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利用粒子群算法设计无线传感器网络中的最优安全路由模型（Matlab代码实现）

news 2025/8/16 10:46:05

💥1 概述

📚2 运行结果

🎉3 参考文献

👨‍💻4 Matlab代码

💥1 概述

无线传感器网络（WSN）由数十个、数百个甚至数千个自主传感器组成。这些传感器以无线方式嵌入环境中，彼此相距一定距离进行通信，其任务是发现和聚集环境信息，并将其传输到监测中心。传感器节点的连续移动及其有限的电池功率会导致这些类型网络的路由问题。因此，在无线传感器网络中提供可靠和安全的协议似乎至关重要。本文的主要重点是利用人工智能技术，如聚类和粒子群优化（PSO）算法，在无线传感器网络中找到安全高效的路由模型。

📚2 运行结果

主函数部分代码：

function Init

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%

%Field Dimensions - x and y maximum (in meters)
run=1;
iteration=1;
PopNum=30;
VelocityLimit=3.83;
xRank=13.33;  
RepLimit = 100;    % repository limitation number . we can not exceed this number of record in repository
ObjNum=2;
R = 6;
C1 = [1.3 1.5 1.8 1.9 2];      % C1 nd C2 range
Weight = [0.6 0.7 0.8 0.9 1];          % W range
%Energy Model (all values in Joules)
%Initial Energy 
Eo=8;
NodeNum=400;
PlotSizeX=100;
PlotSizeY=100;
X=PlotSizeX*rand(1,NodeNum);
Y=PlotSizeY*rand(1,NodeNum);


E=Eo*ones(1,NodeNum); %Initil Energy
sender.x=0;
sender.y=0;
%x and y Coordinates of the Sink
sink.x=PlotSizeX/2;
sink.y=PlotSizeY/2;





% % %Number of Nodes in the field
% % n=300;
% % %Optimal Election Probability of a node
% % %to become cluster head
% % p=0.2;


%Eelec=Etx=Erx
ETX=50*0.000000001;
ERX=50*0.000000001;
%Transmit Amplifier types
Efs=10*0.000000000001;
Emp=0.0013*0.000000000001;
%Data Aggregation Energy
EDA=5*0.000000001;
%maximum number of rounds
rmax=5;

%%%%%%%%%%%%%%%%%%%%%%%%% END OF PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%
%Computation of do
do=sqrt(Efs/Emp);
MutualDominance=1;
MobilityRate=2;%movement rate of sink
Step=150; %step of modeling
BsTrackX=[sink.x];
BsTrackY=[sink.y];  % save the track of x and y coordinate of sink

energy_values=[];
fitness=[];
clusters=[];
index_energy=1;
index_fitness=1;
cluster_index=1;

for w=1:StepTrackSize=size(BsTrackX);[sink.x sink.y]=NodeMovement(BsTrackX(TrackSize(2)),BsTrackY(TrackSize(2)),PlotSizeX,PlotSizeY,MobilityRate);BsTrackX=[BsTrackX sink.x];BsTrackY=[BsTrackY sink.y];SenderNum=ceil(rand()*7);for jj=1:SenderNumI=ceil(NodeNum*rand());while (E(I)<=0)I=ceil(NodeNum*rand());endsender.x=X(I);sender.y=Y(I);for ii=1:run[OutputParticleAC OutputParticleVal OutputFitness OutputRepSize Energy]=PSO(iteration,X,Y,E,sink,sender,xRank,RepLimit,PopNum,C1(3),Weight(4),ETX,EDA,Emp,NodeNum,PlotSizeX,ObjNum,do,VelocityLimit,R,Efs,MutualDominance,I,MobilityRate);end      endE=Energy;energy_values(index_energy,1:NodeNum)=E;fitness(index_fitness,1)=OutputFitness(1,1);     clusters(cluster_index,1)=-OutputFitness(1,2);cluster_index=cluster_index+1;index_fitness=index_fitness+1;index_energy=index_energy+1;disp(['end of step ',num2str(w),' ']);
end