International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
Enhance the Life Time of Wireless Sensor Network
by Blemish Node Recuperation
S.Saranya1, M.V.B.Chandra Sekhar2
Final MTech student 1, associate professor2
Dept of CSE, Aditya Institute of Technology And Management , Tekkali , Srikakulam
Abstract: Efficient communication between wireless
sensor networks is always an interesting research
issue in the field of wireless sensor networks. Batter
levels (power consumption) and route reuse are
important factors while transmission of data between
sensor nodes or sink node to sensor node. In this
paper we are proposing an efficient approach for fault
node recovery, it identifies the fault nodes and
minimizes the battery consumption and reuses the
path which is previously used for transmission of data
packets.
I. INTRODUCTION
Directed Diffusion Algorithm is one of the
basic and efficient routing algorithm [1][2] in wireless
sensor networks, the main objective of algorithm is to
minimize the consumption of power, reduction of data
delay and reusability of path. It is a query driven
approach, transmits the data packets which meets the
query from sink node. Here sink node maintains the
attribute or key value pairs to other sensor nodes by
broadcasting the query packets to entire network.
Vice versa sensor nodes send data packets when
query meets.
Grade Diffusion Algorithm H. C. Shih et al.
presented the Grade Diffusion (GD) algorithm [3] in
2012 to improve the ladder diffusion algorithm using
ant colony optimization (LD-ACO) for wireless
sensor networks .The GD algorithm not only creates
the routing for each sensor node but also identifies a
set of neighbor nodes to reduce the transmission
loading. Each sensor node can select a sensor node
from the set of neighbor nodes when its grade table
lacks a node able to perform the relay. The GD
algorithm can also record some information regarding
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 extra relay operation.
That is, the GD algorithm updates the routing path in
real time, and the event data is thus sent to the sink
node quickly and correctly. We can improve the
efficiency or performation of our proposed work with
ISSN: 2231-5381
evolutionary algorithms like GELS for more optimal
solution, but time complexity is the major factor in
this scenario .It is gives best results if we can
minimize the complexity in GELS evolutionary
approach for generation of chromosomes[4][5].
II. RELATED WORK
To resolve the issue of failures, here we are
replacing the nodes with active nodes which are
suitable to the threshold value by using genetic
algorithm. Genetic algorithm is one of the efficient
evolutionary algorithms for generation of optimal
route by generating the chromosome and performs the
operations like cross over and mutation over the
chromosomes. In the genetic approach it increases the
chances of generating the optimal path[6].
In the initialization process, chromosome can be
generated with collection of failure and non-failure
nodes, number of chromosomes can be finalized
based on the population and each individual element
in the chromosome is a gene, it can be either 0 or 1,1
indicates node id failure and it needs to be replaced
and o indicates node that need not to be replaced.
The crossover step is used in the genetic algorithm to
change the 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[7][8]. 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.
The mutation step can introduce traits not
found in the original individuals and prevents the GA
from converging too fast. In this algorithm[9][10], we
simply flip a gene randomly in the chromosome, The
chromosome with the best fitness value is the solution
after the iteration. The FNR algorithm will replace the
http://www.ijettjournal.org
Page 13
International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
sensor nodes in the chromosome with genes of 1 to
extend the WSN lifetime[11][12].
II. PROPOSED WORK
In this paper we are proposing an efficient
fault node recovery approach with grade diffusion
algorithm followed by pso algorithm. This approach
creates route for every sensor node and also identifies
the set of neighbor nodes to reduce the transmission
loading and every node can communicate with other
sensor node and relay on it and can select lighter
loading and more power and the algorithm updates
the path dynamically whenever node status updated
and it is followed by the genetic approach.
Construct Network
Main objective of this approach is to eliminate the
failure nodes(nodes can be failed by any cause, it can
be battery power depletion or requirement of more
relay nodes) from the list and update with optimal
node. network can be established between sensor
nodes and sink nodes, in between there are various
nodes are failure, so these paths or routes can be
modified based on the basic threshold values of the
sensor nodes in the network .
Detect
GD and
Fault Nodes
PSO algorithm
Result
Failure Node
Network
Recovery
Fig1: Architecture
Particle swarm optimization (PSO) is a computational
method that optimizes a problem by iteratively trying
to improve a candidate solution with regard to a given
measure of quality. PSO optimizes a problem by
having a population of candidate solutions, here
dubbed particles, and moving these particles around
in the search-space according to simple mathematical
formulae over the particle's position and velocity.
Each particle's movement is influenced by its local
best known position but, is also guided toward the
best known positions in the search-space, which are
updated as better positions are found by other
particles. This is expected to move the swarm toward
the best solutions.
behavior, as a stylized representation of the
movement of organisms in a bird flock or fish school.
The algorithm was simplified and it was observed to
be performing optimization. The book by Kennedy
and Eberhart describes many philosophical aspects of
PSO and swarm intelligence. An extensive survey of
PSO applications is made by Poli.
Let S be the number of particles in the swarm, each
having a position xi ∈ℝn in the search-space and a
velocity vi ∈ℝn. Let pi be the best known position of
particle i and let g be the best known position of the
entire swarm. A basic PSO algorithm is then:
Algorithm :
PSO is originally attributed to Kennedy, Eberhart and
Shi and was first intended for simulating social
For each particle i = 1, ..., S do:
o
o
Initialize the particle's position with a uniformly distributed random vector: xi ~ U(blo, bup), where
blo and bup are the lower and upper boundaries of the search-space.
Initialize the particle's best known position to its initial position: pi ← xi
ISSN: 2231-5381
http://www.ijettjournal.org
Page 14
International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
If (f(pi) <f(g)) update the swarm's best known position: g ← pi
Initialize the particle's velocity: vi ~ U(-|bup-blo|, |bup-blo|)
Until a termination criterion is met (e.g. number of iterations performed, or a solution with adequate
objective function value is found), repeat:
o For each particle i = 1, ..., S do:
 Pick random numbers: rp, rg ~ U(0,1)
 For each dimension d = 1, ..., n do:
 Update the particle's velocity: vi,d ← ω vi,d + φprp (pi,d-xi,d) + φgrg (gd-xi,d)
 Update the particle's position: xi ← xi + vi
 If (f(xi) <f(pi)) do:
 Update the particle's best known position: pi ← xi
 If (f(pi) <f(g)) update the swarm's best known position: g ← pi
Now g holds the best found solution.
o
o


The parameters ω, φp, and φ g are selected by the practitioner and control the behaviour and efficacy of the PSO
method,
PSO is a Meta heuristic as it makes few or no
assumptions about the problem being optimized and
can search very large spaces of candidate solutions.
However, Meta heuristics such as PSO do not
guarantee an optimal solution is ever found. More
specifically, PSO is a pattern search method which
does not use the gradient of the problem being
optimized, which means PSO does not require that the
optimization problem be differentiable as is required
by classic optimization methods such as gradient
descent and quasi-Newton methods. PSO can
therefore also be used on optimization problems that
are partially irregular, noisy, change over time, etc.
IV. CONCLUSION
We are concluding our current research work
with node recovery failure system, optimization is
always an interesting research field. Battery
consumption or power of wireless sensor nodes and
reusability of route are basic parameters to optimize
while transmission of data packets from sensor nodes
to sink nodes and our approach gives efficient results
with failure node recovery approach and gives
optimal solution than the traditional approach.
[2]. W. H. Liao, Y. Kao, and C. M. Fan, “Data
aggregation in wireless sensor networks using ant
colony algorithm,” J. Netw. Comput. Appl., vol. 31,
no. 4, pp. 387–401, 2008
[3]. 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.
[4]. 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.
[5].
Hong-Chi
Shih,
Jiun-HueiHo,
BinYihLiao,andJengShyang Pan, “Fault Node Recovery
Algorithm for aWireless Sensor Network,” IEEE
Sensors Journal, vol. 13, no. 7, pp. 2683-2689, 2013.
[6]. 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
[7] J. H. Ho, H. C. Shih, B. Y. Liao, and J. S. Pan,
“Grade diffusion algorithm,” in Proc. 2nd Int. Conf.
Eng. Technol. Innov., 2012, pp. 2064–2068.
REFERENCES
[1]. 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.
ISSN: 2231-5381
[8] T. P. Hong and C. H. Wu, “An improved weighted
clustering algorithm for determination of application
nodes in heterogeneous sensor networks,” J. Inf.
Hiding Multimedia Signal Process., vol. 2, no. 2, pp.
173–184, 2011.
http://www.ijettjournal.org
Page 15
International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 1- April 2015
[9] 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.
[10] W. H. Liao, Y. Kao, and C. M. Fan, “Data
aggregation in wireless sensor
networks using ant colony algorithm,” J. Netw.
Comput. Appl., vol. 31,
no. 4, pp. 387–401, 2008.
[11] 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.
[12] J. Pan, Y. Hou, L. Cai, Y. Shi, and X. Shen,
“Topology control for
wireless sensor networks,” in Proc. 9th ACM Int.
Conf. Mobile Comput.
Netw., 2003, pp. 286–299.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 16