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ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue I, March 2015
13
All Rights Reserved © 2015 IJARTET
An Efficient and Robust Trust Management for Wireless
Sensor Networks
Praghash.K, PG Scholar, M.E. Communication Systems, Francis Xavier Engineering College, Tirunelveli, India1
Mrs.J.Friska, Assistant Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli, India2
Abstract - Unattended Wireless Sensor Networks (UWSNs) are defined by fixed or irregular intervals between sink visits
and long periods of disconnected operations. Unattended Wireless Sensor Networks are more capable of providing computation,
communication and power capabilities than peer to peer networks and ad hoc networks. UWSNs are mostly used for environment
monitoring applications. An UWSN have thousands of sensors. Until their energy is depleted, these sensor nodes provide services
throughout their whole lifetime. The existing WSN trust management schemes are not applicable to UWSNs when there is an
absence of an online trusted third party implies that. To provide efficient and robust trust data storage and trust generation this
method proposes a trust management scheme for UWSNs. To identify storage nodes and to significantly decrease storage cost,
this method employs a geographic hash table for trust data storage. For mitigating trust fluctuations caused by environmental
factors this method uses subjective logic based consensus techniques. It exploits a set of trust similarity functions to sustain trust
pollution attacks and to detect trust outliers.
Keyword Terms: Unattended wireless sensor network (UWSN), distributed trust management, subjective logic
1. INTRODUCTION
The telecommunications network allows the
computers to exchange data. In computer networks,
networked computing devices pass data to each other along
data connections. Data is transferred in the form of packets.
The connections (network links) between nodes are
established using either cable media or wireless media. The
best-known computer network is the Internet. Network
computer devices that originate, route and terminate the data
are called network nodes. Nodes can include hosts such as
personal computers, phones, servers as Ill as networking
hardware. Two such devices are said to be networked
together when one device is able to exchange information
with the other device, whether or not they have a direct
connection to each other. Computer networks differ in the
physical media used to transmit their signals, the
communications protocols to organize network traffic, the
network's size, topology and organizational intent. In most
cases, communications protocols are layered on (i.e. work
using) other more specific or more general communications
protocols, except for the physical layer that directly deals
with the physical media. Computer networks support
applications such as access to the World Wide Ib, shared use
of application and storage servers, printers, and fax
machines, and use of email and instant messaging
applications. A network is a group of two or more computer
systems linked together. There are many types of computer
networks. They are Local Area Networks, Wide Area
Networks, Campus Area Networks, Metropolitan Area
Networks and Home Area Networks. Local-area networks
(LANs) are the computers that are geographically close
together (that is, in the same building). Wide Area
Networks (WBNs) are the computers that are farther apart
and are connected by telephone lines or radio waves.
Campus-area networks (CANs) are the computers that are
within a limited geographic area, such as a campus or
military base. Metropolitan-area networks (MANs) are a
data network designed for a town or city. Home-area
networks (HANs) are a network that contained within a
user's home that connects a person's digital devices. In
cryptography, a trusted third party (TTP) is an entity which
facilitates interactions between two parties who both trust
the third party; The Third Party reviews all critical
transaction communications between the parties, based on
the ease of creating fraudulent digital content. In TTP
models, the relying parties use this trust to secure their own
interactions. TTPs are common in any number of
commercial transactions and in cryptographic digital
transactions as Ill as cryptographic protocols; for example, a
certificate authority (CA) would issue a digital identity
ceritificate to one of the two parties in the next example.
The CA then becomes the Trusted-Third-Party to
that certificates issuance. Likewise transactions that need a
third party recordation would also need a third-party
repository service of some kind or another. Trust in
Distributed and Peer-to-Peer Systems Reputation and trust
systems in the context of distributed and peer-to-peer (P2P)
networks are distributed; there is no centralised entity to
oversee the behaviour of nodes in a network, so users keep
track of their peers’ behaviour and exchange this
information directly with others; and also maintain a
statistical representation of the reputation by borrowing

14
tools from the realms of game theory. These systems try to
counter selfish routing misbehavior of nodes by enforcing
nodes to cooperate with each other. Security is a
fundamental component of every network design.
2. PROPOSED METHOD
2.1 INTRODUCTION
Wireless Sensor Networks (WSNs) have been used
in challenging, hostile environments for various applications
such as forest fire detection, battlefield surveillance, habitat
monitoring, traffic management, etc. One common
assumption in traditional WSNs is that a trusted third party,
e.g., a sink, is always available to collect sensed data in a
near-to-real-time fashion. Although many WSNs operate in
such a mode, there are WSN applications that do not fit into
the real time data collection model. Consider an example of
a monitoring system deployed in a natural park to detect
poaching activities. The lack of regular access routes and the
size of the surveillance area would require a mobile sink to
collect data periodically. Another example is an underwater
mobile sensor network for submarine tracking and harbor
monitoring. The inaccessibility of the protected area and
other technical problems make it difficult to maintain
continuous connections betIen sink and sensors.
Trust management becomes very important for
detecting malicious nodes in unattended hostile
environments. It can also assist in secure routing secure data
distribution and trusted key exchange. An efficient trust
management system is required to handle trust related
information in a secure and reliable way. It should deal with
uncertainty caused by noisy communication channels and
unstable sensor behavior.
A trust management scheme for efficient trust
generation which is scalable and robust trust data storage in
UWSNs is proposed. A central issue for trust management
in UWSNs is how to store trust data without relying on a
trusted third party. Initially, the proposed method consider
two simple trust management schemes as a first-step attempt
to address the existing trust storage problems in UWSNs.
After analyzing the shortcomings of these simple schemes,
an advanced scheme based on a Geographic Hash Table
(GHT) is introduced. This advanced scheme allows sensor
nodes to put and get trust data to and from designated
storage nodes based on node IDs. Sensor nodes do not need
to know the IDs of storage nodes. They use a hash function
to find locations of the storage nodes, which significantly
reduce the storage cost. This scheme also proposes a set of
similarity threshold functions to remove outliers from trust
opinions. This prevents attackers from generating false trust
opinions and from polluting trustworthiness. Furthermore, it
provides a detailed analysis of the proposed scheme and
conducts a comprehensive simulation-based study to
demonstrate that our scheme is efficient, robust, and
scalable.
Fig.1. Relationship between trust producer, trust consumer and trust
manager
2.2 SECURITY MODEL
The UWSNs can be attacked in many ways. In this
study, the proposed method focuses on an adversary ADV
launching attacks against trust data. The proposed method
divide the attacks into two categories: trust eraser and trust
pollution attacks. The effect of the trust eraser attack
(denoted as ADV_Del) is that the trust data stored in sensors
are lost and cannot be retrieved by trust consumers. For
instance, ADV could try to compromise sensors and to erase
the trust data stored in them. Moreover, when sensors are
nonfunctional (e.g., due to energy depletion, natural
disasters, etc.) their stored trust data are lost and are
considered as non-recoverable in this study. In case of trust
pollution attack, ADV does not delete the trust data but
rather pollute them. The proposed method considers the
following pollution strategies. Environmental effect
(ADV_Noise) means since sensors’ trust opinions are
generated based on sensors’ previous behavior; they may
generate some noise due to environmental effects.
Homogeneous attack (ADV_Homo) means that for a given a
sensor sj, ADV tries to increase sj trustworthiness j
monotonically or, it tries to decrease j
monotonically. To
do so, at each time interval ADV can compromise a subset
of sensors to generate false trust opinions. Hybrid attack,
denoted as ADV_Hbd. This attack is more severe since
ADV aims to manipulate the trustworthiness, i.e., it is able
to increase or decrease trustworthiness in any way. For
clarity, the proposed method assumes that the number k of
compromised sensors in each time interval is fixed. The
proposed method refer to this number as the compromising
capability. The compromised sensor can occur anywhere in
the network.
2.3. Design Goals
The trust management scheme is designed with the
following goals in mind that is robustness, resilience,
Trust
Produce
r
Trust
Manager
Trust
Consum
Trust
Opinion
Trust
worthine

15
scalability, efficiency, consistency. Robustness means the
scheme remains functional even after certain amount of
sensor nodes lose battery poIr or are physically damaged.
Moreover, the trust data stored in the system should remain
available to queries even if some sensor nodes fail due to the
ADV_Del attack. Resilience means the generated
trustworthiness should be as close as possible to its real
value even though ADV tries to inject false trust opinions in
order to pollute it. In other words, the system should be
resilient to ADV_Noise, ADV_Homo and ADV_Hbd.
Scalability means that this scheme should work for a very
large unattended physical area with a high density of sensor
nodes. Efficiency means that the designed trust management
scheme should be efficient in terms of both communication
cost and storage cost. Consistency means that the Trust
opinions generated by trust producers and trust queries sent
from trust consumers should be routed correctly to trust
managers where the trust data are stored.
2.4.Advanced Scheme (AS)
The Advanced Scheme (AS) utilizes a hash-table-
like interface of GHT where nodes can put and get data
based on their data type, i.e., Put(Data Type, Data Value)
and Get(Data Type). Since a sensor ID is unique in the
network, trust producers are able to put trust opinions to
trust managers based on the ID, i.e., Put (sj, ). Trust
consumers are able to get trustworthiness from trust
managers using the same sensor ID, i.e., Get (sj). In other
words, trust opinions are pushed by, and stored at the same
trust manager node. Meanwhile it enables trust consumers to
pull trustworthiness from the trust manager nodes
consistently. Neither trust producers nor trust consumers
need to store the IDs of trust manager nodes, reducing
storage cost significantly. Furthermore, the scheme should
not be sensitive to node failures. That is, the scheme should
be resilient to ADV_Del. Using the Get(sj, r) function, trust
consumers are able to get sj’s trustworthiness j
from the r-
th trust manager node of sj. That is, each node has α trust
manager nodes to store its trust opinions from its neighbors
for data redundancy. Trust opinions regarding a sensor sj are
hashed by the sensor ID sj to a geographical location. The
node closest to the hashed geographical location is referred
to as the trust manager node where data is sent to and
retrieved from.
The closest node to the location , namely trust manager
can receive the trust opinions and trust query requests. The
AS includes the following phases:
3. RESULTS AND DISCUSSION
Fig.2. Network Formation
Fig.2. shows the network formation. In this
network formation one base station and two clusters are
formed. Each cluster having 35 nodes. These nodes share
their routing table to their neighbors mutually. So that the
efficient routing path is to be determined for transferring
network credentials.
Fig.3. Cluster Formation
Fig.3.shows cluster formation of nodes. In this
network there are two clusters are formed. Here node 0
represents the base station. These cluster heads are the trust
managers whose are responsible for the production of trust
opinions about the sensor nodes.

16
Fig.4. Cluster Head Selection
Fig.4. shows the cluster head selection. In this
network there are two clusters are formed. For that clusters
select the cluster head. Here node 33 and 38 are the cluster
heads. At once the cluster heads are formed; they stared to
generate the trust opinions about their neighbors; so as to
find the efficient path
Fig.5. Base station send hello packet to cluster nodes
Fig.5. shows sending hello packet from base station
to cluster nodes. In this sending Mac address of each nodes
to its neighbor node. This diagram explains how the base
station activates the trust managers ie) the cluster heads to
investigate the neighborhood sensors.
Fig.6. Collect trust information and send to node 43
Fig.6. shows the trust information collection and
transmission. The information of the node 52 collects the
trust information from its neighbor and send them to node
43. Here the trust manager is checking whether the nodes
are attacked or not. So finally we find out the node no 14 is
malicious /attacked
Fig.7. Cluster head 38 collect trust information
Fig.7. shows the cluster head 38 collect the information and
send it to base station. The cluster head 38 collect the
information from its cluster nodes. Similarly in the same
way the cluster head II determines the trustworthiness of the
neighbors.

17
Fig.8.Packet Dropping
Fig.8. shows the packet dropping. The packet will be
dropped due to malicious nodes. Due to the malicious node
present in the network the packet will be dropped.
Fig.9.Compromised node detection
Fig.9. shows the compromised node detection. The node 13
and 43 are the compromised nodes. Because they are not
sending the data to its neighbors it only drop the packets.
Fig.10. Elimination the compromised node and select alternate route nodes
Fig.10. shows the elimination the compromised node and
select the alternate route. The node 13 and 43 are the
compromised nodes. These compromised are eliminated
because of secure data communication. Then select the
alternate route.
Fig.11. Collection of trust information from cluster nodes and send to base
station
Fig.11. shows the information collection from cluster nodes.
The cluster head 33 collect the information from their
cluster nodes. Then the cluster head sends the information to
base station.
4.ANALYSIS

18
Fig.12. Packet Loss Determination
Fig.12. shows the packet loss. The graph is plotted between
time versus packet loss. The packet loss will be decreases as
time increases. Initial time instant, due to the malicious node
there will be a large amount of packet loss observed; shows
the abnormal increase in the packet loss graph. Then after
finding the alternate route to transfer the packets , the packet
loss gets controlled immediately, and thereafter there will be
less packet loss.
Fig.13.Throughput Analysis
Fig.13. shows the throughput. The throughput graph is
plotted between times versus through put. The throughput
will be increased as the time increases. As we know that the
packet loss and throughput are inversely proportional, we
get the throughput analysis with well known result. Here in
this graph the throughput is decreased between ( 2.000-
4.000) ms due to packet loss and then it is get overcome.
5. CONCLUSION
In this scheme, an efficient and robust trust
management schemes for UWSNs based on Subjective
Logic is introduced. This advanced trust storage scheme
(AS) facilitates distributed trust data storage to ensure high
reliability of trust data. It takes the advantage of both GHT
and GPSR routing to find storage nodes and to route trust
data. I have also proposed several methods to mitigate trust
pollution attacks based on various trust similarity measures.
I demonstrated that our trust management schemes are
resilient to major attack categories including ADV_Del,
ADV_Noise, ADV_Homo, and ADV_Hbd. Moreover, the
simulation results demonstrated that AS has much loIr
storage costs compared with the less sophisticated
approaches. Combining AS with similarity threshold
measures, these schemes are able to significantly reduce
trust storage costs and perform efficient node invalidation
and mitigation of ADV’s pollution attacks.
REFERENCES
[1]. A. Ma, C. Soriente, and G. Tsudik, “New adversary and
new threats: Security in unattended sensor networks,” IEEE
Netw., vol. 23, no. 2, pp. 43–48, Mar. 2009.
[2]. C.. Ren, V. Oleshchuk, F. Y. Li, and S. Sulistyo,
“SCARKER: A sensor capture resistance and key refreshing
scheme for mobile WSNs,” in Proc. IEEE LCN, Bonn,
Germany, 2011.
[3]. E. Di Pietro, L. V. Mancini, C. Soriente, A. Spognardi,
and G. Tsudik, “Catch me (if you can): Data survival in
unattended sensor networks,” in Proc. IEEE PERCOM,
Hong Kong, 2008.
[4]. E. Di Pietro, G. Oligeri, C. Soriente, and G. Tsudik,
“United I stand: Intrusion-resilience in mobile unattended
WSNs,” IEEE Trans. Mobile Comput., vol. 12, no. 7, pp.
1456–1468, Jul. 2013.

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