Energy Balanced Clustering Algorithm in Large Scope WSN ...

Energy Balanced Clustering Algorithm in Large Scope

WSN Based on Cooperative Transmission Technology

Abstract—In this paper, a clustering algorithm based on the

cooperative transmission technology is proposed with respect to

uneven energy consumption in large scope wireless sensor

network. In this algorithm, to ensure the even distribution of the

cluster head nodes in large scope wireless sensor network which

has even clusters, the cooperative transmission technology is

applied in data transmission among the clusters. System

performance is improved by virtual multi-antenna system which

is constituted through cooperative transmission between the

sensor nodes. The experimental results show that the energy

consumption is balanced and the network lifetime is extended. Index Terms—Wireless sensor network, energy consumption

unbalanced, clustering algorithm, cooperative transmission,

cluster head node

I.

INTRODUCTION

Wireless sensor network (WSN) is a self-monitoring

system through MANET way, which is composed of a

large number of sensor nodes deployed in the monitoring

area. It has been gradually used in military, civilian and

industrial areas. However, due to the huge number of

sensor nodes and the general use of battery as the power

source, the energy issue has become a bottleneck for

further development.

As multi-hop manner is generally used in data

transmission of the wide range of WSNs, the loads of the

nodes near the SINK will be inevitably increased and

more energy is consumed due to transponding the data of

the peripheral nodes. It results in the uneven energy

consumption of the entire network. In papers [1]-[4] the

relay nodes, which can transpond data but do not perceive

information, are added to the network, thus avoiding the

problem of uneven energy consumption caused by

excessive load. However, a huge number of relay nodes

are added in [4], which is not applicable in practice. Then

an energy consumption balancing strategy based on

adjustable transmit power is proposed in [5]. When the

residual energy of the node is high, the transmitting

power is increased and the data is transmitted to longer

distance; otherwise, the transmitting power will be

reduced [6]. A co-operative transmission strategy based

on residual energy stimulation is proposed in literature

This work is supported by the Major National Scientific and

Technological Project of China (No. 1010122720110046). Corresponding author email: [email protected]

[7]. When the co-operation conditions are met, data will

be sent to the SINK through the cooperative transmission

node. To some extent, it solves the problem of uneven

energy consumption in the network. However, the

method is not applicable to large scope WSNs. The

energy consumption cannot reduce linearly as the number

of cooperative nodes increase. When over a certain range,

the increase in the number of cooperative nodes cannot

improve the performance of the network significantly [8],

[9]. George N. Bravos did a deep research for

collaborative MIMO and further demonstrated the

cooperative MIMO communication has better energy

performance than the multi-hop SISO algorithm [10].

Based on the current status of research it can be seen

that clustering can solve the problem of local uneven

energy, and that the cooperative transmission can oppose

the multipath fading effects [11]. The combination of

them can effectively solve the problem of the uneven

energy consumption. For example, the LEACH routing

protocol requires that the cluster head nodes can

communicate directly with the SINK node. However,

according to the energy consumption model of the

wireless transmission, the energy consumption of the

wireless communication is proportional to the n-th power

of the transmission distance (n = 2, 3, 4). Thus, the local

energy balance can be ensured by the LEACH routing

protocol cluster, but the uneven energy problem brought

by the distance cannot be resolved [12]. If the

transmission technology is collaborated, such uneven

energy problem of distances can be solved. This paper is

studied based on this combination.

In this paper, a clustering algorithm based on the

cooperative transmission technology is proposed. With

such a method, the energy consumption in large scope

wireless sensor network is balanced and the network

lifetime is extended.

The rest of this paper is organized as follows: Section

II presents some foundations and the model for the

wireless sensor network system including the energy

consumption is discussed. In Section III, the proposed

methods for constitution of clusters, the determination of

the optimal cluster head nodes and the inter-cluster

communication strategy based on cooperative

transmission are described. Section IV presents the

simulation parameters and results. Finally, Section V

concludes the paper.

627

Journal of Communications Vol. 9, No. 8, August 2014

©2014 Engineering and Technology Publishing

doi:10.12720/jcm.9.8.627-633

Manuscript received March 12, 2014; revised August 27, 2014.

Yang Jiang, Yiqian Yan, and Yang Yu

College of Communication Engineering, Chongqing University, Chongqing 400044, China

Email: {cqjiangsun, yanyiqiancq}@126.com; [email protected]

II. SYSTEM MODEL

Since the wireless sensor networks use the clustering

algorithm, the network model is a hierarchical structure,

as shown in Fig. 1.

SINK

Head node

Member node

Fig. 1. The model of the network

For this model, the following assumptions are made: (a)

Once deployed, the nodes in the network are stationary;

(b) Except the SINK nodes have no energy limitation, the

other nodes in the network have the same initial energy;

(c) The nodes in the network can automatically adjust the

transmitting power.

The common energy model of wireless sensor net-

works is as follows.

_ _

2

0

4

0

( , ) ( ) ( , )

,

,

Tx Tx elec Tx amp

elec fs

elec amp

E k d E k E k d

kE k d d d

kE k d d d

(1)

_( ) ( )

Rx Rx elec elecE k E k kE (2)

where ( , )Tx

E k d is the energy consumed when the sensor

node transmits k bits of data, _ ( )Tx elecE k is the energy

consumed by the transmitting circuit when the sensor

node transmits k bits of data, _ ( , )Tx ampE k d is the energy

consumed by the amplifier when the sensor node

transmits k bits of data, ( )RxE k is the energy consumption

when the node receives k bits of data, and only the

energy consumption of the circuit is considered and elecE

indicates the loss energy of the transmitting circuit. If the

transmission distance is less than the threshold value 0d ,

the loss of the power amplifier uses the free space loss

model. Otherwise, the multi-path fading model is used.

fs and amp are the energies that the power amplifier

required for the free space model and the multi-path

fading model, respectively.

In the literature [13], for vMISO system, it is assumed

that the synergistic encode is STBC and the modulation

method is BPSK. When the bit error rate is the 10-3

, the

multi-path fading parameter =3 , the diversity gain,

G(Nc), under different cooperative node numbers, Nc,

and range extension, βext, when the nodes use the same

transmitting power are shown in Table I[10]

.

TABLE I: DIVERSITY GAIN AND RANGE EXTENSION

Nc 2 3 4 5 10

G(Nc)(dB) 10 13.5 14 14.5 15.9

ext ( =3) 2.71 4.07 4.65 5.2 7.3

The number of cooperative nodes can be determined

according to the parameters in Table I. When

2.71link linkd d d , two cooperative nodes can transmit

data to the SINK node. And if 2.71 4.07link linkd d d ,

three nodes are needed to accomplish the transmission.

The impact of the cooperative transmission on the

transmission range is described in Table I. Assume that

the transmission distance is constant, with the cooperative

transmission added the transmitting power of the nodes

will inevitably decline. The transmitting power of the

node after collaboration is shown in

( )10

10 cG N

c

t tP P (3)

where c

tP represents the transmitting power of a single

node with cooperative transmission, and Pt represents

that without cooperative transmission. Thus, the equation

(3) describes the relationship of the transmitting power

with and without cooperative transmission.

If Rs

is the energy consumed when the sensor node

transmits s bits of data and the transmitting time is 1s,

the energy model is as follows.

2

0

4

0

* * ,

* * ,

s elec fs

s elec amp

E Rs E Rs d d d

E Rs E Rs d d d

(4)

According to (3) and (4), when the number of

cooperative nodes is tM , the consumption per second

can be present as

2 10

0

4 10

0

* * * *10 ,

* * * *10 ,

c

c

G N

t elec fs

G N

t elec amp

E M Rs E Rs d d d

E M Rs E Rs d d d

(5)

Thus, we get the mathematical model of the energy

consumption with the cooperative transmission.

0 20 40 60 80 100 120 140 160 180 2000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

the distance from SINK /m

the e

nerg

y c

onsum

ption o

f th

e n

odes/J

no cooperative nodes

two cooperative nodes

three cooperative nodes

four cooperative nodes

Fig. 2. relationship between energy consumption and the number of

cooperative nodes

628



app:ds:network

Fig. 2 shows the relationship between energy

consumption and the number of cooperative nodes. In the

case of the same transmitting distance, the cooperative

nodes can relatively reduce the energy consumption of

the nodes. However, the energy consumption is not

always less as the number of cooperative nodes is more.

From Fig. 2 it can be seen that, the energy consumption

of the four cooperative nodes is greater than that of three

cooperative nodes.

III. EBBMCC_LS CLUSTERING ALGORITHM

A. Constitution of Clusters

1) Constitution of the initial uniform clusters

When all the nodes are deployed to the monitoring area,

the nodes obtain their location by initialization. It is

assumed that the location of SINK is (x0,y0) and the

number of sensor nodes is n. When the nodes are

deployed, their locations are fixed and expressed as

[(x1,y1),(x2,y2)….(xi,yi)….(xn,yn)]. The nodes calculate

their distances from the SINK d as

2 2

0 0( ) , 1,2,..... .i id x x y y i n (6)

If 1 2d r A n , select the node as the candidate

cluster head node, where r is the radius of the cluster

domain and A is the area of the network. Assume there

are m candidate cluster head nodes. The candidates

broadcast their identities. Then the nodes received the

broadcasted information report their locations to the

candidates. The candidates calculate their distances from

these nodes as

2

2( ) , =1,2,...... k k j k jd x x y y k m (7)

where j is the number of the nodes which have reported

their locations. If 02 kr d d , node j will be selected as

a candidate. And the marked candidates which do not

satisfy the conditions will be canceled. Thus, each

candidate performs the operation to find the nearby

candidates, until the whole network is covered. Then the

candidates distribute uniformly in the network.

When the candidates are confirmed, the candidates

broadcast their identities with the level of the transmitting

power corresponding to the radius of the cluster domain.

The nodes received this message join the cluster, which

forms an initial uniformly distributed cluster structure.

The flow chart of building initial cluster is shown in Fig.

3.

2) Determine the optimal cluster head nodes

Compared to other nodes, the cluster head nodes

consume more energy, so the nodes with more residual

energy should be selected. While in order to minimize

energy consumption, the nodes should have minimum

communication cost. The initial cluster structure is just

uniformly distributed without considering the problem of

energy consumption. Therefore, the cluster head must be

optimized.

Start

Deploy n sensor nodes

The Nodes

initialize and get

their locations

The nodes calculate their distances from the SINK

1

2An

d r

2 2

0 0( )i id x x y y

be the candidate cluster

head node and join in

The candidates calculate their distances from node j

2

2( )k k j k jd x x y y

kd

12

2A

k nd r

Add node j to

Delete node j from

headS

headS

headS

is traversed headSk=k+1

RNHm is traversed

( )NHmR

j=j+1

Yes

Yes

i>n?i=i+1No

Yes

No

No

Broadcast their identities as the

candidates

Joining the

corresponding clusters

End

No

Yes

No

Yes

Fig. 3. The flow chart of building initial cluster

The minimum communication cost of the cluster head

node j is calculated as follows.

min

1

1cos

m

j k

k

t Pm

(8)

where m is the number of cluster members and Pk is the

629



power level that the cluster member k uses to send data.

The above equation represents the average trans-

mitting power when all the cluster members send data to

the cluster head node. With the consideration of the

residual energy level, the minimum communication cost

is

_ min

1cos _ cos

( )j i

req

t adjusted tE j

(9)

where ( )reqE j is the residual energy of the j-th sensor

node.

According to the amended smallest cluster

communication cost, the optimal cluster nodes are

determined as the cluster head nodes. Thus, a single

round cluster is built. At this time, it starts the data

transfer phase for data sensing and transmission. After a

round ends, the optimization process of the cluster heads

repeats until the network dies.

After the optimal cluster head nodes are confirmed,

they broadcast their identities to the cluster members.

Thus, uniformly clustering phase in the network is

completed. The network enters the data transfer phase.

Data transmission in WSNs includes transmission in a

cluster and between clusters. Considering the impact of

energy consumption, the clustering range can meet the

free space loss model. Thus, each member node sends

data directly to the cluster head node in its own slot. The

algorithm aims at large-scale wireless sensor networks, so

data transmission between clusters is much more complex.

The flow chart of confirming best cluster head is shown

in Fig. 4.

j=1

i=1

_ min

( )

1cos _ cos

i i

req j

t adjusted tE

j=m?(m is the number of cluster

members)

Select the node with

smallest

as the head nodecos _

it adjusted

i=n?

(n clusters)

j=j+1

i=i+1

Yes

End

No

No

Yes

Fig. 4. The flow chart of confirming best cluster head

B. Inter-Cluster Communication Strategy Based on

Cooperative Transmission

In general, the cluster head can send integrated

information to the base station with the following two

means. One is the single hop communication method, that

is, the fusion of data is sent to the remote base station

directly. Another is obtaining the path of the minimum

energy cost from the cluster head to the base station and

realizing multi-hop data transmission by relaying and

forwarding of the other cluster head nodes. The first

method cannot meet the needs of energy conservation.

The other one with a multi-hop manner can effectively

reduce the number of cluster head nodes which directly

communicate with the base station. The data can be

transmitted by the cluster head close to the base station.

Although the algorithm is more complex, the study

focuses on the second communication method because of

the energy efficiency of WSN. Therefore, the proposed

EBBMCC_LS (Energy Balanced Based Multi-hop and

Cooperative transmission technology in Clustered Large

Scope WSN) clustering algorithm combines the initial

multi-hop with cooperative transmission.

The key issue of inter-cluster communication is the

problem of cooperative transmission.

1) Conditions of cooperative transmission

Assume CHnT and CHmR are the two cluster head nodes

of multi-hop communication, in which CHnT is the

sending node, where the average residual energy of the

cluster is re nTE CH and CHmR is the receiving node,

where the average residual energy of the cluster is

re mRE CH .

If re nT re mRE CH E CH , the cooperative trans-

mission is used for the expansion of the transmission

distance; if re nT re mRE CH E CH , the initial multi-hop

transmission is used.

2) Selection of cooperative nodes

If re nT re mRE CH E CH , the sending node uses

cooperative transmission to extend the transmission

distance, thus avoiding the traffic load of the next hop.

The cooperative node is selected from the collection RNHm.

According to Table I, when the number of cooperative

nodes is 2, 3 or 4, and single transmission power is Pt, the

extension is 2.71, 4.07 or 4.65, which means that the

expansion is good. If the cooperative nodes are more than

4, the effect of the expansion is significantly reduced. In

order to better reflect the expansion characteristics of the

cooperative transmission, only the cases of 2, 3 and 4

cooperative nodes are considered. When condition of

cooperative transmission is met and there is data to be

sent, according to the principle of a minimum number of

hops, the number of cooperative nodes can be determined

by the following equation

min , 2,3,4. s

ci

di

d

(10)

where ds represents the distance of the sending node from

the SINK node and dci represents the transmitting range

when the number of nodes is i.

To maximizing the energy in the cluster after the data

is sent, the cooperative nodes have to satisfy the two

conditions: a) the residual energy of the nodes is as much

630



as possible. Because more energy will be consumed in

the collaborative process; b) the distances of the nodes

from the cluster head node need to be as small as possible

to avoid the extra energy consumption.

Thus, select the sensor nodes meeting the following

condition as the cooperative nodes:

max( ), 1,, , ,

re i

ci

E CHi n

d (11)

where re iE CH is the residual energy of the i-th node,

cid is the distance from the i-th node to the cluster head

node and n is the number of cluster members. The flow

chart of communication among clusters is shown in Fig. 5.

Determine the next hop of

the cluster head by The

way of simple multi-hop

>req tE req rE

Send data to the

next hop of the

original multi-hop

Choose as the

cooperative node

Cooperative transmission

max( )

re i

ci

E CH

d

Yes

No

Fig. 5. The flow chart of communication among clusters

IV. SIMULATION

A. Simulation Settings

MATLAB is used as the simulation platform. The

energy loss of the network is calculated with the model of

radio communication system. The parameters in the

simulation experiment are shown in Table II. In the

simulation, when the node energy is less than Emin

(0.002J), the node is considered to be death.

TABLE II: SIMULATION PARAMETERS

parameters value

Network Range (300×300) , (400×400) , (500×500)

Number of nodes 300, 500, 700 Location of base station (150,150), (200,200), (250,250)

Initial energy of node 0.5J

Radius of cluster 30m Eelec 50nJ/bit

Efs 10pJ/bit/m2 Emp 0.0013pJ/bit/m4

Threshold(d0) 75m

B. Distribution of the Cluster

Clustering constitutes the hierarchical structure of

WSN. The energy consumption of the network can be

better balanced with the cyclical cluster head rotation

strategy. Meanwhile, the clustering algorithm also has its

own shortcomings, such as the energy consumption is

only balanced at a local scale for uneven clustering, etc.

Especially in large-scale wireless sensor networks, due to

inter-cluster data forwarding, uneven energy consumption

is caused inevitably by the distance. Simple clustering

algorithm is not an effective solution. EBBMCC_LS

algorithm is a clustering routing algorithm to balance the

energy consumption of a wide range of wireless sensor

network. The distribution of the clusters is verified first.

0 50 100 150 200 250 3000

50

100

150

200

250

300

Fig. 6. Distribution of nodes

Fig. 6 is the distribution of 300 sensor nodes randomly

distributed in a square area of 300 * 300 m2. The nodes

are deployed randomly, so the distribution of them is

uneven as in the figure.

After running the clustering algorithm to build clusters

based on the node deployment in Fig. 6, the resulted

network is as in Fig. 7. The dark nodes represent the

cluster head nodes. From the figure, the cluster head

nodes are relatively evenly distributed in the network.

Therefore, EBBMCC_LS algorithm can achieve a

uniform distribution of nodes in the network clustering.

0 50 100 150 200 250 3000

50

100

150

200

250

300

Fig. 7. Distribution of initial cluster heads in WSN

C. Analysis of Energy Consumption

This study focuses on the problem of energy

consumption of wireless sensor networks. Now the

energy consumption of EBBMCC_LS algorithm is

evaluated by deaths of nodes. If all nodes die in a short

631



period, it indicates that the energy consumption of all

nodes in the network is relatively even; otherwise, the

energy consumption of the network is seriously uneven.

0 200 400 600 800 1000 1200 1400 1600 1800 20000

50

100

150

200

250

300

回合数/轮

死亡节点数

/个

LEACH

LEACH(mhop)

LEACH-C(mhop)

EBBMCC-LS

Th

e n

um

be

r o

f th

e d

ea

d n

od

es

The round of the network Fig. 8. Dead nodes in 300*300 m2 network areas

0 200 400 600 800 1000 1200 1400 1600 1800 20000

50

100

150

200

250

300

350

400

450

500

回合数/轮

死亡节点数

/个

LEACH

LEACH(mhop)

LEACH-C(mhop)

EBBMCC-LS

The

nu

mb

er o

f th

e d

ead

no

des

The round of the network

Fig. 9. Dead nodes in 400*400 m2 areas

0 200 400 600 800 1000 1200 1400 1600 1800 20000

100

200

300

400

500

600

700

回合数/轮

死亡节点数

/个

LEACH

LEACH(mhop)

LEACH-C(mhop)

EBBMCC-LS

The

nu

mb

er

of

the

de

ad n

od

es

The round of the network

Fig. 10. Dead nodes in 500*500 m2 network areas

Fig. 8, Fig. 9, Fig. 10 show the number of dead nodes

with the change of the number of rounds in LEACH

clustering single-hop transmission, LEACH clustering

multi-hop transmission, LEACH-C clustering multi-hop

transmission and EBBMCC_LS algorithm. The network

ranges are 300300 m2, 400400 m

2, 500500 m

2,

respectively. From the three figures, it can be seen that

death nodes appears firstly under LEACH clustering

single-hop transmission, and death of all nodes requires

the longest time. Then the LEACH clustering multi-hop

transmission follows. The next is LEACH-C clustering

multi-hop transmission. For EBBMCC_LS algorithm

death nodes appear the latest, and all the nodes die

quickly after the first death node. By comparing the

LEACH clustering single-hop transmission with the

LEACH clustering multi-hop transmission, it can be seen

that the single-hop approach is no longer applicable in the

wide range of wireless sensor networks; from the

comparison of LEACH clustering multi-hop transmission

and the LEACH-C clustering multi-hop transmission, a

uniform distribution of nodes can better balance the

energy consumption. Therefore, it is obvious that

EBBMCC_LS algorithm has better energy consumption

equalization.

D. Analysis of Network Lifetime

The lifetime of wireless sensor network is generally

defined as the moment when the first node dies, called

FND (First Node Died) Lifetime. Network lifetime is one

of the key measures of wireless sensor networks. In three

different network ranges of 300300 m2, 400400 m

2,

500500 m2, the improvements of the network lifetime

with EBBMCC_LS algorithm are verified by comparing

the time of the first dead node with the LEACH

clustering single-hop transmission, LEACH clustering

multi-hop transmission, LEACH-C clustering multi-hop

transmission.

300*300 400*400 500*5000

500

1000

1500

2000

网络范围/m*m

第一个死亡节点出现轮数

/轮

LEACH

LEACH(mhop)

LEACH-C(mhop)

EBBMCC-LS

The

mo

men

t o

f th

e fi

rst

no

de

die

s

The range of the network/m*m

Fig. 11. Comparison of network lifetime

Fig. 11 represents the comparison of network lifetime

under the above four algorithms in different ranges. In the

range of 300 * 300 m2, the lifetime of the EBBMCC_LS

algorithm is 1.4 times larger than the LEACH-C multi-

hop transmission, 1.8 times larger than the LEACH

multi-hop the transmission and 4.2 times larger than the

LEACH single-hop transmission. In the range of 400 *

400 m2, the lifetime of the EBBMCC_LS algorithm is

respectively 1.7 times, 2.2 times and 9 times than the

other methods. And in the range of 500 * 500 m2, the

lifetime of the EBBMCC_LS algorithm is respectively

1.9 times, 2.7 times and 23 times than the other methods.

It can be seen that, with the increase of network range,

improvements in the network lifetime of EBBMCC_LS

algorithm are increasingly apparent.

632



According to the above simulation and analysis about

distribution of nodes, death of nodes and network lifetime

of the four algorithms, EBBMCC_LS algorithm has a

good capability of energy consumption equalization in a

wide range of wireless sensor networks, and is able to

postpone the first death of the nodes to extend the FND of

the network.

V. CONCLUSIONS

A clustering algorithm based on the cooperative

transmission technology with respect to uneven energy

consumption in large scope wireless sensor network has

been proposed. In this algorithm, the cooperative

transmission technology has been applied in data

transmission among the clusters, which has ensured the

even distribution of the cluster head nodes in large scope

wireless sensor network. It has been shown that the

energy consumption is balanced and the network lifetime

is extended. The performance of the network is improved

significantly.

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633



Yang Jiang was born in Sichuan Province,

China, in 1963. He received his first class

honors B.S. degree in electronics from

Chongqing University in 1987, and his M.S.

and Ph.D. degrees both from Chongqing

University in 2000 and 2011, respectively.

Since 1987, he has been with the Department

of Electronic Engineering in Chongqing

University, where he is currently a professor.

His current research interest is in wireless communications,

communication systems and Internet of Things technology.

Yiqian Yan was born in Hebei Province,

China, in 1991. She received the B.S. degree

from Chongqing University, in 2012 in

Communication Engineering. She is currently

pursuing the M.S. degree with the College of

Communication Engineering, Chongqing

University. Her research interest is in wireless

communications, especially in wireless sensor

networks.

Yang Yu was born in Zhejiang Province,

China, in 1989. He received the B.S. degree

from Chongqing University, in 2012 in

Communication Engineering. He is currently

pursuing the M.S. degree with the College of

Communication Engineering, Chongqing

University. His research interest is in

Electronics and Communications.

Energy Balanced Clustering Algorithm in Large Scope WSN ...

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