International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 3- Dec 2013
An Efficient and Improved Load Balancing Factor
with TM for Multi agent Systems
Yatirajula Ramesh babu1, Jayanthi Rao Madina2
1,2
M.Tech Scholar1, Assistant Professor2
Dept of CSE, Sarada Institute of Science, Technology and Management, Srikakulam, Andhra Pradesh
Abstract: Trust computation is still an important factor while
communicating each node with other in mobile adhoc
networks. In this paper we are proposing a novel and
dynamic trust computation measure approach with an
efficient model TC with LBF, Static measures may not give
the optimal solution,Most of our approaches works on
statistical measures like trust computations, (i.e. Direct and
indirect trust computations), these approaches are not
optimal, because anonymous user may not have(or use) same
set of characteristics as previous connection. In this paper a
proposal to an efficient and empirical trust computation
method for identifying and allowing the nodes based on trust
measures was done. Here, we are proposing an improved
trust computation method by enhancing the traditional trust
computation method with balancing factor and the results are
shown at the last for the efficiency of proposed system.
I.
INTRODUCTION
Cooperative communication is the basic issue in mobile
adhoc networks, every individual node acts as server
application, with their characteristics of continuous
listening to the incoming node and process the incoming
query and returns the result.High-quality, personalized
recommendations are a key fea ture in many online
systems. Since these systems often have explicit
knowledge of social network structures, the
recommendations may incorporate this information. This
paper focuses on networks which represent trust and
recommendations which incorporate trust relationships.
The goal of a trust-based recommendation system is to
generate personalized recommendations from known
opinions and trust relationships. In analogy to prior work
on voting and ranking systems, we use the axiomatic
approach from the theory of social choice. We develop an
natural set of five axioms which we desire any
recommendation system exhibit. Then we show that no
system can simultaneously satisfy all these axioms. We
also exhibit systems which satisfy any four of the five
axioms. Next we consider ways of weakening the axioms,
which can lead to a unique recommendation system based
on random walks. We consider other recommendation
systems (personal page rank, majority of majorities, and
min cut)and search for alternative axiomatizations which
uniquely characterize these systems.
ISSN: 2231-5381
In Peer-to-peer (P2P) communication electronic commerce
(E-Commerce) communities can be seen as truly
distributed computing applicationsin which peers
(members) communicate directlywith one another to
exchange information, distribute tasks,or execute
transactions. P2P E-Commerce communities canbe
implemented either on top of a P2P network[1,2,3] orusing
a conventional client-server platform. Gnutella is
anexample of P2P E-Commerce communities that are built
ontop of a P2P computing platform. Person-to-person
onlineauction sites such as eBay and many business-tobusiness(B2B) services such as supply-chain-management
networksare examples of P2P communities built on top of
a client server computing architecture.
II.
RELATED WORK
Trust computation is the basic factor for measure the node
while communicating with the service provider to preserve
the services from the provider in an optimal manner. Load
balancing factor also an important factor communication of
nodes in mobile adhoc networks and peer to peer
communications, Even though various providers available
for the agents, agent needs to pass the data packets in
optimal manner with respect to the load balancing factor,
because multiple agents communicates with multiple
agents
Recognizing the importance of trust in such
communities,an immediate question to ask is how to build
trust.There is an extensive amount of research focused on
buildingtrust for electronic markets through trusted third
partiesor intermediaries [14, 8]. However, it is not
applicableto P2P eCommerce communities where peers are
equal intheir roles and there are no entities that can serve as
trustedthird parties or intermediaries.Reputation systems
[11] provide a way for building trustthrough social control
without trusted third parties. Mostresearch on reputationbased trust utilizes information suchas community-based
feedbacks about past experiences of peers to help making
recommendation and judgment onquality and reliability of
the transactions. Communitybasedfeedbacks are often
simple aggregations of positiveand negative feedbacks that
peers have received for thetransactions they have
performed and cannot accurately capture the
trustworthiness of peers. In addition, peers canmisbehave
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 3- Dec 2013
in a number of ways, such as providing falsefeedbacks on
other peers.
The challenge of building a trustmechanism is how to
effectively cope with such maliciousbehavior of peers.
Another challenge is that trust contextvaries from
communities to communities and from transactionsto
transactions.
It
is
important
to
build
a
reputationbasedsystem that is able to adapt to different
communitiesand different situations.Furthermore, there is
also a need for experimental evaluationmethods of a given
trust model in terms of the effectivenessand benefits. Most
traditional trust models onlygive an analytical model
without any experimental validationdue to the subjective
nature of trust. There is a need ofgeneral metrics for
evaluating the effectiveness and benefitsof trust
mechanisms.With these research problems in mind, we
developPeerTrust, a peer-to-peer trust model for
quantifying andassessing the trustworthiness of peers in
P2P E-Commercecommunities. Our goal is to build a
general trust metric thatprovides an effective measure for
capturing the trustworthinessof peers, addresses the fake or
misleading feedbacks,and has the capability to adapt to
different communities andsituations.
III.
TRUST COMPUTATION WITH LOAD
BALANCING FACTOR
Our approach introducing an integrated approach of trust
computation with load balancing factor for efficient
communication of agents and service providers. Agent can
communicate in optimal manner. In this paper we are
proposing an empirical trust computation method with load
balancing factor, when nodes or agents tries to connect
with service providers, connection needs to optimize the
optimal service providers, for trust worthyvarious
mechanisms implemented as trust measures for measuring
the trust of the service providers. Even though trust
measure is the basic measure we need to consider the load
balancing factor, because even though service provider is
good, if load is high, agent can not choose as its service
provider because it may not leads to optimal results.
Now a day’s identifying the unauthorized user in networkis
still an important research issue during the peer to
peerconnections. Networks are protected using many
firewallsand encryption software’s. But many of them are
notsufficient and effective. Most trust computation systems
formobile ad hoc networks are focusing on either
routingprotocols or its efficiency, but it fails to address
thesecurity issues. Some of the nodes may be selfish,
forexample, by not forwarding the packets to the
destination,thereby saving the battery power. Some others
may actmalicious by launching security attacks like denial
ISSN: 2231-5381
ofservice or hack the information. The ultimate goal of
thesecurity solutions for wireless networks is to
providesecurity services, such as authentication,
confidentiality,integrity, anonymity, and availability, to
mobile users.
Thispaper incorporates agents and data mining techniques
toprevent
anomaly
intrusion
in
mobile
adhocnetworks.Home agents present in each system collect
the data fromits own system and using data mining
techniques toobserve the local anomalies. The Mobile
agentsmonitoringthe neighboring nodes and collect the
information fromneighboring home agents to determine the
correlationamong the observed anomalous patterns before
it will sendthe data. This system was able to stop all of the
successfulattacks in an adhoc networks and reduce the false
alarmpositives.
We define transaction success rate as a metric to
measurethe productivity and security level of a community.
A transactionis considered successful if both of the
participatingpeers cooperate. Otherwise one or both of the
peers is facedwith the risk of malicious behaviors from the
other peer.The successful transaction rate is defined as a
ratio of thenumber of successful transactions over the total
number oftransactions in the community up to a certain
time. A communitywith a higher transaction success rate
has a higherproductivity and a stronger level of security.
We expect thata community with an effective trust
mechanism should have a higher transaction success rate as
peers are able to make informed trust decisions and avoid
unreliable and dishonestpeers.
Trust Computation Model (TCM) is based on a set of
required factors to be known prior to making a trust
decision [10]. These factors include information about
other agents’ knowledge base, and interactions during the
current task. A key feature of this model is that it considers
how much knowledge one should have about the trustee
agents in order to make a trust decision. TCM model
defines direct trust based on three concepts: familiarity,
similarity and past experience, whereas indirect trust is
defined based on recommendations.
Overall trust metric can be calculated with expected
trust(i.e Expected trust reflects expected performance of
the target agent and it is deduced from both recent and
historical trust.) and deviation reliability.(i.e Deviation
reliability is a measure of how much deviation we are
willing to tolerate. Malicious agents sometimes
strategically oscillate between raising and milking their
reputation which seriously affects the performance of the
network),coming to the load balancing factor,we are
enhancing the previous load balancing factor with
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 3- Dec 2013
additional trust measures as by integrating the transaction
trust and again recursively calculate the distance again if it
is minimum then lamda. The following algorithm shows
the load balancing factor with enhances trust measure as
follows.
IV.
LOAD BALANCING FACTOR
Algorithm 1:
Selection of service providing agent(p,S)
Input: Evaluating agent p and the set of agents responding
to a service request S
Output: Service providing agent q.
For each x € S do
Trust(p,x) = ET(p,x)*DR(p,x)
If(Trust(p,x)<¥) then
Trust(p,x)
=
ET(P,x)*DR(p,x)+TransTrust(p,x).
If(Trust(p,x)<¥) then
UUu {x}
Else
previous traditional and our proposed approach asfollows:
5
4
3
2
1
0
Time
Load
TA with TA with PA with
out LB LB Factor LB Factor
Factor
TA-Traditional approach
PA-Proposed approach
LB-Load Balance
VI.
GG u {x}
End if
Else
GG u {x}
End if
End for
If G ≠ Ø then
For each x € G do
N(p,x)= I(p,x)+∑ FC(p,y)*I(y,x).
End for
Sort G in increasing order of load N
return agent q with the smallest load N
else
Total_trust0
For each x € U do
Total_trustTotal_trust+ Trust(p,x)
End for
If Total_trust>0 then
For each x € U do
Compute Prob(p,x)
End for
return agent q with probability Prob(p,q)
End if
End if
V.
RESULTS
Our experimental results show the efficient
performance than the traditional trust computation with
previous load balancing factor. Performance evaluation of
ISSN: 2231-5381
Trust
measures
CONCLUSION
We are concluding our proposed work by enhancing the
load balancing factor with integrated trust computation for
selection of the optimal service provider with balancing
load factor and SecuredTrust can ensure secured
communication among agents by effectively detecting
strategic behaviors of malicious agents.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 3- Dec 2013
[11] P. Resnick, K. Kuwabara, R. Zeckhauser, and E.
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BIOGRAPHIES
Yatirajula Ramesh bbabu (s/o y.satya
narayana)received B.Tech from Dadi
Institute
of
Engineering
&
Technologyafflicated
to
JNTUniversity,Kakinada.,
Visakhapatnam.He is pursuing M.Tech
in Sarada Institute of Science, Technology
and
Management, Srikakulam, Andhra Pradesh. Interesting
areas are Information security.
Jayanthi Rao Madina is working as a
HOD in Sarada Institute of Science,
Technology
And
Management,
Srikakulam, Andhra Pradesh. He
received his M.Tech (CSE) from Aditya
Institute
of
Technology
And
Management, Tekkali. Andhra Pradesh.
His research areas include Image Processing, Computer
Networks, Data Mining, Distributed Systems.
ISSN: 2231-5381
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