Article directory
-
- [Other algorithms can be replaced, `Get resources`, please refer to Section 5 of the article: Resource acquisition]
- 1. SSA algorithm
- 2. 3D overlay model
- 3. Partial code display
- 4. Simulation result display
- 5. Resource Acquisition
【Other algorithms can be replaced, Get resources, please refer to Section 5 of the article: Resource acquisition】
1. SSA algorithm
2. 3D overlay model
The 3D overlay model is shown in Figure 1 below.
Since the nodes are scattered randomly, the distribution of sensor nodes will affect the network coverage, so
R
c
o
v
R_{cov}
Rcov? is used as the coverage evaluation standard. In the three-dimensional coverage area, the coverage area of sensor nodes is a sphere with a certain radius. Randomly sprinkled in the 3D monitoring area
N
N
N sensor nodes, forming a node set
S
=
{
the s
1
,
the s
2
,
.
.
.
,
the s
N
}
(1)
S=\left \{ s_{1},s_{2},…,s_{N} \right \} \tag{1}
S={s1?,s2?,…,sN?}(1)
Among them, the first
i
i
The coordinates of i nodes are
the s
i
(
x
i
,
the y
i
,
z
i
)
s_{i}(x_{i},y_{i},z_{i})
si? (xi?, yi?, zi?). The set of three-dimensional monitoring nodes is
L
=
{
l
1
,
l
2
,
.
.
.
,
l
N
}
(2)
L=\left \{ l_{1},l_{2},…,l_{N} \right \} \tag{2}
L={l1?,l2?,…,lN?}(2) Among them, a target point in the three-dimensional monitoring area is
l
v
(
x
v
,
the y
v
,
z
v
)
l_{v}(x_{v},y_{v},z_{v})
lv?(xv?,yv?,zv?), the distance between the three-dimensional monitoring point and the target point is:
d
(
the s
i
,
l
v
)
=
(
x
i
?
x
v
)
2
+
(
the y
i
?
the y
v
)
2
+
(
z
i
?
z
v
)
2
(3)
d(s_{i},l_{v})=\sqrt{(x_{i}-x_{v})^{2} + (y_{i}-y_{v})^{2} + (z_ {i}-z_{v})^{2}} \tag{3}
d(si?,lv?)=(xixv?)2 + (yiyv?)2 + (zizv?)2
d
(
the s
i
,
l
v
)
≤
R
the s
d(s_{i},l_{v})\le R_{s}
d(si?,lv?)≤Rs?, the target point is within the three-dimensional coverage area, and the perception is marked as 1; otherwise, the perception is marked as 0 outside the three-dimensional coverage area. Using the Boolean perception model, the perception degree is:
p
(
the s
i
,
l
v
)
=
{
1
,
d
(
the s
i
,
l
v
)
≤
R
S
0
,
d
(
the s
i
,
l
v
)
>
R
S
(4)
p(s_{i},l_{v})=\left\{\begin{matrix} 1,d(s_{i},l_{v})\le R_{S} \ 0,d(s_{ i},l_{v})> R_{S} \end{matrix}\right. \tag{4}
p(si?,lv?)={1,d(si?,lv?)≤RS?0,d(si?,lv?)>RS(4)
Among them, R_{s} is the communication radius of the node, assuming that there is
k
k
k nodes to be tested
the s
1
,
the s
2
,
.
.
.
,
the s
k
s_{1},s_{2},…,s_{k}
s1?,s2?,…,sk?, corresponding points
l
l
The coverage of l is
p
(
the s
i
,
l
v
)
p(s_{i},l_{v})
p(si?,lv?), where
k
a
l
l
k_{all}
kall? is all sensor nodes to be tested in the monitoring area,
R
p
(
k
a
l
l
,
l
v
)
R_{p}(k_{all},l_{v})
Rp?(kall?,lv?) is the joint perception probability, the expression is:
R
p
(
k
a
l
l
,
l
v
)
=
1
?
Π
i
=
1
k
(
1
?
p
(
the s
i
,
l
v
)
)
(5)
R_{p}(k_{all},l_{v})=1-\prod_{i=1}^{k}(1-p(s_{i},l_{v})) \tag{5}
Rp?(kall?,lv?)=1?i=1∏k?(1?p(si?,lv?))(5)
The overall coverage of the network is:
R
c
o
v
=
∑
i
=
1
k
R
p
(
k
a
l
l
,
l
v
)
k
(6)
R_{cov}=\frac{\sum_{i=1}^{k}R_{p}(k_{all},l_{v}) }{k} \tag{6}
Rcov?=k∑i=1k?Rp?(kall?,lv?)?(6)
in,
R
c
o
v
R_{cov}
Rcov? is the overall coverage of the sensor network,
P
P
P is any monitoring point in the area. The coverage performance of the wireless sensor network can be tested by using the coverage rate as a fitness function.
3. Partial code display
FoodNumber=30; % population number
maxCycle=500; %Maximum number of iterations
dim=30; % number of parameters to be optimized
P_percent = 0.2; %finder ratio
pNum = round( FoodNumber * P_percent ); % number of discoverers
objfun = 'WSNcover';
c=0; % lower limit
d=50; % upper limit
r=10; % border width
lb= c.*ones( 1,dim ); % Lower limit bounds
ub= d.*ones( 1,dim ); % Upper limit bounds
for i = 1 : FoodNumber
FoodsX( i, : ) = lb + (ub - lb) .* rand( 1, dim );
FoodsY( i, : ) = lb + (ub - lb) .* rand( 1, dim );
FoodsZ( i, : ) = lb + (ub - lb) .* rand( 1, dim );
ObjVal(i)=feval(objfun,FoodsX( i, : ),FoodsY( i, : ),FoodsZ( i, : ),dim,r,d);
Fitness(i)=calculateFitness(-ObjVal(i));% Get the fitness value, the higher the coverage, the higher the fitness value
end
pFit = Fitness;
pObj = ObjVal;
pX = FoodsX;
pY = FoodsY;
pZ = FoodsZ;
[ ObjMax, ObjbestI ] = max( ObjVal );
[ fMax, fbestI ] = max( Fitness );
bestX = FoodsX( fbestI, : );
bestY = FoodsY( fbestI, : );
bestZ = FoodsZ( fbestI, : );
HistoryObjMax = [1,maxCycle];
% draw
figure(1)
for i=1:dim
x = bestX(1,i);
y = bestY(1,i);
z = bestZ(1,i);
cc(x,y,z,r);
hold on;
end
xlabel('X(m)');
ylabel('Y(m)');
zlabel('Z(m)');
title('Overlay effect before optimization');
4. Simulation result display
5. Resource acquisition
Full source code is available.