Research on Energy Saving

outing Algorithm of Cluster Wireless

Sensor Networks

Jinglei Zhang

, Qing Liu

and Yong Wang

Nanjing Institute of Industry Technology, Nanjing, China

ZhongShan Vocational College, Nanjing, GHINA

Nanjing University of Posts and Communications, Nanjing, China

njwy1961@163.com

Keywords: Wireless sensor networks (WSN), cluster routing algorithm, network lifetime, energy efficiency, LEACH.

Abstract: Based on the analysis of LEACH protocol, this paper proposes an improved algorithm: taking into account

the two factors of node energy and distance, we improve the probability of cluster head selection. The

probability formula of the cluster head selection of the original protocol is modified by using the weighting

factor. According to the improved algorithm proposed in this paper, simulation experiments are carried out

respectively upon different adjustment parameters selected. The results show that the improved algorithm

can avoid the existence of extremely large and small clusters compared with the LEACH protocol, which

will balance the energy consumption of network nodes, reduce the network energy consumption, improve

energy efficiency and extend the network’s life cycle.

1 INTRODUCTION

At present, many clustering algorithms are based on

Low Energy Adaptive Clustering Hierarchy

(LEACH) (Deepshikha, 2017) routing protocol.

These routing protocols focuse on different

optimization targets according to different

application requirements. They have their own

advantages and disadvantages according to their

different performance in energy efficiency, data

fusion and network scalability. Clustering routing

algorithm has obvious advantages in reducing

energy consumption and prolonging network

lifetime. Therefore, the improvement research on

clustering-based low energy routing protocol has

become a hot and developing trend in this field.

Energy-efficient routing algorithm based on

clustering of WSN has obvious advantages on

reducing energy consumption and prolonging

network lifetime. This is the focus of this paper.

2 ENERGY CONSUMPTION

ANALYSIS

Energy consumption of network nodes is one of the

most important issues in WSN. The network nodes

are composed of four modules, sensing, processing,

wireless communication and energy supply. Energy

consumption comes from the first three modules.

The energy consumption of each component of

the sensor node is as shown in Fig. 1.

Fig.1 Energy consumption distribution of each component

3 ANALYSIS OF DEFECTS OF

LEACH ALGORITHM

The traditional LEACH protocol is easy to

implement and has strong addictiveness.. Each node

in the run time takes turns as the cluster head,

492

Zhang, J., Liu, Q. and Wang, Y.

Research on Energy Saving Routing Algorithm of Cluster Wireless Sensor Networks.

In 3rd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), pages 492-497

ISBN: 978-989-758-312-4

averaging the energy consumption of the entire

network. Compared with the surface routing

protocol, it has a longer network lifetime. Through

the simulation analysis of the LEACH protocol, we

find some shortcomings of the LEACH protocol.

(1) In the selection of cluster heads, the LEACH

protocol cannot produce a fixed number of cluster

heads in each round because of the uncertainty of the

random number produced by the node itself. The

instability of cluster number will lead to the situation

that some network rounds may generate more cluster

heads while the other rounds may produce less

cluster head. When the number of cluster heads is

very small, the cluster member nodes in the network

are usually more. Cluster heads require more time

and energy to deal with data processing, which

eventually leads to a sharp energy drop of cluster

heads. If there are a large number of cluster heads,

there are few member nodes within clusters or even

there are cluster head nodes left in clusters. As the

cluster head conducts direct communication with the

base station, too many nodes’ long-distance

communication with the base station will lead to the

increase of the energy consumption of the whole

network.

(2) The number of nodes in each round of cluster

is greatly different. Some clusters are very large with

more node members. Some clusters have few or

even no member nodes. In this way, it is very easy to

cause the load imbalance of the whole network. In

the end, this situation will cause some nodes to die

because of the premature exhaustion of energy.

(3) The location of the cluster head nodes is

unevenly distributed. Some cluster heads are too

concentrated or adjacent, while some cluster heads

are distributed on the edge of the entire network

area. In this way, the member nodes in some clusters

have to communicate with the cluster heads through

long distance transmission, resulting in a large

amount of energy consumption.

(4) With the operation of the network, some

nodes may have undertaken too many tasks in

advance and have little residual energy. Some other

nodes may have more residual energy due to the

opposite situation. If we do not consider the residual

energy of nodes, and when the residual energy of

nodes in the later stage is generally low, we will

unfortunately choose the nodes with extremely low

energy in the cluster head selection. They will not be

able to undertake the related tasks and lead to the

failure of communication.

4 IMPROVEMENT SCHEME

AND ANALYSIS

This paper optimizes the algorithm based on the

shortcomings of the LEACH protocol. A series of

improvements are proposed. The simulation and

analysis are carried out according to the improved

scheme.

4.1 Evaluation standard of network

energy consumption performance

In order to compare the improved protocol with the

original LEACH protocol, we need to propose an

evaluation criterion for measuring the performance

of the network. In the evaluation criteria of energy

consumption of sensor networks, the two common

indicators refer to the network life cycle and the total

energy consumption per round of network.

4.1.1 The network lifetime

As an important evaluation index, the network

lifetime cycle is now widely used to measure the

performance of a WSN.

The most widely defined definition: the network

lifetime is defined as the time from network starting

to work, to the death of the first node in the network,

or to the energy exhaustion of any node in the

network.

The network lifetime is in fact closely related to

the number of surviving nodes in the network.

During the simulation test, we will use the number

of remaining surviving nodes in each round of

network or the percentage of remaining surviving

nodes in each round network as a more objective

criterion.

4.1.2 Total energy consumption per round

The total energy consumption of every node in every

network per round or the total residual energy of

every node in the whole network per round can also

be used as an index to measure the energy

consumption of the whole network and to illustrate

the setup of sensor network. In the comparison of

the simulation experiments in this paper, we will

measure the residual total energy of all nodes in the

whole network per round.

Research on Energy Saving Routing Algorithm of Cluster Wireless Sensor Networks

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4.2 LEACH Protocol Improvement

Scheme

In this section, we will propose some improved

algorithms for some of the defects. The main

purpose is to improve the threshold T(n) of the

cluster head election to reduce the energy

consumption of the network and improve the

network performance.

The improved algorithm also takes the round as

the smallest cycle unit. Each round is divided into

the initialization stage established by the cluster

group and the stable work stage for data

transmission. Among them, the cluster establishment

stage includes the election of cluster heads and the

formation of clusters. The stable transmission phase

of data mainly completes the two tasks of node

routing and data forwarding between nodes.

4.2.1 Improved scheme based on residual

energy and distance

One of the shortcomings of the LEACH protocol: in

the process of network operation, the residual energy

of each node fails to be considered. When the next

round of cluster heads is re-selected, the distribution

of energy consumption in the network is uneven,

leading to the premature death of some nodes. Now

we bring the residual energy into the scope of the

cluster head election, and improve the original

protocol.

The cluster head nodes selected randomly are

usually not well distributed because of the location

distribution. Some are too concentrated. Some are

too scattered or even on the edge of the network

area. There are often more clusters where the cluster

heads are too concentrated. A large number of

cluster heads and remote base stations will consume

a lot of energy when communicating, and the cluster

heads distributed at the edge of the network will

consume a lot of energy because of the long-distance

transmission with the remote member nodes.

We add the distance D(n) to all nodes in the

regional centre as a measure. The basic goal of the

scheme is to maximize the coverage area of the

selected cluster heads as far as possible. The

probability of reducing the cluster head

marginalization is reduced. Let the cluster head

close to the centre of the whole region to shorten the

data transmission distance (Takhellambam S. S.,

2016).

The calculation of the center of mass in the

region surrounded by clusters: for simplicity, we

will use the arithmetic mean of all node coordinates

within the cluster to replace the centroid location.

The following is the centroid formula, and D (X

) is the centroid position:

()

(, )

()

DX Y

⎧

⎪

⎨

⎪

⎩

∑

(1)

In formula (1), (X (i), Y (i)) is the coordinate

position of the internal nodes of the cluster, and N is

the number of all the nodes within the cluster.

Considering the influence of two factors on the

threshold T(n), such as the residual energy of nodes

and the distance from nodes to centers, an

optimization scheme based on residual energy and

distance factors is proposed.

The calculation formula of the cluster head

threshold T (n) of the optimization scheme is:

(,) ()

; ( )

()

1(*d )

()

0 ;

⎧

⎛⎞

−

⋅⋅ ∈

⎪

⎜⎟

⎪

⎝⎠

−×

⎨

⎪

⎩

Enr d dnp

qt ifnG

En d

prmo

otherwise

(2)

In the formula (2), p is the percentage of cluster

heads in the expected network. R is the number of

the current running rounds, and n is the number used

to identify nodes in the network. The meaning of G

is LEACH, which does not act as a set of cluster

head nodes in the past

1 p

rounds.

Er(n, r) represents the residual energy of node n

in the current r round. Ei(n) represents the initial

energy of node n when the network starts running. qi

is an energy-related regulation parameter, which is

used to regulate the influence of the node energy

consumption factor on the threshold T(n).

The two parameters of Er and Ei are the internal

information of the node itself. It is maintained by the

node itself and does not need to communicate with

other nodes. Through adjusting the threshold T(n) by

Er Ei

, the nodes with large energy consumption

ratio can reduce their probability of becoming

cluster heads by decreasing the value of T(n). On the

contrary, for smaller nodes whose energy

consumption is smaller, the probability of cluster

head will be increased by increasing the value of

T(n).

Dm represents the maximum distance between

all nodes in the network to the center of the network

area. D(n) represents the distance from a node n to

the regional center in the network. Ti is a distance

dependent regulation factor, which is used to

regulate the proportion of distance factor in the

cluster head threshold T(n) calculation.

ICECTT 2018 - 3rd International Conference on Electromechanical Control Technology and Transportation

494

We use

()

()−dm d n dm

to adjust the threshold

T(n). The probability of a node at the edge of a

cluster to become a cluster head is reduced, and the

probability of raising the node in the cluster's

endoplasmic reticulum becomes the cluster head.

Through the adjustment of probability, the covering

area is maximized and the cluster head is

marginalized as much as possible when the cluster

head is selected, so the cluster head is closer to the

center of the region.

= q

* t

, formula (2) can be reexpressed as:

(,) ()

; ( )

()

1( mod )

()

0 ;

⎧

⎛⎞

−

⋅⋅ ∈

⎪

⎜⎟

⎪

⎝⎠

−×

⎨

⎪

⎩

Enr d dn

wifnG

En d

otherwise

(3)

In formula (3), q

is a parameter of energy

regulation and ti is a range adjustment parameter. By

changing the values of these two parameters, the

proportion of the energy factor and the distance

factor in the cluster head threshold T(n) can be

dynamically adjusted.

Here, w

is a synthetic parameter. It weighs the

influence of the proportion of energy and the

specific gravity of the distance on the T(n) of the

cluster head threshold. A suitable w

value can make

the network performance to the best state.

4.3 Simulation analysis

4.3.1 Simulation test scene and parameter

setting

In order to compare the experiment results of the

improved scheme and the traditional LEACH

protocol, we adopted the same network model

assumption as LEACH protocol, wireless

communication model between nodes, data fusion

model and so on(Heinzelman W. B., 2002; Say S.,

2014). Among them, the formula of energy is still

used in the first order radio model in the LEACH

protocol.

It is assumed that 225 nodes in the network are

randomly distributed in the area of 150m*150m, and

all nodes have the same initial energy. The network

simulation parameters are shown in table 1.

Table1 network simulation parameters

Network covera

e area 150m×150m

Total number of nodes N 225

Base station coordinates (located in

the center of the network)

(75m

，75m)

ected cluster head

ercenta

e P 0.1

Parameter

10pJ/bit/m

Parameter

amp

0.0013pJ/bit/m

Energy consumption for receiving

and receiving E

elec

50nJ/bit

Cluster head data fusion energy

consumption E

5nJ/bit/signal

Packet size L 400byte

Node initialization energy E0 1.5J

4.3.2 Simulation and analysis

For the improved scheme of considering the energy

factor and the distance factor in the calculation of

the cluster head threshold, the simulation experiment

is carried out by using the formula (3). The

experimental results are analysed from two aspects

of the total residual energy of the network and the

number of survival nodes.

a. Comparison of total residual energy of

network

In Figure 4.10, the blue solid line reflects the

relationship between the energy of the LEACH

protocol and the number of running rounds. The red

point line indicates the relationship between the

energy of the improved scheme and the number of

running rounds. The black dotted line at the top of

the graph represents the total energy of the entire

network node that is initialized at the time of

initialization.

As you can see in Figure 2, the improved scheme

has obvious advantages over the LEACH protocol in

terms of energy consumption. For example, it can be

obtained from the data that the improved scheme

postpones the Δr=2507 round in comparison with

the LEACH protocol under the same energy

consumption of only 10% of the remaining energy.

From figure 2, it is obvious that the red dot is on the

right side of the solid blue line. The red point line

drops slower than the blue line. It shows that the

energy consumption per round of the improved

scheme is less than LEACH.

Research on Energy Saving Routing Algorithm of Cluster Wireless Sensor Networks

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Fig. 2 Network running round number

b. Comparison of the number of surviving nodes

Fig. 3 can be seen that the improvement scheme

for the LEACH protocol has obvious superiority in

the network lifetime. The number of death rounds of

the first node, the improved scheme increases the

delta r=1054 round than the LEACH, and the

number of all nodes of the network nodes is

increased by the Δr=2790 round. From fig. 3 can be

seen, the improved scheme of LEACH protocol in

solid blue red dotted right, that improved scheme

prolongs the network life cycle.

Through the analysis, it is not difficult to draw

the conclusion that the improved scheme has lower

energy consumption and longer network lifetime

than the LEACH protocol.

Fig.3 Comparison of network life cycle

c. Influence of adjustment parameter w

The influence of the parameter w

on the total

energy consumption of the network: in Fig. 4, it can

be seen that the smaller the w

value, the lower the

total energy consumption of the network. At

=0.24, the energy consumption is the lowest;

when w

=2.0, the energy consumption is the largest.

Fig.4 Relationship between the residual energy and

rounds r

Fig.5 The relationship between the network surviving

node and the number of r

From fig. 5, we can see that when w

=2.0, the

number of the first dead node in the network is the

largest, the number of r=2540 rounds is the largest.

When w

=0.5, the number of death nodes of all

nodes in the network is the largest, which is r=12491

round.

If the number of running cycles of only 10% (or

death 90%) nodes is defined, the parameter w

=0.24

in the improved scheme has a longer network

lifetime.

5 Conclusions

According to the improved algorithm proposed in

this paper, different parameters w

and q

) are

chosen to go through simulation experiment

respectively. The simulation results show that the

improved algorithm can avoid the existence of a

extremely large and clusters compared with the

LEACH protocol and it can also better balance the

energy consumption of nodes in the network, reduce

ICECTT 2018 - 3rd International Conference on Electromechanical Control Technology and Transportation

496

energy consumption, improve energy efficiency, and

prolong the network life cycle.

ACKNOWLEDGEMENTS

Project Supported by Top-notch Academic Programs

Project of Jiangsu Higher Education Institutions

(PPZY2015A087)

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