Wireless sensor networks (WSNs) have actively been considered in vast amount of applications in fields of science and engineering. The node location technology is one of the most critical technologies of WSNs. Aiming at the problem of distance vector-hop (DV-HOP) algorithm's excessive estimation error, we propose in this article a multi-objective DV-HOP localization algorithm based on differential evolution quantum particle swarm optimization (DQPSO-DV-HOP). First, the set of anchor nodes generated during the deployment phase that would cause large errors is eliminated, and a correction factor is introduced to modify the average hop distance to reflect the actual situation of the network better. In the node localization phase, the objective function we propose is optimized under a combination of the DE and QPSO algorithms, so the estimated results of unknown nodes are optimized and modified by using the QPSO algorithm of fast convergence, which is easy to converge to the optimal global value. Simulation results show that the localization stability, accuracy, and convergence given by the proposed DQPSO-DV-HOP algorithm are better than other schemes. High precision positioning algorithm can improve the accuracy of energy consumption monitoring and provide more accurate data for energy saving management.

中文翻译：

---

一种基于差分进化量子粒子群优化的多目标距离矢量跳跃定位算法

无线传感器网络 (WSN) 已在科学和工程领域的大量应用中得到积极考虑。节点定位技术是无线传感器网络最关键的技术之一。针对距离向量跳跃（DV-HOP）算法估计误差过大的问题，本文提出了一种基于差分进化量子粒子群优化（DQPSO-DV-HOP）的多目标DV-HOP定位算法。首先，剔除部署阶段产生的会导致较大误差的锚节点集合，并引入修正因子来修正平均跳距，以更好地反映网络的实际情况。在节点定位阶段，我们提出的目标函数在DE和QPSO算法的组合下进行优化，因此对未知节点的估计结果采用收敛速度快的QPSO算法进行优化和修正，易于收敛到最优全局值。仿真结果表明，所提出的DQPSO-DV-HOP算法给出的定位稳定性、精度和收敛性优于其他方案。高精度定位算法可以提高能耗监测的准确性，为节能管理提供更准确的数据。