基于鸡群算法的无线传感器网络定位研究
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摘要
针对无线传感器网络(WSN)节点定位精度不足的问题,提出一种改进鸡群算法与典型定位模型相结合的ICSO(Improve Chicken Swarm Optimization)算法。首先,提出基于pareto距离分级的分类算法,优化鸡群算法种群比例;然后,在母鸡位置公式中引入随机游走策略,增大搜索范围;最后,将净能量增益引入小鸡的位置公式,进一步提高定位精度。仿真结果表明,ICSO与改进后的粒子群算法(MPSO)和鸡群算法(BIDCSO)相比,在参考节点比例、节点密度、通信半径和定位区域面积等方面的平均定位精度分别提高了19.2%、22.1%、12.1%、8.5%和6%、10.5%、4.4%、4.7%。实验结果表明,ICSO算法能够有效提高定位精度。
Aiming at the problem of insufficient positioning accuracy of wireless sensor network(WSN)nodes,an improved algorithm of integrated Chicken Swarm Optimization(ICSO)is proposed. Firstly,a classification algorithm based on pareto distance grading is proposed to optimize the population ratio of flock algorithm. Then,a random walk strategy is introduced in the hen position formula to increase the search range. Finally,the net energy gain is introduced into the position formula of the chick. Further improve the positioning accuracy.The simulation results show that compared with the Modified particle swarm optimization(MPSO)and Bio Inspired Distributed Chicken Swarm Optimization(BIDCSO),the average positioning accuracy of ICSO in terms of the proportion of reference nodes,node density,communication radius and location area is improved by 19.2%,22.1%,12.1%,8.5% and 6%,10.5%,4.4%,4.7%,respectively. Experimental results show that the ICSO algorithm can effectively improve the positioning accuracy.
Aiming at the problem of insufficient positioning accuracy of wireless sensor network(WSN)nodes,an improved algorithm of integrated Chicken Swarm Optimization(ICSO)is proposed. Firstly,a classification algorithm based on pareto distance grading is proposed to optimize the population ratio of flock algorithm. Then,a random walk strategy is introduced in the hen position formula to increase the search range. Finally,the net energy gain is introduced into the position formula of the chick. Further improve the positioning accuracy.The simulation results show that compared with the Modified particle swarm optimization(MPSO)and Bio Inspired Distributed Chicken Swarm Optimization(BIDCSO),the average positioning accuracy of ICSO in terms of the proportion of reference nodes,node density,communication radius and location area is improved by 19.2%,22.1%,12.1%,8.5% and 6%,10.5%,4.4%,4.7%,respectively. Experimental results show that the ICSO algorithm can effectively improve the positioning accuracy.
引文
[1] 纪杰,施伟斌.改进的无线传感器网络节点定位算法[J].电子科技,2016,29(10):86-88.
[2] 庞新苗.无线传感器网络DV-Hop定位算法和定向扩散协议研究[D].长沙:中南大学,2010.
[3] 金纯,叶诚,韩志斌,等.无线传感器网络中Dv-Hop定位算法的改进[J].计算机工程与设计,2013,34(2):401-404.
[4] 王亚子,杨建辉.改进粒子群算法的无线传感器网络节点定位[J].计算机工程与应用,2014,50(18):99-102.
[5] 夏少波,邹建梅,朱晓丽,等.无线传感器网络DV-Hop定位算法的改进[J].计算机应用,2015,35(2):340-344.
[6] 马淑丽,赵建平.无线传感器网络中DV-Hop定位算法的改进[J].通信技术,2015,48(7):840-844.
[7] 夏少波,朱晓丽,邹建梅,等.基于跳数修正的DV-Hop改进算法[J].传感技术学报,2015,28(5):757-762.
[8] Shayokh M A,Shin S Y.Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization[J].Wireless Personal Communications,2017(10):1-16.
[9] SAKURADA M,FUKUDA M.An RSSI-Based Error Collection Applied to Estimated Sensorlocations[C]//Communications,Computers and Signal Processing.IEEE,2016:58-63.
[10] Ruiz A E,Cruz E R,Urrea M D J T,et al.Performance Comparison Between Simulated Andreal Case Scenario of RSSI-Based Localization Algorithms on a WSAN[J].IEEE LatinAmerica Transactions,2016,14(1):115-121.
[11] Shabazian R,Ghorashi S A.Distributed Cooperative Target Detection and Localization Indecentralized Wireless Sensor Networks[J].Journal of Supercomputing,2017,73(4):1715-1732.
[12] MENG X,Liu Y,Gao X,et al.A New Bio-Inspired Algorithm:Chicken Swarm Optimization[M]//Advances in Swarm Intelligence.Springer International Publishing,2014:86-94.
[13] 张建春,康凤举,梁洪涛,等.基于鸡群优化的粒子滤波算法研究[J].系统仿真学报,2017,29(2):295-300,308.李鹏(1995-),男,硕士研究生,长春理工大学电子信息工程学院,主要研究方向为无线传感器网络、智能算法,mlf0916@126.com;陈桂芬(1964-),女,吉林长春人,博士,教授,博士生导师,主要研究方向为无线传感器网络理论与技术、光通信及无线通信理论与技术、智能信息处理,chenguif@163.com;胡文韬(1994-),男,吉林长春人,硕士研究生,主要研究方向为复杂电子信息处理技术。
[2] 庞新苗.无线传感器网络DV-Hop定位算法和定向扩散协议研究[D].长沙:中南大学,2010.
[3] 金纯,叶诚,韩志斌,等.无线传感器网络中Dv-Hop定位算法的改进[J].计算机工程与设计,2013,34(2):401-404.
[4] 王亚子,杨建辉.改进粒子群算法的无线传感器网络节点定位[J].计算机工程与应用,2014,50(18):99-102.
[5] 夏少波,邹建梅,朱晓丽,等.无线传感器网络DV-Hop定位算法的改进[J].计算机应用,2015,35(2):340-344.
[6] 马淑丽,赵建平.无线传感器网络中DV-Hop定位算法的改进[J].通信技术,2015,48(7):840-844.
[7] 夏少波,朱晓丽,邹建梅,等.基于跳数修正的DV-Hop改进算法[J].传感技术学报,2015,28(5):757-762.
[8] Shayokh M A,Shin S Y.Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization[J].Wireless Personal Communications,2017(10):1-16.
[9] SAKURADA M,FUKUDA M.An RSSI-Based Error Collection Applied to Estimated Sensorlocations[C]//Communications,Computers and Signal Processing.IEEE,2016:58-63.
[10] Ruiz A E,Cruz E R,Urrea M D J T,et al.Performance Comparison Between Simulated Andreal Case Scenario of RSSI-Based Localization Algorithms on a WSAN[J].IEEE LatinAmerica Transactions,2016,14(1):115-121.
[11] Shabazian R,Ghorashi S A.Distributed Cooperative Target Detection and Localization Indecentralized Wireless Sensor Networks[J].Journal of Supercomputing,2017,73(4):1715-1732.
[12] MENG X,Liu Y,Gao X,et al.A New Bio-Inspired Algorithm:Chicken Swarm Optimization[M]//Advances in Swarm Intelligence.Springer International Publishing,2014:86-94.
[13] 张建春,康凤举,梁洪涛,等.基于鸡群优化的粒子滤波算法研究[J].系统仿真学报,2017,29(2):295-300,308.李鹏(1995-),男,硕士研究生,长春理工大学电子信息工程学院,主要研究方向为无线传感器网络、智能算法,mlf0916@126.com;陈桂芬(1964-),女,吉林长春人,博士,教授,博士生导师,主要研究方向为无线传感器网络理论与技术、光通信及无线通信理论与技术、智能信息处理,chenguif@163.com;胡文韬(1994-),男,吉林长春人,硕士研究生,主要研究方向为复杂电子信息处理技术。