基于遗传模拟退火优化的DV-Hop定位算法OA北大核心CSTPCD
Dv-Hop localization algorithm based on genetic simulated annealing optimization in wireless sensor network
为进一步提升无线传感器网络的定位精度,提出了一种利用遗传模拟退火法改进的dv-hop定位优化算法(DGSA).首先引入跳数调整因子对节点间跳数信息进行修正,利用共线度去除会产生较大误差的信标节点,再用加权处理方式优化平均跳距,降低了 dv-hop算法因本身局限性产生的定位误差;其次将局部搜索能力优良的模拟退火算法引入遗传算法中进行寻优,使算法的寻优效率和定位精度得以提升.仿真结果表明:在相同环境下,DGSA算法相较现有的无线传感器网络定位算法有更好的搜索效率,定位结果也更加准确.
In order to further improve the positioning accuracy of WSN,an improved dv-hop positioning optimization algorithm based on genetic simulated annealing method was proposed.Firstly,the number of hops adjustment factor was introduced to correct the number of hops information between nodes.After the introduction of collinearity,the beacon nodes with large errors were removed.Then the average hop distance of dv-hop beacon nodes was optimized by weighting method,which reduced the localization error caused by the limitations of dv-hop algorithm.Secondly,the local search ability of genetic algorithm was enhanced by combining genetic algorithm with simulated annealing algorithm.Simulation results show that DGSA algorithm is more accurate than other localization algorithms in the same situation.
余修武;穆静;刘永
南华大学资源环境与安全工程学院,湖南衡阳 421001
计算机与自动化
无线传感器网络节点定位dv-hop遗传算法模拟退火算法
wireless sensor networksnodes positioningdv-hopgenetic algorithmsimulated annealing algorithm
《华中科技大学学报（自然科学版）》 2024 (003)
149-155 / 7
湖南省市联合自然科学基金资助项目(2021JJ50093);国家自然科学基金资助项目(11875164);湖南省重点研发计划资助项目(2018SK2055).
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