Based on Heterogeneity and Electing Probability of Nodes Improvement in LEACH

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 9, 2013 | ISSN (online): 2321-0613
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I. INTRODUCTION
A. Wireless sensor networks
Efficient design, performance and implementation of
wireless sensor networks have become a hot area of research
in recent years, due to the vast potential of sensor networks
to enable applications that connect the physical world to the
virtual world. By networking large numbers of tiny sensor
nodes, it is possible to obtain data about physical
phenomena that was difficult or impossible to obtain in
more conventional ways. In the coming years, as advances
in micro-fabrication technology allow the cost of
manufacturing sensor nodes to continue to drop, increasing
deployments of wireless sensor networks are expected, with
the networks eventually growing to large numbers of nodes.
Potential applications for such large-scale wireless sensor
networks exist in a variety of fields, including medical
monitoring, environmental monitoring, surveillance, home
security, military operations, and industrial machine
monitoring.
Fig. 1 Sensor nodes scattered in a sensor field And the
Components of a single sensor node
(Source [1]).
B. Network model
1) The single hierarchy network model identical with
the one of LEACH with assumptions as follow
[21]:
2) All nodes are the same, static, and have enough
computing capacity to support different MAC
protocols and data processing.
3) The radio signal has identical energy attenuation in
all directions, and the wireless channel is
symmetric.
4) All nodes can communicate with each other and the
sink in single hop mode.
5) All nodes can be aware of their own residual
energy and adapt transmission power according to
communication distances.
6) Sinks are static, and with enough power supply.
7) Each node transmits data at given time slot. The
data sensed by adjacent nodes are correlative, so
the cluster head can fuse the collective data.
Fig. 2: Network Model
C. Energy model
Heinzelman et al. [1] used the first order radio model. To
transmit a k-bit data to a distance d, the radio hardware
energy consumption is:
Where Eelec is the factor of electronics energy consumption.
εfs and εmp are identical to the ones in receiver.
d0 is the reference distance between transmitter and
receiver, which is given by
To receive a k-bit data, the radio expends:
It is assumed that the sensed information is correlated,
thus cluster-head can always aggregate the data gathered
from its members into a single length-fixed packet. Cluster-
head aggregates k-bit data from n members to expend:
Where EDA is the factor of data aggregation
Based on Heterogeneity and Electing Probability of Nodes
Improvement in LEACH
Zaki Anwer1
Amandeep Kaur Brar2
1, 2
Punjabi University, Patiala

Based On Heterogeneity and Electing Probability of Nodes Improvement in LEACH
(IJSRD/Vol. 1/Issue 9/2013/0065)
Fig. 3: First order radio model [1]
D. ROUTING PROTOCOLS IN WSNs
1) Flooding
Flooding [23] is an old routing mechanism that may also be
used in sensor networks. In flooding, a node sends out the
received data or the management packets to its neighbors by
broadcasting, unless a maximum number of hops for that
packet are reached or the destination of the packets is
arrived.
2) GOSSIPING
Gossiping protocol is an alternative to flooding mechanism.
In Gossiping [24], nodes can forward the incoming
data/packets to randomly selected neighbor node. Once a
gossiping node receives the messages, it can forward the
data back to that neighbor or to another one randomly
selected neighbor node. This technique assists in energy
conservation by randomization.
3) SPIN
SPIN (Sensor Protocols for Information via Negotiation)
[25] is a family of adaptive protocols for WSNs. Their
design goal is to avoid the drawbacks of flooding protocols
mentioned above by utilizing data negotiation and resource
adaptive algorithms.
4) Directed Diffusion
Directed diffusion [26] is another data dissemination and
aggregation protocol. It is a data-centric and application
aware routing protocol for WSNs. It aims at naming all data
generated by sensor nodes by attribute-value pairs. Directed
diffusion consists of several elements; first of all, naming;
where task descriptors, sent out by the sink, are named by
assigning attribute-value pairs.
5) PEGASIS
PEGASIS (Power-Efficient Gathering in Sensor Information
Systems) is a greedy chain-based power efficient algorithm
[29]. Also, PEGASIS is based on LEACH (the scenario and
the radio model in PEGASIS are the same as in LEACH).
6) GEAR
GEAR (Geographical and Energy Aware Routing) [30] is a
recursive data dissemination protocol WSNs. It uses energy
aware and geographically informed neighbor selection
heuristics to rout a packet to the targeted region. Within that
region, it uses a recursive a geographic informed mechanism
to disseminate the packet.
7) LEACH PROTOCOL
LEACH (Low Energy Adaptive Clustering Hierarchy) [27]
is a self-organizing, adaptive clustering-based protocol that
uses randomized rotation of cluster-heads to evenly
distribute the energy load among the sensor nodes in the
network.
LEACH based on two basic assumptions:
(a) base station is fixed and located far away from the
sensors, and
(b) all nodes in the network are homogeneous and energy
constrained.
The idea behind LEACH is to form clusters of the sensor
nodes depending on the received signal strength and use
local cluster heads as routers to route data to the base
station.
The key features of LEACH are:
1) Localized coordination and control for cluster set-
up and operation.
2) Randomized rotation of the cluster ”base stations”
or ”cluster-heads” and the corresponding clusters.
3) Local compression to reduce global
communication.
In LEACH, the operation is separated into fixed-length
rounds, where each round starts with a setup phase followed
by a steady-state phase. The duration of a round is
determined priori. LEACH algorithm works as follows:
1) Advertisement phase:
In this phase, nodes elect themselves to be a cluster-
heads for the current round (r) through a cluster-head
advertisement message. For this cluster-head
advertisement, the cluster heads use CSMA MAC
protocol. After the completion of this phase, and
depending on the received advertisement signal
strength; the non cluster-head nodes (their receivers
must be kept on during this phase to hear the
advertisements of all cluster-heads) determine the
cluster to which they will belong to for this current
round (r). At each round, a node n selects a random
number k that is between 0 and 1. If k is less than a
threshold T(n), then the node becomes a cluster-head
for the current round (r).
Where P is the desired percentage of cluster-heads, r
is the current round, and G is the set of nodes that
have not been cluster heads in the last 1/P rounds.
Since k is randomly selected, then the number of
cluster heads may not be fixed.
2) Cluster set-up phase: After each non-cluster-head
node will has decided to which cluster it belongs, it
informs the cluster-head node that it will be a
member of the cluster. So, each node transmits this
information back to the cluster head using CSMA
MAC protocol.
3) Schedule Creation phase: The cluster-head node
receives all the messages for nodes that would like to
be included in the cluster. Based on the number of
nodes in the cluster, the cluster-head node creates a
TDMA schedule telling each node when it can
transmit. This schedule is broadcast back to the nodes
in the cluster.
4) Data Transmission phase: After the creation of both
the clusters and the TDMA schedule

(TDMA is fixed), nodes in the cluster start
transmitting the data they already have during their
allocated transmission time to the cluster-head
(cluster-head node keeps its receiver on all the time to
receive the sent data). Once all the data (sent by
nodes in the cluster) have been received by the
cluster-head node, it will perform signal processing
function to compress the data into a single signal (the
steady-state operation of LEACH networks).
E. Heterogeneous Model for WSN
Nowadays, WSNs attracted lots of researchers because of its
potential wide applications and special challenges. For past
few years, wireless sensor networks mainly focused on
technologies based on the homogeneous wireless sensor
network in which all nodes have same system resource but
recently heterogeneous wireless sensor network is becoming
more and more popular and the results of researches show
that heterogeneous nodes can prolong network lifetime and
improve network reliability without significantly increasing
the cost.
1) Types of Heterogeneous Resources
The heterogeneous resources are basically divided into three
categories: computational heterogeneity, link heterogeneity,
and energy heterogeneity . Computational heterogeneity
means that the heterogeneous node has a more powerful
microprocessor or microcontroller and more storage
memory than the normal node. The sensor nodes with more
powerful computational resources can provide complex data
processing and long-term storage. Link heterogeneity means
that the heterogeneous node has high-bandwidth and long-
haul network transceiver (Ethernet or 802.11 networks) than
the normal node. Link heterogeneity can provide more
reliable data transmission. Therefore, the reliability of the
data transmission will increase by link heterogeneity.
Energy heterogeneity means that the heterogeneous node is
line powered, or its battery is replaceable.
Among above three categories of resource heterogeneity, the
energy heterogeneity is most important because both
computational heterogeneity and link heterogeneity will
consume more battery energy resource. If there is no energy
heterogeneity, computational heterogeneity and link
heterogeneity will bring negative impact to the sensor
network.
2) Impact of Heterogeneous Resources on WSNs
i) Response time: Computational heterogeneity can
decrease the processing latency and link
heterogeneity can decrease the waiting time, hence
response time is decreased.
ii) Lifetime: The average energy consumption will be
less in heterogeneous sensor networks for forwarding
a packet from the normal nodes to the sink, hence life
time is increased. Further, it is also known that if in a
network, heterogeneity is used properly then the
response of the network is tripled and the network’s
lifetime can be increased by 5fold (Yarvis 2005).
II. PROBLEM FORMULATION
A. PROBLEM WITH EXISTING PROTCOLS
There are so many existing protocols but LEACH (Low-
energy Adaptive Cluster Hierarchy) is considered in this
dissertation. The limitations of existing LEACH are found
and formulated in section 6.1.1.
B. PROBLEMS WITH LEACH
1) The time duration of the setup phase is non-
deterministic [3] and the collisions will cause the
time duration too long and hence the sensing
services are interrupted. Due to that Leach may be
unstable during the setup phase that depends on the
density of sensors.
2) Leach [4] is not applicable to networks that are
deployed in large region as it uses single hop
routing where each node can transmit directly to
the cluster head and the sink
3) The cluster heads [3] – [4] used in the LEACH will
consume a large amount of energy if they are
located farther away from the sink.
4) Leach [3] – [4] does not guarantee good cluster
head distribution and it involves the assumption of
uniform energy consumption for the cluster heads.
5) Leach uses dynamic clustering [5] which results in
extra overhead such as the head changes
,advertisement that reduces the energy
consumption gain
C. PROBLEMS WITH ENERGY LEACH
1) Energy leach is not applicable to the network
deployed in large region. Which clearly shows that
Energy leach is not scalable in nature and puts too
many area limitations on the sensor nodes?
2) Energy leach is based on dynamic clustering which
comes up with too many overheads.
3) The protocol assumes [7] that all the nodes brings
same amount of energy capacity in each election
round assuming that being a CH consumes
approximately the same amount of energy for each
node.
III. 7. PROBLEM STATEMENT
In heterogeneous sensor networks, certain nodes become
cluster heads which aggregate the data of their cluster nodes
and transfer it to the sink. An Improved Energy leach
protocol for cluster head selection in a hierarchically
clustered heterogeneous network to reorganize the network
topology efficiently is proposed in this research work. The
proposed algorithm will use thresholding to improve the
cluster head selection. The presented algorithm considers
the sensor nodes in wireless network and randomly
distributed in the heterogeneous network. The coordinates of
the sink and the dimensions of the sensor field are known in
prior.
This research work has proposed a new improved
Energy leach clustering heterogeneous network where the
advanced nodes elect themselves as cluster heads for the
increasing number of rounds based on their higher initial
energy relative to other nodes. However in existing work we
have found that the advance nodes has almost same
probability so which make the selection non-deterministic,
so to remove this problem we have modified the probability
function of the existing Energy leach to give more priorities

to advance nodes than normal nodes. The overall objective
is to extend the time when all the nodes are available (i.e.
Stability time of sensor networks).
Comparison of proposed algorithm is also drawn with
existing methods to evaluate the performance of proposed
algorithm.
IV. PERFORMANCE PARAMETERS
1) Stability Period: is the time interval from the start of
network operation until the death of the first sensor
node. We also refer to this period as “stable region.”
2) Instability Period: is the time interval from the death of
the first node until the death of the last sensor node.
We also refer to this period as “unstable region.”
3) Network lifetime: is the time interval from the start of
operation (of the sensor network) until the death of the
last alive node.
4) Number of alive (total, advanced and normal) nodes
per round: This instantaneous measure reflects the total
number of nodes and that of each type that has not yet
expended all of their energy.
5) Throughput: We measure the total rate of data sent
over the network, the rate of data sent from cluster
heads to the sink as well as the rate of data sent from
the nodes to their cluster heads.
V. RESEARCH METHODOLOGY
To attain the objective, step-by-step methodology is used in
this dissertation. To attain the objectives of this dissertation
a suitable algorithm will be proposed and design in
MATLAB. Different type of tests will be implemented using
improved algorithm to test various aspects of the Wireless
Sensor Network. Visualization of the experimental results
will be done and based upon the simulated results suitable
performance results will be drawn.
VI. RESULTS
The simulation has been done in MATLAB. Let us assume a
heterogeneous sensor network with 100 number of sensor
nodes are distributed randomly in the 100 x100 m2
area, as
shown in Figure 3, we denote a normal node with ‘o’, an
advanced node with ‘+’. The base station with ‘x’ is located
at point (50, 50). The values used in the first order radio
model are described in Table 1.
Parameters Values
Sensor Deployment area 100m*100m
Base Station Location 50m*50m
Number of nodes 100
Data packet size 4000 bits
Control packet size 100 bits
Initial energy of sensor 0.5 J
Electronics energy 50 nJ/bit
Free space factor ϵfs 10 pJ/bit/m2
Multipath factor ϵmp 0.0013 pJ/bit/m4
Table 1: Parameter values
The horizontal and vertical coordinates of each sensor are
randomly selected between 0 and maximum value of the
dimension. The size of the message that nodes send to their
cluster heads as well as the size of the (aggregate) message
that a cluster head sends to the base station is set to 50 bytes.
Fig. 4: Node Distribution in EX-LEACH
Lifetime is the criterion for evaluating the performance of
routing protocols in sensor networks. In this work, we
measure the lifetime in terms of the round when the first
node last node dies. We have simulated LEACH in the
presence of homogeneous parameters. Our proposed
protocol is simulated in the presence of different
heterogeneity parameters in the network. The results of
proposed and LEACH simulations are shown in Figure.5
Fig. 5: Dead nodes in LEACH AND EXLEACH
and normal nodes in LEACH and EX-LEACH
A detailed view of the behavior of LEACH and proposed
protocol is illustrated in fig. 5 for different distributions of
heterogeneity. Figure 5 shows that the first node die earlier
in case of LEACH after 803 rounds whereas last node
remain alive for 1388 rounds in LEACH to base station, and
in Proposed scheme the first node died after 867 rounds and
last nodes remain alive for 4106 rounds, which is more than
LEACH . This extended the lifetime and stability of network
system. On the other hand, Figure 6 shows the number of
messages received by the BS. Since in the proposed protocol
more number of alive nodes exists, therefore the packets
received by the proposed protocol is more over more
number of rounds.
Fig. 6: messages received by BS in case of LEACH and
messages received by BS case of EX-LEACH
Figure 6 shows the number of messages received by the BS.
Since in the Proposed protocol more number of alive nodes
exists, therefore the packets received by the Proposed

protocol is more over more number of rounds but in case of
existing LEACH protocol the lifetime of the network is less
than the proposed protocol and number of nodes alive is less
than the proposed protocol so the packets sent to BS is less.
VII. CONCLUSION
Wireless sensor network is a combination of wireless
communication and sensor nodes. The network should be
energy efficient with stability and longer lifetime. In this
paper, we have presented clustered heterogeneous wireless
sensor networks where more powerful sensor nodes act as
cluster heads for more number of rounds. The energy drain
rate of battery source is less in advance and super nodes as
compared to normal nodes in the system. Based upon the
simulation results, the proposed protocol has confirmed that
it provides a longer network lifetime as existing LEACH
protocol.
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