dcim是什么意思无线传感器网络模型设计-英文文献翻译.doc

2021年1月21日发(作者：supei)


Model
Design
of
Wireless
Sensor
Network
based
on
Scale- Free
Network Theory
ABSTRACT
The key issue of researches on wireless sensor networks is to balance the energy costs
across
the
whole
network
and
to
enhance
the
robustness
in
order
to
extend
the
survival
time of the whole sensor network. As a special complex network limited especially by the
environment,
sensor
network
is
much
different
from
the
traditional
complex
networks,
such
as
Internet
network,
ecological
network,
social
network
and
etc.
It
is
necessary
to
introduce a way of how to study wireless sensor network by complex network theory and
analysis methods, the key
of which lies
in
a successful
modeling which
is
able to
make
complex network theory and analysis methods more suitable for the application of wireless
sensor network in order to achieve the optimization of some certain network characteristics
of wireless sensor network. Based on
generation
rules
of traditional scale-free networks,
this paper added several restrictions to the improved model. The simulation result shows
that
improvements
made
in
this
paper
have
made
the
entire
network
have
a
better
robustness to the random failure and the energy costs are more balanced and reasonable.
This
improved
model
which
is
based
on
the
complex
network
theory
proves
more
applicable to the research of wireless sensor network.

Key-words: Wireless sensor network; Complex network; Scale-free network














I. INTRODUCTION


In
recent
years,
wireless
sensor
networks
have
attracted
more
and
more
related
researchers for its advantages. Sensor nodes are usually low-power and non- rechargeable.
The integrity
of the original
networks will be destroyed and other nodes will have more
business burden for data transmission if the energy of some certain nodes deplete. The key
issue of sensor network research is to balance the energy consumption of all sensor nodes
and to minimize the impact of random failure of sensor nodes or random attacks to sensor
nodes on the entire network [1].

Complex network theory has been for some time since first proposed by Barabasi and
Albert in 1998, but complex network theory and analysis method applied to wireless sensor
networks research is seriously rare and develops
in slow progress. As a special complex
network limited especially by the environment, sensor network is much different from the
traditional
complex
network,
and
the
existing
complex
network
theory
and
analysis
methods
can
not
be
directly
applied
to
analyze
sensor
networks.
Based
on
scale-free
network theory (BA model) [2], (1) this paper added a random damage mechanism to each
sensor
node
when
deployed
in
the
generation
rule;
(2)
considering
the
real
statement
of
wireless sensor networks, a minimum and maxinum restriction on sensor communication
radius
was
added
to
each
sensor
node;
(3)
in
order
to
maintain
a
balanced
energy
comsuption of the entire network, this paper added a limited degree of saturation value to
each
sensor
node.
This
improved
scale-free
model
not
only
has
the
mentioned
improvements
above,
but
also
has
lots
of
advantages
of
traditional
scale-free
networks,
such as the good ability to resist random attacks, so that the existing theory and analysis
methods of complex network will be more suitable for the researches of wireless sensor
network.












II. PROGRESS OF RELATED RESEARCH

Hailin
Zhu
and
Hong
Luo
have
proposed
two
complex
networks-based
models
for
wireless
sensor
networks
[3],
the
first
of
which
named
Energy-aware
evolution
model
(EAEM) can organize the networks in an energy- efficient way, and can produce scale-free
networks
which
can
improve
the
networks
reliance
against
random
failure
of
the
sensor
nodes.
In
the
second
model
named
Energy-balanced
evolution
model
(EBEM),
the
maximum number of links for each node is introduced into the algorithm, which can make
energy consumption more balanced than the previous model (EAEM).

CHEN Lijun and MAO Yingchi have proposed a topology control of wireless sensor
networks
under
an
average
degree
constraint
[4].
In
the
precondition
of
the
topology
connectivity
of
wireless
sensor
networks,
how
to
solve
the
sparseness
of
the
network
topology is a very important problem in a large number of sensor nodes deployed randomly.
They
proved
their
proposed
scheme
can
decrease
working
nodes,
guarantee
network
topology sparseness, predigest routing complexity and prolong network survival period.

LEI Ming and LI Deshi have proposed a research on self- organization reliability of
wireless sensor network[5], which aiming on the two situations: deficiency of WSN nodes
and
under
external
attack,
analyzes
the
error
tolerance
ability
of
different
topologies
of
WSN, and eventually obtains optimized self
—
organized topological models of WSN and
proposes a refined routing algorithm based on WSN.















III. IMPROVED SCALE-FREE MODEL FOR WSN

Because of the limited energy and the evil application environment, wireless sensor
networks may easily collapse when some certain sensor nodes are of energy depletion or
destruction by the nature, and even some sensor nodes have been damaged when deployed.
There is also a restriction on maxinum and mininum communication radius of sensor nodes
rather
than
the
other
known
scale-free
networks
such
as
Internet
network,
which
has
no
restriction
on
communication
radius.
To
have
a
balanced
energy
consumption,
it
is
necessary to set up a saturation value limited degree of each sensor node [6].

In response to these points, based on the traditional scale-free model, this paper has
made the following improvements in the process of model establishment:

(1) A large number of researches have shown that many complex networks in nature
are not only the result from internal forces, but also the result from external forces which
should
not
be
ignored
to
form
an
entire
complex
network.
Node
failure
may
not
only
occour by node energy depletion or random attacks to them when sensor networks are in
the
working
progress,
but
also
occour
by
external
forces,
such
as
by
the
nature,
when
deployed.
In
this
paper,
a
mechanism
of
small
probability
of
random
damage
has
been
added to the formation of sensor networks.

(2) Unlike Internet network where two nodes are able to connect directly to each other
and their connection are never limited by their real location, sensor network, two nodes in
which connect to each other by the way of multi-hop, so that each node has a maximum of
length
restriction
on
their
communication
radius.
To
ensure
the
sparse
of
the
whole
network, there must also be a minimum of length restriction on their communication radius.
In
this
paper,
a
length
restriction
on
communication
radius
of
sensor
nodes
has
been
proposed in the improved model.

(3)
In
sensor
network,
if
there
exists
a
sensor
node
with
a
seriously
high
degree,
whose
energy
consumption
is
very
quickly,
it
will
be
seriously
bad.
The
whole
sensor
network would surely collapse if enough energy were not supported to the certain node. To
avoid this situation, this paper has set up a saturation value limited degree of each sensor
node. By adding the mentioned restrictions above to the formation of the scale-free model,




the new improved model will be more in line with the real statement of sensor network.
Complex
network
theory
and
analysis
methods
will
be
more
appropriate
when
used
to
research and analyze the sensor network.

IV
. DESCRIPTION OF THE IMPROVED ALGORITHM

The specific algorithm of the improved model formation are described as follows :

(1) A given region (assumed to be square) is divided into HS*HSbig squares (named
as BS);

(2) Each BS (assumed to be square) is divided into LS*LS small squares (named as
SS), and each SS can have only one node in its coverage region;

(3)
m0
backbone
nodes
are
initially
generated
as
a
random
graph,
and
then
a
new
node will be added to the network to connect the existing m nodes with m edges at each
time interval. (m< m0, mis a quantity parameter);

(4) The newly generated node v, has a certain probability of Peto be damaged directly
so that it will never be connected with any existing nodes;

(5)
The
newly
generated
node
vconnects
with
the
existing
node
i,
which
obeyes
dependent-preference
rule
and
is
surely
limited
by
the
degree
of
the
certain
saturation
value .
(6) The distance div between the newly generated node v connects and the existing
node
i
shall
be
shorter
than
the
maximum
dmax
of
the
communication
radius
of
sensor
nodes.

Above all, the probability that the existing node i will be connected with the newly
generated node v can be shown as follows:



In
order
to
compute
it
conveniently,
here
assumed
that
few
nodes
had
reached
the
degree of saturation value kimax . That is, N is very minimal in Eqs.
(1) so that it can be ignored here. And in Eqs.

?
?
ak
i
?
Kj
j
?
1
N

N=m
0
?
t
?
1

(2)





With The varying rate with time of ki, we get:

?
k
i
amk
amk
i

(3)
?
m
?
i
?
m
?
t
?
1
i
?
?
t
2
mt
?
m
?
j?
1
0
k
j

When t→∞,


t
?
2
（
t
）
=m
（
）
,
?
?
condition: k
i
(t
i
)=m, we get the solution:
k
(4)

i
t
i
a
The probability that the degree of node I is smaller than k is:

P
{k
i
(t)
?
k}
?
P{t
i
?
m
1
?
t
}

k
1
?







(5)

The
time
interval
when
each
newly
generated
node
connected
into
the
network
is
equal, so that probability density of t
i
is a constant parameter:

P
(t
i
)
?
1
1/β

m
0
?
t
we replace it into Eqs. (5), then we get:

P
{k
i
(t)
?
k}
?
P{t
i
?
m
1
?
t}
?
1
?
1
?
k
m
1
?t
k
1
?
t
i
?
1
?
P(t
)

i





(6)
m
1
?
t

So we get:

1
?
1
?
k
(t
?
m
0
)
?
P
(k
i
(t)
?
k)< br>2
m
1
?
t
1
P
(k)
?
?
.
1
?




?
k
m
0
?
t
k
When t →∞, we get:

P
(k)
?
2
m
2
k
?
r




























(7)






(8)

In which
?
=1+
2
=1+
,
and the degree distribution we get and the degree distribution of
?
a
1
traditional scale-free network are similar. Approximately, it has nothing to do with the time
parameter t and the quantity of edges m generated at each time interval.

P{d
iv
?
d
max
}
could
be
calculated
by
the
max
in
um
restriction
dmax
on
communication
radius
of
each
sensor
node
and
the
area
of
the
entire
coverage
region
S,
that
is





P{d
iv
?
d
max
}
=
?
d
2
S
?
d
2

Then we replace
P{d
iv
?
d
max
}
?
1
?
2
a
P{d
iv
?
d
max
}
(1-P< br>e
)
=
S

and
a=
into
2S
?
1
?
（
1-P
e
）
?
d< br>2
Eqs. and eventually we get:

P
(k)
?
2m
2
k









































?
2
m
2
k
.



V
. SIMULATION

This
paper
used
Java
GUI
mode
of
BRITE
topology
generator
to
generate
the
topology, and parameter settings were as follows:

1) N=5000
N means the quantity of the sensor nodes at the end of the

topology generation.

2) m=m
0
=1
M means the quantity of the new generated edges by the new generated node at each
time interval.

3) HS=500

HS means the given region was divided into HS*HS big squares.

4) .LS=50 LS means each big square was divided into LS*LS small squares.

5)
d
min

=10
d
min
is the mininum restriction on communication radius of each sensor node.


=128
6)
d
max
d
max

is the maxinum restriction on communication radius of each sensor node.

7) PC=1

PC means wether preferential connectivity or not.

8) .IG=1

IG means wether incremental grouth or not.

9)
P
e
=0.01, m=1
This means that any newly generated node has 1% chance to be node failure and the
newly generated node if normal only connect with one existing node .







Then we got each degree of the sensor network nodes from BRITE topology generator.
To analyze the degree distribution, we use Matlab to calculate datas and draw graph. As
can
easily
be
seen
from
Fig.
1,
the
distribution
of
degree
k
subjected
approximately
to
Power-
Law distribution. However, the value of γ is no longer between 2 and 3, but a very
large value, which is caused by the random damage probability P
e
to new generated nodes
when deployed and the max in um of communication radius d
max
of each sensor node. It
can be easily seen that the slope of P(k) is very steep and P(k) rears up because sensor node
has a limited degree of saturation value by 180. The existence of 0 degree nodes is result
from the random damage to new generated nodes when deployed.












Fig. 1 Degree distribution of Improved Model
Compared with the degree distribution produced by traditional scale-free network as
is
shown
in
Fig.
2,
the
generation
rule
proposed
in
this
paper
has
produced
a
degree
distribution
in
a
relatively
low
value
as
is
shown
in
Fig.
1;
there
are
some
nodes
of
0
degree as is shown in Fig. 1 on the left for the random damage rule; as is shown on the
right in Fig. 1, there are no nodes with higher degree than the quantity of 180 while there
are some nodes whose degree are of higher degree than the quantity of 180.







Fig. 2 Degree distribution of traditional Scale-free Model
















VI. CONCLUSION

This paper has added a random damage to new generated nodes when deployed;
considering multi-hop transmission of sensor network, this paper has proposed a maximum
restriction on the communication radius of each sensor node; in order to improve the
efficiency of energy comsumption and maintain the sparsity of the entire network, this
paper has also added a minimum restriction on the communication radius of each sensor
node to the improved model; to balance the energy comsuption of the entire network, this
paper has proposed a limited degree of saturation value on each sensor node.

In this paper, an improved scale-free network model was proposed to introduce the
theory of traditional scale-free network and analysis methods into the researches of
wireless sensor networks more appropriately, which would be more approximate to the real
statement of wireless sensor networks.

















REFERENCES
[1] R. Albert, H. Jeong and A.-L. Barabasi. Error and attack tolerance of complex networks. Nature,
2000; 406: 378-382.

[2] Albert R, Barabasi A. Statistical mechanics of complex networks. Rev Mod Phys 2002; 74: 47
–
97..

[3]
Zhu
HL,
Luo
H.
Complex
networks-based
energy-efficient
evolution
model
for
wireless
sensor
networks. Chaos, Solitons and Fractals; 2008: 1-4.

[4]
Chen
LJ,
Mao
YC.
Topology
Control
of
Wireless
Sensor
Networks
Under
an
Average
Degree
Constraint. Chinese Journal of computers 2007; 30: 1-4.

[5]
Lei
M,
Li
DS.
Research
on
Self-Organization
Reliability
of
Wireless
Sensor
Network .
Complex
system and complexity science 2005, 2: 1-4.

[6] Chen LJ, Chen DX. Evolution of wireless sensor network

. WCNC 2007; 556: 3003
–
7.

[7] Peng J, Li Z. An Improved Evolution Model of Scale-Free Network . Computer application. 2008 , 2;
1: 1-4.