International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 7 – Oct 2014
A Hybrid Approach for Efficient Communication in
Wireless Sensor Networks
K.DurgaBhavani1,K.Srinivasa Rao2
1
Final Mtech student,2Assistant Professor
1
Dept of CSE, Siet(Swarnandhra Institute Of Engineering And Technology),Seetharampuram(Narasapur).
2
Dept of CSE, Gvvit(GrandhiVaralakshmiVenkataRao Institute Of Technology,Tunduru.
Abstract: Cooperative communication is the one of the
current interesting research issue in the field of wireless
sensors, Various cache based approaches proposed by
various authors but cache individually cannot increase the
performance over MANET. Topology architectures defined
for data transmission and cooperative communication in
MANET, In this approach we are introducing an empirical
model for cooperative communication with one of the
evolutionary algorithm along with cache implementation
which is proposed in previous mechanism, In this approach
we consider the factors of signal strength and channel
capacity for calculating the communication cost then we
generates the chromosomes for data transmission between
source and destination through intermediate nodes.
I. INTRODUCTION
In wireless networks the end user nodes are
available are not known. At the time of transferring the
data from one node to another node it find the shortest path
and sends the data to destination node. In path finding
process it finds the intermediate node and sends the data to
destination node. If the intermediate node is available or
not active the process will stuck and the data is not sent to
destination node. [1]
To achieve this problem there are so many
solutions are proposed such as the nodes in the network
which sends the status to other nodes in the network those
are active. There are cache present in the network , due to
cache presentation in the network it stores the status of the
node inn the network. The node that protects the
intermediate node which is choose from network and send
data to destination node which is active. It is not possible
that all nodes status is store in cache because cache stores
small amount of data and it has some storage limits.[2]
That is what the researchers introduced a concept
cooperative caching which is implemented in peer to peer
networks. It is based on the asymmetric approach that
ISSN: 2231-5381
cache the data and send data to others. It is mainly
depends on the network layer and designed the cache in
this at every node in the network. This solution is not only
copies the data in user storage and the local storage.[3,4]
Although
cooperative
cache
has
been
implemented by many researchers these implementations
are in the Web environment, and all these implementations
are at the system level. As a result, none of them deals with
the multiple hop routing problem and cannot address the
on-demand nature of the ad hoc routing protocols. To
realize the benefit of cooperative cache, intermediate nodes
along the routing path need to check every passing-by
packet to see if the cached data match the data request. In ,
the authors explored several system issues regarding the
design and implementation of routing protocols for ad hoc
networks. They found that the current operating system
was insufficient for supporting on-demand or reactive
routing protocols, and presented a generic API to augment
the current routing architecture. However, none of them has
looked into cooperative caching in wireless P2P networks.
II. RELATED WORK
In cooperative caching the status of the node are stored
and the data send to network using DSR routing protocol
using the status of the node which is stored in cache. The
data initially send to the intermediate node that are active
and the source node does not know the it is sent to
destination node or not.[9]
In asymmetric approach the above stated problems
are there. So we another method that is centroid based
method. The source node selects the centroid node within
all active nodes. The centroid calculations are shown
below.
A=(a1+a2+a3+……….+an)/n and
B=(b1+b2+b3+……….+bn)/n
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 7 – Oct 2014
2. Centriod for n points is calculated as follows:
between the nodes, after the mutation again calculate the
communication cost between the source and destination
nodes followed by relay nodes
In node communication establishment module
we construct a general node to node communication
through the socket programming, Every node can
communicate with each other .data packet can be
transmitted from source node to destination node, Each
node acts as server, it can accept the any connection and
receives the data packets from any other node and transmits
the data packets to other node.
A = (a1+a2+…….+an)/n and
Evolutionary algorithm:
B = ( b1+b2+…….+bn)/n
Genetic Algorithm is an evolutionary algorithm uses
genetic operator to generate the offspring of the existing
population. This section describes three operators of
Genetic Algorithms that were used in GA algorithm:
selection, crossover and mutation.
The shortest path consisted node is taken as group head. It
is calculated by taking the Euclidian distance of every node
within the range of the nodes and the centroid. The distance
is calculated as
Ed=((a2-a1)2 + (b2-b1)2)/2
1. Initialize Cache cluster heads and gateways by finding
centriods for different transmission ranges.
3. Cache the status of the nodes by updating cache tables
maintained by Cache cluster heads.
4. Choose source and destination nodes.
5. Find shortest path from source to destination by using
DSR or AODV[6,7]
6. Transmit the data if the nodes on the chosen path are all
active by retrieving status from Cache cluster heads.
III. PROPOSED WORK
In this proposed architecture we introduced
an evolutionary algorithm for optimal cooperative
communication between the nodes with the parameters
channel capacity and signal strength and cache
implementation for accessing the previously accessed or
transmitted data, it leads to the communication cost
Node 2
Selection: The selection operator chooses a chromosome in
the current population according to the fitness function and
copies it without changes into the new population.GA
algorithm used route wheel selection where the fittest
members of each generation are more chance to select.
Crossover: The crossover operator, according to a certain
probability, produces two new chromosomes from two
selected chromosomes by swapping segments of genes GA.
evolutionary algorithm for efficient cooperative
communication over nodes in MANET with the parameters
channel capacity and signal strength, it leads to the
Node 3
Node 6
Node 1
Node 4
between the nodes, here genetic algorithm finds the optimal
communication cost by applying the process of optimal
chromosome or path selection and mutation operators
ISSN: 2231-5381
Node 5
communication cost between the nodes, here genetic
algorithm finds the optimal communication cost by
applying the process of optimal chromosome or path
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 7 – Oct 2014
4.
3. Compute Path
Centralized
Server
Source Node
9.
Res
po
nse
8.
Sa
me
Re
qu
est
2
.
7.
Forward
data
Packets
5.
4. Forward
Request
6.
5.
R
R
e
eq
u
q
e
u
s
Cache
6. Data
Packets
selection and mutation operators between the nodes, after
the mutation again calculate the communication cost
between the source and destination nodes followed by relay
nodes.
The above figure shows complete architecture of
the proposed work, Source node makes the request to the
centralized server through cache if data not available in
cache. Centralized server computes the optimal path by
Computing optimal cost and transfers the data packets
through the path. If requested data packet available at
cache, no need to connect to centralized server.
Empirical cost computation:
During the cooperative communication between the nodes,
nodes communicate with each other with optimal path,
which is generated by evolutionary approach, When a node
transmits the data to receiver request made to evolutionary
processing module for path computation and it computes
all the paths between the source to destination and selects
the optimal path and transmits the data.
Communication cost=Signal strength + channel capacity
Obtains the optimal path which has the best communication
cost and transmits the data over the path.
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Destination Node
Intermediate
Node
Step1: Initially Source node selects the destination to
transmit data packets
Step2: Request received by the Processing module and
generates the paths in topology
Step3: The Processing module computes the path with their
signal strength and channel capacity
Step4: Compute communication cost with signal strength
and channel capacity for fitness
Step5: select optimal path (optimal communication cost)
and transmits the data.
Consider an example of nodes A,B,C,D,E,F and
if a node(gene) ’A’ wants to transfer the data to the
receiver ’F’ , The processing module computes the all the
available paths from source to destination and applies the
fitness function and obtains the optimal path and transmits
the data over that path, the following Evolutionary
approach as shown below.
ABCDEF
ABEDCF
AEDCBF
ACDBEF
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 7 – Oct 2014
Now compute the fitness value based on the signal strength
and channel capacity as communication cost and Obtains
the optimal path which has the best communication cost
and transmits the data over the path.
IV. CONCLUSION
Finally we are concluding our research work with efficient
cache implementation and evolutionary routing protocol
based on signal strength and channel capacity for
calculation of communication cost. Our primary factors
gives optimal performance than the traditional weight
based approaches an cache improves the performance by
reducing the response time of the requested node.
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