

Sensors (Basel). 2023 Apr 3;23(7):3698. doi: 10.3390/s23073698.

DOI:10.3390/s23073698

PMID:37050758

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10098855/

Abstract

The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs a DV-Hop algorithm based on multi-objective salp swarm optimization. Firstly, hop counts in the DV-Hop algorithm are subdivided, and the average hop distance is corrected based on the minimum mean-square error criterion and weighting. Secondly, the traditional single-objective optimization model is transformed into a multi-objective optimization model. Then, in the third stage of DV-Hop, the improved multi-objective salp swarm algorithm is used to estimate the node coordinates. Finally, the proposed algorithm is compared with three improved DV-Hop algorithms in two topologies. Compared with DV-Hop, The localization errors of the proposed algorithm are reduced by 50.79% and 56.79% in the two topology environments with different communication radii. The localization errors of different node numbers are decreased by 38.27% and 56.79%. The maximum reductions in localization errors are 38.44% and 56.79% for different anchor node numbers. Based on different regions, the maximum reductions in localization errors are 56.75% and 56.79%. The simulation results show that the accuracy of the proposed algorithm is better than that of DV-Hop, GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop algorithms.

摘要

传感器节点的定位是无线传感器网络中的一个重要问题。DV-Hop 算法是一种典型的无测距算法，但定位精度不高。为了进一步提高定位精度，本文设计了一种基于多目标沙蚕群优化的 DV-Hop 算法。首先，对 DV-Hop 算法中的跳数进行细分，并基于最小均方误差准则和加权对平均跳距进行修正。其次，将传统的单目标优化模型转换为多目标优化模型。然后，在 DV-Hop 的第三阶段，采用改进的多目标沙蚕群算法来估计节点坐标。最后，将所提出的算法与两种拓扑结构中的三种改进的 DV-Hop 算法进行了比较。与 DV-Hop 相比，在具有不同通信半径的两种拓扑环境下，所提出的算法的定位误差分别降低了 50.79%和 56.79%。不同节点数量的定位误差分别降低了 38.27%和 56.79%。对于不同的锚节点数量，定位误差的最大减少量分别为 38.44%和 56.79%。基于不同区域，定位误差的最大减少量分别为 56.75%和 56.79%。仿真结果表明，所提出的算法的准确性优于 DV-Hop、GWO-DV-Hop、SSA-DV-Hop 和 ISSA-DV-Hop 算法。