Sensor Energy Optimization Using Fuzzy Logic in Wireless Sensor Networking

IJRREST: International Journal of Research Review in Engineering Science and Technology (ISSN 2278- 6643) | Volume-2 Issue-1, March 2013
11 | P a g e
Sensor Energy Optimization Using Fuzzy Logic in Wireless Sensor
Networking
*Neeraj Kumar Mishra
*Asst. Professor, R.D. Foundation Group of Institutions, Modinagar, Ghaziabad, U.P., India
Abstract— Wireless sensor networks is
challenging in that it requires an enormous
breadth of knowledge from an enormous variety
of disciplines. A lot of study has been done to
minimize the energy used in routing and number
of protocols has been developed. These protocols
can be classified as - Hierarchical, data centric,
location based and Network flow protocols. In this
paper, we are particularly focusing on
hierarchical protocols. In such types of protocols,
the energy efficient clusters are formed with a
hierarchy of cluster heads. Each cluster has its
representative cluster head which is responsible
for collecting and aggregating the data from its
respective cluster and then transmitting this data
to the Base Station either directly or through the
hierarchy of other cluster heads. Fuzzy logic has
been successfully applied in various areas
including communication and has shown
promising results. However, the potentials of
fuzzy logic in wireless sensor networks still need to
be explored. Optimization of wireless sensor
networks involve various tradeoffs, for example,
lower transmission power vs. longer transmission
duration, multi-hop vs. direct communication,
computation vs. communication etc. Fuzzy logic is
well suited for application having conflicting
requirements. Moreover, in WSN, as the energy
metrics vary widely with the type of sensor node
implementation platform, using fuzzy logic has the
advantage of being easily adaptable to such
changes.
Keywords: WSN, LEACH, CHEF, FUZZY
LOGIC, MULTIHOPE.
1. INTRODUCTION
Micro sensor can contain hundreds or thousands of
sensing nodes. It is desirable to make these nodes as
cheap and energy efficient as possible and relay on
their large numbers to obtain high quality result.
Network protocol must be designed to achieve fault
tolerance in the presence of individual node failure
while minimizing energy consumption. The limited
wireless channel bandwidth must be shared among all
the sensors in the network routing protocol for these
networks should be able to perform local
collaboration to reduce bandwidth requirement.
Sensing communication processing and battery units
are the primary components of a sensor node.
Individual sensors have the capacity to detect events
occurring in their area of deployment. Reliable data
transport is an important facet of dependability and
quality of services in several applications of wireless
sensor networks. Different application have different
reliability requirement.
2. RELATED WORK
A lot of study has been done to minimize the energy
used in routing and number of protocols has been
developed. These protocols can be classified as -
Hierarchical, data centric, location based and
Network flow protocols. In this paper, we are
particularly focusing on hierarchical protocols. In
such types of protocols, the energy efficient
clusters are formed with a hierarchy of cluster
heads. Each cluster has its representative cluster
head which is responsible for collecting and
aggregating the data from its respective cluster and
then transmitting this data to the Base Station
either directly or through the hierarchy of other
cluster heads. The existing hierarchical protocols are:
2.1. LEACH (Low Energy Adaptive Clustering
Hierarchy)
LEACH is one of the most popular clustering
algorithms. The main idea behind LEACH is to form
clusters based upon the signal strength of the sensors.
Some of the nodes are randomly chosen as the cluster
heads(CH) and a node is assigned to the CH based
upon the signal strength received by that node from the
CH. CHs have to do a lot more work than the s, hence
they dissipate a lot more energy and may die quickly.
In order to maintain a stable network, CHs keep on
rotating, in every round. So, a node which had become
CH may not get an opportunity to become CH again
before a set interval of time. A node can become the
cluster head for the current round if its value is less

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than the threshold T (n) where T (n) is given by: P is
the percentage of cluster heads, r is the rth round, and
G is the set of nodes which are not cluster heads in
the last 1/P rounds. Figure 1 show how clusters are
formed in 400*400 regions in one round. Nodes
having same symbol belong to the same cluster. The
cluster heads are shown in red. Base Station (or sink)
is situated at (50, 50) and is shown by X. Compared
to direct communication, LEACH achieves 7 times
more reduction in energy dissipation and about 4-8
times more reduction as compared to MTE routing
protocol . A drawback of LEACH is that LEACH is a
single hop routing. Each node transmits its data to
either CH or BS directly. Moreover, for a
network of larger regions, the dynamic clustering
may become overhead since rotation of CH at every
round and advertisements of CHs also dissipate
energy. LEACH also gives no idea about the
placement of nodes and the distribution of clusters is
totally randomized.
2.2. LEACH-C protocol
LEACH-C is a centralized clustering algorithm in
which BS has the right to select the clusters based
upon the 1-P*(r mod 1/p) annealing algorithm to find
k optimal number of clusters. BS selects the CHs for a
particular round. The protocol guarantees optimum
clusters but has a drawback that each node sends
information about its current location and residual
energy to the sink during the set up phase which
results in an extra overhead.
2.3. CHEF (Cluster-Head Election Mechanism
using Fuzzy Logic)
In CHEF chance has been calculated using Fuzzy if-
then rules to elect the cluster head (CH). Two
variables namely proximity distance and energy
are considered as key parameters in calculating
the chance. The fuzzy logic based approach allows
the node with High energy and locally optimal node
to elect as a cluster head. The CHEF is 22.7% more
efficient than LEACH. In this paper, an effort has been
done to compare the three protocols LEACH, CHEF
and the proposed protocol F-MCHEL (Fuzzy based
Master cluster head election protocol), based on the
energy dissipation model shown in the figure 1.
For a particular node, the energy is dissipated because
of receiving and transmitting. The energy expanded in
transmitter to transmit k-bit message is given by:
ET(k,d) = (Eelec * k) + (Efs*k*d2
) if d<=d0
Fig.1. Energy Dissipation Model
(Eelec * k) + (Emp*k*d4
) if d>d0
Eelec is the energy dissipated to run the
electronics circuit’s k is the packet size Efs and mp
are the characteristics of the transmitter amplifier d
is the distance between the two
communicating ends. Energy dissipation to receive a
k-bit message is given by- ER (k) = Eelec* k
The values of radio characteristics are: Eelec =50
nJ/bit
Efs = 10 pJ/bit/m2
Emp = 0.0013pJ/bit/m4
In addition to above energy expansions, a CH also
dissipates energy because of data aggregation. The
data aggregation energy EDA has the value of
5nJ/bit/signal.
3. F-MCHEL: FUZZY BASED MUSTER
CLUSTER HEAD ELECTION LEACH
PROTOCOL
In this section F-MCHEL(Fuzzy based Master Cluster
Head Election Leach protocol), a homogeneous energy
protocol ,has been introduced, which uses fuzzy if -
then rules to maximizing the lifetime of WSNs. F-
MCHEL is similar to the CHEF but it has the Master
cluster head election mechanism. Cluster heads
election process is similar to CHEF. In CHEF the
cluster head has been elected using the fuzzy
approach based on two input parameters namely,
Energy of the node and roximity distance. In CHEF
all the elected cluster heads are used to send the
aggregated information to Base station, whereas
in F-MCHEL one Master cluster head has been
elected out of these elected cluster heads. Cluster
head node which has the maximum residual energy
is elected as Master cluster head. After the
formation of clusters all non-Cluster heads nodes are
used to sends the information to the respective cluster
head.
All Cluster head will aggregate the information

13 | P a g e
received from non-Cluster head nodes and they
send the aggregated information to Master Cluster
head. Master cluster head again aggregate the
information received from cluster heads and sends this
information to Base station. Like LEACH and
CHEF, the proposed protocol F-MCHEL also
assumes two phases at the beginning of each
round, one is the setup phase and other is the
steady state phase.
3.1 Proposed Approach
Two well known clustering algorithms are used in
association with a visualization interface to simulate
the possible the cluster network with optimized
energy consumption.
3.1.1 Subtractive Clustering
Assumes each node as a potential cluster head and
calculate the measure of the likelihood that each node
defines the cluster head, based on the density of
surrounding nodes. The algorithm has the following
features:
 Select the node with the highest potential to be the
first cluster head.
 Removes all nodes near to the first cluster head, in
order to determine the next node cluster and its
head location.
 Iterates this process until the entire nodes are
within radii of a cluster. Subtractive clustering
based algorithm is implemented using MATLAB
function. [C,S]= SUBCLUST (X, RADII,
XBOUNDS, OPTIONS).
 Matrix X contains data points.
 RADII are vector that specified a cluster head's
range of influence in each of the data dimensions.
 XBOUNDS is a 2xN matrix that specific how to
map the data in X into a unit hyper box.
 The returned vector C contains the position of a
cluster head.
 S contains the sigma values that specify the range
of influence of a cluster head in each of the data
dimension.
 Option is use for specifying clustering algorithm
parameter to over ride the default values.
3.2.2 FUZZY C-mean (FCM) Clustering
Fuzzy C-means (FCM) Algorithm was introduced by
Bezdek. The FCM based algorithm is a data
clustering technique wherein each data point belongs
to a cluster to some degree that is specified by a
membership grade. Pseudo code for fuzzy C-mean
based solution is as follows:
 Initialize the data points.
 For cluster unit 1 to number of nodes call fuzzy C-
mean algorithm. Assign attributes to the cluster
head. Calculate the energy loss according to
equation. End for all the nodes.
 Calculate the total energy loss. If total energy loss
is less then minimum total cost. Total energy loss
is the new minimum total energy. End for cluster
unit 1 to number of nodes.
3.2 Cluster head Election Using Fuzzy Logic
Parameters Experiment
No. of needs (n) 150
BS position 200,50
E0 (0-100)J
E elect 50 nj/bit
E emp 100pj/bit/m4
E fs 10 pj/bit/m2
E da 5 nj/bit/signal
NO. OF BITS(K) 200
4. SIMULATION RESULTS
Number of experiments has been carried out to
compare the three protocols LEACH, CHEF and F-
MCHEL with same network and parameter
settings. Simulation results on MATLAB depict
that F-MCHEL has better stability period and less
energy dissipation per round. Simulation results
shows that the Fuzzy logic systems, which can
manipulate the linguistic rules in a natural way, can
avoid the necessity of accurate representation of the
environment, which generally does not exist in
reality.
Network Settings we are using a 400*400 region
having 100 sensor nodes placed randomly. The
packet size is considered to be of 2000 bits. The
various parameter values taken for experiments are
shown in the Table 1.
Performance of the suggested protocol has been
measured on the basis of following parameters:
(i) Stability Period: Stability period is the period (or
round) up to which all nodes are alive. This period lies
between Rounds 1 to the round at which the first node
dies.
(ii) Instability period: Instability Period is the period
between the first dead node and last dead node. This
period should be kept as small as possible.

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(iii) Energy dissipation.
(iv) Number of packets transmitted to base station.
Table.1. Improvement of Stable Region
Table.2. Declination of Unstable Region
As compared to CHEF and LEACH, F-MCHEL
has an improvement over the stability period as shown
in the Fig below. As per the simulation results, the
first node dead in LEACH and CHEF are at 1132
and 1457 respectively whereas in F-MCHEL, the
first dead is at 1952 which shows an
improvement of (33.97%) over CHEF and (72%)
over LEACH. The result obtained has been shown
in Table 1 and Table 2. The instability period is
given by the time between first dead node and last
dead node. While calculating instability period,
we are considering only those rounds in which
some data is transferred to the BS.
Table.3. Simulated Results
All those rounds in which no data is transferred to
BS are not counted. It is clear from the graph that the
unstable region for F-MCHEL shows decline over
LEACH, however CHEF shows better unstable
region as compare to F-MCHEL.
5. CONCLUSION
Clustering is one of the most important approaches to
energy saving by keeping portion nodes active. There
are several advantage in LEACH protocol namely,
uneven distributed of energy among nodes, no cluster
head selection in any round etc. In this paper,
LEACH protocol was compared with the improved
clustering hierarchy scheme (DBS) and fuzzy logic
based approach.
6. REFERENCES
[1]W. Heinzelman, A chandrakasan and H.
Balakrishanan,"Energy efficient comn. protocol for
wireless micro sensor ntwork" in proc. of the 33rd
annual Hawaii International conference on system
science (HICSS), maui, HI Jan 2000, pp,3005-3014.
[2]C. Chee-yee and S.P. kumar ,"Sensor networks:
evolution, opportunities, and channges" in
proceedings of the IEEE, Aug.2003,pp.1247-1256.
[3]http://satandards.ieee.org/getieee802.
[4]IEEE, IEEE 802.15.4. Wireless medium access
control and physical layer (PHY) specification for
Low-rate wireless personal area network, 2003.
[5]A.Wang, A. Chandrakasan, "Energy efficient DSP
for wireless sensor network” IEEE signal processing
magazine, July 2002, app.68-78.
[6]T. Rappaport, wireless communications: principle
& practice, Englewood cliffs,NJ: Prentice-Hall, 1996.
[7]Jong-Myoung Kim, Seon-Ho Park, Young-Ju
Han, Tai-Myoung Chung,” CHEF: Cluster Head
Election mechanism using Fuzzy logic in
Wireless Sensor Networks “ICACT 2008,PP-654-
659, Feb 2008.
[8]Heizleman W., Chandrakasan A., and
Balakrishnan H., “Energy Efficient Communication
Protocol for Wireless Sensor Networks,” in
Proceedings of The 10th
IEEE/ACM International
Symposium on Modeling Analysis and Simulation of
Computer and Telecommunication Systems
(MASCOTS), San Francisco, pp. 20-27, 2002.
[9]Klues K., Xing G., and Lu C., “Towards a
Unified Radio Power Management Architecture for
Wireless Sensor Networks,” in Proceedings of
International Workshop on Wireless Sensor Network
Architecture (WWSNA'07), USA, pp. 55-60, 2007.
LEACH CHEF F-
MCHEL
Improvement
over LEACH
First
Node
Dies
1132 1457 1952 72%
LEACH CHEF F-MCHEL Impro-
ement
over
LEACH
Instability
Period =
Last node
dies -First
node dies
441 176 300 31%

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