无线传感网络下的固定节点优化定位仿真
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摘要
在对无线传感网络下固定节点定位时,由于传感器信道节点分布不均匀产生节点稀疏性,使得出现大量节点信号特征与正常节点信号特征相差很大。传统的定位算法在进行定位时需要对这些节点信息进行大量的迭代计算处理,转换成具有相同信号特征的节点才能完成定位,导致定位时间长、效率差的问题。提出改进可控制粒子群算法的无线传感网络下的固定节点优化定位方法。先融合经典多维标度法定位算法,构建固定节点的距离矩阵,并计算无线传感网络下固定节点的原始坐标,将粒子释放原理和速度约束方案相融合优化固定节点定位过程,并利用信标节点修正了固定节点平均每跳距离,利用已知固定节点对估计坐标进行迭代寻优,从而实现无线传感网络下的固定节点优化定位。仿真结果表明,改进的定位方法有效的提高了固定节点的定位精度。
A fixed node optimization location method under the wireless sensor network based on the improved controllable particle swarm optimization( pso) algorithm is proposed. Firstly,the location algorithm based on classic multidimensional scaling is fused,a distance matrix of fixed node is constructed,and the original coordinate of the fixed node under wireless sensor network is calculated. The particle release principle and speed constraint scheme are integrated to optimize the fixed node location process,and the average hop distance of fixed node is corrected by the beacon node. The estimated coordinates are made iterative optimization by using the known fixed node,so as to realize the optimization location of the fixed node in wireless sensor network. Simulation result verifies that the improved location method can effectively increases the location accuracy of fixed node.
A fixed node optimization location method under the wireless sensor network based on the improved controllable particle swarm optimization( pso) algorithm is proposed. Firstly,the location algorithm based on classic multidimensional scaling is fused,a distance matrix of fixed node is constructed,and the original coordinate of the fixed node under wireless sensor network is calculated. The particle release principle and speed constraint scheme are integrated to optimize the fixed node location process,and the average hop distance of fixed node is corrected by the beacon node. The estimated coordinates are made iterative optimization by using the known fixed node,so as to realize the optimization location of the fixed node in wireless sensor network. Simulation result verifies that the improved location method can effectively increases the location accuracy of fixed node.
引文
[1]王媛媛,杜永贵.无线传感器网络定位算法[J].计算机仿真,2014,(5):300-303.
[2]吴凡,彭力.基于节点密度及路径优化的无线传感器网络定位算法研究[J].传感技术学报,2014,(11):1539-1544.
[3]张双双,王延年.节点分布不均匀的无线传感网络低功耗算法.[J].西安工程大学学报,2015,6(29):720-723.
[4]吕家亮,等.基于微粒群优化的三维无线传感网定位算法研究[J].山东大学学报(理学版),2013,48(5):78-82.
[5]汪华斌,曾少宁,罗伟.基于粒子群优化的无线传感器网络节点定位算法[J].惠州学院学报,2014,34(3):67-70.
[6]郑志龙,等.运用最优化原理的新型非视距抑制无线定位算法[J].传感技术学报,2013,26(5):722-727.
[7]杨剑宁,蔺坤.无线传感网络通信优化算法仿真[J].计算机仿真,2014,31(2):382-385.
[8]李坤,何月顺.一种基于蒙特卡洛的移动传感网节点定位的优化算法[J].数字技术与应用,2014,(8):123-124.
[9]肖巍巍.不确定性环境下传感节点跟踪优化过程仿真[J].计算机仿真,2014,31(7):309-312.
[10]张炜.基于节点映射的井下无线传感器网络定位算法[J].工矿自动化,2013,39(11):8-11.
[2]吴凡,彭力.基于节点密度及路径优化的无线传感器网络定位算法研究[J].传感技术学报,2014,(11):1539-1544.
[3]张双双,王延年.节点分布不均匀的无线传感网络低功耗算法.[J].西安工程大学学报,2015,6(29):720-723.
[4]吕家亮,等.基于微粒群优化的三维无线传感网定位算法研究[J].山东大学学报(理学版),2013,48(5):78-82.
[5]汪华斌,曾少宁,罗伟.基于粒子群优化的无线传感器网络节点定位算法[J].惠州学院学报,2014,34(3):67-70.
[6]郑志龙,等.运用最优化原理的新型非视距抑制无线定位算法[J].传感技术学报,2013,26(5):722-727.
[7]杨剑宁,蔺坤.无线传感网络通信优化算法仿真[J].计算机仿真,2014,31(2):382-385.
[8]李坤,何月顺.一种基于蒙特卡洛的移动传感网节点定位的优化算法[J].数字技术与应用,2014,(8):123-124.
[9]肖巍巍.不确定性环境下传感节点跟踪优化过程仿真[J].计算机仿真,2014,31(7):309-312.
[10]张炜.基于节点映射的井下无线传感器网络定位算法[J].工矿自动化,2013,39(11):8-11.