西南大学学报 (自然科学版)  2019, Vol. 41 Issue (2): 117-127.  DOI: 10.13718/j.cnki.xdzk.2019.02.016 0
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1. 河南工业职业技术学院 电子信息工程学院, 河南 南阳 473009;
2. 西安邮电大学 通信与信息工程学院, 西安 710121

1 本文高速移动WSN网络信号源定位算法

 图 1 本文算法流程图
1.1 基于旋跳修正机制的信号强度节点跳数区域定位

 $H\left( {A,B,n + 1} \right) = \frac{{L\left( {A,B} \right) + L\left( {B,C} \right)}}{{M\left( {A,B,n} \right) + M\left( {B,C,n} \right)}}$ (1)
 $H\left( {B,C,n + 1} \right) = \frac{{L\left( {B,C} \right) + L\left( {A,C} \right)}}{{M\left( {B,C,n} \right) + M\left( {A,C,n} \right)}}$ (2)
 $H\left( {C,A,n + 1} \right) = \frac{{L\left( {B,C} \right) + L\left( {A,C} \right)}}{{M\left( {B,C,n} \right) + M\left( {A,C,n} \right)}}$ (3)
 图 2 区域节点示意图

 图 3 三角定位法

 $\nu \left( {A,N + 1} \right) = \frac{{\nu \left( {B,\mathit{sink},N} \right) + \nu \left( {C,\mathit{sink},N} \right)}}{{\nu \left( {A,\mathit{sink},N} \right)}}$ (4)
 $\nu \left( {B,N + 1} \right) = \frac{{\nu \left( {A,\mathit{sink},N} \right) + \nu \left( {C,\mathit{sink},N} \right)}}{{\nu \left( {B,\mathit{sink},N} \right)}}$ (5)
 $\nu \left( {C,N + 1} \right) = \frac{{\nu \left( {B,\mathit{sink},N} \right) + \nu \left( {A,\mathit{sink},N} \right)}}{{\nu \left( {C,\mathit{sink},N} \right)}}$ (6)

 $H\left( {A,B,n + 1} \right) = \frac{{c\nu \left( {A,N + 1} \right)\left( {L\left( {A,B} \right) + L\left( {B,C} \right)} \right)}}{{M\left( {A,B,n} \right) + M\left( {B,C,n} \right)}}$ (7)
 $H\left( {B,C,n + 1} \right) = \frac{{c\nu \left( {B,N + 1} \right)\left( {L\left( {B,C} \right) + L\left( {A,C} \right)} \right)}}{{M\left( {B,C,n} \right) + M\left( {A,C,n} \right)}}$ (8)
 $H\left( {C,A,n + 1} \right) = \frac{{c\nu \left( {C,N + 1} \right)\left( {L\left( {B,C} \right) + L\left( {A,C} \right)} \right)}}{{M\left( {B,C,n} \right) + M\left( {A,C,n} \right)}}$ (9)

1.2 基于自漂移误差修正机制的锚节点初始化

 ${L_i} = \frac{{\int\limits_{i \ne j} {\sqrt {{{\left( {{x_i} - {x_j}} \right)}^2} + {{\left( {{y_i} - {y_j}} \right)}^2}} } }}{{\sum\limits_{i \ne j} {{L_{ij}}} }}$ (10)

 $R\left( {i,j} \right) = \sqrt {{{\left( {{x_i} - {x_j}} \right)}^2} + {{\left( {{y_i} - {y_j}} \right)}^2}}$ (11)

 $L\left( {i,j} \right) = R\left( {i,j} \right){L_i}$ (12)

 ${M_i} = \frac{{\sum\limits_{i \ne j} {L = \left( {i,j} \right)} /\sum\limits_{i \ne j} {{L_{ij}}} }}{{G + 1}}$ (13)

 ${\mu _i} = \frac{{\sum\limits_{i = 1}^G {\frac{1}{{{M_i}}}} }}{{\frac{1}{{{M_i}}}}}$ (14)

 图 4 平均拓扑间距漂移指数的获取
1.3 粒子群精度提升机制

Step 1：对网络中全部锚节点进行初始化，即sink节点通过搜寻，寻找到当前处于存活状态的锚节点，并记录每一个锚节点的当前最优值popt及网络最优值gopt

Step 2：初始化过程完成后，记录获取的全部N个锚节点的坐标(xnyn)，设待定位信号源的坐标估计为(xy)，di为待定位信号源isink节点间的物理距离，记录完毕后，转Step 3；

Step 3：根据锚节点坐标(xnyn)及信号源坐标(xy)的物理估计距离di构建距离方程：

 $\begin{array}{*{20}{c}} {\sqrt {{{\left( {{x_1} - x} \right)}^2} + {{\left( {{y_1} - y} \right)}^2}} = {d_1}}\\ \vdots \\ {\sqrt {{{\left( {{x_n} - x} \right)}^2} + {{\left( {{y_n} - y} \right)}^2}} = {d_n}} \end{array}$ (15)

 $f\left( i \right) = \sqrt {{{\left( {{x_i} - x} \right)}^2} + {{\left( {{y_i} - y} \right)}^2}}$ (16)

 $F\left( {i,T} \right) = \sum {{{\left( {f\left( i \right) - {d_i}} \right)}^2}}$ (17)

Step 4：对比全部的锚节点当前最优值popt，若优于目标函数F(iT)，则保持不变，见图 5；否则将当前目标函数的值赋予popt，其中T表示当前传输周期，T+1表示下一个传输周期.

 图 5 粒子群精度提升机制
 ${p_{{\rm{opt}}}}\left( {T + 1} \right) = \left\{ {\begin{array}{*{20}{c}} \begin{array}{l} F\left( i \right)\\ {p_{{\rm{opt}}}}\left( T \right) \end{array}&\begin{array}{l} {\rm{if}}\;F\left( {i,T} \right) < {p_{{\rm{opt}}}}\left( T \right)\\ {\rm{if}}\;F\left( {i,T} \right) > {p_{{\rm{opt}}}}\left( T \right) \end{array} \end{array}} \right.$ (18)

Step 5：对比全部的锚节点网络最优值gopt，若优于目标函数F(iT)，则保持不变，见图 5；否则将当前目标函数的值赋予gopt，其中T表示当前传输周期，T+1表示下一个传输周期.

 ${g_{{\rm{opt}}}}\left( {T + 1} \right) = \left\{ {\begin{array}{*{20}{c}} \begin{array}{l} F\left( i \right)\\ {g_{{\rm{opt}}}}\left( T \right) \end{array}&\begin{array}{l} {\rm{if}}\;F\left( {i,T} \right) < {g_{{\rm{opt}}}}\left( T \right)\\ {\rm{if}}\;F\left( {i,T} \right) > {g_{{\rm{opt}}}}\left( T \right) \end{array} \end{array}} \right.$ (19)

Step 6：在数据传输周期T内，根据计算获取的当前最优值popt及网络最优值gopt来不断调整锚节点的坐标，并用于信号源定位，传输周期T完毕后，返回Step 1，继续进行锚节点坐标的修正过程.

1.4 算法复杂度分析

2 仿真实验

 图 6 3种算法在不同信道条件下的信号定位精度

 图 7 定位错误发生频率

 图 8 信号上传带宽测试

 图 9 不同算法的能耗率测试结果
3 结束语

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The Signal Source Localization Algorithm for WSN Network Based on Spin-Jump Self-Drift Correction
DU Heng1, YU Hui-fang2, GONG Qian-ru1
1. School of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, Henan 473009, China;
2. School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
Abstract: Due to the difficulty in signal source localization, the poor anti-jitter performance of the data links and limited bandwidth of signal transmission of the current mobile wireless sensor networks (WNSs) in the process of their deployment, this paper proposes a new algorithm for ultra-high speed mobile WSN network signal source localization based on the spin jump drift correction mechanism. Taking into consideration the signal stability characteristics of the anchor node in an area, and based on the inverse relationship between signal intensity and number of hops, we first design a method to search the signal intensity-node hop strength, thus succeeding in achieving the positioning of the signal source and selecting the best performance of the regional nodes. Then, based on the fact that a frequency drift occurs in the high-speed mobile process of the mobile WSN nodes, we calculate the error of anchor node hop drift, and improve the coverage of the nodes through the correction of the physical difference between the anchor nodes. As a result, the frequency interference coverage of the signal sources outside the region by the regional nodes is reduced and the bandwidth of signal transmission is improved. Finally, taking into account the fact that the sink node has the characteristics of central control, this paper establishes a mobile anchor node particle swarm model through the sink node control mode, and eliminate the frequency jitter problem of mobile anchor node coordinate in the process of mobility brought by the high cycle using recursive way. Simulation results show that compared with the chaotic offset correction localization algorithm, spiral recursive adaptive localization algorithm and localization algorithm convergence of successive jump down commonly used in the mobile location in WSN networks, this algorithm has higher positioning accuracy and signal upload bandwidth and lower positioning error in complex network conditions.
Key words: wireless sensor network (WSN)    signal source localization    intensity search    frequency drift    frequency interference coverage    particle swarm motion model    coordinate jitter