A Novel Cluster-Head Selection Algorithm for Wireless Sensor Networks

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A Novel Cluster-head Selection Algorithm for Wireless

Sensor Networks

Journal: International Journal of Parallel, Emergent and Distributed Systems

Manuscript ID: GPAA-2009-0090

Manuscript Type: Original paper

Date Submitted by the

Author: 01-Dec-2009

Complete List of Authors: Ding, Rong; Beihang University, Computer Science Yang, Bing; Beihang University, Computer Science Yang, Lei; Beihang University, Computer Science Wang, Jiawei; Beihang University, Computer Science

Keywords: cluster-head selection algorithm, clustering algorithm, soft threshold, wireless sensor networks

URL: http:/mc.manuscriptcentral.com/gpaa Email: [email protected]

International Journal of Parallel, Emergent and Distributed Systems

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A brief flowchart of LEACH algorithm 133x188mm (96 x 96 DPI)

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The flowchart of soft threshold calculation 139x99mm (96 x 96 DPI)

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The lifetime of 100-node network with initial energy 2J/node. 169x127mm (72 x 72 DPI)

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Performance comparison when 1%, 50% and 100% of nodes died. 127x84mm (96 x 96 DPI)

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Comparison for STCS algorithm with parameter of p2, p2/2 and p2/4. 169x127mm (72 x 72 DPI)

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The Comparison of STCS with different initial probability and LEACH. 169x127mm (72 x 72 DPI)

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Comparison of STCS and LEACH for 200 nodes network. 169x127mm (72 x 72 DPI)

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A Novel Cluster-head Selection Algorithm for Wireless Sensor

Networks

Rong Ding, Bing Yang, Lei Yang, Jiawei Wang

State Key Laboratory of Software Development Environment, Beihang University,

Beijing, China,

[email protected], {ybing, rockyanglei, wangjiawei}@cse.buaa.edu.cn

In recent years, many clustering algorithms have been proposed. Among them, LEACH

is the most famous one. However, in LEACH, within each 1/p rounds, once a node has

been selected as a cluster-head (CH), its threshold will be set to 0, and thus it will lose

the chance to participate cluster-head selection, even if it still has enough energy. In this

paper, we present a novel cluster-head selection algorithm. Instead of changing the

threshold to 0 directly, the proposed algorithm adjusts the threshold of each node

gradually according to the roles they have played in the last round, so more nodes could

have the opportunity to be CHs. Simulation results show that the proposed algorithm

outperforms LEACH in network lifetime by an average of 30% approximately, In

addition, when the scale of network is expanded, the STCS algorithm can also perform

better than LEACH.

Keywords: wireless sensor networks; clustering algorithm; cluster-head selection algorithm; soft

threshold;

1. Introduction

Wireless Sensor Network (WSN) comprises of micro sensor nodes, which are usually

battery operated sensing devices with limited energy resources. In most cases,

replacing the batteries is not an option [1][2]. In order to extend the network lifetime,

many routing protocols have been devised. One of these is network clustering, in

which network is partitioned into small clusters and each cluster is monitored and

controlled by a node, called Cluster Head (CH). A CH is responsible for conveying

any information gathered by the nodes in its cluster and may aggregate and compress

the data before transmitting it to the base station (BS). Other nodes send the data

sensed from the environment to these CHs. However, the added responsibility results

in a higher rate of energy drain at the CHs, and thus power-efficiency is important in

designing clustering protocols. LEACH (Lower Energy Adaptive Clustering

Hierarchy) [3], which is one of the most popular clustering mechanisms, addresses

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this by probabilistically rotating the role of CH among all nodes. However, its cluster-

head selection mechanism is based on a kind of hard threshold. This kind of selection

mechanism causes that a node cannot continue to participate the cluster-head election

process, once it has acted as a CH in the current 1/p rounds. Therefore, as the

algorithm continues, the nodes which can be chosen as CHs will become fewer and

fewer. This paper focuses on how to resolve it by developing a novel approach of

cluster-head selection.

This paper builds on the work described in [4] by giving a detailed description

and analysis of Soft Threshold based Cluster-head Selection (STCS) Algorithm, a

novel cluster-head selection algorithm for wireless sensor networks. In our previous

work, we simulate a 100-node network to observe the performance of STCS. This

paper is an extended version of the previous one. In this paper we expand the network

size and try different initial probabilities of becoming cluster-head of each node, then

observe the influence of network size on performance of STCS and find the optimal

initial probability.

The rest of this paper is organized as follows: Section 2 presents a brief

description of the related work. Section 3 describes the limitations of the current

LEACH algorithm and our algorithm on cluster-head selection in detail. Finally,

simulation results are presented in Section 4, while Section 5 concludes the paper.

2. Related work

In recent years, many algorithms and protocols for cluster-head selection have been

proposed. These algorithms improve the performance of cluster-head selection from

different perspectives. In this section, we detail the related work on these

improvements. As what is mentioned in the above paragraph, LEACH is the most

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popular clustering algorithm. Lots of cluster-head selection algorithms are based on

LEACH’s architecture.

LEACH-C (Centralized) [5] uses a centralized controller to select CHs. By

using a central control algorithm to form the clusters, it can produce better clusters by

dispersing the CH nodes throughout the network. The main drawbacks of this

algorithm are non-automatic cluster-head selection and the requirement that the

position of all sensors must be known. LEACH’s stochastic algorithm is extended in

[6] with a deterministic cluster-head selection, which utilizes the remaining energy

level of each node to determine the threshold. However, to get the energy level of

sensors in a real network usually needs a routing protocol which will create new cost.

HEED (Hybrid Energy-Efficient Distributed Clustering) [7] is a distributed clustering

scheme in which CH nodes are picked from the deployed sensors. HEED considers a

hybrid of energy and communication cost when selecting CHs. In CEFL (Cluster-

head Election Using Fuzzy Logic) algorithm [8], a kind of fuzzy logic method is

adopted to select the CH. In literature [9], the author propose a approach to optimally

determine the location of cluster-heads for minimizing communication power, which

require that each sensor node connects to at least p cluster-heads for reliability, and

each cluster-head can accept at most q connections. In order to overcome the fact that

a Mixed Integer Nonlinear Programming (MINLP) problem, the author proposes an

iterative decomposition algorithm and use a randomized multi-start technique for

global optimization. In literature [10], the author propose an Adaptive Clustering

Protocol for Medium-scale (ACPM) WSNs which is a LEACH-like clustering

protocol, it has a new cluster head selection strategy with an adaptive back-off scheme

to overcome the power limitation of broadcasting ADV messages in LEACH. In [11],

the author propose an ELCH(Extending Lifetime of Cluster Head) routing protocol

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that has self-configuration and hierarchal routing properties, which organize the

clusters on the basis of radio radius and the number of cluster members. In addition,

the selection of cluster-head is according to the votes from their neighbors. In

literature [12], the author proposed A new algorithm for building and regroup clusters,

which is called Improved Distributed Cluster Organization Algorithm (IDCOA). By

the proposed algorithm, nodes are clustered via the way that some nodes propose

invitations, and other sensors respond.

Different from the algorithms mentioned above, our algorithm focuses on

improving the cluster-head selection by adjusting the threshold of each node to

become CH gradually to extend the network lifetime.

3. Algorithm description

In this section, we review LEACH algorithm and discuss its limitations, and then put

forward our improvement on cluster-head selection in detail.

3.1 LEACH

The operation of LEACH is broken up into rounds. Each round begins with a set-up

phase, in which the clusters are organized. Then there is a steady-state phase, in which

data is transferred to the base station. At the beginning of each round, each sensor

node chooses a random number between 0 and 1, and compares it with a threshold

T(i). If the number is less than T(i), the node becomes a cluster-head in the current

round. The threshold is set as:

i1

1 *( mod )( ) (1)

0 otherwise

pG

p rT ip

∈ −=

Where

kp

N=

( N is the total number of network nodes, and k is the desired

cluster-head number of each round) is the desired (optimal) percentage of cluster

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heads, r represents the current round (

10 r

p≤ <

) and G is the set of nodes that have not

been selected as CHs in the last r rounds. Thus, each node will be a CH at some point

within 1/p rounds. The cluster head nodes broadcast their status to other sensors in the

network. Each non-cluster-head node determines to which cluster it wants to belong

by choosing the CH that requires the minimum communication energy. Once all the

nodes are organized into clusters, each CH creates a TDMA schedule for the nodes in

its cluster. This allows the radio components of each non-cluster-head node to be

turned off all time except its transmitting time, and thus the energy dissipated in the

individual sensors is minimized. The CH node selects a CDMA spreading code

randomly, which is to be used for communication within its cluster so that the signals

of different clusters (using different CDMA codes) don't interfere. The CH node

aggregates the data obtained from the nodes within its cluster and then transmits the

compressed data to the base station.

LEACH has been a landmark in clustering protocols in wireless sensor

network with its simplicity and efficiency. However, there are some limitations which

make LEACH not so effective. For example, during the probability based cluster-head

selection, once a node has acted as a CH, its threshold will be set to 0. Therefore, the

nodes available to be selected as CHs become fewer and fewer within 1/p rounds. In

the next section, we will describe the STCS (Soft Threshold Based Cluster-head

Selection) algorithm, which uses the basic LEACH architecture but changes the CH

selection procedure to prolong the network lifetime.

Figure 1 shows the brief flowchart of LEACH algorithm.

Figure 1. A brief flowchart of LEACH algorithm.

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3. STCS algorithm

To meet the unique requirements of wireless sensor networks, and more evenly

distribute energy load among the nodes in the networks, we proposed STCS (Soft

Threshold Based Cluster-head Selection) algorithm to make an improvement on the

CH selection algorithm.

In the STCS algorithm, CHs are also chosen according to the probability. The

traditional LEACH algorithm adopts a kind of hard-threshold CH selection method.

That is to say, after a node has been selected as a CH once, the threshold in its cluster-

head selection will be changed to 0 directly and this node will lose its opportunity to

be selected as a CH again, even if it still has enough energy. This may not be the best

method to evenly distribute energy consumption on each node and to prolong the

lifetime of the network. In our algorithm, the probability of a node to become a CH is

also determined by a threshold T(i). However, different from the hard threshold in

LEACH, in STCS is a soft threshold. That is, once a node has acted as a CH, T(i)

will be adjusted step by step instead of being changed to 0 directly.

The detailed operations of STCS algorithm are shown in Figure 2.

Figure 2. The flowchart of soft threshold calculation.

From Figure 2, we can see that in the first round ( 0r = ), all nodes are chosen to

be CHs with the probabilityk

pN

=, which is the desired percentage of cluster heads.

The threshold of each node in the following rounds is based on whether this node is a

CH or not and the member number of the cluster in the last round.

The soft threshold is adjusted by parameter , whose value may affect the

probability of nodes to become CHs in each round. How to set the adjusting

parameter will be discussed in the following paragraph. In Section 4.1, we will

introduce several different parameter values in the simulation, and try to find the

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optimal value. The concrete operation of adjusting threshold is as follows, if a node

has been selected as a CH in the last round, its probability of becoming a CH in the

current round will be decreased by ε; else it will be increased by( )( )NUM C i

ε

. Where

( )C i represents the cluster that node i belongs to, and

( )( )NUM C i represents the

member number of ( )C i.

3.3 Analysis of STCS

The STCS algorithm mainly focuses on improving the cluster-head selection

procedure, while the cluster forming is similar to LEACH by using a distributed

algorithm, where nodes make individual decisions without any centralized control. Its

goal is to design a cluster formation algorithm so that there are approximately k

clusters in each round. Therefore, the STCS algorithm should ensure that the

expectations of the number of CHs to be k in every round. Set N as the total number

of network nodes, and p is the desired percentage of the CHs, thus k N p= × .

As shown in Figure 2, in the first round ( 0r = ), each node i set its initial

threshold top

:

0

0

( ) | (2)1

1 * ( mod )

r

r

pT i p

p rp

=

=

= =

−

Set

0 node i is a non-CH node in the r th round|

1 node i is a CH node in the r th roundi r

X = , then 1

|N

r ii r

Y X=

= ∑denotes the number

of CHs in the r th round, and then 0 0

1 10

( | ) ( ) 1* ( ) |N N

r i ri ir

E Y E X T i N p k= == ==

= = = × =∑ ∑.

As mentioned in Section 3.2, in the other (

11 r p≤ <

) rounds,

1 1

1

( ) |

( ) | (3)( ) | otherwise

NUM(C(i))

r r

r

r

T i i G

T iT i

ε

ε− −

−

− ∈

=+

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Where Gr-1 is the set of nodes which were selected to be CHs in the 1r − th

round, and C(i) represents the cluster that node i belongs to. For the r th round, there

is:

1 1

( | ) [ | ] 1* ( ) | (4)N N

r i r ri i

E Y E X T i= =

= =∑ ∑

For each cluster C, according to equation (3), we can get:

1 1 01 1

( | ) 1* ( ) | 1* ( ) | ( | ) ... ( | )N N

r r r ri i

E Y T i T i E Y E Y k− −= =

= = = = = =∑ ∑

4. Simulation results and analysis

In order to evaluate the performance of the STCS algorithm, we used NS-2 simulator

to simulate STCS and LEACH algorithm for comparison. The code for our

experiments was a modification based on the code of LEACH algorithm [13].

In the simulation, similar to LEACH, we used a random 100-node network,

the BS was located at (50, 175) in a 100m x 100m field. In these experiments, each

node began with only 2J of energy and calculated its probability as described in

Figure 2 to determine its cluster head status at the beginning of each round. In

addition, in order to compare with LEACH, we also set the desired percentage of

cluster heads p to 0.05. The parameter in STCS was set to p2.

We compared the time of communication when 1%, 50% and 100% of the

nodes die between LEACH and STCS with each node having the same initial energy

level. At the beginning of the simulation, since the nodes using the STCS are allowed

to be CHs more than once, some nodes may be selected as CHs many times. It caused

their energy to drain rapidly, so the first node died earlier in STCS than in LEACH.

However, as the simulation continued, the advantage of STCS appeared gradually,

and the time when 50% and 100% of nodes died were both later in STCS than in

LEACH (see Figure 3 and Figure 4).

Figure 3. The lifetime of 100-node network with initial energy 2J/node.

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Figure 4. Performance comparison when 1%, 50% and 100% of nodes died.

Table 1 summarizes the results with initial energy per node of 1J, 2J, 3J, 4J

and 5J in the 100m x 100m network. The result of table 1 shows that the conclusion

made in the above paragraph is still tenable with different initial energy.

Table 1. The time(s) when 1%, 50% and 100% nodes died for different initial energy.

Energy(J

/node)

protocol 1% 50% 100%

LEACH 110 220 274 1

STCS 80 250 422

LEACH 380 490 550 2

STCS 130 510 648

LEACH 480 690 758 3

STCS 120 760 1100

LEACH 550 930 1005 4

STCS 190 990 1574

LEACH 860 1230 1350 5

STCS 150 1160 1871

The experimental data listed above indicates that STCS does not perform well

enough at the initial period of algorithm. However, as the algorithm continues, STCS

extends the network lifetime and performs much better than LEACH. In the next

section, we will use different parameters and observe its impact on the performance of

STCS. In addition, we will also simulate 200 nodes to observe what influence will be

brought by the increase of the number of network nodes.

4. Simulation parameter setting

As shown in Figure 2, we adjust the threshold ( )T i with a parameter. In this section,

we will discuss how to choose the parameter.

In the worst case, if one node is always selected to be CH from the first round

to the last round, the total number of rounds is 1/p, while the initial threshold of each

node is p . So we must make sure that

1) 0(

pp ε× ≥−

, i.e., 2

pε ≤ . In the simulation

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described above, we just set parameter ε to p2 simply. In Figure 5, we simulated the

proposed algorithm with parameter ε equalling to p2, p2/2 and p2/4 for comparison.

Figure 5. Comparison for STCS algorithm with parameter of p2, p2/2 and p2/4.

As can be seen from Figure 5, STCS with parameter p2/2 performs better than

STCS with parameter p2, but when parameter ε decreases from p2/2 to p2/4, the

difference of their performance is no longer obvious. The result of the simulation

reflects that the parameter ε may have an optimal value between 0 and p2.

In addition, the desired percentage of cluster heads in LEACH is set to 0.05,

which is also the initial probability of each network node to become cluster-head.

Here we simulate STCS algorithm with initial probability of 0.03, 0.035, 0.04, 0.045,

0.05 and 0.06, and to observe how the initial probability influences the performance

of STCS. Figure 6 shows the simulation result.

Figure 6. The Comparison of STCS with different initial probability and LEACH.

From Figure 6, we can see that the performance of STCS is not increased

linearly with the increase of initial probability of network nodes. When we set the

probability to 0.035, the STCS performs best in compare with LEACH and STCS of

other probabilities.

In order to observe the influence of network size on the performance of STCS,

we double the network size to 200 nodes and locate the BS at (100, 350) in a 200m x

200m field, while the other parameters is the same as before. Figure 7 shows the

simulation result. We can infer from Figure 7 that STCS algorithm can also

outperform LEACH with the expansion of the network size.

Figure 7. Comparison of STCS and LEACH for 200 nodes network.

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5. Conclusion and future work

In wireless sensor networks, the energy consumption and the network lifetime are

important issues for the research of the route protocol. In this paper, we present a

novel cluster-head selection algorithm. Its main idea is to adjust the threshold of each

node step by step instead of changing it to 0 directly, and thus nodes have more

chance to become CHs. Simulation results show that the proposed algorithm performs

better than LEACH algorithm, and the lifetime of the network is extended by an

average of 30% approximately. In addition, when the scale of network is enlarged, the

STCS algorithm can also outperform LEACH.

In this paper, we just adopt a way of adjusting the threshold to achieve our

purpose of increasing the network lifetime. In order to further save energy and extend

the lifetime of the network, our future plans will involve how to optimize the selection

of the parameter ε.

Acknowledgments

Supported by the Research Fund for the Doctoral Program of Higher Education of

China(RFDP), the fund of the State Key Laboratory of Software Development

Environment (Grant No. SKLSDE-2009ZX-04)and the National High-tech R&D

Program of China (863 Program) (Grant No. 2008AA12A216).

References

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A Novel Cluster-Head Selection Algorithm for Wireless Sensor Networks