International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
A Novel and Optimal Cooperative Communication
in MANET
Jampana Venkata Sudheer Varma1,N. Tulasi Raju2
1
1,2
Final Mtech student,2Assistant Professor
Dept of CSE, Swarnandhra College of Engg & Tech, Seetharampuram, W.G.Dt, (A.P),India.
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 cooperative increases the efficiency of the
networking system by communicating the clients and
allows the clients requests by satisfying the cache. There
are two technologies which consider cooperative caching.
First one is performance increasing much more frequently
than the disk performance. The makes reducing the disk
access by the file system. And the second system is
making of high speed switching communication networks
can supply blocks over the network and more efficient
standard[1][2].
In previous systems there is three level storage
phases are there and which is the limited form of
cooperative caching by finding the location by sharing the
memory to substitute the other two storage levels. Initial
cooperative caching provide efficient performance and also
it improves the central hit rate and then it avoids the
systems memory access. Cooperative caching introduces
another cache system such as systems cache hierarchy.
Based on cooperative caching there is a novel level and that
may be found in local memory and the server memory. The
entire study of the cooperative caching summarizes to
provide perfect benefits over the communicative networks
and evaluate the caching and workloads of nodes.
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For the data with varying the level of popularity a
greedy approach for each node that would store as unique
data as its storage allows. This method adds to
noncooperation and can increase heavy network-wide data
duplications. In the other case it is fully cooperative and a
terminal try to make the best of the total of single contents
stored within the SWNET by avoiding duplication[3].
Based on a flexibility service and pricing a
stochastic method for the content service provider cost
calculation is developed. A cooperative caching approach
that is Split Cache proposed in previous researches and it is
numerically analyzed and also theoretically proven to
provide best available object placement for systems with
similar content demands. A benefit based schema
Distributed Benefit proposed to reduce the provisioning
cost in different networks which includes the nodes with
different content request rates and patterns[4][5].
To analyze and understand the modern object
placement under similar object request model researchers
introduced the following Split Cache policy in which the
available cache space in each device is classified into a
duplicate part and a unique segment. In the first part the
nodes can store the very famous objects without bothering
about the object duplication and in the second part is
distinct objects which are allowed to be stored. The Split
Cache replacement method the immediately following an
object is retrieving from the cloud service servers and it is
divided as single type object as there is single copy of this
object in the network. Moreover at the time of downloads
by node an object and as a replica object as there are
present at least two duplicates of their SWNET node and
that object is classified of that object in the network[6].
For storing the information a novel object which is
least object in the entire cache is selected as a candidate
and it is replaced with new object if it is low popular than
the new received object. The evictee candidate is selected
for duplicate object it is only from the first duplicate
segment of the cache. A distinct object is never
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
dispossessed in order to keep a duplicated object. Split
Cache object replacement method analyzes the modern
strategy. By using this method at static state of all devices
the cache preserves the similar objects and set in their
duplicate locations but distinct objects in their unique
areas[7].
upon on client piece of work. The N-Chance method
manipulates the Greedy Forwarding algorithm to consists
of clients cooperate to preferably cache singlets and the
data blocks stored in one client cache. Except for the
singlets the N-Chance Forwarding performance as Greedy
Forwarding operation can be done.
II. RELATED WORK
N-Chance forwarding tries to prevent ignore
singlets from client memory. At the time of client discards
a block the method checks to verify that if the block is the
final copy cached by any client node. This verifies a
message to server or may do by consulting the flags related
with every block as explained below. If the block is a
singlet and it throws the block away then the client links
the block’s recirculation number of times to n and it sends
the data to peer and it sends the server a message by
explaining it that the block has moved to
somewhere[11][12].
There so many algorithms for cooperative caching
and some of them is direct client caching, greedy caching,
central coordinated caching. Coming client caching it
allows an activated client to use a static storage. The client
gathers the entries of the local cache directly to a static
device. The client can access the remote cache as private
and its requests the remote device to become active and
leaves the cooperative caching. Its requests become simple
and it can implement without server manipulation.
Consider the server it uses the remote storage available to
have temporarily increasing the local cache. In this method
the drawback is server coordination with active clients and
do not have any advantages from the data of the active
clients storage memories.
Greedy forwarding method considers cache as
global memory which is accessed by the authenticated
clients. In every client it maintains a local cache memory
without concerning the data of other cache in the system. If
any client does not find any block in its cache then it
requests for the server data. If server does not contain the
requested data it requests another cache in the
communication network. Using this greedy caching we can
change only the file system[8][9].
Central coordinated caching includes coordinating
with the greedy forwarding. It behaves like moving
memory physically from the clients to the server. The
server maintains the central managing the client cache
using the replace of the existing algorithms. At the time of
evicting the block and it sends the block to replace the
recently utilized block over all of the clocks centrally
coordinated distributed cache. The main advantage of this
is more central hit rate and it achieves maintaining the
group of its memory. The main disadvantage of is that the
clients hit rates may reduce when their local caches are
effectively made low and global coordination may produce
the load on the server.
The last method that we can evaluate the NChance Forwarding it dynamically sets the fraction of
every client’s cache maintained cooperatively and depends
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The node that receives the data links the block to
its LRU list and if the block had recently. If a recirculating
block reaches the end of the LRU list and its count is
decremented. It forwarded again the count is set to zero and
in case it is simply ignored. If a client checks a singlet it
resets the block’s recirculation count and gathers data
generally while the client had been cooperatively caching
the singlet ignores the block from its cache system[10].
Coming to privacy of content after publishing is
the main task when data transferring from source node to
destination node. In centralized server also the data privacy
is the main task. For this some traditional researches
introduced cryptographic techniques. By these techniques
the data convert into un-formatted text and more privacy
options are available for source node and the destination
node also. In centralized server also we will secure the
content by these cryptographic techniques.
In next section we introduced a new architecture
which gives moderate solution for cooperative caching and
it improves the processing efficiency while transferring and
receiving the data in the secure communication channel.
We used genetic algorithm for finding the optimal
solutions.
III. PROPOSED WORK
In our work we propose a topology, we presented as special
algorithm for moderate cooperative communication
between the nodes having parameters network channel
capability, strength of signal and temporary memory that is
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
cache implementation for previous accessed / transferred
data for accessing. It tends to the increasing of
communication cost; hence we introduced genetic
algorithm leads to the optimal solution for decreasing the
communication cost. It applies the method for path
selection and mutation operation between the nodes. Then
mutation
operation
once again computes the
communication cost between the source node and the
destination node followed by relay nodes.
Node 2
In the initialize the communication between the
nodes we connect through socket programming. The node
which is connected with another node communication each
other at the time of data packet transfer. Each node in the
communication acts as a server and accept requests from
another nodes. It receives data packets from accepted nodes
and vice versa.
Node 3
Node 6
Node 1
Node 5
Node 4
Fig1: Node Construction
Proposed Approach:
Genetic algorithm is a process which uses the operators to
generate off spring of the previous group of chromosomes.
Here we explained about the operators present in genetic
algorithm such as Selection, Crossover and Mutation.
Selection: This operator selects a chromosome in existing
set of chromosomes based on fitness. It copies that selected
chromosome without any changes into the new
chromosomes. It uses wheel selection that depends on
fitness value of the chromosomes in each generation and
the best fittest chromosomes more chances to get selected.
Crossover: This operator generates new chromosomes
based on particular probability from two selected
chromosomes. It swaps segments in chromosomes at
particular position in chromosomes produces new
chromosome.
3. Compute path Response
Source node
Destination
node
Centralized server
4. Forward
Request
1.Request
9. Response
2. Request
5. Data Packets
nse
7. Forward
Data Packets
8. Same Request
Cache
6. Data Packets
Intermediate
Node
Fig2: Proposed Architecture
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
Mutation: This operator generates new chromosomes by
interchanging the genes in chromosomes itself.
Our Architecture:
The below architecture show our complete
proposed work. The Source node sends request to the
global (centralized) server using cache. If the data is not
present in cache the centralized server calculates the
optimal path by calculating the optimal cost and sends the
data packets using the path in secure channel. If the
requested data packet is available at cache there is no need
to connect with centralized server otherwise it connects
with server and copy data packet from the centralized
server and then copy to cache.
Analysis of Optimal Communication cost:
IV. CONCLUSION
At cooperative communication between the nodes
are communicate each other with optimal path which is
generated by genetic algorithm. At the time of data transfer
to receiver the source node calculates communication cost
and optimal path using genetic algorithm (evolutionary
algorithm). Then the source node selects one of the paths
from the set of optimal paths to transfer the data to
destination node.
Finally we conclude 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
give optimal performance than the traditional weight based
approaches an cache improves the performance by
reducing the response time of the requested node.
Communication cost (complexity) =Signal – strength +
channel capability gets the optimal path which has the best
communication cost and transfer the data packet through
the path.
1. Source node chooses the destination to transfer
the data.
2. If the request received by the processing
method it generates the paths in architecture.
3. The Processing method calculates the path with
their signal strength and channel capability.
4. Then compute the communication complexity
with signal strength and network channel capacity for
fitness value.
5. Select optimal communication cost and transfers the
data.
Here we explain an example, Take some set of
nodes A,B,C,D,E,F and if a node ’A’ wants to send the
data to receiver ’F’ , The processing module calculates all
the available paths from source to destination. Then apply
the fitness value and obtains the optimal path and transfer
the data over that path using the following Evolutionary
approach as shown below
V. FUTURE ENHANCEMENT
ABCDEF
REFERENCES
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Web Cache Replacement Strategies STEFAN PODLIPNIG AND
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ACDBEF
Then 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.
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We are concluding our research work with efficient routing
approach for cooperative communication and cache
implementation for frequently accessed information. It
leads to optimal of usage of bandwidth, reduces the
network traffic and improves in terms of time complexity.
We can enhance our approach by reducing the time
complexity issues in the split cache replacement and by
implementing in our current approach.
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Algorithms,” IEEE Trans. Mobile Computing, vol. 6, no. 6,pp. 606-620,
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BIOGRAPHIES
Venkata Sudheer Varma received the
degree in Information Technology from
Jawaharlal Nehru Technological university
Kakinada, pursuing M.Tech (computer
science and engineering). He is an p.g
student, department of computer science
and engineering, Swarnandhra Collage of Engineering &
Technology, Seetharampuram, Narsapur, AP, India. His
research areas of interests are Computer Networks.
Tulasi Raju Nethala received the master’s
degree in computer science and
engineering from jntu kakinada. He is an
Asst. professor in the department of
computer science in Swarnandhra College
of Engineering & Technology. His
research areas of interests are in Computer Networks.
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