基于改进蚁群算法的WSN路由优化研究
文章目录
- 一、理论基础
-
- 1、LEACH
- 2、LEACH-ANT
-
- （1）阈值公式
- （2）能见度系数(启发函数)
- 3、LEACH-IACA
-
- （1）簇首建立阶段
- （2）簇间传输阶段
- 二、仿真与分析
- 三、参考文献
一、理论基础
1、LEACH
请参考文献[1]。
2、LEACH-ANT
针对LEACH存在的缺陷，改进点主要有以下两点：
（1）阈值公式
首先改进LEACH协议的簇首选举规则。修改阈值 T i T_i Ti的生成公式为： T i = { p 1 − p × ( r m o d ( 1 / p ) ) ( E i , c u r r e n t E i , t o t a l ) α ( E i , c u r r e n t E a v e ) β , i ∈ G 0 o t h e r w i s e (1) T_i=\begin{dcases}\frac{p}{1-p×(rmod(1/p))}\left(\frac{E_{i,current}}{E_{i,total}}\right)^\alpha \left(\frac{E_{i,current}}{E_{ave}}\right)^\beta,i∈G\\0\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad otherwise\end{dcases}\tag{1} Ti=⎩⎪⎨⎪⎧1−p×(rmod(1/p))p(Ei,totalEi,current)α(EaveEi,current)β,i∈G0otherwise(1)其中， E i , c u r r e n t E_{i,current} Ei,current表示 i i i节点当前的能量， E i , t o t a l E_{i,total} Ei,total表示节点的总能量， E i , a v e E_{i,ave} Ei,ave表示网络中所有节点的平均能量， α \alpha α、 β \beta β为修正因子，取值范围为 1 ≤ α ≤ β ≤ 2 1≤\alpha≤\beta≤2 1≤α≤β≤2。
（2）能见度系数(启发函数)
其次，将蚁群算法中的能见度系数 η i j = 1 / d i j \eta_{ij}=1/d_{ij} ηij=1/dij更改为由距离和节点剩余能量共同决定；加入系数 α \alpha α ，主要是由于无线电传输模型中信号的衰减反比于距离的三次方，这里系数 γ \gamma γ可以在3~4间取值。 μ \mu μ为矫正系数，表示无线电信号在信道中的传播质量，一般 μ ≤ 1 \mu≤1 μ≤1。新的能见度系数 η i j = μ × 1 d i j γ × E i , c u r r e n t E a v e (2) \eta_{ij}=\mu×\frac{1}{d_{ij}^{\,\gamma}}×\frac{E_{i,current}}{E_{ave}}\tag{2} ηij=μ×dijγ1×EaveEi,current(2)
3、LEACH-IACA
（1）簇首建立阶段
阈值 T ( n ) T(n) T(n)的计算公式如下： T ( n ) = { p 1 − p [ r m o d ( 1 / p ) ] ⋅ E c u r r e n t ω E l a s t + ( 1 − ω ) E a v g , i f n ∈ G 0 , o t h e r w i s e (3) T(n)=\begin{dcases}\frac{p}{1-p[rmod(1/p)]}\boldsymbol\cdot\frac{E_{current}}{\omega E_{last}+(1-\omega)E_{avg}},if\, n∈G\\0,otherwise\end{dcases}\tag{3} T(n)=⎩⎨⎧1−p[rmod(1/p)]p⋅ωElast+(1−ω)EavgEcurrent,ifn∈G0,otherwise(3)式中， p p p表示簇首在所有节点中所占的百分比； r r r是选举轮数； G G G表示本轮循环中未当选过簇首的节点集合； E c u r r e n t E_{current} Ecurrent表示本轮节点的剩余能量； E l a s t E_{last} Elast表示上轮该节点的剩余能量； E a v g E_{avg} Eavg表示当前所有节点的平均剩余能量，公式如下： E a v g = ∑ i = 1 n E i n (4) E_{avg}=\frac{\displaystyle\sum_{i=1}^nE_i}{n}\tag{4} Eavg=ni=1∑nEi(4)
（2）簇间传输阶段
参考文献[3]4.2.2部分内容。
二、仿真与分析
具体代码如下：
%% 清空环境变量
clc;
clear;
%% 参数设置
xm = 100; % x轴范围
ym = 100; % y轴范围
sink.x = 50; % 基站x轴
sink.y = 150; % 基站y轴
n = 100; % 节点总数
p = 0.1; % 簇头比例
E0 = 0.5; % 初始能量
Eelec = 50*10^(-9);
ETX = 50*10^(-9); % 传输消耗能量，每bit
ERX = 50*10^(-9); % 接收消耗能量，每bit
Efs = 10*10^(-12); % 自由空间
Emp = 0.0013*10^(-12); % 多路径衰减
d0 = sqrt(Efs/Emp); % 距离阈值
EDA = 5*10^(-9); % 融合能耗，每bit
rmax = 1500; % 防止簇头过早死亡，需定期更换簇头，即为总轮数
CM = 100; % 控制信息大小，包含自己簇内需要转发的总的数据包个数
DM = 4000; % 要传输的信息大小，一个数据包Bit
w = 0.6;
alpha = 1;
beta = 2;
%%
for i = 1:n
figure(1);
%随机产生节点
S1(i).xd = rand(1,1)*xm;
S2(i).xd = S1(i).xd;
S3(i).xd = S2(i).xd;
S1(i).yd = rand(1,1)*ym;
S2(i).yd = S1(i).yd;
S3(i).yd = S2(i).yd;
S1(i).G = 0; % 若为0，标示当前节点可为簇头，每一周期全部置为0，重新指定全部簇头
S2(i).G = 0;
S3(i).G = 0;
S1(i).E = E0; % 设置初始化能量为E0
S2(i).E = E0;
S3(i).E = E0;
Elast(i) = S1(i).E;
S1(i).type = 'N'; % 节点类型为普通型
S2(i).type = 'N';
S3(i).type = 'N';
plot(S1(i).xd, S1(i).yd, 'bo');
hold on; % 保持所画的图像
end % 为每个节点随机分配坐标，并设置初始化能量为E0，节点类型为普通型
S1(n+1).xd = sink.x;
S1(n+1).yd = sink.y;
S2(n+1).xd = sink.x;
S2(n+1).yd = sink.y;
S3(n+1).xd = sink.x;
S3(n+1).yd = sink.y;
plot(S1(n+1).xd, S1(n+1).yd, 'x'); % 绘制基站节点
%%%%%%%%%%%%%%%%LEACH-ANT%%%%%%%%%%%%%%
% 死亡节点标志
flag_first_dead = 0;
flag_teenth_dead = 0;
flag_all_dead = 0;
% 死亡节点数
first_dead1 = 0;
teenth_dead1 = 0;
all_dead1 = 0;
% 活动节点数
alive1 = n;
% 传输到基站和簇头的比特计数器
packets_TO_BS1 = 0;
packets_TO_CH1 = 0;
%% 迭代
for r = 0:rmax
r
% 如果轮数正好是一个周期的整数倍，则设置S(i).E为0---此时所有节点均参见竞争
if mod(r,round(1/p)) == 0 % round一个四舍五入函数
for i = 1:n
S1(i).G = 0;
end
end
cluster = 0; % 初始化簇头为0
dead = 0; % 初始死亡节点数0
Eavg = 0;
%%
for i = 1:n
if S1(i).E <= 0
dead = dead+1; % 节点死亡后变为红色，死亡节点数加一
if dead == 1 && flag_first_dead == 0
first_dead1 = r; % 第一个节点的死亡轮数
flag_first_dead = 1;
end
if dead == 0.1*n && flag_teenth_dead == 0
teenth_dead1 = r;
flag_teenth_dead = 1;
end
if dead == n && flag_all_dead == 0
all_dead1 = r;
flag_all_dead = 1;
end
else
S1(i).type = 'N';
Eavg = Eavg+S1(i).E;
end
end
Eavg = Eavg/n;
STATISTICS.DEAD1(r+1) = dead; % 每轮死亡节点数
STATISTICS.ALIVE1(r+1) = alive1-dead; % 每轮存活节点数
%% 获取簇头节点数组
for i = 1:n
Elast(i) = S1(i).E;
if S1(i).E > 0 && Eavg > 0
if S1(i).G <= 0
temp_rand = rand; % 取一个随机数
if temp_rand <= p/(1-p*mod(r,round(1/p)))*(S1(i).E/(w*Elast(i)+(1-w)*Eavg))
S1(i).type = 'C'; % 此节点为此轮簇头节点
S1(i).G = round(1/p)-1; % S(i).G设置为大于0，此周期不能再被选为簇头节点
cluster = cluster+1; % 簇头数加1
C(cluster).xd = S1(i).xd;
C(cluster).yd = S1(i).yd; % 将此节点标志为簇头
distance = sqrt((S1(i).xd-S1(n+1).xd)^2+(S1(i).yd-S1(n+1).yd)^2); % 簇头到基站的距离
C(cluster).distance = distance; % 标志位此簇头到基站的距离
C(cluster).id = i; % 此簇头节点的id
distanceBroad = sqrt(xm*xm+ym*ym);
if distanceBroad > d0
S1(i).E = S1(i).E- (Eelec*CM + Emp*CM*distanceBroad^4);
else
S1(i).E = S1(i).E- (Eelec*CM + Efs*CM*distanceBroad^2);
end
% plot(S1(i).xd, S1(i).yd, 'r*');
% hold on;
% 簇头发数据到基站
% if distance > d0
% S1(i).E = S1(i).E- ((Eelec+EDA)*DM+Emp*DM*distance^4);
% else
% S1(i).E = S1(i).E- ((Eelec+EDA)*DM+Efs*DM*distance^2);
% end
end
end
end
end
%% 普通节点选择簇头
for i = 1:n
if S1(i).type == 'N'&&S1(i).E > 0
if cluster > 0
min_dis = sqrt((S1(i).xd-C(1).xd)^2+(S1(i).yd-C(1).yd)^2); % 计算此节点到簇头1的距离
min_dis_cluster = 1; % 初始设置到簇头1的距离最近
for c = 2:cluster
temp = sqrt((S1(i).xd-C(c).xd)^2+(S1(i).yd-C(c).yd)^2);
if temp < min_dis
min_dis = temp;
min_dis_cluster = c;
end
end
% plot(S1(i).xd, S1(i).yd, 'bo');
% hold on;
% plot([S1(i).xd; S1(C(min_dis_cluster).id).xd], [S1(i).yd; S1(C(min_dis_cluster).id).yd], 'k--');
% hold on;
% 接收簇头发来的广播的消耗
S1(i).E = S1(i).E - Eelec*CM;
% 加入簇
if min_dis > d0
S1(i).E = S1(i).E- (ETX*(DM+CM) + Emp*(DM+CM)*min_dis^4);
else
S1(i).E = S1(i).E- (ETX*(DM+CM) + Efs*(DM+CM)*min_dis^2);
end
packets_TO_CH1 = packets_TO_CH1+1;
% 簇头接收
S1(C(min_dis_cluster).id).E = S1(C(min_dis_cluster).id).E-((ERX+EDA)*DM+ERX*CM);
% 如果只有一个簇头
if cluster == 1
dist = sqrt((C(1).xd-sink.x)^2+(C(1).yd-sink.y)^2);
if dist > d0
S1(C(1).id).E = S1(C(1).id).E-(ETX*DM + Emp*DM*dist^4);
else
S1(C(1).id).E = S1(C(1).id).E-(ETX*DM + Efs*DM*dist^2);
end
packets_TO_BS1 = packets_TO_BS1+1;
end
else % 无簇头选出
dist = sqrt((S1(i).xd-S1(n+1).xd)^2+(S1(i).yd-S1(n+1).yd)^2);
if dist > d0
S1(i).E = S1(i).E- (ETX*DM + Emp*DM*dist^4);
else
S1(i).E = S1(i).E- (ETX*DM + Efs*DM*dist^2);
end
packets_TO_BS1 = packets_TO_BS1+1;
end
end
end
%% 簇头路由生成
if cluster > 1
[Route, D] = ACCHRA1(S1, C, cluster, sink.x, sink.y, Eelec, Efs, Emp, DM, Eavg);
order = [C([Route]).id];
for i = 1:cluster-1
if i == 1
S1(order(i)).pos = 0; % pos=0 表示位于起点
else
S1(order(i)).pos = 1; % pos=0 表示位于中间
end
S1(order(i)).dis = sqrt((S1(order(i)).xd-S1(order(i+1)).xd)^2+(S1(order(i)).yd-S1(order(i+1)).yd)^2);
% S1(order(i)).dis = D(Route(i), Route(i+1));
% plot([S1(order(i)).xd; S1(order(i+1)).xd], [S1(order(i)).yd; S1(order(i+1)).yd], 'o-');
% hold on;
end
% plot([S1(order(cluster)).xd; sink.x], [S1(order(cluster)).yd; sink.y], 'o-');
% hold on;
% plot(sink.x, sink.y, '*');
S1(order(cluster)).pos = 2; % pos=2 表示位于终点
S1(order(cluster)).dis = sqrt((S1(order(cluster)).xd-sink.x)^2+(S1(order(cluster)).yd-sink.y)^2);
for i = 1:cluster
% if S1(order(i)).E > 0
% 起点
if S1(order(i)).pos == 0
if S1(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S1(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S1(order(i)).dis^2;
end
S1(order(i)).E = S1(order(i)).E - ETx;
packets_TO_CH1 = packets_TO_CH1+1;
end
% 中间节点
if S1(order(i)).pos == 1
ERx = (EDA+ETX)*DM;
if S1(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S1(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S1(order(i)).dis^2;
end
S1(order(i)).E = S1(order(i)).E - ETx;
packets_TO_CH1 = packets_TO_CH1+1;
end
% 终点
if S1(order(i)).pos == 2
ERx = (EDA+ETX)*DM;
if S1(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S1(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S1(order(i)).dis^2;
end
S1(order(i)).E = S1(order(i)).E-ETx;
packets_TO_BS1 = packets_TO_BS1+1;
end
% end
end
end
STATISTICS.PACKETS_TO_CH1(r+1) = packets_TO_CH1;
STATISTICS.PACKETS_TO_BS1(r+1) = packets_TO_BS1;
end
%%%%%%%%%%%%%%%%LEACH%%%%%%%%%%%%%%
% 死亡节点标志
flag_first_dead = 0;
flag_teenth_dead = 0;
flag_all_dead = 0;
% 死亡节点数
first_dead2 = 0;
teenth_dead2 = 0;
all_dead2 = 0;
% 活动节点数
alive2 = n;
% 传输到基站和簇头的比特计数器
packets_TO_BS2 = 0;
packets_TO_CH2 = 0;
%% 迭代
for r = 0:rmax
r
% 如果轮数正好是一个周期的整数倍，则设置S(i).E为0---此时所有节点均参见竞争
if mod(r,round(1/p)) == 0 % round一个四舍五入函数
for i = 1:n
S2(i).G = 0;
end
end
cluster = 0; % 初始化簇头为0
dead = 0; % 初始死亡节点数0
% 重新绘制整体节点
%%
for i = 1:n
if S2(i).E <= 0
dead = dead+1; % 节点死亡后变为红色，死亡节点数加一
if dead == 1 && flag_first_dead == 0
first_dead2 = r; % 第一个节点的死亡轮数
flag_first_dead = 1;
end
if dead == 0.1*n && flag_teenth_dead == 0
teenth_dead2 = r;
flag_teenth_dead = 1;
end
if dead == n && flag_all_dead == 0
all_dead2 = r;
flag_all_dead = 1;
end
else
S2(i).type = 'N';
end
end
STATISTICS.DEAD2(r+1) = dead; % 每轮死亡节点数
STATISTICS.ALIVE2(r+1) = alive2-dead; % 每轮存活节点数
%% 获取簇头节点数组
for i = 1:n
if S2(i).E > 0
if S2(i).G <= 0
temp_rand = rand; % 取一个随机数
if temp_rand <= p/(1-p*mod(r,round(1/p)))
S2(i).type = 'C'; % 此节点为此轮簇头节点
S2(i).G = round(1/p)-1; % S(i).G设置为大于0，此周期不能再被选为簇头节点
cluster = cluster+1; % 簇头数加1
C(cluster).xd = S2(i).xd;
C(cluster).yd = S2(i).yd; % 将此节点标志为簇头
distance = sqrt((S2(i).xd-S2(n+1).xd)^2+(S2(i).yd-S2(n+1).yd)^2); % 簇头到基站的距离
C(cluster).distance = distance; % 标志位此簇头到基站的距离
C(cluster).id = i; % 此簇头节点的id
distanceBroad = sqrt(xm*xm+ym*ym);
% 簇头广播
if distanceBroad > d0
S2(i).E = S2(i).E- (Eelec*CM + Emp*CM*distanceBroad^4);
else
S2(i).E = S2(i).E- (Eelec*CM + Efs*CM*distanceBroad^2);
end
% 簇头发数据到基站
if distance > d0
S2(i).E = S2(i).E- ((Eelec+EDA)*DM+Emp*DM*distance^4);
else
S2(i).E = S2(i).E- ((Eelec+EDA)*DM+Efs*DM*distance^2);
end
packets_TO_BS2 = packets_TO_BS2+1;
end
end
end
end
%% 普通节点选择簇头
for i = 1:n
if S2(i).type == 'N'&&S2(i).E > 0
if cluster > 0
min_dis = inf; % 计算此节点到簇头1的距离
min_dis_cluster = 0; % 初始设置到簇头1的距离最近
for c = 1:cluster
temp = sqrt((S2(i).xd-C(c).xd)^2+(S2(i).yd-C(c).yd)^2);
if temp < min_dis
min_dis = temp;
min_dis_cluster = c;
end
end
% 接收簇头发来的广播的消耗
S2(i).E = S2(i).E - ERX*CM;
% 加入簇
if min_dis > d0
S2(i).E = S2(i).E- (ETX*(DM+CM) + Emp*(DM+CM)*min_dis^4);
else
S2(i).E = S2(i).E- (ETX*(DM+CM) + Efs*(DM+CM)*min_dis^2);
end
% 簇头接收
S2(C(min_dis_cluster).id).E = S2(C(min_dis_cluster).id).E-((ERX+EDA)*DM+ERX*CM);
packets_TO_CH2 = packets_TO_CH2+1;
else % 无簇头选出
dist = sqrt((S2(i).xd-S2(n+1).xd)^2+(S2(i).yd-S2(n+1).yd)^2);
if dist > d0
S2(i).E = S2(i).E- (ETX*DM + Emp*DM*dist^4);
else
S2(i).E = S2(i).E- (ETX*DM + Efs*DM*dist^2);
end
packets_TO_BS2 = packets_TO_BS2+1;
end
end
end
STATISTICS.PACKETS_TO_CH2(r+1) = packets_TO_CH2;
STATISTICS.PACKETS_TO_BS2(r+1) = packets_TO_BS2;
end
%%%%%%%%%%%%%%%%LEACH-ANT%%%%%%%%%%%%%%
% 死亡节点标志
flag_first_dead = 0;
flag_teenth_dead = 0;
flag_all_dead = 0;
% 死亡节点数
first_dead3 = 0;
teenth_dead3 = 0;
all_dead3 = 0;
% 活动节点数
alive3 = n;
% 传输到基站和簇头的比特计数器
packets_TO_BS3 = 0;
packets_TO_CH3 = 0;
%% 迭代
for r = 0:rmax
r
% 如果轮数正好是一个周期的整数倍，则设置S(i).E为0---此时所有节点均参见竞争
if mod(r,round(1/p)) == 0 % round一个四舍五入函数
for i = 1:n
S3(i).G = 0;
end
end
cluster = 0; % 初始化簇头为0
dead = 0; % 初始死亡节点数0
Eavg = 0;
% 重新绘制整体节点
%%
for i = 1:n
if S3(i).E <= 0
dead = dead+1; % 节点死亡后变为红色，死亡节点数加一
if dead == 1 && flag_first_dead == 0
first_dead3 = r; % 第一个节点的死亡轮数
flag_first_dead = 1;
end
if dead == 0.1*n && flag_teenth_dead == 0
teenth_dead3 = r;
flag_teenth_dead = 1;
end
if dead == n && flag_all_dead == 0
all_dead3 = r;
flag_all_dead = 1;
end
else
S3(i).type = 'N';
Eavg = Eavg+S3(i).E;
end
end
Eavg = Eavg/n;
STATISTICS.DEAD3(r+1) = dead; % 每轮死亡节点数
STATISTICS.ALIVE3(r+1) = alive2-dead; % 每轮存活节点数
%% 获取簇头节点数组
for i = 1:n
if S3(i).E > 0
if S3(i).G <= 0
temp_rand = rand; % 取一个随机数
if temp_rand <= p/(1-p*mod(r,round(1/p)))*(S3(i).E/E0)^alpha*(S3(i).E/Eavg)^beta
S3(i).type = 'C'; % 此节点为此轮簇头节点
S3(i).G = round(1/p)-1; % S(i).G设置为大于0，此周期不能再被选为簇头节点
cluster = cluster+1; % 簇头数加1
C(cluster).xd = S3(i).xd;
C(cluster).yd = S3(i).yd; % 将此节点标志为簇头
distance = sqrt((S3(i).xd-S3(n+1).xd)^2+(S3(i).yd-S3(n+1).yd)^2); % 簇头到基站的距离
C(cluster).distance = distance; % 标志位此簇头到基站的距离
C(cluster).id = i; % 此簇头节点的id
distanceBroad = sqrt(xm*xm+ym*ym);
% 簇头广播
if distanceBroad > d0
S2(i).E = S2(i).E- (Eelec*CM + Emp*CM*distanceBroad^4);
else
S2(i).E = S2(i).E- (Eelec*CM + Efs*CM*distanceBroad^2);
end
end
end
end
end
%% 普通节点选择簇头
for i = 1:n
if S3(i).type == 'N'&&S3(i).E > 0
if cluster > 0
min_dis = sqrt((S3(i).xd-C(1).xd)^2+(S3(i).yd-C(1).yd)^2); % 计算此节点到簇头1的距离
min_dis_cluster = 1; % 初始设置到簇头1的距离最近
for c = 2:cluster
temp = sqrt((S3(i).xd-C(c).xd)^2+(S3(i).yd-C(c).yd)^2);
if temp < min_dis
min_dis = temp;
min_dis_cluster = c;
end
end
% plot(S1(i).xd, S1(i).yd, 'bo');
% hold on;
% plot([S1(i).xd; S1(C(min_dis_cluster).id).xd], [S1(i).yd; S1(C(min_dis_cluster).id).yd], 'k--');
% hold on;
% 接收簇头发来的广播的消耗
S3(i).E = S3(i).E - Eelec*CM;
% 加入簇
if min_dis > d0
S3(i).E = S3(i).E- (ETX*(DM+CM) + Emp*(DM+CM)*min_dis^4);
else
S3(i).E = S3(i).E- (ETX*(DM+CM) + Efs*(DM+CM)*min_dis^2);
end
packets_TO_CH3 = packets_TO_CH3+1;
% 簇头接收
S3(C(min_dis_cluster).id).E = S3(C(min_dis_cluster).id).E-((ERX+EDA)*DM+ERX*CM);
% 如果只有一个簇头
if cluster == 1
dist = sqrt((C(1).xd-sink.x)^2+(C(1).yd-sink.y)^2);
if dist > d0
S3(C(1).id).E = S3(C(1).id).E-(ETX*DM + Emp*DM*dist^4);
else
S3(C(1).id).E = S3(C(1).id).E-(ETX*DM + Efs*DM*dist^2);
end
packets_TO_BS3 = packets_TO_BS3+1;
end
else % 无簇头选出
dist = sqrt((S3(i).xd-S3(n+1).xd)^2+(S3(i).yd-S3(n+1).yd)^2);
if dist > d0
S3(i).E = S3(i).E- (ETX*DM + Emp*DM*dist^4);
else
S3(i).E = S3(i).E- (ETX*DM + Efs*DM*dist^2);
end
packets_TO_BS3 = packets_TO_BS3+1;
end
end
end
%% 簇头路由生成
if cluster > 1
[Route, D] = ACCHRA(S3, C, cluster, sink.x, sink.y, Eavg);
order = [C([Route]).id];
for i = 1:cluster-1
if i == 1
S3(order(i)).pos = 0; % pos=0 表示位于起点
else
S3(order(i)).pos = 1; % pos=0 表示位于中间
end
S3(order(i)).dis = sqrt((S3(order(i)).xd-S3(order(i+1)).xd)^2+(S3(order(i)).yd-S3(order(i+1)).yd)^2);
% S1(order(i)).dis = D(Route(i), Route(i+1));
% plot([S1(order(i)).xd; S1(order(i+1)).xd], [S1(order(i)).yd; S1(order(i+1)).yd], 'o-');
% hold on;
end
% plot([S1(order(cluster)).xd; sink.x], [S1(order(cluster)).yd; sink.y], 'o-');
% hold on;
% plot(sink.x, sink.y, '*');
S3(order(cluster)).pos = 2; % pos=2 表示位于终点
S3(order(cluster)).dis = sqrt((S3(order(cluster)).xd-sink.x)^2+(S3(order(cluster)).yd-sink.y)^2);
for i = 1:cluster
% if S1(order(i)).E > 0
% 起点
if S3(order(i)).pos == 0
if S3(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S3(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S3(order(i)).dis^2;
end
S3(order(i)).E = S3(order(i)).E - ETx;
end
% 中间节点
if S3(order(i)).pos == 1
ERx = (EDA+ETX)*DM;
if S3(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S3(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S3(order(i)).dis^2;
end
S3(order(i)).E = S3(order(i)).E - ETx;
packets_TO_CH3 = packets_TO_CH3+1;
end
% 终点
if S3(order(i)).pos == 2
ERx = (EDA+ETX)*DM;
if S1(order(i)).dis > d0
ETx = (Eelec+EDA)*DM + Emp*DM*S3(order(i)).dis^4;
else
ETx = (Eelec+EDA)*DM + Efs*DM*S3(order(i)).dis^2;
end
S3(order(i)).E = S3(order(i)).E-ETx;
packets_TO_BS3 = packets_TO_BS3+1;
end
% end
end
end
STATISTICS.PACKETS_TO_CH3(r+1) = packets_TO_CH3;
STATISTICS.PACKETS_TO_BS3(r+1) = packets_TO_BS3;
end
%% 绘图比较
figure;
r = 0:rmax;
plot(r, STATISTICS.ALIVE1, 'r', r, STATISTICS.ALIVE3, 'g', r, STATISTICS.ALIVE2, 'b', 'linewidth', 2);
legend('LEACH-IACA', 'LEACH-ANT', 'LEACH');
xlabel '时间(轮)'; ylabel '存活节点数/个';
grid on;
figure;
r = 0:rmax;
plot(r, STATISTICS.PACKETS_TO_CH1, 'r', r, STATISTICS.PACKETS_TO_CH3, 'g',...
r, STATISTICS.PACKETS_TO_CH2, 'b', 'linewidth', 2);
legend('LEACH-IACA', 'LEACH-ANT', 'LEACH');
xlabel '时间(轮)'; ylabel '簇头接收数据包的数量/个';
grid on;
figure;
r = 0:rmax;
plot(r, STATISTICS.PACKETS_TO_BS1, 'r', r, STATISTICS.PACKETS_TO_BS3, 'g',...
r, STATISTICS.PACKETS_TO_BS2, 'b', 'linewidth', 2);
legend('LEACH-IACA', 'LEACH-ANT', 'LEACH');
xlabel '时间(轮)'; ylabel '基站接收数据包的数量/个';
grid on;
三、参考文献
[1] Heinzelman W R , Chandrakasan A , Balakrishnan H . Energy-Efficient Protocol for Wireless Microsensor Networks[C]// hicss. IEEE Computer Society, 2000.
[2] 郦元宏, 王泽民. 基于蚁群算法的LEACH协议在WSN中的研究[J]. 声学与电子工程, 2017, 000(003):37-39.
[3] 方正. 基于改进蚁群算法的WSN路由优化研究[D].重庆邮电大学,2020.