Optimized sensor nodes by fault node recovery algorithm

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 250
OPTIMIZED SENSOR NODES BY FAULT NODE RECOVERY
ALGORITHM
S. Sofia1
, M.Varghese2
1
Student, Department of CSE, IJCET
2
Professor, Department of CSE, IJCET
Abstract
This paper proposes fault node recovery algorithm to enhance the lifetime of wireless sensor networks when some of the sensor nodes
shut down due to absence of battery power. The proposed algorithm combined Grade diffusion algorithm with genetic algorithm. The
algorithm can result replacement of fewer sensor nodes and more reused routing paths. The proposed algorithm increases the number
of active nodes, reduces dataloss during transmission, and reduces energy consumption.
Keywords: Wireless Sensor Networks, Energy Consumption, Fault Node Recovery.
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1. INTRODUCTION
The Sensor networks are mainly used in fields such as
Scientific, Military, Healthcare applications. However Sensor
size represents a significant limitation mainly in terms of
energy and their life period, for the batteries have to be too
tiny. This is the reason why intensive research is being
conducted nowadays on how to control sensor energy
consumption within network. However power sources in
sensor node limit life time of whole system. A sensor Network
with Distributed system made up with large number of small
sensors, equipments, transmitters and receivers. One of the
major problems in these networks is to reduce energy and
enhance lifetime of networks. Energy is an important factor
important in Wireless Sensor Networks.
This paper proposed Fault Node Recovery algorithm to
enhance the lifetime of a wireless sensor network
(WSN).when some of the sensor nodes shut down, either
because they no longer have battery energy or they have
reached their operational threshold. Using the FNR algorithm
can result in fewer replacements of sensor nodes and more
reused routing paths. Thus, the algorithm not only enhances
the WSN lifetime but also reduces the cost of replacing the
sensor nodes.
2. RELATED WORKS
The traditional approaches to sensor network routing include
the Fault Node Recovery (FNR) algorithm. FNR combines
both Grade Diffusion (GD) with Genetic Algorithm (GA) The
algorithm proposed in this paper is based on Fault Node
Recovery algorithm (FNR). These optimizations will enhance
WSN lifetime and reduce replacement cost.
2.1 Directed Diffusion Algorithm
The goal of DD algorithm is to reduce the data relay
transmission for power management. The DD algorithm is an
query driven transmission protocol. The collected data is
transmitted only if it matches the query from the sink node. In
the DD algorithm, the sink node provides the queries in the
form of attribute-value pairs to the other sensor nodes by
broadcasting the query packets to the whole network.
Subsequently, the sensor nodes send the data back to the sink
node only when it ﬁts the queries.
2.2 Grade Diffusion Algorithm
The Grade Diffusion (GD) algorithm not only creates the
routing for each sensor node but also identifies a set of
neighbour nodes to reduce the transmission loading. Each
sensor node can select a sensor node from the set of neighbour
node by using Grade tables. The GD algorithm can select a
node with more available energy than the other nodes to
perform relay operation. A grade diffusion algorithm is
proposed to solve the sensor node’s transmission problem and
the sensor node’s loading problem. In addition to them, the
sensor node also can save some backup nodes to reduce
energy consumption for re-looking routing. By using this
method more than one sensor node’s detect the same event is
also possible.
2.3 Genetic Algorithm
Genetic Algorithm based on 5 steps namely Initalization,
Evaluation, Selection, CrossOver, Mutation. The initial
population of candidate solution is generated randomly in this
step. In evaluation the fitness value of the candidate solutions
are evaluated. In selection method select higher fitness value.
In cross over step combines two or more parental solutions to

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create new offsprings. Mutation randomly modifies a
solution.The sensor routing paths are shown in Fig. 1. In Fig.
2, the situation in which the outside nodes transfer event data
to the sink node via the inside nodes (the sensor nodes near the
sink node) in a WSN illustrate the accommodation measures
for
Fig. 1.Wireless sensor node routing
Fig. 2.Wireless sensor node routing path when some nodes are
not functioning.
In Fig. 2, the situation in whichthe outside nodes transfer
event data to the sink node via the inside nodes (the sensor
nodes near the sink node) in a WSN illustrate the
accommodation measures for non-working nodes. The inside
nodes thus have the largest data transmission loading,
consuming energy at a faster rate. If all the inside nodes
deplete their energy or otherwise cease to function, the event
data can no longer be sent to the sink node, and theWSN will
no longer function.
2.4. Fault Node Recovery Algorithm:
This paper proposes a Fault Node Recovery(FNR) algorithm
for WSNs based on the Grade Diffusion algorithm combined
with Genetic algorithm. The FNR algorithm creates the grade
value, routing table, neighbour nodes, and payload value for
each sensor node using Grade Diffusion algorithm. The sensor
nodes transfer the event data to the sink node according to the
GD algorithm when events appear. Then is calculated
according to in the FNR algorithm The algorithm will be
invoked and replace non functioning sensor nodes by
functioning nodes selected by the Genetic algorithm.
The power consumption of the sensor nodes in WSN’s are
unavoidable. However this paper proposes an algorithm to
search for and replace fewer sensor nodes and to reuse the
most routing paths. The current paper proposes a Partial
Scram Optimization algorithm based on.
3. PROPOSED SYSTEM
This paper proposes a fault node recovery (FNR) algorithm for
WSNs based on the grade diffusion algorithm combined with
the genetic algorithm. The ﬂow chart is shown in Fig. 3. The
FNR algorithm creates the grade value, routing table, neighbor
nodes, and payload value for each sensor node using the grade
diffusion algorithm. In the FNR algorithm, the number of
nonfunctioning sensor nodes is calculated during the wireless
sensor network operation, and the parameter B is calculated
.In Fig. 3, the FNR algorithm creates the grade value, routing
table, a set of neighbor nodes, and payload value for each
sensor node, using the grade diffusion algorithm.
In Fig. 3, the FNR algorithm creates the grade value, routing
table, a set of neighbor nodes, and payload value for each
sensor node, using the grade diffusion algorithm. In Fig. 3, the
FNR algorithm creates the grade value, routing table, a set of
neighbor nodes, and payload value for each sensor node, using
the grade diffusion algorithm. The sensor nodes transfer the
event data to the sink node according to the GD algorithm
when events appear. If Bth is larger than zero, the algorithm
will be invoked and replace nonfunctioning sensor nodes by
functional nodes selected by the genetic algorithm. Then the
wireless sensor network can continue to work as long as the
operators are willing to replace sensors. There are 5 steps in
the genetic algorithm: Initialization, Evaluation, Selection,
Crossover, and Mutation. Descriptions of the steps follow.

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Fig. 3.System Architecture
3.1 Initialization
In the initialization step, the genetic algorithm generates
chromosomes, and each chromosome is an expected solution.
The number of chromosomes is determined according to the
population size, which is defined by the user. Each
chromosome is a combination solution, and the chromosome
length is the number of sensor nodes that are depleted or
nonfunctioning. The elements in the genes are either 0 or 1. A
1 means the node should be replaced, and a 0 means that the
node will not be replaced.Fig. 4 represents a chromosome. The
chromosome length is 10 and the gene is 0 or 1, chosen
randomly in the initialization step. In this case, there are 10
sensor nodes not functioning, and their node numbers are 9, 7,
10, 81, 23, 57, 34, 46, 66, and 70.
Fig. 4.Chromosome and its gene
3.2 Evaluation
In general, the fitness value is calculated according to a fitness
function, and the parameters of the fitness function are the
chromosome’s genes. However, we cannot put genes directly
into the fitness function in the FNR algorithm, because the
genes of the chromosome are simply whether the node should
be replaced or not. In the FNR algorithm, the goal is also to
reuse the most routing paths and to replace the fewest sensor
nodes. Hence, the number of routing paths available if some
nonfunctioning sensor nodes are replaced is calculated.
3.3 Selection
The selection step will eliminate the chromosomes with the
lowest fitness values and retain the rest. We use the elitism
strategy and keep the half of the chromosomes with better
fitness values and put them in the mating pool. The worse
chromosomes will be deleted, and new chromosomes will be
made to replace them after the crossover step. The process is
showninFig.5.
Fig. 5 Selection step.
3.4 Cross Over
The crossover step is used in the genetic algorithm to
changethe individual chromosome. In this algorithm, we use
the one-point crossover strategy to create new
chromosomes.Two individual chromosomes are chosen from
the mating pool to produce two new offspring. A crossover
point is selected between the first and last genes of the parent
individuals. Then, the fraction of each individual on either side
of the crossover point is exchanged and concatenated. The rate
of choice is made according to roulette-wheel selection and
the fitness values.

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3.5 Mutation
The mutation step can introduce traits not found in the original
individuals and prevents the GA from converging too fast. In
this algorithm, we simply ﬂip a gene randomly in the
chromosome, as shown in Fig.6. The chromosome with the
best ﬁtness value is the solution after the iteration. The FNR
algorithm will replace the sensor nodes in the chromosome
with genes of 1 to extend the WSN lifetime.
Fig. 6 Mutation step.
4. 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
eachdimension was set at 0 to 100. The radio ranges
(transmissionrange) of the nodes were set to 15 units. In each
of thesesimulations, the sensor nodes were distributed
uniformly overthe space. There are three sensor nodes
randomly distributedin 10 × 10 × 10 space, and the Euclidean
distance is atleast 2 units between any two sensor nodes.
Therefore, thereare 3000 sensor nodes in the 3-D wireless
sensor networksimulator, and the center node is the sink node.
The FNR, DD, and GD algorithms were implemented. The
active sensor nodes and total data loss after 90000 events are
shown in Figs.7 and 8. The active nodes mean that the sensor
node has enough energy to transfer data to other nodes. The
FNR, DD, and GD algorithms were implemented. The active
sensor nodes and total data loss after 90 000 events are shown
in Figs.7 and 8. 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, algorithms because the algorithm can replace the
sensor nodes after the number of nonfunctioning nodes
exceeds the threshold,by using the GA algorithm. In Fig. 8,
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 913 449
data losses. This new algorithm can reduce data loss by 98.8%
compared to the traditional algorithms.
Fig. 7.Number of active nodes
Fig. 8 Total data loss
Fig. 9 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 after
8 000 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 and
3298.29 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 the replaced sensor nodes are usually used the
most. After 90 000 events, 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 of 31.1% and 27%, respectively.

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Fig. 9.Average energy consumption
Fig. 10.Average number of messages reaching the sink node
The average number of messages that reach the sink node
when each algorithm manages the network is compared inFig.
10. 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 this algorithm is
higher than when using the other algorithms.By using this
algorithm, the sensor nodes are not onlyreplaced, but the
replacement cost is reduced, and more routing paths are
reused.
Fig. 11.Average residual energy after 8000 events
Fig.12 Average residual energy after 90000 events
In Fig. 11, using the GD algorithm, after 8000 events the
grade 1 sensor nodes only have 145.57 Ws energy remaining,
and the other grade sensor nodes still have enough energy to
function. Using the FNR algorithm, the grade 1 sensor nodes
still have 1568.34 Ws. The grade 1 sensor nodes are near the
sink node, and they are relay nodes for the other grade sensor
nodes, so they consume their energy rapidly. The FNR
algorithm can replace some of the energy-depleted sensor
nodes. Hence, the available sensor nodes are more numerous
than when using the traditional algorithms. In Fig. 12, the
average energy consumption for each grade is calculated after
90 000 events. Using the GD algorithm, the 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

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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.
Fig. 13.Rate of total messages to recovery nodes
The FNR algorithm tends to replaces Grade 1 sensor nodes in
the ﬁrst place, since the loading of the Grade 1 sensor nodes is
larger than the loading of others.
5. CONCLUSIONS AND FUTURE WORK
In real wireless sensor networks, the sensor nodes use battery
power supplies and thus have limited energy resources. In
addition to the routing, it is important to research the
optimization of sensor node replacement, reducing the
replacement cost, and reusing the most routing paths when
some sensor nodes are nonfunctional. This paper proposes 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, increasingthe WSN lifetime and
reducing the replacement cost. In the simulation, the proposed
algorithm increases the number of active nodes up to 8.7
times.
The number of active nodes is enhanced 3.16 times on average
after replacing an average of 32 sensor nodes for each
calculation. 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. It is of great interest to extend our approach
to handle Energy Efficiency in WSN. As with most existing
work on Fault Node Recovery Algorithm. Our work is limited
in the sense that its consider only. More powerful adversaries
may apply Partial Scram Optimization algorithm. Studying the
Partial Scram Optimization under this adversarial model is an
interesting future direction.
REFERENCES
[1] T. H. Liu, S. C. Yi, and X. W. Wang, “A fault
management protocol for low-energy and efﬁcient
wireless sensor networks,” J. Inf. Hiding Multimedia
Signal Process., vol. 4, no. 1, pp. 34–45, 2013.
[2] C. Intanagonwiwat, R. Govindan, D. Estrin, J.
Heidemann, and F. Silva,“Directed diffusion for
wireless sensor networking,” IEEE/ACM
Trans.Netw., vol. 11, no. 1, pp. 2–16, Feb. 2003.
[3] E. M. Royer and C. K. Toh, “A review of current
routing protocols for ad-hoc mobile networks,” IEEE
Personal Commun., vol. 6, no. 2,pp. 46–55, Apr.
1999.
[4] H. C. Shih, S. C. Chu, J. Roddick, J. H. Ho, B. Y.
Liao, and J. S. Pan,“A reduce identical event
transmission algorithm for wireless sensor
networks,” in Proc. 3rd Int. Conf. Intell. Human
Comput.Interact.,2011, pp. 147–154.
[5] Z. He, B. S. Lee, and X. S. Wang, “Aggregation in
sensor networks with a user-provided quality of
service goal,” Inf. Sci., vol. 178, no. 9, pp. 2128–
2149, 2008.
[6] S. Corson and J. Macker, Mobile Ad Hoc
Networking (MANET): Routing Protocol
Performance Issues and Evaluation
Considerations.NewYork,NY, USA: ACM, 1999.
[7] J. H. Ho, H. C. Shih, B. Y. Liao, and S. C. Chu, “A
ladder diffusion algorithm using ant colony
optimization for wireless sensor networks,” Inf. Sci.,
vol. 192, pp. 204–212, Jun. 2012.
[8] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C.Hu, and J.
Jetcheva A Performance Comparison of Multi-Hop
Wireless Ad-Hoc Network Routing Protocols.In
Proceedings of the Fourth Annual ACM/IEEE
International Conference on Mobile Computing and
Networking (Mobicom’98), Dallas, TX, 1998.
[9] M. Perillo, and W. B. Heinzelman, Providing
application QoS throughintelligent sensor
management, Proc. of the 1st IEEE International
Workshop on Sensor Network Protocols and
Applications, pp. 93-101, 2003.

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