Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks
Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks
复制标题
基于改进的DV-Hop和DE算法增强无线传感器网络中的传感器节点定位算法
DOI：
10.3390/s20020343
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
de Mello, Rodrigo Fernandes
中科院分区：
文献类型：
--
作者：
Han, Dezhi;Yu, Yunping;de Mello, Rodrigo Fernandes
距离向量跳(DV-Hop)算法是无线传感器网络中最著名的基于距离向量路由协议的无距离定位算法，但众所周知其定位精度有限。本文提出了一种基于差分进化(DE)和改进的DV-Hop算法的改进的无线传感器节点定位算法DEIDV-Hop，改善了每跳平均距离的潜在误差问题。在随机个体的变异操作中引入了增加种群多样性的随机变异，增强了DE算法的搜索停滞和早熟收敛。在生成个体的基础上，将粒子群算法的社会学习部分嵌入到交叉操作中，加快了算法的收敛速度，改善了算法的优化结果。应用改进的DE算法得到与未知节点估计位置对应的全局最优解。仿真结果表明，在四种不同的网络环境下，该算法比以往的算法具有更小的定位误差和更好的稳定性。尽管如此，对于定位精度和稳定性要求更高的应用场景，它仍然是有前景的。
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.