A Fault Tolerant Approach to Enhances Wsn Lifetime in Star Topology

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 958
A FAULT TOLERANT APPROACH TO ENHANCES WSN LIFETIME IN STAR
TOPOLOGY
M.Anitha Jayaseeli Dept of Informatio Technology,Sathyabama University Chennai, India.
Dr. S. Murugan, Department HOD Dept of Information Technology , Sathyabama University
Chennai, India.
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Abstract - In a Wireless Network, there may be a problem of
broadcast delay while transmitting the data from one node to
another node. Therefore there are chances that there may be a
loss of packets. The proposed fault-tolerant methodology
enables a system to continue the broadcasting without caring
about packet loss. By using minimized delay in broadcast for
transmission, data loss can be minimized. If the delay occurs in
sink node there must be a possible to loss of both node and
packets. To avoid this, Gradient Diffusion algorithm (GD) is
applied to increase the lifetime of the node. One more issue
that arises while reducing the delay during the transmission is
high interference and more concurrent transmissions at
expense of data rate. To address above problem, Standard
Deviation of Average Remaining Broadcast Time is computed
for determining the priority of these parameters. The result of
proposed algorithm is to increase the lifetime of the node by
reusing it and replace it. From this method the active node
increased up to 8 to 9 times, the data loss reduced up to 98 %
and the energy consumption is 32 % approximately
Key Words: Minimum broadcast delay, Multi-hop
Routing, FNR algorithm, wireless STAR networks.
I. INTRODUCTION
Usually, in Wireless Sensor Network every node has a
limitation in computational power which is used to process
and transfer the active data to the base station or data
collection center. Based on the sensor nodes only the WSN
increase the Sensor area and the Transmission area. but each
sensor nodes has a low energy that must be replenished. As
soon as it became exhausted. It will lead to the leaks and the
node became failed. Due to the failed nodes will not relay
data to the other nodes during transmission. Therefore the
problem of overload to other sensor nodes with increasing
transmission processing.
In proposes of this paper is, a Fault tolerant approach to
increase the lifetime of a wireless sensor network (WSN)
when some of the sensor nodes died, either because they no
longer have battery energy or they have reached their
operational threshold. A network of Wireless Sensor Node is
created with limited energy. Power and transmission
scheduling are assigned to reduce packet-based broadcast
from a root node to others. Although it minimizes the
broadcast delay the lifetime of a node is not determined
here. Since we include a gradient algorithm to increase the
node lifetime and the fault tolerant is maintained. Using the
approach can result in reusing and replacing of sensor nodes
Thus, the approach increases the WSN lifetime and also there
is no replacement cost for sensor nodes.
II. RELATED WORK
Broadcast scheduling is a fundamental problem in multi-
hop wireless networks. The objective of a broadcast schedule
is to deliver a message from a given source to all the other
nodes in the network in a minimum amount of time with the
constraint that parallel transmissions cannot interfere with
each other. [1]In this paper, we address the problem of joint
power control and scheduling for minimizing broadcast
delay in wireless mesh networks. Given a set of mesh routers
and a routing tree, we aim to assign power for relay nodes
and compute an optimal transmission schedule such that the
total delay for a packet broadcast from the root to all the
routers is minimized. We consider rate adaptation in our
scheme. This is a difficult issue. High power enables high
data rate but causes high interference, whereas low power
allows more concurrent transmissions at the expense of data
rate. With regard to multicast scheduling and power control,
several proposals have appeared in the literature.
The authors in [3], [4] addressed the problem of
cooperative multicast scheduling for power allocation in

multi-channel condition to enhance network performance.
Since some nodes can relay messages to the destinations, the
main idea of those works is to combine multiple replicas of
the same message from different transmitters at the
receivers to enhance the quality of signals. However, both of
the two works solve the problem from physical layer’s
perspective and they only consider the cooperation in two-
hop transmission. Topology control is defined as controlling
the neighbor set of nodes in a WSN by adjusting transmission
range and/or selecting specific nodes to forward the
messages [5].
Topology control approaches can be divided into two
main categories, namely, homogeneous and non
homogeneous [6]. In homogeneous approaches transmission
range of all sensors are the same whereas in non
homogeneous approaches nodes can have different
transmission ranges. There are many topology control
methods proposed in literature and they can be classified
according to the techniques they use. Many topology control
methods are built on the transmit power adjustment
technique which depends on the ability of sensors to control
their transmit power.
III.PROPOSED WORK
A. Diffusion Algorithm
A sequence of routing algorithm [10], [14] for wireless
sensor networks has been introduced in recent years. C.
Intanagonwiwat et al. proposed the Direct Diffusion (DD)
algorithm [9] in 2003. The aim of the DD algorithm is to
decrease the data relay transmission counts for power
management. The DD algorithm is a query-driven
transmission protocol. The arranged data is transmitted only
if it matches the query from the sink node. In the DD
algorithm, the sink specifies the queries in the form of
attribute –value pairs to the other sensor nodes by
forwarding the query packets to the entire network.
Simultaneously, the sensor nodes send the data back to the
sink node only when it fits the queries.
B. Grade Diffusion Algorithm
H. C. Shih et al. proposed the Grade Diffusion (GD)
algorithm [7] in 2012 to enhance the ladder diffusion
algorithm using ant colony optimization (LD-ACO) for
wireless sensor networks [6]. The GD algorithm not only
creates the routing for each sensor node but also notices a
pair of nearby nodes to decrease the transmission loading
.Each sensor nodes can select a sensor node from the pair of
nearby nodes when its grade table lacks a node able to
perform the relay. The GD algorithm can also store some
information based on the data relay. Then a sensor node can
select a node with a lighter loading or more available energy
than the other nodes to perform the additional relay
operation. That is, the GD algorithm updates routing path in
real time and the event data is thus send to sink node quickly
and correctly.
The WSN may fail due to different type of causes,
including the following: the routing path may experience a
break; the WSN sensing area may experience a leak; the
batteries of some sensor nodes may be reduced depending
on more relay nodes; or other nodes wear out after the WSN
has been in use a long interval of time.
The power conception of the sensor node in WSN
can never be avoided. This paper however illustrates an
algorithm to identify for and replace lesser sensor nodes and
to reuse the most routing paths. Conventional identification
techniques are always incapable of optimizing nonlinear
operation with several variables. One scheme,
C .Fault Node Recovery Algorithm
This paper illustrates a fault node recovery (FNR)
algorithm for WSNs depends on the grade diffusion
algorithm along with the genetic algorithm. The FNR
algorithm discovers the grade value, routing table, nearby
nodes, and payload value for each sensor node using the
grade diffusion algorithm. In the FNR algorithm, the
sequence of non operating sensor nodes is determined the
wireless sensor network operation, and the parameter Bth is
determined according to (1).The FNR algorithm identifies
the grade value, routing table, a pair of nearby nodes, and
payload value for each sensor node, using the grade diffusion
algorithm. The sensor node transfers the event data to the
sink node which depends on the GD algorithm when event
occurs. Then, Bth is greater than zero, the algorithm will be
called and released non operating sensor nodes by functional
nodes determining by the genetic algorithm. Then the
wireless sensor network can continue to function as long as
the operators are ready to replace senor.
The parameter is encoded in binary string and
serves as the, chromosomes of the GA. the elements (or bits),
the genes in the binary strings are adjusted to reduce or
increase the fitness value. The fitness operation generates its
fitness value, which is composed of many variables to be
optimized by the GA. At each iteration of the GA, a already
defined values along with the chromosomes. There are 5

steps in the genetic algorithm: Initialization, Evaluation,
Selection, Crossover and algorithm.
IV. SIMULATION
A simulation of the fault node recovery algorithm as
described in Section 3 was performed to verify the method.
The experiment was designed based on 3-D space, using
100×100 × 100 units, and the scale of the coordinate axis for
each dimension was set at 0 to 100. The radio ranges
(transmission range) of the nodes were set to 15 units. In
each of these simulations, the sensor nodes were distributed
uniformly over the space. There are three sensor nodes
randomly distributed in 10 × 10 × 10 space, and the
Euclidean distance is at least 2 units between any two sensor
nodes. Therefore, there are 3000 sensor nodes in the 3-D
wireless sensor network simulator, and the center node is
the sink node. The data packages were exchanged between
random source/destination pairs with 90 000 event data
packages. In our simulations,
Fig -1: Wireless sensor node routing.
Fig -2: Wireless sensor node routing path when some nodes
are not functioning.
Chart -1: Number of active nodes.
Chart -2: Total data loss.
the energy of each sensor node was set to 3600 Ws that is
the actual available energy. Each sensor consumed 1.6 Ws
when it conducts a completed data transformation (Rx +T
x).In the GA algorithm, the population size was 20; the
crossover rate was 50%; and the mutation rate was 2%.The
FNR, DD, and GD algorithms were implemented. The active
sensor nodes and total data loss after 90 000 events are
shown in Figs. 3 and 4.
The active nodes mean that the sensor node has enough
energy to transfer data to other nodes, but some sensor
nodes can be deleted from the active nodes list if their
routing tables do not have a sensor node that can be used as
a relay node, or if they are not in the routing table of any
other sensor nodes. This new algorithm enhances the
number of active nodes by8.7 and 10.8 times, respectively.
The FNR algorithm has the most active sensor nodes
compared with the DD and GD algorithms because the
algorithm can replace the sensor nodes after the

number of nonfunctioning nodes exceeds the threshold,
by using the GA algorithm. Fig. 4 compares the total data loss
using the FNR algorithm to the total data loss using the DD
and GD algorithms. In this simulation, event data was
destroyed and recorded into the loss count if the data had
already been relayed over 20 times.
Chart -3: Average energy consumption
and transfer them to the sink node if the WSN lifetime is
increased. In Fig. 4, the FNR algorithm exhibits smaller data
losses because the algorithm can replace fewer sensor nodes
and reuse more routing paths if the number of sensor nodes
that are nonfunctioning exceeds the threshold. After the
simulation, the FNR algorithm had only suffered 11 025 data
losses, but the DD and GD algorithm had suffered 912 462
and 913 449data losses.
This new algorithm can reduce data loss by
98.8%compared to the traditional algorithms. Fig. 5
compares the average energy consumption of a WSN
managed using the FNR algorithm to the average energy
consumption using the DD and GD algorithms. The DD and
GD algorithms allow the WSN to consume more energy
after8 000 events because the inside nodes are energy-
depleted, but the outside nodes continue to attempt to
transfer event data to the sink node through the inside nodes
until they are also energy-depleted. After 90 000 events, the
DD and GD algorithm-managed WSNs had consumed
3495.17 Ws and3298.29 Ws, respectively.
The proposed algorithm increases the WSN lifetime
by replacing some of the sensor nodes that are not
functioning. In addition to enhancing the active nodes and
reducing the data losses, the FNR algorithm reduces the
relayed energy consumption by reducing the number of data
relayed, as there placed sensor nodes are usually used the
most. After 90 000events, using the proposed algorithm, the
WSN had consumed only 2407.68 Ws, and, compared to
using the DD and GD algorithms, exhibited a reduction in
energy consumption of31.1% and 27%, respectively. After
that, we experiment different node densities in our
simulation environment to compare the average energy
consumption. The simulate result is shown in Table I. We
can find that the FNR algorithm has the least average energy
consumption in all case, and it can save 31.73% energy at
most in Table I.
Hence, the FNR algorithm has the best energy saving
performance no matter under any node densities. The
average number of messages that reach the sink node when
each algorithm manages the network is compared in Fig. 6.
Using the traditional DD and GD algorithms, the sink node
can receive no messages after 8000 events because all of the
inside nodes are energy-depleted, and the WSN lifetime is
ended. This proposed algorithm replaces energy depleted
sensor nodes to increase the WSN lifetime. Therefore, the
average number of messages received using
Chart -4: Average number of messages reaching the sink
node.
Chart -5: Total number of sensor nodes recovered.

Using this algorithm is higher than when using the other
algorithms. By using this algorithm, the sensor nodes are not
only replaced, but the replacement cost is reduced, and more
routing paths are reused. The total number of sensor nodes
recovered is shown in Fig. 7.From Fig. 7, 1085 sensor nodes
were recovered, and the FNR algorithm continues to run for
34 iterations after 90 000events. In the simulation, the
algorithm replaced, on average, approximately 32 sensor
nodes for each calculation, extending the lifetime of the WSN.
Using the FNR algorithm, if the grade 1 sensor nodes
consume their energy rapidly because they try to transfer
event data to the sink node using neighbor nodes if the grade
1 sensor nodes are energy-depleted or their routing table is
empty. The FNR algorithm has ample energy for each grade
sensor node because the algorithm can replace the sensor
nodes, but it reuses more routing paths compared to using
the traditional algorithm. The number of replaced sensor
nodes and the total number of messages.
Chart -6: Total messages reaching the sink node for each
replaced node.
Chart -7: Rate of total messages to recovery nodes.
V. CONCLUSION
In this paper, we have studied the issue of scheduling for
minimizing broadcast delay from the gateway node to all the
star routers in WSNs and Fault Node recovery when some of
the nodes shut down. We introduce standard deviation of
average remaining broadcast time to determine the priority
of data-rate-first approach and concurrency-first approach.
Based on this concept, we propose a balanced method to
solve the problem. A fault node recovery algorithm for WSN
based on the grade diffusion algorithm combined with a
genetic algorithm. The FNR algorithm requires replacing
fewer sensor nodes and reuses the most routing paths,
increasing the WSN lifetime and reducing the replacement
cost.
Extensive simulations have demonstrated that our
proposed method can reduce the broadcast delay
significantly compared with the methods using fixed
transmission power. In addition, increases the number of
active nodes up to 8.7 times. The algorithm reduces the rate
of data loss by approximately 98.8% and reduces the rate of
energy consumption by approximately 31.1% Therefore, the
FNR algorithm not only replaces sensor nodes, but also
reduces the replacement cost and reuses the most routing
paths to increase the WSN lifetime.
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