IEEE SENSORS JOURNAL, VOL. 12, NO. 9, SEPTEMBER 2012
2891
Fuzzy-Logic-Based Clustering Approach for
Wireless Sensor Networks Using
Energy Predication
Jin-Shyan Lee, Senior Member, IEEE, and Wei-Liang Cheng
Abstract— In order to collect information more efficiently,
wireless sensor networks (WSNs) are partitioned into clusters.
Clustering provides an effective way to prolong the lifetime of
WSNs. Current clustering approaches often use two methods:
selecting cluster heads with more residual energy, and rotating
cluster heads periodically, to distribute the energy consumption
among nodes in each cluster and extend the network lifetime.
However, most of the previous algorithms have not considered
the expected residual energy, which is the predicated remaining
energy for being selected as a cluster head and running a round.
In this paper, a fuzzy-logic-based clustering approach with an
extension to the energy predication has been proposed to prolong
the lifetime of WSNs by evenly distributing the workload. The
simulation results show that the proposed approach is more
efficient than other distributed algorithms. It is believed that
the technique presented in this paper could be further applied
to large-scale wireless sensor networks.
Index Terms— Cluster head selection, energy predication, fuzzy
reasoning, wireless sensor networks.
I. I NTRODUCTION
I
N THE past years, there have been increasing advances
in digital electronics, semiconductor manufacturing
technology, and wireless communications leading to the
development of low-power, low-cost, and small-size devices
with embedded sensing, computing, and communication
capabilities. A wireless sensor network (WSN) is composed of
hundreds or even thousands of such sensor devices which use
radio frequencies to perform distributed sensing tasks [1]–[6].
In general, since these sensor devices are equipped with
non-rechargeable batteries, energy efficiency is a major design
issue in order to increase the life-time of sensor networks.
Cluster-based design is one of the approaches to conserve
the energy of the sensor devices since only some nodes, called
cluster heads (CHs), are allowed to communicate with the
base station. The CHs collect the data sent by each node
in that cluster, compress it, and then transmit the aggregated
Manuscript received January 17, 2012; revised April 19, 2012; accepted
May 31, 2012. Date of publication June 13, 2012; date of current version
August 1, 2012. This work was supported by the National Science Council
(NSC), Taiwan, under Grant NSC 100-2221-E-027-010 and Grant NSC 992221-E-027-102. The associate editor coordinating the review of this paper
and approving it for publication was Prof. Ralph Etienne-Cummings.
The authors are with the Department of Electrical Engineering,
National Taipei University of Technology, Taipei 10608, Taiwan (e-mail:
[email protected]; [email protected]).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JSEN.2012.2204737
data to the base station. The representative design is
low-energy adaptive clustering hierarchy (LEACH) protocol
[7], [8], which uses a pure probabilistic model to select CHs
and rotates the CHs periodically in order to balance energy
consumption. However, in some cases, inefficient CHs can
be selected. Because LEACH depends on only a probabilistic
model, some cluster heads may be very close each other and
can be located in the edge of WSNs. These inefficient cluster
heads could not maximize the energy efficiency.
Appropriate cluster-head selection can significantly reduce
energy consumption and prolong the lifetime of WSNs. Some
of the clustering algorithms employ fuzzy logic to handle
uncertainties in WSNs. Generally, fuzzy clustering algorithms
use fuzzy logic for blending different clustering parameters
to select cluster heads. To overcome the defects of LEACH,
Gupta et al. [9] proposed to use three fuzzy descriptors
(residual energy, concentration, and centrality) during the
cluster-head selection. The concentration means the number
of nodes present in the vicinity, while the centrality indicates
a value which classifies the nodes based on how central
the node is to the cluster. In every round, each sensor node
forwards its clustering information to the base station at which
the CHs are centrally selected. However, this mechanism is
a centralized approach. Kim et al. [10] proposed a similar
approach (namely CHEF: Cluster Head Election mechanism
using Fuzzy logic), but in a distributed manner by using two
fuzzy descriptors (residual energy and local distance). The
local distance is the total distance between the tentative CH
and the nodes within predefined constant competition radius.
Hence, the base station does not need to collect clustering
information from all sensor nodes. Moreover, since selecting
the cluster head is not easy in different environments which
may have different characteristics, Anno et al. [11] employed
different fuzzy descriptors, including the remaining battery
power, number of neighbor nodes, distance from cluster
centroid, and network traffics, and evaluated their
performance. The sensor nodes closer to the base station
consume much more energy due to the increased network
traffic near the base station. Hence, the sensor nodes closer
to the base station quickly run out of battery. Besides the
residual energy, Bagci et al. [12] further considered a fuzzy
descriptor, distance to the base station, during the cluster
head selection. This unequal clustering approach is based on
the idea of decreasing the cluster sizes when getting close to
the base station.
1530–437X/$31.00 © 2012 IEEE
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IEEE SENSORS JOURNAL, VOL. 12, NO. 9, SEPTEMBER 2012
Based on LEACH, most existing fuzzy clustering
approaches [9]–[15] considered the residual energy of sensor
nodes during the CH selection. However, the remaining energy
after being selected as a CH and running a round has never
been discussed. A round refers to the interval between two
consecutive cluster formation processes. In this paper, a fuzzylogic-based clustering approach with an extension to the
energy predication has been proposed to prolong the lifetime
of WSNs by evenly distributing the workload. In addition
to the residual energy, the expected residual energy (ERE)
has been introduced to act as a fuzzy descriptor during the
on-line CH selection process. In order to estimate the ERE,
the expected energy consumption (EEC) is required. In our
work, the EEC would be quickly calculated via an off-line
trained neural network model. The proposed approach adopts
the LEACH architecture with an extension to the energy
predication based on the ERE, and thus the approach is named
LEACH-ERE. To the best of our knowledge, it is the first time
that expected/estimated remaining energy is used in clusterhead selection for wireless sensor networks.
The rest of this paper is organized as follows. Section II
briefly introduces the predication of the energy consumption
scheme. Next, a fuzzy-logic-based clustering approach is
proposed in Section III. Then, in Section IV, an example of a
100-node wireless sensor network is provided to evaluate the
proposed approach. Finally, Section V concludes this paper.
II. P REDICATION OF THE E NERGY C ONSUMPTION
Set-up
Phase
Steady-state Phase
Clusters
formed
• ••
Slot for node i
Fig. 1.
Frame
Cluster formation and operation.
As the distance between the transmitter and receiver is less
than a threshold value d0 , the free space model (d 2 power loss)
is employed. Otherwise the multipath fading channel model
(d 4 power loss) is used. Equation (2) shows the amount of
energy consumed for transmitting l bits of data to d distance,
while (3) represents the amount of energy consumed for
receiving l bits of data.
Tx + l ∗ ε ∗ d 2 , d < d
l ∗ E elec
fs
0
E Tx (l, d) =
(2)
Tx + l ∗ ε
4
l ∗ E elec
mp ∗ d , d ≥ d0
Rx
E Rx (l) = l ∗ E elec
(3)
Tx and E Rx are the energy consumption per bit in the transE elec
elec
mitter and receiver circuits. Also, εfs and εmp are the energy
consumption factor of amplification for the free space and
multipath radio models, respectively. The threshold value d0
could be obtained via (4).
εfs
d0 =
.
(4)
εmp
A. LEACH Clustering Algorithm
C. Expected Residual Energy
LEACH [7], [8] is one of the clustering mechanisms to
achieve the energy efficiency in the communication between
sensor nodes. The operation of LEACH is divided into rounds.
Each round begins with a set-up phase when the clusters are
organized, followed by a steady-state phase when data are
transferred from the nodes to the CH and on to the base station.
LEACH forms clusters by using a distributed algorithm, where
nodes make autonomous decisions without any centralized
control. Each node i elects itself to be a CH at the beginning
of round r + 1 (which starts at time t) with probability Pi (t).
Pi (t) is chosen such that the expected number of CHs for this
round is k. If there are N nodes in the network, each node
would choose to become a CH at round r with the probability
as (1).
⎧
: Ci (t) = 1
k
⎨
N
Pi (t) = N−K ∗ r mod K
(1)
⎩
0
: Ci (t) = 0
Before the cluster formation, the number of cluster members
is unknown. However, since it is proportional to the number
of neighbors near a potential CH (in a specific transmission
range), the number of neighbors (defined as value n) could
be used to obtain the expected energy consumption during the
CH selection. As shown in Fig. 1, after the cluster formation,
the steady-state operation is broken into frames, where nodes
send their data to the CH at most once per frame during their
allocated transmission slot. In a frame, suppose a CH has n
cluster members, it would receive n messages from all the
members and then transmit one combined message to the base
station with a distance dtoBS . The number of frames could be
obtained by (5).
tssPhase
(5)
Nframe =
n ∗ t slot + tCHtoBS
where tssPhase is the operation time of the steady-state phase
(i.e. the time of a node to be a CH), tslot is the slotted time
required for the transmission from members to the CH, and
tCHtoBS is the time required for the transmission from CH to
the base station.
The expected consumed energy of a node to be a CH after
a steady-state phase could be represented as (6).
E expConsumed(l, dtoBS , n) = Nframe ∗ E Tx (l, dtoBS )
+n ∗ E Rx (l) .
(6)
where Ci (t) is the indicator function determining whether or
not node i has been a CH within the most recent (r mod N/K )
rounds (Ci (t) = 0 means node i has been a CH). Thus, only
nodes that have not already been CHs recently (i.e. Ci (t) = 1)
may become CHs at round r + 1.
B. Radio Energy Dissipation Model
Currently, there is a great deal of research in the area of lowenergy radios. In this paper, the first-order radio model shown
in [16] has been adopted to model the energy dissipation.
All the sensor nodes are assumed to transmit and receive the
same size of messages, i.e. l bits of data. The distance to the
LEE AND CHENG: FUZZY-LOGIC-BASED CLUSTERING APPROACH FOR WSNs USING ENERGY PREDICATION
2893
Data size (l)
Distance (
Radio Energy
Dissipation Model
)
Chance
Neighbors (n)
Fuzzy Inference System
Residual Energy (
)
+
_
Fuzzifier
Inference
Engine
Defuzzifier
Fuzzy Rule Base
Fig. 2.
Fig. 3.
Proposed scheme of the probability reasoning during cluster head selection.
Fuzzy set for input variable. (a) Residual energy. (b) Expected residual energy.
base station, dtoBS , could be computed based on the received
signal strength. Then, the expected residual energy of a node
to be a CH after a steady-state phase could be obtained via (7).
E expResidual(l, dtoBS , n) = E residual − E expConsumed
5) The radio link is symmetric such that energy consumption of data transmission from node A to node B is the
same as that of transmission from node B to node A.
(7)
where the E residual is the residual energy of a sensor node
before the cluster head selection.
III. P ROPOSED C LUSTERING A PPROACH
A. System Assumptions
This paper considers network applications in which sensor
nodes are deployed randomly in order to continuously monitor
the environment. The information collected by sensor nodes
is sent to a base station located outside of the deployment
field. Each sensor nodes can operate either in sensing mode
to monitor the environment parameters and transmit it to the
associated CH or in CH mode to gather data, compress it and
forward to the base station. In addition, some assumptions are
made as follows:
1) All sensor nodes and the base station are stationary after
deployment.
2) The network is considered homogeneous and all sensor
nodes have the same initial energy.
3) Nodes have the capability of controlling the transmission
power according to the distance of receiving nodes.
4) The distance between nodes can be computed based on
the received signal strength.
B. Handing Uncertainties Using Fuzzy Inference Systems
To handle uncertainties, this paper has used fuzzy inference
systems (FIS) for the chance computation of each node. As
show in Fig. 2, two input variables for the FIS are the residual
energy E residual and the expected residual energy E expResidual,
and one output parameter is the probability of a node to be
selected as a CH, named chance. The bigger chance means
that the node has more chance to be a CH.
The fuzzy set that describes the residual energy input variable is depicted in Fig. 3(a). The linguistic variables for this
fuzzy set are high, rather high, medium, rather low, low, and
very low. A trapezoidal membership function is used for high
and very low, while a triangular membership function is used
for the rest linguistic variables. The other fuzzy input variable
is the expected residual energy of the CH candidate. The fuzzy
set that describes expected residual energy input variable is
illustrated in Fig. 3(b). The linguistic variables of this fuzzy set
are high, medium and low. A trapezoidal membership function
is used for high and low, while a triangular membership
function is used for medium. The only fuzzy output variable
is the chance of a CH candidate. The fuzzy set for the chance
output variable is demonstrated in Fig. 4. Seven linguistic
variables are very high, high, rather high, medium, rather
low, low, and very low. The very high and very low have a
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IEEE SENSORS JOURNAL, VOL. 12, NO. 9, SEPTEMBER 2012
Algorithm 1 Proposed Clustering Algorithm
Input:
N: a network
a : a node of N
V: {v | v is a’s vicinity node which is a CH candidate}
T: a threshold value to become a CH candidate
chance(a): a suitability value of the node a to be a CH
k: the number of clusters
r: the number of times to be a CH
Output:
CH(a): the cluster head of the node a
isClusterHead(a): true if CH(a)=a
Fig. 4.
Fuzzy set for output variable chance.
TABLE I
F UZZY M APPING RULES
Function:
broadcast(data, distance);
send(data, destination);
fuzzylogic(
,
Initialization:
1.
chance(a) ← fuzzylogic(
2.
isClusterHead(a) = false;
3.
r ←
);
,
);
Main:
4.
/* for every clustering round */
)
5.
if (r ==
6.
isClusterHead(a) ← false;
7.
T ← 1;
8.
else T ←
;
trapezoidal membership function, and the remaining linguistic
variables are represented by using triangular membership
functions. In this work, for simplicity and reducing the cost of
computation, the triangular membership functions are mostly
chosen here.
The chance calculation is accomplished by using predefined
fuzzy if-then mapping rules to handle the uncertainty. Based
on the two fuzzy input variables, 18 fuzzy mapping rules are
defined in Table I. From the fuzzy rules, we can get the fuzzy
variable chance. This fuzzy variable has to be transformed to
a single crisp number that is a form we can use in practice.
In our approach, the center of area (COA) method is used
for defuzzification of the chance. Generally, fuzzy rules can
be generated either from heuristics or from experimental data.
In this paper, the heuristic fuzzy rule generation method is used
with the principle: A node which holds more residual energy
and more ERE has a higher probability to become a CH.
C. Proposed LEACH-ERE Clustering Algorithm
Similar to the LEACH, our proposed clustering method
configures clusters in every round. The pseudo code of the
clustering method is described as Algorithm 1. In every
9.
end if
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
if (rand(0,1) > T )
CH(a) ← a ;
chance(a) ← fuzzylogic(
,
);
broadcast(chance(a), V); //Candidate-Message
On receiving Candidate-Messages from CH candidates;
for each v V
if (chance(a) < chance(v) )
CH(a) ← v ;
isClusterHead(a) ← false;
broadcast(Quit-Election-Message, V)
else isClusterHead(a) ← true;
;
end if
end if
24.
25.
26.
27.
28.
29.
30.
if (isClusterHead(a) == true)
broadcast(CH-Message, V)
On receiving JOIN-REQ messages;
else
On receiving CH-Message;
Send JOIN-REQ messages to the closest CH;
end if
clustering round (lines 4-30), each sensor node generates a
random number between 0 and 1. If the random number for
a particular node is bigger than a predefined threshold T ,
which is the percentage of the desired tentative CHs, the
node becomes a CH candidate. Then, the node calculates the
chance using the fuzzy inference system which is mentioned
above and broadcasts a Candidate-Message with the chance.
This message means that the sensor node is a candidate
for CH with the value of chance. Once a node advertises
a Candidate-Message, the node waits Candidate-Messages
from other nodes. If the chance of itself is bigger than every
chance values from other nodes, the sensor node broadcasts
a CH-Message which means that the sensor node itself is
TABLE II
C ONFIGURATION PARAMETERS
Type
Network topology
Radio model
Application
Parameter
Value
Number of nodes
Expected number of
clusters Network
coverage Base
station location
100
5
(0, 0) (100, 100) m
at (50, 175) m
Startup energy
Tx /E Rx
E elec
elec
εfs
εmp
2J
50 nJ/bit
10 pJ/bit/m2
0.0013 pJ/bit/m4
Simulation times
Packet header size
Broadcast packet size
Data packet size
Competition radius
Bandwidth
15
25 bytes
16 bytes
500 bytes
25 m
1 Mb/s
Number of Alive Nodes
LEE AND CHENG: FUZZY-LOGIC-BASED CLUSTERING APPROACH FOR WSNs USING ENERGY PREDICATION
2895
100
90
80
LEACH
70
CHEF
LEACH-E
ERE
60
50
40
30
LEA
ACH-C
20
10
0
0
100
0
200
300
400
500
600
Num
mber of Rounds
Fig. 6. Distribution of alive sensor nodes according to the number of rounds.
The Round at which Half of the Nodes Alive (HNA)
600
500
400
300
200
100
0
LEACH
LEACH-C
CHEF
LEACH-ERE
Fig. 5. Round at which half of the nodes alive for each clustering approaches.
elected as the CH. If a node which is not a CH receives the
CH-Message, the node selects the closest cluster head as its
CH and sends a JOIN-REQ request to the head.
Fig. 7. Distribution of the number of clusters according to the number of
rounds.
TABLE III
AVERAGE AND S TANDARD D EVIATION OF THE N UMBER
OF
IV. P ERFORMANCE E VALUATION
In this section, we present the results of experimental
simulations to evaluate our proposed approach. Moreover,
we compare our proposed clustering algorithm LEACH-ERE
with three different algorithms, namely LEACH [8], LEACHCentralized [8], and CHEF [10]. Simulation results have
shown that our approach reveals better performances compared
with others.
A. Simulation Environments
The simulation was implemented based on the network
simulator, NS-2 [17]. The 100 number of nodes are randomly
distributed in a 100 × 100 area. The base station located at a
point (50, 175). The values used in the first order radio model
are described in Table II.
B. Simulation Results
Handy et al. [18] proposed the metric Half of the Nodes
Alive (HNA) which denotes an estimated value for the round
in which half of the senor nodes die. This metric is useful
in densely deployed sensor networks. As shown in Fig. 5,
our proposed LEACH-ERE approach outperforms LEACH and
CHEF. LEACH-ERE is more efficient than LEACH about
42.61% and CHEF about 2.87%. LEACH performance is the
C LUSTERS U P TO ROUND 600
LEACH
LEACH-C
CHEF
LEACH-ERE
Ave.
4.2
4.7
4.7
4.7
Std. Dev.
2.11
1.03
1.6
1.58
poorest one, since it does not consider the residual energy level
of sensor nodes during clustering. Moreover, the distributed
LEACH-ERE has the similar performance as compared with
the centralized LEACH-C.
Fig. 6 illustrates the distribution of the alive sensor nodes
with respect to the number of rounds for each algorithm.
This figure clearly shows that our proposed approach is
more stable than the other distributed clustering algorithms
(LEACH and CHEF), because sensor node deaths begin later
in LEACH-ERE and continue linearly until all sensor nodes
die. As compared with the centralized clustering approach
LEACH-C, the proposed approach has an approximated result
without requiring global network knowledge.
Fig. 7 shows the distribution of the number of clusters with
respect to the number of rounds for each algorithm. LEACHC generates a constant number of clusters until around the
round 560 while the numbers of clusters in LEACH, CHEF,
and LEACH-ERE are varied. Table III shows the average and
standard deviation of the number of clusters up to round 600.
It is apparent that the number of clusters in LEACH-ERE is
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IEEE SENSORS JOURNAL, VOL. 12, NO. 9, SEPTEMBER 2012
Averrage of Receive
ed Packets per Second
140
0
120
0
100
0
80
0
60
0
40
0
20
0
0
LEACH
Fig. 8.
LEACH-C
C
CHEF
LEACH-ER
RE
Average number of received packets per second at the base station.
steadier than that in other distributed clustering algorithms
(LEACH and CHEF). LEACH uses a fully random approach
to produce cluster heads, thus it results in a fairly variable
number of clusters, although the expected number of cluster
heads per round is deterministic.
Fig. 8 shows the average number of received packets
per second at the base station. Obviously, the centralized
LEACH-C has the best performance since the base station
receives the most information from the sensor nodes during
the network lifetime. On the other hand, the proposed
distributed LEACH-ERE has the better result as compared
with the LEACH and CHEF.
V. C ONCLUSION
Energy is a major factor in designing WSNs. To achieve the
energy efficiency, many clustering algorithms are proposed and
LEACH is the representative one. LEACH uses the probability
model to distribute the concentrated energy consumption of the
CHs. However, it depends on only a probability model and
the energy efficiency is not maximized. In this paper, a fuzzylogic-based clustering approach based on LEACH architecture
with an extension to the energy predication has been proposed
for WSNs, namely LEACH-ERE. The main objective of our
algorithm is to prolong the lifetime of the WSN by evenly
distributing the workload. To achieve this goal, we have mostly
focused on selecting proper CHs from existent sensor nodes.
LEACH-ERE selects the CHs considering expected residual
energy of the sensor nodes. The simulation results show
that the proposed LEACH-ERE is more efficient than other
distributed algorithms, such as LEACH and CHEF.
In this paper, the proposed LEACH-ERE algorithm is
designed for the WSNs that have stationary sensor nodes. As
a future work, it can be extended for handling mobile sensor
nodes. Also, a further direction of this work will be to find
the optimal fuzzy set and to compare the enhanced approach
with other clustering algorithms.
ACKNOWLEDGMENT
The authors would like to thank the editor and anonymous
referees for their valuable comments to improve the quality of
this paper.
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Jin-Shyan Lee (M’10–SM’11) received the B.S.
degree in mechanical engineering from the National
Taiwan University of Science and Technology,
Taipei, Taiwan, in 1997, and the M.S. and Ph.D.
degrees in electrical and control engineering from
National Chiao Tung University, Hsinchu, Taiwan,
in 1999 and 2004, respectively.
He was a Visiting Researcher with the Department
of Electrical and Computer Engineering, the New
Jersey Institute of Technology, Newark, from 2003
to 2004. He was a Researcher with the Information
and Communications Research Laboratory, Industrial Technology Research
Institute from 2005 to 2009. Since August 2009, he has been an Assistant
Professor with the Department of Electrical Engineering, the National Taipei
University of Technology, Taipei, Taiwan. His current research interests
include Petri nets, wireless sensor networks, remote monitoring and control,
supervisory control, and hybrid systems.
Dr. Lee was a recipient of the Early Career Award from the IEEE Industrial
Electronics Society in 2010, the Youth Automatic Control Engineering Award
from the Chinese Automatic Control Society in 2008, and the International
Scholarship from the Society of Instrument and Control Engineers in 2004.
He has served on various IEEE conferences as a technical program committee
member and for several journals as an active reviewer.
LEE AND CHENG: FUZZY-LOGIC-BASED CLUSTERING APPROACH FOR WSNs USING ENERGY PREDICATION
Wei-Liang Cheng received the B.S. degree in electrical engineering from National Ilan University,
Ilan, Taiwan, in 2009, and the M.S. degree in electrical engineering from the National Taipei University
of Technology, Taipei, Taiwan, in 2011.
His current research interests include hierarchy
routing and clustering in wireless sensor networks.
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