The document reviews techniques for enhancing energy efficiency in wireless sensor networks (WSNs), emphasizing clustering and cluster head selection as crucial methods. It discusses three primary techniques: Low-Energy Adaptive Clustering Hierarchy (LEACH), fuzzy-based clustering, and neural network approaches, highlighting their roles in reducing energy consumption and extending network lifetimes. The paper includes MATLAB simulations and analyses energy models to evaluate the effectiveness of these strategies.

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A survey on Energy Efficient Protocols
LEACH, Fuzzy-based approach and Neural
Network Technique in Wireless Sensor Network
Shaveta Gupta1
, Vinay Bhatia2
1,2
ECE Deptt. Baddi University, Baddi
shavetaqca@gmail.com, Vinay4research@gmail.com
Abstract— Wireless Sensor Networks (WSN) plays a very
important role in transmitting the data from source to
destination but energy consumption is one of the major
challenges in these networks. WSN consists of hundreds to
thousands of nodes which consume energy while transmitting
the information and with a span of time whole energy get
consumed and network life time gets reduced. Clustering and
Cluster head (CH) selection are important parameters used to
enhance the lifetime of the WSN. Clustering use two methods:
rotating CH periodically in every round to distribute the
energy consumption among nodes and the node with more
residual energy becomes CH.This research paper is focused
on the performance of the techniques used to enhance the
energy efficiency in Wireless Sensor Networks (WSNs). Low-
Energy Adaptive Clustering Hierarchy (LEACH), Fuzzy-
Based and Neural Network are some of the important
techniques used. MATLAB simulation tool is considered in
this paper.
Keywords: Cluster Head, Clustering, Energy Efficiency,
Fuzzy, LEACH, Neural Network and WSNs.
.
I. INTRODUCTION
Wireless sensor network (WSN) is most important
technology and is therefore widely used in today life in a
wide area of different applications. The WSN sensor nodes
operate on battery power which is often deployed in a
complex, rough or sometimes in extreme environment and
consists of hundreds to thousands of nodes so it becomes
difficult to change the batteries. This problem has forced
engineers /scientists to develop some protocols to
minimize the energy consumption especially of the nodes
in order to increase the lifetime of the whole network [1-2].
The main challenge in WSN is energy perspective. The
WSNs used to collect data from the environment, process
it and transmit to the base station (BS). This process
consumes energy so a most important and crucial
technique used is clustering and cluster head selection
(CH). Various protocols are used to overcome the problem
of energy constrain.
Low Energy Adaptive Clustering Hierarchy (LEACH)
is the first hierarchical cluster-based routing set technique
used for energy optimization in WSNs. It involves hard
clustering, CH selection based on energy and distance
between CH and nodes. This distance is determined by
Euclidean distance formula [1]. Head is dependable for
designing and employing a TDMA
It can be seen that the architecture of the WSN and
communication between the nodes with neuron network in
the brain. Artificial Neural Network (ANN) concept is
used for data prediction and communication amongst the
WSNs. The ANN has the capability to predict the data and
helps to avoid unnecessary data communication and thus
saving energy in WSNs Neural Networks is energy
efficient approaches of WSN because of their simple and
parallel distributed computation, distributed storage, data
robustness, auto-classification of sensor nodes and sensor
reading.
The CH selection is not an easy task , fuzzy logic
employed different fuzzy descriptors, including the
residual energy, expected residual energy, number of
neighboring nodes, distance from cluster centroid, and
network traffics, and finally evaluated their performance.
This paper attempts to study three important techniques
LEACH, Neural approach and fuzzy based approach for
improving WSN efficiency.
II. ENERGY EFFICIENT TECHNIQUES
C. LEACH
LEACH is one of the most important, earliest, basic and
dynamic hierarchical routing protocols used in WSNs to
minimize the energy consumption of the nodes. The
clusters are formed and one of the node acting as CH. If a
CH remains fixed throughout the network lifetime then the
CH would die quickly and ends the useful life of all nodes
belonging to that particular cluster. Each node in a cluster
elects itself as local CH at any round with a certain
probability and broadcast its information to the other nodes
[1]. In every cluster one of sensor nodes acts as CH and
remaining sensor nodes act as member nodes of that
cluster. CHs collect the data from all nodes, aggregate
received data, process/compress them and finally transmit
information to BS.
LEACH uses a Time Division Multiple Access
(TDMA)/code-division multiple access (CDMA) MAC to
avoid or reduce inter-cluster and intra-cluster collisions.
After a given interval of time, random rotation for the
selection of CH is conducted so that uniform energy get
consumed by every node in each cluster. The operation of
LEACH consists of two phases, the setup phase and the
steady state phase.
Setup phase: The clusters are organized and CHs are
selected. In the steady state phase, the actual data transfer
to the BS takes place. The duration of the steady state
phase is longer than the duration of the setup phase in
order to minimize overhead. During the setup phase, a
predetermined fraction of nodes ‘p’ elects themselves as

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BUEST, Baddi RIEECE-2014 218
CHs. A sensor node or set of nodes that have not been
selected as a CH in the last (1/p) rounds chooses a random
number between 0 and 1 and if this random number is less
than a threshold value T(n) the node becomes a CH for the
current round. The threshold value is calculated by
T (n) = (p/ (1-p (mod (1/p)))) n € G
where G is the set of nodes that are involved in the CH
election. All the nodes elected as CHs broadcast an
advertisement message to the rest of the nodes in the
network that they are the new CHs. All the non-CH nodes,
after receiving this advertisement, decide on the cluster to
which they want to belong on the basis of signal strength
of the advertisement. The non-CH nodes inform the
appropriate CHs that they will be a member of that
particular cluster. After receiving all the messages from the
nodes, the CH node creates a TDMA schedule and assigns
each node a time slot when it can transmit. This schedule is
broadcast to all the nodes in the cluster.
Steady state phase: The sensor nodes start sensing and
transmitting data to the CHs. The CH node receives all the
data, aggregates it, process it and transmit it to the BS.
After this process again the network goes back into the
setup phase and again selects new CHs for next round.
B. FUZZY- BASED
Clustering techniques used in earlier protocols selects
CHs with more residual energy and rotates CH periodically
so as to distribute the energy consumption among nodes in
each cluster and extend the network lifetime. The previous
algorithms do not consider the expected residual energy,
which is the predicated remaining energy for being
selected as a CH and running a round. A fuzzy-logic-based
clustering approach used predicted as well as residual
energy to prolong the lifetime of WSNs. In fuzzy-logic,
two input variables for the fuzzifier are the residual energy
Eresidual and the expected residual energy EexpResidual and one
output parameter is the probability of a node to be selected
as a CH, named as chance. The bigger chance means that
the node has more chance to become a CH. The EexpResidual
is the difference between the Eresidual and Eexpconsumed while
Eexpconsumed depends upon three factors: size of the data,
distance to BS and number of neighbours (degree of
nodes).
The fuzzy mapping rules describes the variables as
high, rather high, medium, rather low, low, and very
low[3-4]. The fuzzy set that describes that if residual and
expected residual energy (ERE) is high than chance to
become CH is very high. If residual energy is high and
expected residual energy (ERE) is medium than chance to
become CH is high and so on. The chance for becoming
CH is calculated by using predefined fuzzy if-then
mapping rules to handle the uncertainty [5].A node having
more residual energy and ERE has highest probability to
become a CH. The CH selection is same as LEACH. Then,
the nodes calculate the chance using the FIS and
broadcasts a Candidate-Message with the chance. The
nodes broadcasts the massage with a high chance also
receives the massage from other nodes. The node having
high chance elect itself as CH and rest of the nodes
remains normal node.these normal nodes choose the
nearest CH as its CH and sends a JOIN-REQ request to the
CH then starts transmitting the data. Based on the two
fuzzy input variables, 18 fuzzy mapping rules are
discussed in table I [3-4]
Table I. FUZZY MAPPING RULES
H - High RH - Rather High M - Medium
RL -Rather Low L - Low VL -Very Low
1. Energy Model
The energy model used in LEACH and Fuzzy
techniques is same. This model used to calculate the
amount of energy consumed for transferring the data from
simple node to CH, from CH to intermediate CH, from CH
to BS and also from node to BS. The radio dissipation
energy model consists of transmitter having transmit
electronics (Eelec) which depends upon factors like coding,
modulation, filtering and transmit the signal and amplifier
depends on the distance to the receiver and the tolerable
bit-error rate
.Fig. 1. Radio Energy Dissipation Model
If the distance between transmitter and receiver is less
than threshold distance (say do) then free space (d2
power
loss) channel model used and if distance between
transmitter and receiver is greater than threshold distance
(say do) then multi path fading (d4
power loss) channel
model used [1].
S.NO Residual
Energy
ERE Chance
1 H H VH
2 H M H
3 H L M
4 RH H H
5 RH M RH
6 RH L M
7 M H M
8 M M M
9 M L RL
10 RL H VL
11 RL M M
12 RL L RL
13 L H VL
14 L M VL
15 L L L
16 VL H VL
17 VL M VL
18 VL L VL

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219 BUEST, Baddi RIEECE -2015
The energy consumed by the specific nodes/CH for
transmitting k bits of data is:
Energy consumed by transmitter (for d<do)
Etx(L, d) = Eelec * L + L * (Ef s * d2
)
Transmission energy for intermediate node
Etx(L, d) = ((Eelec + EDA) * L) + (Ef s * L * d2
).
Energy consumed by transmitter (for d ≥ do)
Etx(L, d) = Eelec * L + L * (Emp * d4
)
Transmission energy for intermediate node
Etx(L, d) = ((Eelec + EDA) * L) + (Emp * L * d4
)
Energy consumed by Receiver
Erx(L) = Eelec * L
Table II contains first order radio model parameter used
to calculate the energy consumed by each node in a cluster
at various distances.
Table II. Radio Parameters
Parameters Operation Values
Transmitter / Receiver
Electronics
Eelec 50 nJ/bit
Transmit amplifier
(if d to BS<do)
Efs 10 pJ/bit/4m2
Transmit amplifier
(if d to BS>do)
Emp 0.0013 pJ/bit/m4
Data aggregation
energy
EDA 5 nJ/bit/signal
2. Expected Residual Energy in Fuzzy logic
As shown in fig (2), after the cluster formation, the
steady-state operation is broken into data frames, nodes
transmit their data to the CH during their allocated
transmission slot. In a frame,a CH has say 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
Nframe = tssPhase/(n ∗ tslot + tCHtoBS)
where tssPhase is the time of a node to be a CH), tslot is the
slotted time required for the transmission from node to the
CH, and tCHtoBS is the time required for the transmission
from CH to the BS. The expected consumed energy of a
node to be a CH after a steady-state phase could be
calculated as
EexpConsumed(l, dtoBS, n) = Nframe ∗ETx(l, dtoBS)+n ∗ERx(l)
All the sensor nodes are assumed to transmit and receive l
bits of data. The distance to the BS, 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 calculated via
EexpResidual(l, dtoBS, n) = Eresidual − EexpConsumed
where the Eresidual is the residual energy of a sensor node
before the cluster head selection [3].
Fig. 2. Fuzzy-Logic based CH selection
C. NEURAL- BASED
Neural Networks does not conserve energy but they can
help energy conservation methods to work intelligently to
better approach to their goals. However there is enough
motivation to implement full ANNs on each single sensor
node due to analogy between WSNs and ANN as in [6].
Neural Network based energy efficient approaches can also
be classified according to the role Neural Networks play
role in neural topologies applied. In (table.III) you can see
this classification where first column represents the role
that neural network play in, second column represents the
appropriate topology used for it with related references and
third column represents the energy conservation approach
category which it applied to.
S.No The role
N.N can
play N.N
topology
applied
Energy
conservation
category(ies)
1 Energy
efficient
path
discovery
SOM[7-
9],BP[10]
Duty cycling-
routing
2 Energy
efficient
Nodes
clustering
SOM
[11-12]
Duty cycling-
Cluster-based
routing
3 Cluster
head
selection
SOM[13] Duty cycling-
Cluster-based
routing
4 Data
prediction
BP[14], Data reduction-
data
prediction and
duty cycling
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