1. SEP算法简介
% SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks
% This is the LEACH [1] code we have used.
The same code can be used for FAIR if m=1
[1] W.R.Heinzelman, A.P.Chandrakasan and H.Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks"IEEE Transactions on Wireless Communications, 1(4):660-670,2002
2.SEP算法matlab源代码
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Field Dimensions - x and y maximum (in meters)
xm=100;
ym=100;
%x and y Coordinates of the Sink
sink.x=0.5*xm;
sink.y=0.5*ym;
%Number of Nodes in the field
n=100
%Optimal Election Probability of a node
%to become cluster head
p=0.1;
%Energy Model (all values in Joules)
%Initial Energy
Eo=0.5;
%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;
%Values for Hetereogeneity
%Percentage of nodes than are advanced
m=0.1;
%\alpha
a=1;
%maximum number of rounds
rmax=9999
%%%%%%%%%%%%%%%%%%%%%%%%% END OF PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%
%Computation of do
do=sqrt(Efs/Emp);
%Creation of the random Sensor Network
figure(1);
for i=1:1:n
S(i).xd=rand(1,1)*xm;
XR(i)=S(i).xd;
S(i).yd=rand(1,1)*ym;
YR(i)=S(i).yd;
S(i).G=0;
%initially there are no cluster heads only nodes
S(i).type='N';
temp_rnd0=i;
%Random Election of Normal Nodes
if (temp_rnd0>=m*n+1)
S(i).E=Eo;
S(i).ENERGY=0;
plot(S(i).xd,S(i).yd,'o');
hold on;
end
%Random Election of Advanced Nodes
if (temp_rnd0<m*n+1)
S(i).E=Eo*(1+a)
S(i).ENERGY=1;
plot(S(i).xd,S(i).yd,'+');
hold on;
end
end
S(n+1).xd=sink.x;
S(n+1).yd=sink.y;
plot(S(n+1).xd,S(n+1).yd,'x');
%First Iteration
figure(1);
%counter for CHs
countCHs=0;
%counter for CHs per round
rcountCHs=0;
cluster=1;
countCHs;
rcountCHs=rcountCHs+countCHs;
flag_first_dead=0;
for r=0:1:rmax
r
%Operation for epoch
if(mod(r, round(1/p) )==0)
for i=1:1:n
S(i).G=0;
S(i).cl=0;
end
end
hold off;
%Number of dead nodes
dead=0;
%Number of dead Advanced Nodes
dead_a=0;
%Number of dead Normal Nodes
dead_n=0;
%counter for bit transmitted to Bases Station and to Cluster Heads
packets_TO_BS=0;
packets_TO_CH=0;
%counter for bit transmitted to Bases Station and to Cluster Heads
%per round
PACKETS_TO_CH(r+1)=0;
PACKETS_TO_BS(r+1)=0;
figure(1);
for i=1:1:n
%checking if there is a dead node
if (S(i).E<=0)
plot(S(i).xd,S(i).yd,'red .');
dead=dead+1;
if(S(i).ENERGY==1)
dead_a=dead_a+1;
end
if(S(i).ENERGY==0)
dead_n=dead_n+1;
end
hold on;
end
if S(i).E>0
S(i).type='N';
if (S(i).ENERGY==0)
plot(S(i).xd,S(i).yd,'o');
end
if (S(i).ENERGY==1)
plot(S(i).xd,S(i).yd,'+');
end
hold on;
end
end
plot(S(n+1).xd,S(n+1).yd,'x');
STATISTICS(r+1).DEAD=dead;
DEAD(r+1)=dead;
DEAD_N(r+1)=dead_n;
DEAD_A(r+1)=dead_a;
%When the first node dies
if (dead==1)
if(flag_first_dead==0)
first_dead=r
flag_first_dead=1;
end
end
countCHs=0;
cluster=1;
for i=1:1:n
if(S(i).E>0)
temp_rand=rand;
if ( (S(i).G)<=0)
%Election of Cluster Heads
if(temp_rand<= (p/(1-p*mod(r,round(1/p)))))
countCHs=countCHs+1;
packets_TO_BS=packets_TO_BS+1;
PACKETS_TO_BS(r+1)=packets_TO_BS;
S(i).type='C';
S(i).G=round(1/p)-1;
C(cluster).xd=S(i).xd;
C(cluster).yd=S(i).yd;
plot(S(i).xd,S(i).yd,'k*');
distance=sqrt( (S(i).xd-(S(n+1).xd) )^2 + (S(i).yd-(S(n+1).yd) )^2 );
C(cluster).distance=distance;
C(cluster).id=i;
X(cluster)=S(i).xd;
Y(cluster)=S(i).yd;
cluster=cluster+1;
%Calculation of Energy dissipated
distance;
if (distance>do)
S(i).E=S(i).E- ( (ETX+EDA)*(4000) + Emp*4000*( distance*distance*distance*distance ));
end
if (distance<=do)
S(i).E=S(i).E- ( (ETX+EDA)*(4000) + Efs*4000*( distance * distance ));
end
end
end
end
end
STATISTICS(r+1).CLUSTERHEADS=cluster-1;
CLUSTERHS(r+1)=cluster-1;
%Election of Associated Cluster Head for Normal Nodes
for i=1:1:n
if ( S(i).type=='N' && S(i).E>0 )
if(cluster-1>=1)
min_dis=sqrt( (S(i).xd-S(n+1).xd)^2 + (S(i).yd-S(n+1).yd)^2 );
min_dis_cluster=1;
for c=1:1:cluster-1
temp=min(min_dis,sqrt( (S(i).xd-C(c).xd)^2 + (S(i).yd-C(c).yd)^2 ) );
if ( temp<min_dis )
min_dis=temp;
min_dis_cluster=c;
end
end
%Energy dissipated by associated Cluster Head
min_dis;
if (min_dis>do)
S(i).E=S(i).E- ( ETX*(4000) + Emp*4000*( min_dis * min_dis * min_dis * min_dis));
end
if (min_dis<=do)
S(i).E=S(i).E- ( ETX*(4000) + Efs*4000*( min_dis * min_dis));
end
%Energy dissipated
if(min_dis>0)
S(C(min_dis_cluster).id).E = S(C(min_dis_cluster).id).E- ( (ERX + EDA)*4000 );
PACKETS_TO_CH(r+1)=n-dead-cluster+1;
end
S(i).min_dis=min_dis;
S(i).min_dis_cluster=min_dis_cluster;
end
end
end
hold on;
countCHs;
rcountCHs=rcountCHs+countCHs;
%Code for Voronoi Cells
%Unfortynately if there is a small
%number of cells, Matlab's voronoi
%procedure has some problems
%[vx,vy]=voronoi(X,Y);
%plot(X,Y,'r*',vx,vy,'b-');
% hold on;
% voronoi(X,Y);
% axis([0 xm 0 ym]);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% STATISTICS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% DEAD : a rmax x 1 array of number of dead nodes/round
% DEAD_A : a rmax x 1 array of number of dead Advanced nodes/round
% DEAD_N : a rmax x 1 array of number of dead Normal nodes/round
% CLUSTERHS : a rmax x 1 array of number of Cluster Heads/round
% PACKETS_TO_BS : a rmax x 1 array of number packets send to Base Station/round
% PACKETS_TO_CH : a rmax x 1 array of number of packets send to ClusterHeads/round
% first_dead: the round where the first node died
% %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3.总结
SEP算法是基于二级异构的网络,即网络中存在高级节点和普通节点,算法为不同初始能量的节点分配不同的轮转周期来实现延长网络稳定周期的目的,但SEP算法的簇头选举只是基于节点的初始能量,未考虑节点的剩余能量。DEEC算法将二级异构网络扩展到多级异构网络,在SEP算法的基础上根据节点的剩余能量水平和网络的异构性来决定簇头的选举,既能充分利用网络的异构性又能适应节点能量的变化。但DEEC使用估算方案求解网络平均剩余能量,求解的前提是网络能耗均匀,这与实际并不相符，从而削弱了DEEC的实用性。Aderohumu等人提出了E-SEP算法，该算法将网络中的节点根据能量分为了三类，增加了中间节点，相比于SEP算法，该算法进一步改善了网络的稳定性，但是E-SEP仍未考虑到节点与基站的距离。Faisal等人提出了Z-SEP算法，Z-SEP算法将网络中的能量异构节点按照能量的不同部署在不同的区域，簇头只在高级节点部署的区域中选举，这种分区部署策略一定程度上节省了能耗，延长了网络生命周期。自适应高效动态聚类路由算法EDFCM，与DEEC算法相似，也是通过估算网络平均剩余能量，对下一轮理想状态下的平均消耗能量进行预测，将预测值与历史能耗作为计算概率的参考因素。