Performance and Simulation Study of TheProposed Direct, Indirect Trust Distribution Security Architecture in Wireless Sensor Network

Computer Science & Engineering: An International Journal (CSEIJ), Vol.1, No.4, October 2011
DOI : 10.5121/cseij.2011.1403 27
Performance and Simulation Study of
TheProposed Direct, Indirect Trust Distribution
Security Architecture inWireless Sensor Network
1
Mohammad Reza KaghazGaran, 2
Afsaneh KaghazGaran
1
kaghazgaran@yahoo.com
2
af.kaghzagaran@gmail.com
ABSTRACT
In this paper, we propose a routing protocol that is based on securing the routing information from
unauthorized users. Even though routing protocols of this category are already proposed, they are not
efficient, in the sense that, they use the same kind of encryption algorithms (mostly high level) for every
Bit of routing information they pass from one intermediate node to another in the routing path. The
proposed mechanism is evaluated against selected alternative trust schemes, with the results showing that
our proposal achieves its goals.Our research aims at providing a secure and distributed
authentication service in the ad hoc networks.
KEYWORDS
Wireless Sensor Network ; node selection; trust evaluation;
1. Introduction
Mobile host and wireless networking hardware are becoming widely available, and extensive
work has been donein the recent years in integrating these elements into traditional networks such
as internet.
They can be often used in scenarios in which no infrastructure exists, or in which the existing
infrastructure does not meet application requirements for reasons of security or cost. Wireless
sensor Network routing protocols are challenging to design and secure ones are even more so.
Prior research has generally studied the routing problem in a non-adversarial setting, assuming a
trusted environment [3]. These may be sufficient for normal day-to-day applicationsbut for
applications such as military exercises and disaster relief, a secure and a more reliable
communication is a prerequisite.
Trust-based solutions provide a method to select neighbors based on their trust value which is
derived from previous interactions. There have been a number of trust mechanisms proposed for
mobile [1],[4] and peer-to-peer networks [5],[7]. Most of these mechanisms are distributed and
favors the most trustworthy neighbor.
In this paper, we propose a novel probabilistic node selection model which provides a load
balancing within the population of nodes. Hereby, a node’s probability for being selected for an
interaction will correspond to its trustvalue. Furthermore, it gives unknown nodes the
opportunityto be selected, thus enabling these nodes to contribute to the community. trust [5]
briefly discusses a similar selection model, but it does not provide an analysis of themodel’s

28
impact on the quality of interactions and it allocatesthe lowest possible trust value to unknown
nodes.
Achieving key management in mobile Wireless Sensor Network networks is a challenge due to
the lack of a central authority and the autonomous, dynamic nature of these networks which result
in poor connectivity and routing failure. Many secure routing protocols for mobile Wireless
Sensor Network networks are published, e.g. SAODV [9], SEAD [10], ARIADNE [11], and
endairA [12]. Most of these assume pre-existing and pre-sharing keying relationships. Key
management proposed in [7-9] operates on the routing layer to achieve key distribution.
The required certificates are appended to all routing request in an effort to distribute keying
material during the route establishment phase. This approach is not ideal for an on-demand
Wireless Sensor Network in network environment because it results in flooding the network with
route request during its route discovery phase.
Figure 1: Trust and other security models in Trust Establishment security
Architecture for WSN
Secure protocols exist that provide key management tasks such as key distribution [6] but these
schemes lack to consider the delay incurred from the key management task of verification,
assuming it to be negligible. Existing models have such delayed bootstrapping security phases
that security is only delivered after an initial time of setup. This creates a window period of
weaken security or a window period of restricted communication [4, 6]. The aim of our paper is
to design a key management scheme that can be used to distribute and verify certificates in a
wireless on-demand Wireless Sensor Networkin network, with negligible affects upon routing
performance. The proposed scheme establishes trust by distribute and verify certificates for all
the nodes in a network with the following constraints:
• The key management scheme is to operate in an on-demand environment, exclusively
on the routing layer.
• The key management scheme is to distribute and verify certificates between local and
remote nodes providing direct and indirect trust relationships respectively.
• Each node in an indirect trust chain must verify its neighbor, the originator and
destination node’s certificates, before the trust chain is secure.
• The key management scheme aims to minimize the security overheads which affect the
network routing performance. These overheads include certificate verification and
distribution delays.
• The key management scheme should avoid altering the routing mechanism, and strive
for independence between routing and trust establishment. Routing packet size is not to
be extended to incorporate security information.

29
• The certificate scheme is to be designed in a fully distributive manner with no existence
of an on-line trust authority or prior trust relationships.
• Security should be available as a node enters a network with a seemingly timeless
bootstrapping phase for security.
• The key management scheme should be robust to poor connectivity and routing failure
due to shifting mobility, error-prone wireless channels and traffic congestion which are
natural characteristics of Wireless Sensor Network networks.
2. Related work
A detailed survey was presented on key management schemes for mobile ad hoc networks in the
previous chapter. Section-2 focused on the network layer and presented a survey of existing
secure routing protocols. This section provides work directly related to the DITD model.
The authors of [20],[3] propose a completely self-organized public key system for mobile ad hoc
wireless networks. This is a PGP based solution which provides key management in ad hoc
networks without the presence of an off-line or on-line authority, like a CA, TTP or server. Each
node distributes its self-certificates and maintains its own certificate repositories. Nodes
participating in the network share their certificate repositories and repository updates are
performed in a proactive manner. Certificates are reciprocally authenticated and trust chains
formed linking remote nodes to each other. Security is realized on the application layer.
Zapata [21] addresses the issue of verification delays in secure mobile ad hoc networks. Zapata
proposes a protocol to optimize the number of verifications made in a single secure route
discovery phase. Once a route is established only then are the shared certificates verified. This
helps in reducing the computational overhead of verifications on multi-hop paths. By reducing the
total number of verifications made in a network’s life time there is a resultant end-to-end delay
upon the delivery of routes.
[22] proposes a fully distributive conduct based trust model which has PGP characteristics. This
model operates on the application layer and allows for trust to be established without the presence
of a central authority member. PGP models share certificates to establish trust while the work
proposed in [22] allows for other trust evidence, like conduct and location, to influence the trust
establishment. Trust is fully distributed in a proactive manner allowing all nodes to give trust
opinions about other nodes.
[23] has more recently been used to model trust calculations in [22]. Trust opinions are
mathematically aggregated along a path and trust decisions are mathematically represented. The
work in [22] uses Dijsktra’s extended algorithm proposed by [24] to include trust. This finds the
most trusted path between two remote nodes in a proactive manner.
The majority of literature mentioned function in a proactive manner for application layer
solutions. The DITD model is designed on the network layer for a reactive, fully distributive,self-
organized, mobile ad hoc network environment. The ideas of some of these protocols have
inspired the creation of the DITD model and the impact of these protocols is discussed in Section-
6.

30
3. Proposed Scheme
3.1. System Model
To fulfill the constraints given in Section I, we assume the following system model. There is no
pre-existing infrastructure and no online trusted third party present during communication.
The model is a fully distributive network of wireless nodes using anWireless Sensor Network on-
demand routing mechanism. It is assumed that nodes have their own keying material before
joining the network generated by a fully self-organized mobile Wireless Sensor Networkin
network [2], or by an off-line authority issuing keying material before a node enters the network
for example in [6],[8]. Each node is assumed to have a public and private key pair, a certificate
binding the public key and user identification of the node, and a set of network security
parameters common to all nodes in the network. Secure communication is requested from the
start to the end of the network lifetime, unlike [4, 6] which is flawed by its initial setup phase
with weak security.
3.2. Proposed DITD Model
The proposed Direct, Indirect Trust Distribution Model (DITD) aims to distribute and verify self-
certificates to create direct and indirect trust relationships between nodes. DITD is a certificate
based trust model which works with existing mobile Wireless Sensor Network routing schemes.
It is not specific for a single routing protocol but its principals can be applied to any routing
scheme. In the following we introduce the proposed scheme in AODV environment.
AODV [13],[14] routing procedure has three stages: sending the request message; receiving the
request message; and sending the reply message. In the first stage, the originator node A requests
communication with destination node B by broadcasting a routing request RREQ into the
network. This request is forwarded by intermediate nodes and propagated through the network to
B. When the RREQ message is received by an intermediate node P, it may have been sent by A
or forwarded by a neighboring nodeNP. Upon receiving the RREQ message stage two begins.
At stage two a reverse route to A is then set up and P checks if it is the destination B or has a
fresh route to the destination node B. If not, then the RREQ is further broadcast by P and
propagates until the destination is found. When the destination or a fresh route to the destination
is found, stage three commences. A reply message RREP is propagated along the reverse route
until it reaches the originator node A establishing the communication route. When a node
receives a routing control packet, and before that packet is processed, DITD sends certificate
requests using separate unicast messages. The self-certificate distribution is added at stage two
and stage three; the receiving of the route request and the sending of the reply message stages.
At stage two, upon receiving a route request packet, before this packet is processed and the
routing table updated, direct trust and indirect trust establishment is set up. The proposed scheme
is subdivided into three parts: Direct trust establishment, indirect trust establishment and the post
verification optimization.
NP. Upon receiving the RREQ message stage two begins. At stage two a reverse route to A is
then set up and P checks if it is the destination B or has a fresh route to the destination node B. If
not, then the RREQ is further broadcast by P and propagates until the destination is found. When
the destination or a fresh route to the destination is found, stage three commences. A reply
message RREP is propagated along the reverse route until it reaches the originator node A
establishing the communication route. When a node receives a routing control packet, and before
that packet is processed, DITD sends certificate requests using separate unicast messages. The
self-certificate distribution is added at stage two and stage three; the receiving of the route request
and the sending of the reply message stages. At stage two, upon receiving a route request packet,

31
before this packet is processed and the routing table updated, direct trust and indirect trust
establishment is set up. The proposed scheme is subdivided into three parts: Direct trust
establishment, indirect trust establishment and the post verification optimization.
3.2.1. Direct Trust
At stage two, direct trust relationships are made by allowing the neighboring nodes to exchange
certificates. When intermediate node P receives a route request RREQ it first checks its
certificate repository for the certificate of the neighbor, NP, who forwarded the request. If it does
not possess such a certificate, Certificate NP, a local self-certificate exchange is done between
node P and its neighbor NP using two unicast messages.
The exchange of certificates follows the RREQ. This will flood the network in search of a route
to the destination node. Direct trust establishment is illustrated in Figure 2. What can be
expected is an increased initial packet overhead.
3.2.2. Indirect Trust
Similar to direct trust establishment, at stage two node P searches for the originator’s certificate,
Certificate A. If it is not found, node P sends a unicast certificate request for Certificate A to NP
whose address can be found at the next hop on the reverse route. This propagates Certificate A to
the destination B. For indirect certificate trust to be established originator A is required to
possess the destination’s certificate, Certificate B, as well. By not appending the certificate to
the route requests dependency is reduced between the route establishment and trust establishment.
At stage three, sending the reply message, the indirect trust establishment is completed. Sending
a reply is guided by two conditions. Firstly when the destination node is found and secondly
when a fresh route to the destination node is found. For the first condition, the reverse route to A
is already setup with localized direct trust existing between nodes on the route; therefore a trusted
certificate chain of nodes is available towardshe originator node A. It is required only that the
certificate of the destination node,
Figure 2: Direct and Indirect trust establishment
Certificate B, to be piggy backed on the routing reply message RREP toward B. Each
intermediate node stores Certificate B and updates its certificate repository. For the second
condition, if a fresh route to B is found, there exists a route from intermediate node P to
destination B and a route from P to A. Both routes have localized direct trust existing already, so
the two routes can be view as certificate chains. Two RREP messages are then propagated, one

32
toward B with the Certificate A appended and one toward A with the Certificate B appended.
Indirect trust is therefore set up by certificate chaining as illustrated in Figure 2.
4. Proposed Mechanism
We assume that in Wireless Sensor Network content distribution networks, nodes download the
required content from their immediate wireless neighbor’s and hence all the communications are
single hop. Furthermore, we assume that all neighbors have the required content and that content
is sub-divided into several pieces. We propose Real-Time Trust (ReTT), a novel distributed trust
evaluation mechanism that continuouslyevaluates trust during an interaction. The mechanism is
based on the assumption that an interaction between two nodes will consist of a number of steps
which can be individually analyzed in real time.
ReTT introduces two decision points, the decision to connect to a node (decision to start) and the
decision to stay connected after the interaction has started (decision to continue).The term real
time reflects the notion that newly received information is immediately evaluated to form an
evolving evaluation of trust.
The decision to start is determined by a historical trust value of the content-providing node and
the context of the interaction. This value is calculated by aggregating the opinions of the selecting
node and the recommendationsby other nodes. Let us assume that node d has to make a decision
whether to interact with node i. Given that Oj,i is the opinion sent to node d by node j on node i,
Wj,iis node d’s weighting on Oj,i, and node d has opinions on i from k nodes (including its own
opinion), then the historicaltrust value

of node d for node i is calculated as follows:
T

=
∑ W × O ,

∑ W

Oj,i is the average number of good integrity pieces received by node j from node i in a
transaction. The decision to start considers a level of openness, which allows the mechanismto
Figure 3: shows an overview of the proposed mechanism.
be flexible depending on the context and introduces a trust level threshold. In a fully open system,
this threshold would be the lowest possible trust value, and thus, all availablecontent-providing

Computer Science Engineering: An International Journal (CSEIJ), Vol.1, No.4, October 2011
33
nodes are considered as candidates. The remainder of the paper assumes a fully open system.
If the selecting node decides to interact with a content providing node, it then downloads the first
piece and evaluates the piece’s integrity. In this paper, a piece is considered to have good
integrity if it is received as requested and itdoes not contain a virus. The integrity verification
result is then combined with historical information to decide whetherto download the next piece
(i.e., decision to continue in Figure 3).
Given Ti,m is node i’s calculated trust value after mth piece in the current interaction, Ti,m-1 is the
trust valueafter receiving the previous and Ri,m is the verification result of the current piece, then
real-time trust value is calculated as:
, = , + , , {1,2,3, … . , }
and are the weights, with both being positive numbers which sum up to one. Since havingwill
ensure that the mechanism quickly responds to changes in behavior.We also propose a novel node
selection model which probabilistically selects content providing nodes based on their trust value,
which aims to achieve load balancing while still having an appropriate tradeoff for interacting
with nodeswith low trust value. To this end, the selecting node evaluates the trust values T of the
set of all available content-providing nodes C.
The node probabilistically selects a node from this set C, where the probability of selecting a
node n, Pn, is:
P# =
(T#

)
∑ (T

)
|(|

We define T#

=0,5 only for the selection process, to ensure that unknown nodes are preferred
over known misbehaving nodes.
5. TRUST AND REPUTATION
Trust models are an attempt to formalize trust definitions [1] and are often tied to the
establishment of a Public Key Infrastructure (PKI) in WSN [75].
In [37], [38], [41] give a security approach based on trust for pervasive computing [42] in which a
securityagent (fixed device) in each domain is responsible for trust management, authentication
and authorization. A European project, SECURE [28], [66], presents trust and risk frameworks
for enabling secure collaboration between ubiquitous computer systems. Establishing trust by
physical contact between devices is presented in [67]and extended (to include the use of location-
limited channels) in [7].
In addition to this work, the modeling of trust at the network layer has received much attention.
Various authors have proposed methods for nodes to establish trust in one another. In this section
we provide an overview of these proposals, many of which are designedto tackle the problem of
packet forwarding selfishness using preventative and/or reactive measures. The solutions
described below can be classed as follows:
• Routing protocol mechanisms,
• Currency systems, and
• Reputation systems.

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5.1. Trust in Routing Protocols
This acknowledgementconsists of the packet’s unique identifier, concatenated with the
destination node’s address. If the number of unacknowledged packets exceeds a threshold, then a
faultdetection protocol is used, similar to the Secure Trace route protocol proposed by [15].
Thiscontains the addresses of the intermediate nodes from which the originator wants an
acknowledgement, with the destination node’s address as the last entry. The listincludes a HMAC
which is recursively produced, each round using a secret key shared between the originator node
and the intermediate node being ‘probed’ (this is atechnique also known as ‘onion encryption’
[19]).
When a probed intermediate node receives the probe list, it decrypts a layer of the onion
encryption and verifies the HMAC before forwarding the packet, so that the next intermediate
node in the probe list can verifythat it belongs to the list.
After forwarding a probe list, an intermediate node waits for an acknowledgement from the next
node on the probe list. If one is not received with a specific timeoutinterval, then the node must
initiate an acknowledgement chain by creating an acknowledgement. The timeouts arecalculated
in such a way that the last probed node which successfully receives the packet will always initiate
the acknowledgment chain. Thus, when the route is working,this will be the destination node.
This acknowledgement chain is forwarded towards the originator node.
5.2. Reputation Systems
Reputation systems have been proposed for use in Wireless Sensor Network networks to address
some of the threats arising from misbehaving network nodes. These mechanisms, explored in
more detail in this section, are potentially ofparticular value in addressing the threats arising from
selfish nodes. In the context of anWireless Sensor Network, these mechanisms seek to
dynamically assess the trust worthiness of neighboring network nodes, with a view to excluding
untrustworthy nodes. Although reputationcan be seen as a particular trust metric there have been
attempts to draw a distinction between the two.
The use of reputation systems in many different areas of information technology is increasing, not
least because of their widely publicized use in online auctions and product reviews.Reputation
systems are used to decide who to trust, and to encourage trustworthy behavior. Identify three
goals for reputation systems:
1) To provide information to distinguish between a trustworthy principal and an
untrustworthy principal,
2) To encourage principals to act in a trustworthy manner
3) To discourage untrustworthy principals from participating in the service which the reputation
mechanism is present to protect.
Reputation systems can be managed either centrally or in a distributed manner [36]. As was the
case for the discussion of currency systems in section II-B), we concentrate here on distributed
reputation systems, which suit the properties of a stub Wireless Sensor Network.
Reputationsystems rely on principals monitoring sequences of transactions with other principals,
and on communicationsbetween principals that are willing to take part in the reputation system.
Each principal maintains a reputation value for some subset of the other principals in the system.
These values can be shared between principals or they may be unique for each participant. The

35
precisemeaning of the reputation value, how it is calculated and updated, and how it is
communicated between parties,are all system-dependent. However, it is generally true that this
value is intended in some way to measure the trustworthiness of the principal, at least for the
purposesof the system concerned.
5.2.1. The Watchdog and Path rater Mechanisms:
Like the DSR routing protocol, the Watchdog mechanism makes use of passive observations.
Therefore, if a node maintains a buffer containing packets it has sent toa neighboring node, then,
using passive acknowledgements, the node can determine whether this neighbor has forwarded
the packets. If a packet in the bufferremains unacknowledged for a certain period of time, i.e.
it has not been forwarded, then a failure count for that neighbor is incremented. If the failure
count exceeds a threshold, then the node sends a notification to the source of the packet
identifying the selfish node.
The Pathrate mechanism operates only with source routing based protocols such as DSR, and is
essentially a reputation system. A node assigns a null rating of 0.5 for each node connected to the
network, derived fromthe source routes accumulated through route discovery.
The ratings of nodes on actively used source routes are increased by 0.01 every 200 ms, up to a
maximum of 0.8. When a link break occurs, the node upstream of the break can send a route error
message back to the source. On receipt of a route error message, the ratings of the nodes
downstream of the route error originatorare decreased by 0.05, unless the rating is already 0 or
less, in which case the rating is left unchanged1. If a notification of selfishness is received about a
node, then the rating of that node is assigned a value of –100. All negative ratings are either
increased slowly, or reset tozero after a specific period of time, in order to allow a selfish node to
recover. When a node has multiple paths to the same destination, it can calculate the mean
average of the ratings of each path, in order to determine which path is most likely to offer
successful delivery of traffic.
5.2.2. An Overview of CORE:
Any member in Wireless Sensor Network not contributing will find their reputation worsening
until they are gradually excluded from the operation of the network because of their bad
reputation.
CORE defines two types of reputation, namely subjective and indirect, both of which are
calculated for each function being observed. A node maintains the reputation of each neighbor
node for each of a range of functional behaviors. The two types of reputationvalues are computed
as follows:
• Subjective Reputation is based on local observations. If an observed behavior matches
the expected behavior, then the observation will be deemed positive; otherwise it is
deemed negative.
• When updating a reputation value, greater weight is given to past behavior than current
behavior;
• placing more weight on past observations prevents subjective reputation being influenced
by sporadic behavior.
To be able to perform observations reliably is ofextreme importance to the CORE scheme, andthe
authors have suggested the Watchdog mechanism based on promiscuous observation The
expected result of the current operationis stored in a buffer until a matching observation is made.

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While the expected result is still present in the buffer, the reputation for the observed function is
gradually decreased.
6. Performance and simulation study of the proposed DITD Model
There are two main approaches to evaluate routing applications for mobile ad hoc networks:
simulations and real test beds [25],[26]. Real test beds can provide realistic results. However,
they are impractical to set up. A real test bed, for a large network of nodes would requires 50
nodes in operation which is considerably costly. It is also difficult to compare different protocols
because of the difficulty in repeating test conditions, such as mobility and erratic wireless
connectivity. Therefore, real test beds are logistical unfeasibly. Currently, simulations are widely
used to compare proposed routing protocols. Simulation packages like ns2 and [25],[6] provide
an environment to design and compare proposed and existing protocols. The majority of literature
on this subject use ns2 as its enhanced functionality is suitable for wireless scenarios. The ns2
network simulator was selected to perform a simulation study for the DITD model.
This section presents the effects of adding the security functionality, proposed by the
DITDmodel,to the AODV routing protocol. This functionality includes a certificate distribution
mechanism and a trust evaluation mechanism. The environment investigated is a large mobile ad
hoc network which uses an on-demand routing algorithm. We use subsection 6.1 to: describe the
simulation environment, discuss the simulation scenario, and introduce the traffic and mobility
models. The performance metrics used to analyze the simulated routing protocols. Where a
comprehensive simulation study is presented. This is done by comparing the proposed DITD
model with the AODV routing protocol. Results are presented in simple line graphs and
discussed accordingly.
6.1. Simulation setup
The goal of the simulation experiments is to measure the proposed routing protocol’s
performance to a changing network topology and network conditions. To measure this, protocols
are simulated at varied mobility conditions. A comprehensive simulation study ispresented of the
proposed security scheme for mobile ad hoc networks implemented on the network layer. A
summary of the simulation set used in our study is given in Table-1.
6.1.1. Simulation Scenario
The network was set up with 50 wireless nodes allowing data communication to occur in a peer-
to-peer manner. Nodes are mobile in a rectangular space of 1500m x 300m and the simulation is
run for 900 seconds. A rectangular area is preferred to a square area as longer routes can be
expected. Nodes were configured to use the 802.11b standard communicating over wireless
channels with a two-ray ground radio propagation model with a bandwidth of 2Mbps and a
nominal transmission range of 250m.
6.1.2. Traffic Model
Traffic was simulated using a constant bit rate (CBR) traffic generator which models UDP traffic.
TCP traffic was not used because it uses its own flow control mechanism which schedules data
packets based on the network’s ability to carry them. CBR traffic is more useful for a routing
protocol analysis as it allows the routing protocol to manage the flow of traffic. All traffic is
started within the first 180 seconds of the simulation. Simulations were performed with data
packets sizes of 64, 256, 512 and 1024 bytes. At higher data packet sizes traffic congestion causes
a few nodes to drop most of their received packets, this was observed from test simulation runs. A
data packet size of 64 bytes was selected for the simulation analysis. The focus of the simulation
study is to compare the performance of routing protocols against changing topology and as no

37
load balancing is employed in any simulated protocol, congestion is factored out by selecting a
lower data packet size. The traffic analysis model is consistent with routing protocol analysis in
[26]. For topology analysis the traffic load is fixed with a rate of 4 packets per second.
The maximum number of connections is set to 30 connections with a traffic model with 20
sources.
6.1.3. Mobility Model
A modified “random waypoint” mobility model was used to prevent mobility concerns
highlighted in [25]. The modified random waypoint model improves upon the standard model by
selecting a speed which is between 10% and 90% of the given maximum speed. This addition
provides a more balanced mobility and prevents extreme drops in speed during simulation.
Changing network topology is simulated based on network participant speed. The maximum
speed was varied from 0 to 30m/s with 6 different mobility patterns (0.1, 1, 5, 10, 20 and
30m/s)for two different pause time scenarios, 0 and 250 seconds, representing a network with
continuous motion and a partially stable network.
6.2. Performance metric
The following quantitative metrics are used to analyze the performance of the routing protocols in
mobile ad hoc networks.
6.2.1. Packet Delivery Ratio
The packet delivery ratio (PDR) represents the percentage of data packets that are successfully
received by their intended destination. This metric is also known as throughput and isconsidered a
measurement of the effectiveness of a routing protocol.
Simulation Scenario
Physical and MAC model IEEE 802.11b standard
Nominal bit rate 2Mbps
Transmission Range 250m
Number of nodes 50 nodes
Simulation duration 900 seconds
Simulation area 1500m x 300m
Traffic Model
Traffic type CBR
Data packet size 64 byte
Traffic rate 4 packets per second
Traffic started 0 – 180 seconds
Number of connections and sources 30 and 20
Mobility Model
Model Random Waypoint
Max speed 0.1 , 1, 5, 10, 20, 30

38
m/s
Pause time 0 and 250 seconds
Table 1. Simulation Setup for varied topology
The equation for PDR is:
PDR % =
∑ ,-./0
1

∑ CBRsent
1

× 100
where ∑ ,-./0
1
and∑ CBRsent
1
are the number of CBR data packets received and sent
respectively.
6.2.2. Routing Overhead
A routing protocol uses control packets to establish routes on which data packets are transmitted.
Control packets are separate from data packets but share the same communication channel. Due
to the lack of channel capacity in mobile ad hoc networks a large number of control packets can
result in poor network performance. Key management would require additional control packets
to achieve key management functionality this will be reflected in the simulations. The routing
overhead is also known as a routing protocol’s internal efficiency and will represent the number
of control packets used for a given protocol.
6.2.3. Average End-to-End Delay
This is a qualitative measurement of the delay of data packets. The average end-to-end delay of a
data packet is the time from which it is created at the source and when it arrives at the intended
destination. The delay includes propagation and queuing delay. Delay can be caused by a high
number of control packets propagating in the network or a high computational overhead for the
given protocol. The average end-to-end delay is calculated as follows,
End to End Delay =
∑ ((9:_=#_?@=
A
B (9:_C=DE_?@=)
∑ (9:C=D
A
B
where CBR_send_time and CBR_recv_time represent the record times that a CBR data packet
was sent and received.
6.3.DITD simulation
6.3.1. Implementation
A Linux based server was set up to run the Network Simulator ns-2.31. A routing protocol was
designed in C++ based on the AODV routing protocol available in the ns-2.31 package. The
routing protocol DITD is programmed as a routing agent class. The routing agent handles the
establishment of routes, certificate distribution and trust evaluation. Modifications are made to the
AODV routing agent at the Recv_Request, Send_Request, Recv_Reply, and Send_Reply
functions. These modifications allow for the distribution of separate certificate packets, triggered
by the routing packets. The routing agent’s packet header was modified to include a certificate
control packet Cert-S. The size of the certificate included is 450 bytes which correlates with
experiments in [21].
The size of the certificate control packets is increased resulting in an effective delay in
communication simulating the transfer of actual certificates. The authors of [27] use a similar
approach to simulate the effect of security processing. A certificate table is included at each node
Cartable which is updated by certificate control packets. The certificate table is linked to the
routing table and each node is responsible for managing its own certificate table.

39
The trust evaluation scheme assumes that monitoring trust evidence is available. Routing control
packets are modified to include an associated trust variable. As each routing packet propagates
through the network, the trust of the specific route is calculated and stored in the routing table of
each node. Implicit trust revocation and trust path selection is performed at Recv_Request and
Recv_Reply functions respectively.
A simulationtcl file is written to setup the mobile ad hoc network’s desired simulation scenario,
traffic and mobility model. The trace support files in ns-2.31 were modified to support the DITD
routing agent allowing the inclusion of certificate control packets and trust information. As a
result the output trace and nam files reflect the operation of the DITD routing agent. AWK, an
extremely versatile programming language for UNIX based systems, was used to write script files
to analyze the trace data and provide the measured performance metrics. Finally UNIX based
shell script files were written to allow for multiple iterations and simulation scenarios to be run
simultaneously resulting in over 1000 simulation runs and 430 GB of data analyzed and presented
in simple line graphs.
6.3.2. DITD Performance Results
The DITD model is compared with the AODV routing protocol. Further comparisons are
presented against a conventional approach to key distribution. The simulation scenario which is
used throughout the simulation study. The traffic model simulates a moderate traffic load at a rate
of 4 packets per second. The effects of changing topology are investigated by varying the node
speed for a continuously moving network and a partially stable network. The simulation results
were averaged over 10 speeds per scenario, resulting in a total of 360 iterations for the speed
analysis.
6.3.2.1.Packet delivery
The packet delivery results for the AODV and DITD routing protocols are presented in Figure 5
and Figure 6. Figure 5 represents a simulation environment with a pause time of 0 seconds. This
represents a network of nodes that are continually moving, while Figure 6 represents a partially
stable network. The observation is made that as the speed increases both protocols throughput
decreases. At high speeds the network topology changes rapidly causing breakages in routing
links. The reduction in packet delivery at high speeds is because both protocols will drop data
packets as a result of increased routing breakages. The curves for the AODV and DITD packet
delivery ratio have similar shapes. This is expected because the DITD model is based on the
AODV model. In Figure 5 the DITD model shows a 0–10% reduction gap in packet delivery
when compared to the AODV model. The gap increases uniformly as the speed increases
leveling at 10% for speeds of 20 m/s and higher. Similarly for the more stable network, presented
in Figure 5, there is a reduction in packet delivery ratio of 0-5% when compared to the AODV
model. The stable network in Figure 5 shows better performance at higher speeds because the
number of route link breakages is reduced as a result of a larger pause time.
A large pause time represents a network that will move at a given speed then pause in a fixed
location for a set amount of time. During this time routing link breakages are not expected until
movement commences again. The reduction in packet delivery ratio of DITD, when compared to
AODV, can be attributed to the additional certificate packets being distributed and handled by the
routing agent. The packet queue for the routing protocol has a limited capacity and when it is
overloaded, packets are dropped. This will cause a resultant drop in throughput.
The DITD model optimizes its throughput by processing the routing and certificate control
packets independently of each other. A certificate distribution scheme would expect a severe

40
reduction in performance due to an excessive number of packets being transmitted in the network
or the additional size of the control packet. A conventional certificate distribution scheme,
suggested as a possible solution in [29], simply includes the source certificate in the request
packets RREQ and the destinations certificate in the reply packets RREP. This method was
implemented as a separate routing agent AODV-cert in ns2.
A similar method is suggested in [29]. Implementation includes increasing the packet size of the
routing control packets to include a 450 byte certificate. This effectively increased the regular 56
byte AODV route control packets to 506 bytes. Such an approach would result in the simplest
method of certificate distribution but transmitting 450 bytes more data per control packet would
severely reduce the network performance.
Figure5. Packet Delivery Ratio for highly mobile network (0 second pause time)
The AODV-cert routing agent was simulated under the same simulation conditions as AODV and
DITD, and the packet delivery ratio is presented in Figure 7 and Figure 8. It can be observed that
the packet delivery ratio is severely less than both the AODV and DITD model. For a pause time
of 0 seconds, there is an average gap of 55% between AODV-cert and AODV and an average gap
of 49% between AODV-cert and DITD. Similar results are observed for the stable network in
Figure 9. This simulation shows that DITD optimizes the distribution of certificates by sending
them as separate certificate control packets independent of the route control packets. The
certificate control packets are processed independently of the routing packets, allowing
concurrent processing in a fully distributive system. The operation of DITD allows for certificate
distribution with minimal effect upon the routing procedure. During this time routing link
breakages are not expected until movement commences again.
Figure6. Packet Delivery Ratio for partially stable network (250 second pause time)

41
The reduction in packet delivery ratio of DITD, when compared to AODV, can be attributed to
the additional certificate packets being distributed and handled by the routing agent. The packet
queue for the routing protocol has a limited capacity and when it is overloaded packets are
dropped. This will cause resultant drop in throughput. The DITD model optimizes its throughput
by processing the routing and certificate control packets independent of each other.
A certificate distribution scheme would expect a severe reduction in performance due to an
excessive number of packets being transmitted in the network or the additional size of control
packet. A conventional certificate distribution scheme, suggested as a possible solution in [21],
simply includes the source’s certificate in the request packets RREQ and includes the
destination’s certificate in the reply packets RREP. This method was implemented as a separate
routing agent AODV-cert in ns2. A similar method is suggested in [29]. Implementation
includes increasing the packet size of the routing control packets to include a 450 byte certificate.
This effectively increased the regular 56 byte AODV route control packets to 506 bytes. Such an
approach would result in the simplest method of certificate distribution but the result of
transmitting 450 bytes more data per control packet would severely reduce the network
performance.
The AODV-cert routing agent was simulated under the same simulation conditions as AODV and
DITD and the packet delivery ratio is presented in Figure 6 and Figure 7. It can be observed that
the packet delivery ratio is severely less than mobile ad hoc networks. A high speed network is
described by a maximum node speed of 20 and 30 m/s. This simulates mobile units travelling at
a maximum speed of 70–100km/h which is typical of mobile military vehicles. Mobility aids the
distribution of certificates as nodes come in close contact with each other and are able to establish
direct trust relations reducing end-to-end certificate distribution. These benefits are similar to
[19] solution which relies upon mobility to establish trust in a localized manner [20].
[20]solution is aided by mobility but is also dependent upon mobility for trust relations to be
established. Because of this dependency, a period of weakened security is expected as nodes
exchange certificates. DITD does not only distribute certificates in a localized manner but Figure
8 shows that the DITD model has a 0 - 3% reduction in throughput for low speed mobile ad hoc
networks where nodes move at a maximum speed of 0–10 m/s. This type of networks is typical
of infantry units or a nam the ground scenario. DITD allows for mobility to aid the distribution
of certificates but not relying upon mobility for throughput success. This allows DITD to operate
successfully in slow moving and stationary type networks. The packet delivery ratio results show
that DITD provides certificate distribution at a low performance cost for high speed networks and
for low speed networks.
Figure 7: shows that the DITD model has a 10% reduction in throughput for high speed

42
6.3.2.2.Control Packet Overhead
The control packet overhead presents a comparison between the AODV and DITD models. The
overhead is presented in terms of the number control packets. The AODV model will have only
routing control packets while the DITD model will have both routing and certificate packets. The
results are presented in Figure 8 for a highly mobile network with pause time of 0 seconds and a
partially stable network with pause time of 250 seconds. The DITD model aims to distribute
certificates while routes are discovered and a resultant packet overhead is expect.
AODV and DITD are similar in shape and it is observed that the number of control packets
increases as the speed increases. As the speed increases the topology of the network changes
more rapidly causing routing link breakages and forcing nodes requesting communication to re-
establish routes by send new route request messages. For a partially stable network presented in
Figure 8 the effects of speed are reduced. This confirms that a larger pause time provides a more
stable network. Figure 7 and Figure 8 show a consistent control packet overhead for the DITD
model. It is observed that the gradient of DITD’s packet overhead decreases as speed increases.
This is because mobility aids certificate distribution and as the speed increases less certificate
control packets are required. For example in Figure 7 at the low speed of 1 m/s there is a 132%
increase in the number packets when compared to the AODV protocol. This overhead decreases
for higher speeds showing a comparative 38% and 33% packet overhead for speeds of 20 m/s and
30 m/s respectively. This confirms that mobility aids certificate distribution.
A standard AODV request message is 48 bytes and a reply message is 44 bytes. The DITD
model uses request message of 60 bytes and reply messages of 56 bytes. Therefore, DITD
increases the routing control packet size by 12 bytes. DITD’s routing control packets contain
trust associated variables and flags to trigger back-tracked certificate distribution. The DITD
certificate control packets are 508 bytes in size as they included a 450 byte certificate. It is noted
that making the routing and certificate control packets separate and independent from each other
has a greater impact on reducing the per byte packet overhead. This independency allows for
concurrent processing of packets which is optimal in a fully distributive ad hoc network.
Figure 8: Control packet overhead for highly mobile network (0 second pause time)
[

43
Fig.9: Control packet overhead for partially stable network (250 second pause time)
6.4. Trust Evaluation Results
In order to test the performance of the security evaluation scheme, a black hole attack was
simulated to show that DITD’s security evaluation scheme excludes malicious nodes from trust
and route establishment protecting the network from black hole type attacks. A black hole
adversary model was designed on the ns-2.31 link layer (LL) which lies below the routing layer.
Modifications were made to the link layer agent ll.cc to simulate a black hole attack. Each packet
sent by the routing layer is checked at the link layer, the adversary model silently drops all data
packets while still allowing routing packets to be passed. This creates the effect of a black hole
attack. A second black hole adversary model was implemented which includes a rushing type
attack.
The rushing attack was implemented by allowing adversary nodes to forward routing packets
immediately, removing the small jitter delay that AODV implements. AODV uses this small
delay to reduce the number of collisions and ensure the shortest path is selected. The rushing
attack gives an adversary node a time advantage over normal nodes resulting in the adversary
node becoming part of considerably more routes.
The same simulation scenario and traffic model was used to analyze the black hole attack. The
mobility was fixed with a pause time of 0 seconds and three speeds were investigated (0.1m/s,
5m/s and 20m/s). A 50 node network was simulated with 6 different attack scenarios. The attack
scenarios were created by varying the number of black hole adversary nodes added by 0 to10.
Figure 11 shows the nam simulation file for a simulation scenario with 10 adversary nodes. Each
scenario was averaged over 10 seeds resulting in 720 iterations for the security evaluation scheme
analysis. The black hole attack aims to drop data packets and reduce the networks throughput.

44
Fig. 10: Average end-to-end delay for partially stable network (250 second pause time)
The effects of a black hole and rushing attack are analyzed using the packet delivery ratio
performance metric.
A black hole type problem is implemented to simulate the success of DITD’s security evaluation
scheme. The scenario assumes weighted nodes carry a security metric which identifies fault
detection or data transmission errors carried out by a monitoring system at each node. An
example of such a system is found in [29]. The weighted nodes are used to establish a weighted
trust graph where each edge or route carries a trust calculated by DITD’s security evaluation
scheme. The effects of the black hole attack upon AODV and DITD are compared in Figure 10
and Figure 11. It is observed that as the number of adversary nodes increases the packet delivery
ratio for the AODV model decreases. The AODV model is vulnerable to black hole attacks and
in the presence of 10 adversary nodes the packet delivery ratio is below 65%. The reduction in
throughput is expected as more data packets will be dropped by the presence of many adversary
nodes.
DITD avoids the adversary nodes by implicitly excluding these nodes during route establishment.
The success of the protocol at low speeds is presented in Figure 12 and it is observed that even in
presence of 10 adversary nodes the packet delivery ratio is not less than 90%. Figure 11 presents
the success of the DITD model at a higher mobility of 20m/s. The DITD model prevents the
severe effects of black hole attacks showing better results when 4 and greater than 4 adversary
nodes are present. There is approximately a 10% decrease in packet delivery ratio when
compared to the low mobility scenario in Figure 12.
This reduction in packet delivery ratio is attributed to the increase in link breakages apparent at
higher speeds and the overhead incurred from the certificate exchange protocol. The results of
DITD correlate to the packet delivery ratio at 20m/s in Figure 5. A rushing attack was included
for the simulations presented. An adversary node equipped with a rushing type attack will
participate in more routes maximizing the effect of its attack. Figure 11 and Figure 12 show that
when adversary nodes employ a rushing attack the effects of the black hole attack are maximized.
The packet delivery ratio of the AODV protocol is dropped to 40% when 10 adversary nodes are
present. This is considerably less when compared to the 60-65% packet delivery ratio that
AODV experiences under the same conditions with a standalone black hole attack. The results of
DITD under rushing attacks are unnoticeable when compared to DITD with no rushing attacks.
For low speeds, DITD provides a throughput rate of above 90% even in the presence of 10
adversary nodes.

45
Figure 11: Packet Delivery Ratio for slow moving network under black hole attack
DITD provides a security scheme that excludes malicious nodes from participating in trusted
routes, therefore preventing black hole attacks and a number of other attacks targeting the
network layer. The inclusion of this trust evaluation scheme allows the distribution of certificates
to operate in the most trusted routing environment.
Figure12: Packet Delivery Ratio for fast moving network under black hole attack
7. CONCLUSION
This paper proposed the methods to analyze the process of trust establishment in distributed
networks. he tools for performing the analysis were implemented, and validated by simulations.
The proposed analysis methods were utilized to solve an important research problem: quantitative
comparison among different trust models. In the future, we will exploit more applications of the
analysis tool, such as understanding the effects that application context has upon trust
establishment, and guiding the design of better trust establishment methods.

46
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Authors
Mohammad Reza KaghazGaran
He was born in Mashhad-Iran and B.Sc. Software
June 2011of Islamic Azad University
Vehicular Network and Bioinformatics.
Afsaneh KaghazGaran
She was born in Mashhad-Iran and received B.Sc. in Nursing On 1994 Of Islamic Azad University, Branch
of Mashhad. Her researches are Bioinformatics and Medical
network and computer network and
network.
Kschischang F.R., B. J. Frey, and H.-A. Loeliger, Factor graphs and the sum-product algorithm,
IEEE Transactions on Information Theory, vol. 47, pp. 498-519, 2001.
Mohri M., Semiring frameworks and algorithms for shortest-distance problems, J. Autom. Lang.
350, 2002
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339, 2007.
Zeng X., R. Bagrodia, and M. Gerla, GloMoSim: a Library for Parallel Simulation for Large
J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, A performance comparison of multi-hop wireless ad
ng protocols, in Proceedings of the 4th annual ACM/IEEE international conference
on Mobile computing and networking Dallas, Texas, United States: ACM, 1998.
Navidi W., Stationary Distributions for the Random Waypoint Mobility Model, IEEE Transactio
on Mobile Computing, vol. 3, pp. 99-108, 2004.
Stephan Eichler C.R., Challenges of Secure Routing in MANETs: Aimulative Approach using
SEC, in Mobile Adhoc and Sensor Systems (MASS), 2006 IEEE International Conference
uch, D. Holmer, C. Nita-Rotaru, and H. Rubens, An on-demand secure routing protocol
Atlanta, GA, USA: ACM, 2002.
Iran and B.Sc. Software Computer Engineering and graduated on
of Islamic Azad University of Mashhad. His researches are Wireless and recently
Vehicular Network and Bioinformatics.
Iran and received B.Sc. in Nursing On 1994 Of Islamic Azad University, Branch
Her researches are Bioinformatics and Medical Paramedical. His interests
and connections between the medical and paramedical with computer
, October 2011
47
product algorithm,
e problems, J. Autom. Lang.
hoc networks, Ad
rary for Parallel Simulation for Large-scale
hop wireless ad
ng protocols, in Proceedings of the 4th annual ACM/IEEE international conference
Navidi W., Stationary Distributions for the Random Waypoint Mobility Model, IEEE Transactions
Stephan Eichler C.R., Challenges of Secure Routing in MANETs: Aimulative Approach using
SEC, in Mobile Adhoc and Sensor Systems (MASS), 2006 IEEE International Conference
demand secure routing protocol
Iran and received B.Sc. in Nursing On 1994 Of Islamic Azad University, Branch
s in issues of
paramedical with computer

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