Performance Analysis of Malicious Node Detection and Elimination Using Clustering Approach on MANET
Circuits and Systems, 2016, 7, 748-758
Published Online May 2016 in SciRes. http://www.scirp.org/journal/cs
http://dx.doi.org/10.4236/cs.2016.76064
How to cite this paper: Gopalakrishnan, S. and Kumar, P.M. (2016) Performance Analysis of Malicious Node Detection and
Elimination Using Clustering Approach on MANET. Circuits and Systems, 7, 748-758.
http://dx.doi.org/10.4236/cs.2016.76064
Performance Analysis of Malicious Node
Detection and Elimination Using Clustering
Approach on MANET
S. Gopalakrishnan1*, P. Mohan Kumar2
1Department of Electronics & Communication Engineering, PSNA College of Engineering and Technology,
Dindigul, India
2Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
Received 7 March 2016; accepted 9 May 2016; published 12 May 2016
Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativ ecommon s.org/l icenses/by /4.0/
Abstract
Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of
thousands of nodes that are mobile as well as autonomous and they do not requires any existing
network infrastructure. The autonomous nodes can freely and randomly move within the network
which can create temporary dynamic network and these networks can change their topology fre-
quently. The security is the primary issue in MANET which degrades the network performance
significantly. In this paper, cluster based malicious node detection methodology is proposed to
detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the
cluster head and this key is used for the data transaction between cluster head and node. The
cluster head checks this key for every data transaction from node and match with their cluster ta-
ble. If match is valid, and then only it will recognize that this node is belongs to this cluster, other-
wise it is decided as malicious node. This paper also discusses the detection of link failure due to
the presence of malicious node by determining the gain of each link in the network. The perfor-
mance of the proposed method is analyzed using packet delivery ratio, network life time, and
throughput and energy consumption. The proposed malicious node detection system is compared
with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH
(Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and
BCDCP (Base station controlled dynamic clustering protocol).
Keywords
Malicious Node, Cluster Forma tio n, MANET, Cluster Head
*
Corresponding author.
S. Gopalakrishnan, P. M. Kumar
749
1. Introduction
Mobile Ad hoc networks (MANETs) are the currently emerging communication infrastructure which finds its
application in several significant fields such as mobile devices and military applications in case of disaster a nd
other crisis operations. MANETs are defined as the category of wireless networks that utilize multi-hop radio
relaying and are capable of operating without the support of any fixed infrastructure (i.e. infrastructure less). T he
absence of any central coordinator or base station makes the routing a complex one compared to cellular net-
works. Ad hoc wireless network topology for the mobile network is shown in Figure 1. The communication
between two nodes far apart takes place through an intermediate node. In a MANET, the routing and resource
management are done in a distributed manner in which all nodes coordinate to enable communication among
them. T his req uires eac h node to be more intellige nt so that it can fu nction bo th as a net work host for transmit-
ting and receiving data and as a network router for routing packets from other nodes.
MANETs are susceptible to extensive ranges of security attack which is mostly caused due to its rapid real
time exploitation, infrastructure-less wireless communication channels, and the hostile environments in which
they may be deployed, making them susceptible to a wide range of security attacks described in [1]-[3]. Due to
the quick and economically less demanding deployment of MANETs, they are used in military applications,
collaborative and distributed computing emergency operations, etc. The security of communication in MANET
is very important, especially in military applications. The lack of any central coordination and shared wireless
medium makes them more vulnerable to attacks than wired networks.
Many research works have focused on the security of MANETs. Most of the m deal with prevention a nd de-
tection approaches to combat individual misbehaving nodes. Generally, the attacks against MANETs can be
classified into two types: passive and active attacks. Passive attacks refer to the attempts made by malicious
nodes to perceive the activities, whereas the active attacks are attacks performed by the malicious nodes that
bear some energy cost to perform the attack. When more malicious nodes join to get he r, t he n t he y p er fo r m a co l-
laborative attack, causing more destructive damages to the network. In such a situation, a malicious node (black
hole node) attracts all the packets using forged Route Reply (RREP) packet to wrongly choose the “fake” short-
est route towards the destination and then discard these packets without forwarding them to the destination. In
the case of gray-hole attacks, a node is not init iall y reco gnized as maliciou s since it t urns malicio us onl y at a lat-
er stage, thus a secure communication cannot be performed since its existence within the network cannot be
identified. The malicious node then selectively forwards or discards the data packets as the packets go through
it. The various categories of attacks produced by the malicious nodes include Rushing attack, Black hole attack,
Neighbor attack, Jellyfish attack and Denial of Service (DoS) attack. In Black hole attacks, all data packets are
received on other paths instea d of the actual routing path. In Rushing attack, as the source nodes flood the net-
work with route discovery packets to locate routes to the destinations, each intermediate node processes only the
Figure 1 . MANET architecture.
S. Gopalakrishnan, P. M. Kumar
750
first original packets and the duplicate packets are discarded which arrives later on. A rushing attacker makes
use of this duplicate suppression mechanism by quickly forwarding route discovery packets to access the for-
warding group. In Jellyfish attack, the forwarding group is first intruded and then the data packets are delayed
unnecessarily for a certain time before forwarding. These results in significantly high end-to-end delay and thus
the performance of real-time applications get degraded. Neighbor attack is that, upon receiving a packet, an in-
termediate node records its ID in the packet before forwarding the packet to the next node. An attacker simply
forwards the packet without recording its ID in the packet making two nodes that are not within the communica-
tion range of each other to believe that they are neighbors. Denial-of-Service attack is an attack, in which the
nodes are prohibited to send and receive data packets to its destinations.
In this paper we have taken insight of intrusion detection systems and different attacks on MANET security.
Then we propose a technique in cluster based intrusion detection system which eliminates the malicious nodes.
2. Literature Survey
Chand et al. [4] proposed a cluster-based routing protocol Optimized Energy Efficient Routing Protocol
(OEERP) using the principle of uniform battery drain of nodes. The election of Cluster Head (CH) occurs ran-
domly and once the cluster head is selected, the CH broadcasts an advertisement message to all the nodes. A few
nodes that are left out during cluster formation may become a member of any other cluster or may become a
cluster head of any other cluster. Singh et al. [5] presented LEACH (Low Energy Adaptive Clustering Hierarchy)
protocol to form a cluster of self-organizing nodes. The cluster heads were selected in a random manner based
on the highest energy and accessibility. The selected cluster head performed data fusion for data compression
and he lped in increa sing t he ne twork li feti me and t hroughp ut. T he ent ire kno wled ge of the networ k was no t ne-
cessary to cluster the nodes in the wireless e nvironment, in t his pr otocol.
Abidoye et al. [6] have made use of Data Routing for In-Network Aggregation (DRINA) protocol which per-
for ms Rout in g Tr ee c onstr ucti on t o fi nd the s hor test pat h li nkin g all the nod es within the ne twork. T he ba se sta-
tion after receivi ng the node’ s infor matio n, it starts the formation of clusters u sing these n od es. The inter mediate
nodes between the cluster head and the destination node are called Relay nodes and forward the sensed data.
Chatterjee et a l. [7] proposed the Base-station Contro lled Dyna mic Clusteri ng Pro toc ol (BCDCP) for the routing
of a centralized network. The base station after receiving the energy level of all the s enso r nod es, c luste r fo rma-
tion is performed and the cluster head is selected. This method splits the whole network into two sub-clusters,
and then further into many small clusters up to the required level. The Cluster Heads are placed far apart within
the network to provide uniform coverage all over the network. The BCDCP method implemented CH to CH
multi-hop routing sche me using the minimum spanni ng tre e, to identi fy the lowest energy path for routing and to
forward the messages like Cluster formation a nd CH i nformation in this route.
Rejina Parvin and Vasanthanayaki [8] have used Particle Swarm Optimization (PSO) based clustering algo-
rithm for the detection of residual nodes in wireless sensor networks. The implementation of PSO avoids indi-
vidual node formation since clustering is performed until every node becomes a member of any other cluster,
thus improving the network lifetime. Using this method, the term force between the CHs is considered during
route construction phase to determine the next best hop. Chang et al. [9] implemented Cooperative Bait Detec-
tion Approach for the detection of malicious nodes in MANETs. Their method attempte d to d etect the malicio us
nodes by designing a dynamic source routing (DSR)-based routing mechanism, which is referred to as the coop-
erative bait detection scheme (CBDS), which is a combination of both proactive and reactive defense schemes.
Proactive detection schemes [10]-[14] are schemes that constantly detect or monitor the nearby nodes. In
these schemes, despite the existence of malicious nodes, the overhead of detection is constantly created. Liu et
al. [12] proposed a 2ACK Proactive detection scheme to detect the routing misbehaviors in MANET. In their
method, after the data packets are successfully received, the two-hop acknowledgement packets are sent in the
opposite direction to signify the successful reception of packet. Deng et al. [13] designed Mob ility Based Clus-
tering (MBC) protocol, in which all the sensor nodes possess an opportunity in electing the cluster head based
on the threshold value. MBC protocol performed better than LEACH, HEED and other protocols on mobili-
ty-based environment, but failed to address the critical node occurrence problem which causes packet dropping,
link breakage and reduces the network utilization.
Xue and Nahrstedt [15] proposed the best-effort fault-tolerant routing (BFTR) method. Their BFTR scheme
used end-to-end acknowledgements to monitor the quality of the routing path (measured in terms of packet de-
S. Gopalakrishnan, P. M. Kumar
751
livery ratio and delay) to be chosen by the destination node. The source node selects a different route under the
situation that the path deviates from a predefined behavior set for determining “good” routes. The main demerit
of BFTR is that malicious node s may still exist in the new chosen route, and this sche me is subje cted to repeated
route discovery processes.
The conventional methods were based on clustering and no security issues were considered. The link failure
due to malicious nodes was considered in the conventional protocol. This paper proposes a malicious node de-
tection technique using clustering based approach and provides solution for link loss due to malicious nodes. It
will increase the performance of the MANET system.
3. Proposed Method
3.1. Cluster Based Malicious Node Detection
MANET consists of number of nodes spreading over a certain area. The nodes are grouped into smaller regions,
which are called as cluster. Each cluster has cluster head (CH) and it is responsible for controlling all the nodes
within the i r limit. MANET co ns ists of number of CHs and all CH s are li n ked with the sink. One CH can directly
transmit the packet to the sink or through other CHs. Each CH maintains a cluster table and each node maintains
a neighbor table. The cluster table consists of the details of all nodes, the distance from cluster head to each node
within the cluster and cluster key.
Figure 2 shows the cluster formation in MANET environment. It consists of clusters named as Cluster 1 and
Cluster 2. Cluster 1 contains the nodes N1 to N7 and it maintains a cluster table. The format for cluster table of
each cluster is given as described in Table 1.
Each node within the cluster gets the cluster key from the cluster head and this key is used for the data trans-
action between cluster head and node. The cluster head checks this key for every data transaction from node and
match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this
cluster.
Figure 2 . Cluster based malicious node detection.
Table 1. Cluster tab le for the nodes in a cluster.
Nodes in the cluster Euc li dea n distance (ED) Cluster key
N1 0.3 10110101
N2 0.2 11011100
N3 0.1 11110001
N4 0.25 10101010
N5 0.15 10111111
N6 0.6 10001001
N7 0.7 10101111
S. Gopalakrishnan, P. M. Kumar
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3.2. Determination of Euclidean Distance (ED)
The Euclidean distance (ED) between each node within a cluster and cluster head is determined using the fol-
lowing exp ression,
() ()
22
21 21
EDx xyy=− −−
(1)
where, (x1, x2) represents the coordinates of the cluster head location and (y1, y2) represents the coordinates of
the node within the cluster. The Eucl idean distances are variable due to the mobility of the nodes in and out from
the cluster. The mobility of the node can be determined as,
() ()
1 111
nodetttt t
VWVPPW PP
− −−−
=∗ ++∗∗
(2)
where, W denotes the weight of the node, Vt-1 is the previo us veloc ity of t he node , Pt-1 is previous lo cation of the
node and Pt is the current location of the node. The weight of each node can be computed as,
112 233
Waxax ax=++∗∗
(3)
where, a1and a2re presents consta nt value and it follows {a1, a2} ε {0 to 1}; and
( )
1
chi n
xd dc=
(4)
where,
( )
ch i
dd
denotes the distance from cluster head to the neighbouring nodes around the cluster head and
cn denotes number of cluster heads.
2nn
x Ec=
(5)
3
1
n
xc=
(6)
3.3. Determination of Number of Clusters
Number of clusters in a mobile ad-hoc network can be found by the Equation (7),
clusters
MN
NXY
=
(7)
where, M and N represent the width and height of the network area, respectively. X and Y represent the width
and height of each cluster area, respectively. Let us assume the width and height of each cluster is equal and it is
assumed as
XYt
= =
. Then Eq uation (7) can be re-written as,
2
clusters
MN
Nt
=
(8)
The rad i us rof each cluster is related with t and it is given a s,
2tr= ∗
(9)
Then, Eq uation (9) can be written as,
2
2
clusters
MN
Nr
=
(10)
3.4. Determination of Cluster Key
Cluster head in a cluster delivers 8-bit key to each node available within the cluster. The 8-bit key is indivi dual ly
allotted to e a c h node and the f low of generating the node key is illustrated in Figure 3.
Initially a 10 -bit ke y randomly chosen and it is permuted using the permutation table P10 as given belo w:
S. Gopalakrishnan, P. M. Kumar
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Figure 3 . 8-bit key.
The per mutation the 1 0-bit key can be obtained as
Let the 10-bit key be designated as (k1, k2, k3, k4, k5, k6, k7, k8, k9, k10). Then the permutation P10 is de-
fined as:
()()
P10 k1,k2,k3,k4,k5,k6,k7,k8,k9,k10k3,k5,k2,k7,k4,k10,k1,k9,k8,k6
=
.
The following procedure is adopted for generating first 8-bit key as,
Step 1: For example, the key (1010000010) is permuted to (1000001100).
Step 2: Divide (1000001100) into a left part 5-bit value (10000) and a right part 5-bit value (01100).
Step 3: Perform a circular left shift (LS-1), or rotation, separately. The left value (10000) becomes (00001).
The right value (01100) becomes (11000). Concatenate the left part (00001) and the right part (11000) into a
10-bit value (0000111000).
Step 4: Pic k out a nd pe rmute 8, (do n’t use 1 and 2) whic h will gene rate fir st 8-bit key (K1), of the 10 bits ac-
cording to the following rules:
Step 5: Follow the same procedure for LS-2 to generate next 8-bit key K2
Step 6: XOR the two 8-bits K1 and K2 and r e sultant 8-bit key is assi gned as node key fo r the first node within
the cluster.
Step 7: Initial 10-bit key is changed randomly and apply the steps 1 to 6 inorder to generate the next 8-bit ke y
for t he next node within the cluster.
3.5. Algorithm to Remove the Malicious Node
Step 1: The cluster head finds the malicious node and add this node to malicious node list available in cluster
table.
Step 2: Send this malicious node list to a ll the c luster heads in the M A N E T.
Step 3: All cluster head s b roadcast this information to their corresponding nodes within their cluster limit.
Step 4: If the data coming from the malicious node, then the nodes within the cluster does not respond to the
malicious nodes.
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3.6. Detection of Link Faults
The link faults in the mobile adhoc networks will affect the performance of the routing. The faults in the net-
work occur due to the link failure between nodes in the network. The faults are categorized into persistent faults
and transient fau lts. The faults in the network can be occurred due to the following reasons:
Low battery in no des in the network.
Physical/Hardware problem.
Obstacles in the nodes.
The faults in the node due to the above reasons are called as persistent faults. Transient noises are occurred
due to background noises. In this paper, the persistent faults are detected and alternate routing is selected. The
lossy of the link in the node is determined by estimating average loss rate in the node.
Figure 4 shows the detection of link failure based on link cost algorithm. There are three primary source
nodes s1, s2 and s3. All these three source nodes send the data to the sink. Node s1 use the links l2 a nd l1 to reach
the sink. Node s2 use the links l3 and l1 to reach the sink. Node s3 use the links l4 and l1 to reach the sink. The
node s3 also may send the da ta to the sin k thro ugh link l 5. Each l ink in the ne twor k has 1 uni t link co st. Ass ume
link l1 and l5 are lossy links. These lossy links can be determined using link cost algorithm as described in the
following:
Step 1: Determine the probability of the link to be lossy. In Figure 4, there are five links and each l ink has 0.2
prob abilities for a link to be lossy.
Step 2: Determine the number of possible fault path from source node to sink. Here, we have four number of
fault path as fp1 = {l2, l1}, fp2 = {l3, l1}, fp3 = {l4, l1} and fp4 = {l5}.
Step 3: Find the gain of each link in the network as described i n Equat i on (1 1).
( )
1
kk kbkkgk
p pCΦ=Φ +−Φ−⋅⋅
(11)
where,
k
p
is the probability of a link to be fault.
kb
Φ
is the cost o f the link when the particula r link is fa ult.
kg
Φ
is the cost o f the link when the particula r link is not-fault and Ck is the co st of the link to b e te sted.
Step 4: Find the lowest gain of the link and this lin k i s conclud ed as a faulty link.
The following parameters are used to determine the performance of the proposed method.
Packet delivery ratio (PDR):
It is defined as the ratio of the number of packets correctly received at the destination node to the total number
of packets sent by the source node. The average packet delivery ratio is given by the expression as:
Packetsd
Packetss
N
PDR N
=
(12)
where,
Packetsd
N
is the number of packets received at the destination node and
Packetss
N
is the number of pack-
ets sent from the source node.
Network life time:
It is defined as the total time taken for the packet to reach the destination from source node. The network life
time will be high when there are large numbers of nodes between source and destination node.
Figure 4 . Nodes with link cost.
S. Gopalakrishnan, P. M. Kumar
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Throughput:
The rate at which the total amount of packets transmitted from source to destination node over a time period
“t” is called as throughput. It is simply defined as the number of bits transmitted per second. It is expr essed as,
ThroughputTotal number of bitstime t=
‘’
(13)
Throughput is defined as the number of bits successfully transmitted to the destination over a time period.
4. Results and Discussion
The proposed clustering technique is implemented using Network Simulator 2.0. For measuring performance
anal ysi s pur p o se, we ha ve c ar ri ed o ut a tho r ough e xp er i me nt al ana l ysis t o unde r sta nd t he i mpa c t o f the p ro p o sed
clustering mechanism on MANET. Table 2 shows the initial network parameters for performance evaluation.
Total number of nodes used in this paper is 100 and these nodes are spread over 1000 × 1000 m. Each no de has
1000 J of initial energy and packet size of 512 bytes.
The propo sed malicious node detection system is compared with the conventional techniques as OEERP (Op-
timized energ y efficient routing p rotocol) [3], LEACH (Lo w energy adaptive clustering hierarchy) [4], DRI NA
(Data routing for In-network a ggr ega tion) [5] and BCDCP (Base station contro lled dynamic c lustering proto col)
[6] using the foll owi ng performa nce e valuat ion pa rameters.
Packet delivery ratio (PDR):
PDR defines the percentage of number of packets correctly received at the receiver side. For better perfor-
mance of the system, PDR should be high. The PDR is high when there is a high number of nodes between
source and destination and it will reduces the packet losses. Table 3 shows the PDR of the proposed system with
conventional system. The PDR gradually increases over linear increment of number of nodes in the proposed
system. The maximum PDR achieved in proposed system is 98% at 300 ms time duration.
Network life time:
The network life time will be high when there are large numbers of nodes between source and destination
node. Table 4 shows the network life time over different time period of the proposed method with conventional
methods.
Throughput:
Throughput is d efined as the number of bits s ucce ssfully tra ns mitted to the destination over a time period. The
performance of the system is improved if the throughput i s high. Table 5 shows the performance comparisons of
proposed method with different conventional protocols. From Table 5, the throughput of the proposed system is
proved t o be better than the conventional systems.
Energy consumption
The network life time can be improved by reducing total energy consumption. The energy consumption of the
node is based on sensing the data, conversion from one format to another format and transmission. The ener gy
consumption of the individual node in MANET is computed as,
Table 2. Initial network parameters.
Parameter Assigned va lue
Number of nodes 100
Sensi ng region 1000 * 1000 m
Init ial node energy 1000 J
Packet size 512 B
Transmission power 0.02 Watts
Received power 0.01 Watts
Routing protocol DSR
Data rate 1 Mbps
Radio-propagation model Two Ray Ground
S. Gopalakrishnan, P. M. Kumar
756
Table 3. PDR comparisons.
T i me
( ms )
PDR
Proposed method OEERP LEACH DRINA BCDCP
20 85 62 65 90 24
40 89 61 60 95 38
60 92 64 62 96 39
80 95 61 61 85 39
90 96 61 62 75 39
100 98 61 62 64 39
Table 4. Network life ti me comparisons.
T i me
( ms )
Network life time
Proposed method OEERP LEACH DRINA BCDCP
50 7250 7000 3000 8250 4250
100 3500 2800 1800 6200 3000
150 3270 2100 1500 3800 2250
200 2800 1800 1400 1800 1800
250 2278 1600 1200 1000 1500
300 1800 1500 1000 800 1400
Table 5. Throughput comparisons.
Time
(ms)
Network life time
Proposed method OEERP LEA CH DRINA BCDCP
50 75,000 54,000 55,000 58,000 18,000
100 79,000 52,000 54,000 59,000 26,000
150 79,000 51,000 52,000 62,000 28,000
200 70,000 51,000 52,000 62,500 28,000
250 64,000 50,000 51,000 62,800 27,000
300 58,000 51,000 51,000 57,452 30,000
-
nodenode initialr
EE E=
(14)
where, the initial energy of the node is denoted as
-node initial
E
and the energy after processing the data is denoted
as Er
The total energy consumption of the network is computed as,
_
1
n
networknode i
i
EE
=
=
(15)
where, nrepresent number of nodes in the networ k.
The total energy consumption is based on the number of nodes available in the network. The energy con-
sumption will be high when there are large numbers of nodes in the network. The energy consumption of the
proposed method is compared wit h conventional methods at d ifferent time slots and it is illustrated in Table 6.
S. Gopalakrishnan, P. M. Kumar
757
Table 6. Energy consumption comparisons.
T i me
( ms )
Energy Consumption (Jo ule s )
Proposed met h od OEERP LEACH DRINA BCDCP
50 4 10 20 5 8
100 12 15 45 15 16
150 18 20 70 19 19
200 27 30 90 52 30
250 32 35 115 84 40
300 41 45 138 122 48
The energy consumption of the proposed method is compared with other protocols OEERP, LEACH, DRINA
and BCDCP. From Table 6, there is a linear increment of energy consumption over different time slots. The
proposed method consumes low energy consumption when compared with other conventional protocols. The
network life time will be improved by consuming less energy.
5. Conclusion
The malicious nodes in the MANET are detected and removed using clus terin g appr oach. Each no de wit hin t he
cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster
head and node. The cluster head checks this key for every data transaction from node and matches with their
cluster tab le. If the matc h is valid, then only it will recognize that this node is belongs to this cluster, o therwise it
is decided as malicious node. This paper also discusses the detection of link failure due to the presence of mali-
cious nodes by determining the gain of each link in the net work.
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