IJMNDI.2015.069218

Int. J. Mobile Network Design and Innovation, Vol. 6, No. 1, 2015 31
Copyright © 2015 Inderscience Enterprises Ltd.
Trust-based light weight authentication routing
protocol for MANET
Banoth Rajkumar*
College of Computing and Informatics,
Haramaya University,
Dire Dawa, Ethiopia
Email: banothrajkumar0381@gmail.com
*Corresponding author
Gugulothu Narsimha
Department of Computer Science and Engineering,
JNTUH College of Engineering,
Nachupally, KarimNagar, India
Email: narsimha06@gmail.com
Abstract: In mobile ad hoc network (MANET), most of the existing routing technique lags
trusted way of communication among the mobile nodes. The control messages are also prone to
the external threats. Also, the act of performing authentication of the nodes during each routing
process causes increased overhead. Hence, in this paper, we propose a trust-based light weight
authentication routing protocol in MANET. Initially, a multipath route discovery technique is
utilised that selects the path with maximum packet success ratio as optimal path for data
transmission. For each node in the chosen path, global trust value is estimated based on direct
and indirect trust values of the node. If the trust value of any node is below threshold value, then
it is authenticated using the secret sharing technique. This authentication technique enhances the
reliability, redundancy and network lifetime. By simulation results, we show that proposed
protocol improves the reliability and security of routing.
Keywords: mobile ad hoc network; MANET; light weight; routing; trust; authentication;
multipath; packet success rate; security; reliability.
Reference to this paper should be made as follows: Rajkumar, B. and Narsimha, G. (2015)
‘Trust-based light weight authentication routing protocol for MANET’, Int. J. Mobile Network
Design and Innovation, Vol. 6, No. 1, pp.31–39.
Biographical notes: Banoth Rajkumar obtained his Bachelor’s in Computer Science and
Engineering from National Institute of Technology (NITH) Hamirpur, Himachal Pradesh, India.
Then, he obtained his Master’s in Computer Science and Engineering and pursuing his PhD in
Computer Science and Engineering on the topic ‘Authenticated route formation for securing ad
hoc networks – a security perspective’ both from Jawaharlal Nehru Technological University
Hyderabad Andhra Pradesh India. He has also obtained CCNA-Exploration qualifications.
Currently, he is a Lecturer at the College of Computing and Informatics, Haramaya University,
Ethiopia. His specialisations include computer network, networking, mobile computing, compiler
design, design and analysis of algorithm, MANET and discrete mathematics. His current research
interests are MANERT, public key infrastructure, network security, and authentication server.
Gugulothu Narsimha received his BE in Electronics and Communication Engineering form the
University College of Engineering, Osmania University Hyderabad, passed in 1996, MTech in
Computer Science and Engineering from the University College of Engineering, Osmania
University Hyderabad, passed in 1999, and PhD in Computer Science and Engineering from the
University College of Engineering, Osmania University Hyderabad. He has about ten years
and six months of teaching experience. Presently, he is an Assistant Professor in the Computer
Science and Engineering Department at JNTUH College of Engineering, Nachupally. His
research interests are in resource and mobility management for wireless mesh networks, wireless
sensor networks and heterogeneous wireless networks.

32 B. Rajkumar and G. Narsimha
1 Introduction
1.1 Mobile ad hoc networks
Mobile ad hoc networks (MANETs) consist of freely
roaming wireless nodes that kindly make up for the absence
of fixed infrastructure; that is, the nodes themselves support
the network functionality. MANETs have received great
attention in the past few years. Nodes transiently associate
with their peers that are within the radio connectivity range
of their transceiver and implicitly agree to assist in
provision of the basic network services. Since MANETs are
rapidly deployable and self-organising, they are very
attractive in tactical and military applications, such as the
tactical communications in a battlefield. The salient features
of a MANET are: broadcast nature of the wireless channel,
infrastructure less architecture, highly dynamic network
topology and limited resources of mobile devices. All these
salient features of MANET have posed many new
challenges in the design and implementation of such a
network (Lou et al., 2009; Papadimitratos and Haas, 2003).
1.2 Secure routing in MANET
In MANET, secure routing plays an important role because
of the absence of a fixed infrastructure. Instead, nodes are
transiently associated and cooperate with virtually any node
that could potentially disrupt the route discovery and data
forwarding operations. The trouble of the route discovery
may be an ‘effective’ means to systematically block the
flow of data. Adversaries can respond with stale or
corrupted route replies, or broadcast forged control packets
in order to obstruct the propagation of legitimate queries
and routing updates.
All of the routing protocols in MANET depend on
active cooperation of nodes to provide routing between the
nodes and to establish and operate the network. The basic
achievement in such a setup is that all nodes are well
behaving and authentic (Papadimitratos and Haas, 2003;
Khaleel and Ahmed, 2012).
1.3 Attacks against routing in MANET
The malicious nodes can attack MANET in different ways
like sending fake messages several time, fake routing
information, and advertising fake links to disrupt routing
operations. The confidentiality is not only restricted to user
information but also the routing information need to be
remain confidential in certain cases. For example, routing
information might be valuable for an enemy to identify and
to locate targets in a battlefield (Papadimitratos and Haas,
2003; Khokhar et al., 2006).
The current routing attacks and its countermeasures
against MANET protocols are discussed below.
1.3.1 Flooding attack
In flooding attack, attacker exhausts the network resources,
such as bandwidth and to consume anode’s resources,
such as computational and battery power or to upset the
routing operation to cause harsh degradation in network
performance.
1.3.2 Blackhole attack
In blackhole attack, a malicious node sends fake routing
information, claiming that it has an optimum route and
causes other good nodes to route data packets through the
malicious one.
1.3.3 Link spoofing attack
In a link spoofing attack, a malicious node advertises fake
links with non-neighbours to interrupt routing operations.
1.3.4 Wormhole attack
A wormhole attack is one of the most difficult and severe
attacks in MANETs. In this attack, a pair of colluding
attackers record packets at one location and replay them at
another location using a private high speed network.
1.3.5 Colluding misrelay attack
In colluding misrelay attack, multiple attackers work in
collusion to modify or drop routing packets to upset routing
operation in a MANET (Khokhar et al., 2006).
1.4 Problem identification
Tague et al. (2011) have considered the problem of
jamming-aware source routing in which the source node
performs traffic allocation based on empirical jamming
statistics at individual network nodes. This approach will
give a better routing approach in the case of congestion but
it does not consider the trust value of the nodes and also
ignores the authenticated route formation, i.e., the efficient,
secure and effective communication between the mobile
nodes.
An authentication scheme was introduced in Vergados
and Stergiou (2007) using threshold secret sharing. The
authors have distributed the certification authority (CA)
functionality using the threshold cryptography. But it will
result in increased overhead since each time the all the
nodes need be authenticated.
Zakhary and Radenkovic (2010) have proposed different
centrality measures for evaluation of the individual trust
claims and resolving the aggregated ones. EigenTrust has
been incorporated in this protocol to calculate a global
consistent reputation measure between heterogeneous
nodes. But it does not protect the control messages and
leads to external attacks.
So there is a need to design an efficient routing protocol
which should select jamming free reliable paths and provide
light weight authentication with trust values. In this paper,
we propose a trust-based light weight authentication routing
protocol for MANET.

Trust-based light weight authentication routing protocol for MANET 33
2 Related works
Zhao (2010) has proposed to design a secure routing
protocol in a new approach. This protocol starts from a
prerequisite secure status and fortifies this status by
protecting packets using identity-based cryptography and
updating cryptographic keys using threshold cryptography
periodically or when necessary. Compared to existing
schemes, the main contribution of this proposal is the notion
of allowing only legitimate nodes to participate in the
bootstrapping process, rather than trying to detect adversary
nodes after they are participating in the routing protocol.
Besides, the proposal has several improvements in routing
setup and maintenance: it does not need any side channel or
secret channel; it simplifies secret updates without requiring
a node to move around; it does not use flooding to setup
initial routing, and does not use multicast to update secrets.
Khalil and Bagchi (2011) have presented a protocol
called SADEC that can detect and isolate stealthy packet
dropping attack efficiently. SADEC presents two techniques
that can be overlaid on baseline local monitoring: having the
neighbours maintain additional information about the
routing path, and adding some checking responsibility to
each neighbour. Additionally, SADEC provides an
innovative mechanism to better utilise local monitoring by
considerably increasing the number of nodes in a
neighbourhood that can do monitoring.
Ferdous et al. (2010) have developed a threefold
approach: to formalise and evaluate trust, to use trust as a
basis to establish keys between nodes in MANETs, and to
utilise trust as a metric for establishing secure distributed
control in MANETs. They define metrics for nodes to
establish and manage trust, and use this mutual trust to
make decisions on establishing group and/or pair-wise keys
in the network. They also review the routing protocols of
ad hoc networks with trust considerations and select
dynamic source routing (DSR), a protocol that can be used
in distributed ad hoc network settings for path discovery.
El Defrawy and Tsudik (2008) have focused on privacy
aspects of mobility. Unlike most networks, where
communication is based on long-term identities (addresses),
we argue that the location centric communication paradigm
is better-suited for privacy in suspicious MANETs. To this
end, we construct an on-demand location-based anonymous
MANET routing protocol (PRISM) that achieves privacy
and security against both outsider and insider adversaries.
We analyse security, privacy and performance of PRISM
and compare it to alternative techniques. Results show that
PRISM is more computationally efficient and offers better
privacy than prior work.
Zhao et al. (2010) have proposed a risk-aware response
mechanism to systematically cope with the identified
routing attacks. This risk-aware approach is based on an
extended Dempster-Shafer mathematical theory of evidence
introducing a notion of importance factor. In addition, the
experiments demonstrate the effectiveness of the approach
with the consideration of the packet delivery ratio and
routing cost.
Galuba et al. (2008) developed a new approach that
addresses simultaneously three aspects: security, scalability
and adaptability to changing network conditions. The
communication protocol, Castor, occupies a unique point in
the design space: It does not use any control messages
except simple packet acknowledgements, and each node
makes routing decisions locally and independently without
exchanging any routing state with other nodes. Its novel
design makes Castor resilient to a wide range of attacks and
allows the protocol to scale to large network sizes and to
remain efficient under high mobility.
Tague et al. (2011) have considered the problem of
jamming-aware source routing in which the source node
performs traffic allocation based on empirical jamming
statistics at individual network nodes. They formulate this
traffic allocation as a lossy network flow optimisation
problem using portfolio selection theory from financial
statistics. They show that in multisource networks, this
centralised optimisation problem can be solved using a
distributed algorithm based on decomposition in network
utility maximisation (NUM). They demonstrate the
network’s ability to estimate the impact of jamming and
incorporate these estimates into the traffic allocation
problem.
Vergados and Stergiou (2007) have introduced an
authentication scheme using threshold secret sharing. They
discover what the relationship, between the threshold
parameter and the parameter that defines the number of
secret share holders in the network, should be, in order to
ensure reliability, redundancy and survivability.
Zakhary and Radenkovic (2010) have introduced a
number of optimisations to the current reputation schemes
used in MANETs such as selective deviation tests and
adaptive expiration timer that aim to deal with congestion
and quick reputation convergence. They have proposed to
use two different centrality measures for evaluation of the
individual trust claims and resolving the aggregated ones.
3 Trust-based light weight authentication routing
protocol
3.1 Overview
In this paper, we propose a trust-based light weight
authentication routing protocol in MANET. The multiple
disjoint paths are discovered based on any multipath routing
technique based on ad hoc on demand multipath distance
vector (AOMDV) routing protocol. For each node, the
packet success rate is estimated and the path with maximum
packet success ratio is selected as the primary path. For each
node in the selected path, the global trust value is computed
based on the direct and indirect trust values. If the trust
value of any node is below threshold value, then it is
authenticated using the secret sharing technique. This
approach identifies the relationship among the threshold
parameter and the parameter that defines the number of
secret share holders in the network. This ensures the
reliability, redundancy and survivability of the network.

An efficient routing protocol has been designed that
selects the jamming free reliable paths for data transmission
and also offers light weight authentication methodology. It
involves following three phases:
• Phase 1 – multipath route discovery
• Phase 2 – trust computation
• Phase 3 – light weight authentication mechanism.
3.2 Phase 1: multipath route discovery
This phase involves the discovery of multiple disjoint
paths-based AOMDV.
3.2.1 Estimation of packet success rate
The packet success rate (PSRij(t)) is estimated based on the
random variables λij(t) and variance ρ2
(t) at time t that
describes the in progress success rate of data packet. These
variables are modelled as the beta random variable which is
shown below (Tague et al., 2011).
Let T1 and T2 be the update time periods.
Let x1 and x2 be the weight values in the range (0, 1)
( )ijλ t
λij(t) can be estimated using an exponential weighted
moving average (EWMA) as a function of the value of
λij(t – T1) estimated priorly.
( ) ( ) [ ]( )1 1 1 1( ) 1 ,ij ij ijλ t x λ t T x P t T t= − + − − (1)
where Pij represents the packet delivery ratio over link (i, j).
Pij is defined as the ratio of the number of valid data
packets which are successfully received (sij([t – T1, t])) to
the number of the packet received over the link hij ([t – T1,
t]).
[ ]( )
[ ]( )
[ ]( )
1
1
1
,
,
,
ij
ij
ij
s t T t
P t T t
h t T t
−
⎡ ⎤− =⎣ ⎦ −
(2)
2
( )ρ t
ρ2
(t) is estimated using an EWMA as a function of the
ρ2
(t – T2) estimated priorly.
( ) ( ) [ ]( )2 2
2 2 2 2( ) 1 ,ijρ t x ρ t T x Q t T t= − + − − (3)
where Qij ([t – T2, t]) is the variance of the set of packet
delivery ratio estimated in equation (2)
[ ]( )
[ ]( ){ }
2
1 1 1 2 1
,
, : 0, ..., / 1
ij
ij
Q t T t
Var P t bT t bT T b T T
− =
⎡ ⎤− − + = −⎡ ⎤⎢ ⎥⎣ ⎦
(4)
Thus, utilising the above estimated variables λij(t) and ρ2
(t)
for links (i, j) in the routing path (Rk), the end to end packet
success rate (PSRE) is estimated which is as follows:
E
( , )
PSR
k
ij
i j R
PSR
∈
= ∏ (5)
3.2.2 Route discovery algorithm
The steps involved in the route discovery are as follows:
Let S and D be the source and destination respectively.
Let RREQ and RREP be the route request and route reply
respectively.
Let Ni be the intermediate node
1 When S wants to transmit a data packet to D, it verifies its
route cache for path availability.
If path exists
Then
S considers the available path for data transmission.
Else
S proceeds with Step 2
End if
2 S broadcasts RREQ packet towards D through the
intermediate nodes (Ni)
* *
iS N DRREQ RREQ
⎯⎯⎯⎯→ ⎯⎯⎯⎯→
3 Ni upon receiving the RREQ updates the route cache
(shown in Table 1) about the source, sequence number,
destination, previous hop node and packet success rate
(estimated in Section 3.2.1).
4 Ni then either re-broadcasts the RREQ to its neighbours or
sends the route reply (RREP) if the node is D. This process
is repeated till RREQ reaches D.
5 When D receives RREQ, for every received RREQ the
RREP packet is unicasted in the reverse path towards the
source.
6 Every Ni that receives RREP updates its cache for the
next-hop of the RREP and then unicasts this RREP in the
reverse-path using the earlier-stored previous-hop node
information.
7 Step 6 is repeated till RREP reaches S.
8 S then computes end to end packet success rate of the path
(estimated in Section 3.2.1) based on the collected
information from RREP.
9 S selects an optimal path that has high packet success rate
as the primary path. This optimal path is used for data
transmission between the source and the destination. The
path with the next higher level of packet success rate in
chosen as backup (alternate path)
Figure 1 demonstrates the multipath discovery technique.
Figure 2 shows the best path selection technique. The
path [S-3-6-7-D] is the primary path chosen for data
transmission. The path [S-2-10-12-D] is next alternate path
chosen for data transmission.
Table 1 Format of routing table
Source
ID
Sequence
number
Destination
ID
Previous
hop node ID
Packet
success
rate

Figure 1 Multipath discovery technique (see online version
for colours)
Figure 2 Best path selection (see online version for colours)
3.3 Phase 2: trust computation
In this phase, the direct (DTij) and indirect (IDTij) trust
values of each node are estimated utilising EigenTrust
algorithm. Then a resolver is employed to estimate the
global trust value of the node (Zakhary and Radenkovic,
2010).
3.3.1 Direct trust
Let RSS be the signal strength among Ni to Nj.
Each node (Ni) estimates eigen vector centrality (Ci) of
its neighbours using equation (6). It is proportional to the
sum of the nodes that are link to Ni.
,
( ) 1
1 1
n
i j i j j
j W i j
C C V C
∈ =
= =∑ ∑α α
(6)
where
W(i)set of nodes linked to ith
node
n sum of nodes
α constant
Vi,j adjacency matrix of the network (Refer Note(1)).
This value gets updated once the node receives or sends a
message to its neighbours.
Each node periodically computes its connectivity rating
(recent satisfaction index – RSI) with each of its direct
neighbour nodes using the below computed percentages
[using equation (7)]
% ( , ) % ( , )ijRSI f i j e i j= − (7)
where
%f(i, j) percentage of packets initiated from Ni which were
forwarded by Nj over the total number of packets
provided to Nj.
%e(i, j) percentage of packets that were expired over the
total number of packets given to Nj.
Utilising RSIij, the direct reputation DTij can be estimated as
follows:
( )* *(1 )ij ij ijDT DT pr υ RSI υ= + − (8)
where
DTij(pr) previous trust value of Nj in Ni prior to inclusion of
current RSIij value.
υ constant revealing the confidence level for Nj stored in
Ni [refer note (2)]
DTij is normalised using the equation (9).
( )( )max
ij
ij
ij
DT
DT
f t DT
= (9)
where f(t) max is the function reflecting the maximum
observation of DTij over time t.
3.3.2 Note
1 For eigen vector centrality,
If Ni is linked to Nj
Then
,i jV SS=
Else
Vi,j = 0.
2 If there is no link among Ni and Nj, then DTij is reduced
using a constant β instead of υ.

3.3.3 Indirect trust
IDTij is estimated from aggregated form of trust report
received and processed by Ni about Nj which is shown
below:
( )
( )
( )
nj in
ij
in
RT δ n RT
IDT
δ n RT
∗ ∗
=
∗
∑
∑
(10)
where
δ(n) degree centrality of the reporting node (n).
3.3.4 Resultant global trust value (GTij)
A resolver is employed that computes the resultant global
trust value (GTij) of the node based on the direct and
indirect trust values. It also executes trust noise cancellation
mechanism [elaborated in note (3)].
(1 )ij ij ijGT υ DT υ IDT= ∗ + − ∗ (11)
Each node Ni monitors the trust values (GTij) of its
neighbour nodes within its transmission range. Then, it
collects the trust values from the monitored nodes and
exchanges the collected information with its neighbouring
nodes. Following the information exchange, if any node
finds that the trust value of monitored node is below
threshold, then the node is subjected to authentication
(explained in Section 3.3.3).
3.3.5 Trust noise cancellation
Let Th be the pre-defined threshold defined based on the
neighbour node mobility and link quality.
Let PLR be packet loss ratio
If (PLR (Ni) < Th)
Then
Consider the packet loss to be noise
Lost packets are ignored.
Else
The routes that is passing through respective Ni is prevented
and alternate optimum path is chosen
End if
3.4 Phase 3: light weight authentication mechanism
In this phase, the authentication of the node is performed
using Shamir’s threshold technique.
• Let Y be the secret share holders.
• Let Z be the threshold that defines the number of Y
nodes that needs to be contacted by any node for
getting authenticated.
• Let σi be the secret share of Ni.
• Let CA be the certificate authority.
• Let Kpr and Kpu be the private and public key
respectively.
We assume that the trusted entity initially assigns the
private key and public key to all the nodes deployed in the
network. The steps involved in the authentication phase are
as follows
1 When Ni’s trust value is below threshold, it requires to
be authenticated. It broadcasts a hello message to at
least Z nodes.
2 Any node upon receiving Hello message uses share of
its private key to generate the partial signature and
sends it to requested Ni
(mod )i iK dσ p=
where d = message
p = the value from RSA key pairs of CA.
3 Following the reception of at least Z partial signatures,
Ni generates the complete signature KSi and sends d to
the desired neighbour node.
4 The node upon receiving the authenticated message
can recover the message by using the Lagrange
interpolation which is computed at point zero of
the Z nodes.
(0) (0)
(0)
(mod )
i i i
pri
LP σ LP
i ii
ii i
K
π K π d σ LP
d p
−
= = −
= = Ψ
∑
where Kpri = private key.
1 ,
(0) i
i
j K j i j i
u
LP π
u u≤ ≤ ≠
=
−
(12)
5 Using Ψ, the node recovers d since Kpu of the CA is
familiar to the node which is as follows
(mod )p dΨ = (13)
4 Simulation results
4.1 Simulation model and parameters
We use NS-2 (Network Simulator, http:///www.isi.edu/
nsnam/ns) to simulate our proposed routing protocol. In this
simulation, the channel capacity of mobile hosts is set to the
same value: 11 Mbps. 50 mobile nodes are randomly
deployed and move in a 1,000 metre × 1,000 metre region
for 50 seconds simulation time. We have varied the node
speed as 5, 10, 15, 20 and 25 m/s. The transmission range is
set as 250 metres. The simulated traffic is constant bit rate
(CBR). The numbers of attackers performing black hole
attack are varied from 1 to 5. Our simulation settings and
parameters are summarised in Table 2.

Table 2 Simulation settings
No. of nodes 50
Area size 1,000 × 1,000
Mac 802.11
Radio range 250m
Simulation time 100 sec
Traffic source CBR
Packet size 512
Speed 5, 10, 15, 20, 25 m/s
No. of attackers 1 to 5
4.2 Performance metrics
We evaluate mainly the performance according to the
following metrics.
• Average packet delivery ratio: It is the ratio of the
number of packets received successfully and the total
number of packets transmitted.
• Resilience: It is the ratio between number of packets
dropped and the number of packets sent.
• Average packet drop: It is the average number of
packets dropped by the misbehaving nodes.
• End-to-end delay: It is the amount of time taken by the
packets to reach the destination.
We compare our trust-based light weight authentication
routing protocol (TLWAR) with the AODVRB (Zakhary
and Radenkovic, 2010).
4.3 Results
4.3.1 Based on speed
In order to ensure the reliability with respect to mobility, the
speed of the node is varied as 5, 10, 15, 20 and 25 m/s with
the number of attackers as 2.
Figure 3 Speed vs. delay (see online version for colours)
Results for End-to-End Delay
0
5
10
15
5 10 15 20 25
Speed(m/s)
Delay(Sec)
AODVRB
TLWAR
Figure 4 Speed vs. delivery ratio (see online version for colours)
Results for Packet DeliveryRatio
0
0.2
0.4
0.6
5 10 15 20 25
Speed(m/s)
DeliveryRatio
AODVRB
TLWAR
Figure 5 Speed vs. drop (see online version for colours)
Results for Packet Drop
0
10000
20000
30000
40000
50000
5 10 15 20 25
Speed(m/s)
Pkts
AODVRB
TLWAR
Figure 6 Speed vs. fraction of compromised communications
(see online version for colours)
Results for Fraction of Compromized
Communications
0
0.5
1
5 10 15 20 25
Speed(m/s)
Fraction
AODVRB
TLWAR
Figure 3 shows the results of delay for both the protocols.
The route discovery delay increases at higher mobility.
However, the selection of best routes according to success
ratio, reduces the delay of TLWAR up to 30% when
compared to TLWAR.
Figures 4 and 5 illustrate the results of packet delivery
ratio and drop, respectively. When the node speed is
increased, more route disconnections occur and hence the
packet drop increases resulting in decrease in packet
delivery ratio. However since TLWAR chooses reliable
routes, the packet drop is 21% less and delivery ratio is 44%
more, when compared to AODVRB.
Figure 6 shows the results of fraction of compromised
communications. Since TLWAR provides authentication
based on threshold secret, it outperforms AODVRB by
23%.

4.3.2 Based on attackers
In order to ensure the security with respect to attackers, the
number of attackers is varied from 1 to 5 with node speed of
5 m/s.
Figure 7 Attackers vs. delay (see online version for colours)
Results for End-to-End Delay
0
5
10
15
1 2 3 4 5
Attackers
Delay(Sec)
AODVRB
TLWAR
Figure 8 Attackers vs. delivery ratio (see online version
for colours)
Results for Packet DeliveryRatio
0
0.5
1
1.5
1 2 3 4 5
Attackers
DeliveryRatio
AODVRB
TLWAR
Figure 9 Attackers vs. drop (see online version for colours)
Results for Packet Drop
0
10000
20000
30000
40000
50000
1 2 3 4 5
Attackers
Pkts
AODVRB
TLWAR
Figure 7 shows the results of delay for both the protocols.
The delay increases as the attackers are increased due to the
time involved in trust estimation and authentication.
However the light weight authentication technique reduces
the delay of TLWAR up to 35% when compared to
TLWAR.
Figures 8 and 9 illustrate the results of packet delivery
ratio and drop, respectively. When the attackers are
increased, more packets are dropped due to the black hole
attack resulting in decrease in packet delivery ratio.
However, since TLWAR chooses reliable routes and secure
routes, the packet drop is 64% less and delivery ratio is 48%
more, when compared to AODVRB.
Figure 10 shows the results of fraction of compromised
communications. Since TLWAR provides authentication
based on threshold secret, it outperforms AODVRB by
66%.
Figure 10 Attackers vs. fraction of compromised
communications (see online version for colours)
Results for Fraction of compromised
communications
0
0.5
1
1 2 3 4 5
Attackers
Fraction
AODVRB
TLWAR
5 Conclusions
In this paper, we have proposed a trust-based light weight
authentication routing protocol in MANET. The multiple
disjoint paths are discovered based on any multipath routing
technique like AOMDV. For each node, the packet success
rate is estimated and the path with maximum packet success
ratio is selected as the primary path. For each node in the
selected path, the global trust value is computed based on
the direct and indirect trust values. If the trust value of any
node is below threshold value, then it is authenticated using
the secret sharing technique. This authentication enhances
the reliability, redundancy and network lifetime. By
simulation results, we have shown that proposed protocol
minimises the packet drop and delay and enhances the
packet delivery ratio.
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