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CHAPTER 5

AN AODV-BASED CLUSTERING APPROACH FOR

EFFICIENT ROUTING

5.1 INTRODUCTION

Dynamic network topology and limited system resources

characterize mobile ad hoc networking. Many routing protocols were

proposed for routing in MANETs (Chakeres and Belding-Royer 2004; Jiang

et al 1998). In MANETs, performance decreases when network’s size

exceeds a certain threshold, resulting in many routing algorithms performing

only when network’s size is small. To overcome bandwidth and battery power

limitations and reduce routing overhead, it is mandatory to make network

organization smaller and manageable (Garcia et al 2003).

A clustering architecture provides MANET environments with

three features: network scalability, fault tolerance and communication

overhead reduction. Many clustering algorithms have geographical regions as

clusters or form new ones unnecessarily. (Chiang et al 19997; Lin and Gerla

1997). An algorithm by Chatterjee et al (2000) creates necessary clusters

without using routing protocol maintained information.

It has been proved that cluster architecture ensures basic

performance achievement in MANETs with many mobile terminals. A cluster

structure provides at least three benefits, as an effective topology control

measure. First, it facilitates spatial resources reuse to increase system

capacity. With the non-overlapping multi-cluster structure, two clusters can

use the same frequency/code set if they are not neighbours. Also, a cluster

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coordinates transmission events better with a special mobile node, with a

cluster head, inside. This saves retransmission resources resulting in lower

transmission collisions. The second benefit is in routing, as a cluster head set

and cluster gateways normally are the virtual backbone for inter-cluster

routing which in turn restricts creation and spread of routing information.

Last, a cluster structure shrinks an ad hoc network and makes it more stable

with every mobile terminal. When a mobile node changes its cluster

attachment, the information is updated only in those nodes in corresponding

clusters. Thus, local changes do not need updation by the whole network as

this greatly reduces information stored by each mobile node.

5.2 CLUSTERING

Clustering technique is not a routing protocol; it aggregates nodes

into groups to ensure easier network management. An ad hoc network’s

cluster organization is impossible to be achieved in fixed infrastructure,

offline. Clustering scheme implementation ensures better protocol for Media

Access Control (MAC) through improvement of spatial reuse, throughput,

scalability and power consumption.

MANETs have multiple benefits from clustering. To begin with,

cluster structure ensures MANETs scalability and load balancing, increases

system capacity facilitating spatial resources reuse. In a multi cluster

structure, two non-overlapping clusters can have similar frequency if they are

not adjacent. It also elects a mobile host with special features called cluster

head for enhanced transmission activities coordination which in turn

alleviates collision of mobile nodes, saving both energy and resources.

Second, it limits creation and spreading routing information through

formation of a inter-cluster routing backbone consisting of cluster heads and

cluster gateways. Lastly, it provides a stable and smaller vision of ad hoc

network when a mobile node changes its attaching cluster so that the whole

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network need not be aware of local changes in a node set. Only mobile nodes

in a corresponding cluster reflect such changes. Hence each node stores and

processes a fraction of total network routing information saving resources.

Clustering aims to be connected and nodes play various roles in

clustering techniques. The three node types are defined as follows:

1. Ordinary nodes

2. Cluster head

3. Cluster gateways

5.2.1 Cost Involved with Clustering

Construction and maintenance of a cluster structure requires

additional cost compared to flat structure MANETs. Cost of clustering

analysis is carried out either quantitatively or qualitatively to outline both

clustering techniques benefits and drawbacks. Clustering associated cost is

explained below:

1. Due to Frequent network topology changes cluster relatedinformation varies drastically in a dynamically changingcluster structure. Transfer of message packets subsequentlyeats up massive bandwidth, depleting mobile nodes energyand further disabling upper layer applications that are unableto be implemented with meagre resources.

2. Re-clustering occurs in some clustering schemes due toabrupt local instance like mobile nodes moving to otherclusters or due to the demise of a mobile node or shuttingdown of cluster heads, resulting in other cluster heads re-election. This is re clustering ripple effect which stimulates are-clustering effect over the full network.

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3. Clustering stages are divided into two: cluster formation and

maintenance. The first stage assumes that mobile nodes are

static. Every mobile node obtains information from

neighbouring nodes in a specific period which might prove

impractical in a real time scenario.

5.3 CLUSTER MANAGEMENT

Clustering architecture provide network scalability, fault tolerance

resulting in increased use of network resources. It could be used for resource

management, routing and location management to lower communication and

computational overheads. This section discusses cluster formation and

maintenance.

5.3.1 Clustering Algorithm Design Goal

It is intended to integrate clustering with routing functions. The

design aims of the clustering scheme include:

An algorithm using a routing protocol’s control messages to

form clusters with limited overhead.

An algorithm operating in localized and distributed manners

and intertwining with nodes using only AODV.

The algorithm incurring limited cluster formation/

maintenance overhead and supporting formation of on-

demand clusters.

The algorithm minimizing network-wide flooding and being

scalable.

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The proposed scheme constructs/updates clustering architecture

when clusters alone service is required. The on-demand nature of AODV’s is

utilized for the scheme. Nodes participating in clustering are known from CHs

maintained topological information and individual nodes.

5.3.2 Cluster Formation

The aim of clustering is to ensure efficient use of network

resources, enhance availability, reduce overheads and give scalable

architecture (Jiang et al 1998; Garcai et al 2003). The clustering algorithm

choice affects clusters stability. The proposed work divides a network into

several two-hop clusters where in each cluster a node can play one of five

roles: cluster head, ordinary node, secondary cluster head, undecided node or

gateway. A gateway is a node that communicates directly with more than two

clusters.

A Cluster Head (CH) is elected in every cluster and it is responsible

for maintaining inter and intra-cluster communication. Every CH maintains

two tables in addition to a routing table, an intra-cluster node table and a k-

hop cluster table. The intra-cluster node table has the IDs of cluster nodes.

The k-hop table has the IDs of CHs of other clusters in a 2-hop neighborhood.

The CHs coordinate every cluster and store shared information. All nodes

regularly broadcast a hello message to ensure information about neighbors.

To reduce the periodic broadcasting overhead, new/undecided nodes learn of

their nearest CH on demand by forwarding a cluster head request packet. The

nodes act as either ordinary nodes or a gateway based on present location.

A Secondary Cluster Head (SCH) is also chosen to avoid the CH

from becoming into a bottleneck (Lu and Denko 2004). The SCH stores

backup routing and cluster information, with the role being rotated among

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ordinary nodes. This election needs no extra overheads as any node wishing

to serve as a SCH informs the CH and the latter informs cluster members.

5.3.3 Cluster Head Election

Many distributed algorithms were suggested for CH election in

MANETs. Chiang et al (1997) revealed that the Lowest ID (LID) algorithm

performed better than cluster head election algorithms based on Highest

Connectivity (HC). The proposals in Chatterjee et al (2000), Garcia et al

(2003) use varied criteria for CH election. As cluster heads are responsible for

maintenance and intra and inter-cluster communication, they were expected to

work for long durations when elected. Node mobility and link failure are the

chief reasons for cluster head re-election and changes in cluster membership.

Figure 5.1 Cluster head election

Denko (2003), proposed a mobility-based clustering algorithm

where a node is elected as CH only when the mobility index is below a

specific threshold. This index is computed based on cluster membership and

CH changes. When there is a tie, the lowest ID node is selected. In cluster

election algorithm, lowest ID clustering is first used to form clusters. Hence, a

node is elected CH when it has the lowest ID. This forms the initial node

configuration. Later, a node with a mobility index lower than its neighbors is

used as criteria. In Figure 5.1, every node broadcasts mobility information to

Node Gateway Cluster

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neighbors during a CH election. Once information is collected from neighbors

every node checks to see if it is the node with the lowest mobility index. Once

this is confirmed it becomes a CH itself and informs all neighbors. In this

study, the CH is selected based on the lowest ID and mobility index.

5.3.4 Cluster Maintenance

Cluster maintenance consists of two parts: intra-cluster and inter

cluster maintenance.

5.3.4.1 Intra-cluster maintenance

To keep the neighbor table and CH information updated, nodes

exchange hello messages at periodic intervals. A hello message has

information about a node’s ID and roles. If no hello message is received from

a neighbor during the ALLOW_HELLO_LOST interval, the neighbor is

thought lost and removed from the table. An ordinary node checks the

neighbor table to check whether the CH is still alive. When no CH is found to

exist, a new CH is elected in the neighborhood. Local maintenance is carried

out, when a CH fails.

5.3.4.2 Inter-cluster maintenance

Every cluster head has a k-hop cluster table, containing all network

k-hop CHs information. Each CH then informs neighboring CHs that it is

active by forwarding a HeadAlive message. For example, CH1 receives a

HeadAlive message from CH2. If CH1 discovers that CH2 exists in the CH

table, CH2’s expiration time is updated. Otherwise, a new CH entry for CH2

could be inserted and its expiration time would be set by improving CH

update time to the present. When no HeadAlive message is received from

cluster heads during a HEAD_UPDATE_INTERVAL interval, such a cluster

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is assumed to be unavailable. When no HeadAlive message is received during

ALLOW_HEADALIVE_LOST interval, then that CH is removed from the

CH table.

5.4 IMPLEMENTATION OF THE AODV CLUSTERING AND

ROUTING SCHEMES

A k-hop CH table maintains k-hop neighbor cluster information. In

this study k=2, where only two hop clusters are considered. The K-hop CH

table maintains fields such as CH ID, CH status, cluster size, CH expiration

time and number of times the hello message has been lost. The CH’s

expiration time is the current time plus the HeadAliveUpdate interval. A CH’s

status could be either “alive” or “not alive”. A CH’s status will be marked as

“not alive” when its HeadAlive message is not received by other CHs within

its expiration time. A CH entry will be removed from a k-hop CH table if the

number of times that the HeadAlive message has not been received exceeds

three. In the following section, the various data structure added to the AODV

to implement the proposed clustering architecture is presented.

5.4.1 Hello Message and Neighborhood Maintenance

Prior to a hello message being forwarded the sender node adds

status information to it. The hello message sender can be a CH, a gateway,

ordinary node or undecided node. The hello message’s extension includes the

following: source address, lifetime and current status.

A neighbor table stores the neighbor’s ID, expiration time, status

and the many times a hello message was lost. A node on receipt of a hello

message from neighbors, checks whether the neighbor is alive in the neighbor

table. If existence is confirmed, the neighbor’s expiration time is updated or

else a new entry is added. When a node has not received a hello message from

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another in three subsequent hello intervals, it is thought to be unconnected

and is removed from the neighbor table.

5.4.2 Cluster Management Module

The following three modules are used to implement an AODV

based clustering algorithm.

5.4.2.1 Cluster initialization module

A cluster formation module initializes clusters after initializing an

AODV. The module is invoked through the CLUSTER_FORM_PERIOD, a

predefined threshold. Before initialization, all nodes are set at status of

“undecided” and broadcast hello message to neighbors. When a node receives

all the hello messages from neighbors, it invokes a Cluster Head Election

module to elect a CH. This stage uses a LID clustering algorithm. The new

CH notifies neighbors and other CHs. The nodes on receipt of a CH existence

notification, changes its status to an ordinary node and forwards hello

messages to neighbors. When a node locates two different CHs in the

neighbor table, that node changes into a gateway. After the execution of a

cluster information module, all nodes belong to one cluster at least.

5.4.2.2 Cluster head election module

A node invokes a Cluster Head Election module to elect cluster

heads when there is no CH or when multiple CHs are in proximity to each

other. The procedure involves the following steps: A node checks whether it

has the lowest mobility index among non-gateway neighbors. When it has the

lowest mobility index, it declares itself as CH and notifies other nodes.

Otherwise, it changes its status to ordinary node.

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Figure 5.2(a) reveals the topology of a hypothetical clustered-

network at time t. In Figure 5.2(a), nodes 1, 3 and 6 are CHs. Nodes 8 and 9

are gateways while nodes 2, 4, 5 and 7 are ordinary nodes. Assuming that at

time t2 the network’s topology changes to Figure 5.2(b), where node 7 has

moved from its original cluster. When node 7 checks the neighbor table, it

cannot locate a cluster head. Thus node 7 uses the Cluster Head Election

module to elect a new CH and the result is shown in Figure 5.2(c), where

node 7 becomes a new CH and node 4 the new gateway.

Figure 5.2 Cluster re-election when no cluster exists in the neighbor table

(b)

1

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Node

Gateway

Cluster Head(a)

(c)

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Figure 5.3 reveals a situation where many CHs exist in one cluster as shown

in (a). When the network’s topology changes from Figure 5.3(a) to Figure 5.3

(b) at time t2, nodes 6 and 7 exchange positions. Node 6 and node 3 find two

CHs among their neighbors, as shown in Figure 5.3 (b). Then both node 3 and

node 6 invoke the Cluster Head Election Module to select a new cluster head.

After cluster election, node 6 throws away its CH status and changes itself

into an ordinary node. Node 3 remains a CH. Again, the cluster with nodes 5,

7, 8 and 9 elect node 5 as the new CH. Network topology after the CH re-

election is seen in Figure 5.3(c).

Figure 5.3 Cluster re-election when more than one cluster head exists

Figure 5.3 reveals a situation where many CHs exist in one cluster

as shown in (a). When the network’s topology changes from Figure 5.3(a) to

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4

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NodeGatewayCluster Head

(a)

(c)

(b)

1

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Figure 5.3 (b) at time t2, nodes 6 and 7 exchange positions. Node 6 and node 3

find two CHs among their neighbors, as shown in Figure 5.3 (b). Then both

node 3 and node 6 invoke the Cluster Head Election Module to select a new

cluster head. After cluster election, node 6 throws away its CH status and

changes itself into an ordinary node. Node 3 remains a CH. Again, the cluster

with nodes 5, 7, 8 and 9 elect node 5 as the new CH. Network topology after

the CH re-election is seen in Figure 5.3(c).

5.4.2.3 Neighbor table purge module

As all nodes are mobile and network topology’s changes

frequently, network may be unstable. Clusters size and nodes status varies at

any time due to mobility. To ensure that the cluster information is fresh and

updated, the neighbor table is purged.

5.4.3 Cluster AODV Based Routing

The AODV protocol sends many packets in comparison to other

reactive protocols like DSR. So, when network’s size increases, node degree

also increases proportionately, leading to congestion in the network.

Clustering reduces this through localized route discovery and maintenance.

The suggested Cluster AODV scheme uses clustering architecture and AODV

functionalities for routing. This section discusses mechanisms used by

Cluster-AODV to lower routing overhead and allows scalability while

ensuring good packet delivery ratio.

5.4.3.1 Intra-cluster routing

Intra-cluster routing is routing within a cluster. Each node has routing

information on its cluster. When a node lacks a route to a destination that is in

the cluster it sends a Local Route Request (LRREQ). When route failures ensure

lack of reply to a RREP, local route maintenance is undertaken within a cluster.

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5.4.3.2 Inter-cluster routing

Inter-cluster routing is routing among clusters. The CH has a 2-hop

cluster topology also maintained in a SCH to minimize one point of failure.

When routes cannot be found in a cluster once a LRREQ message has been

issued, a CH uses a RREQ message to locate a destination via a gateway to 2-

hop neighbor clusters. To lower RREQ flooding packet overhead only

gateways and CHs forward the RREQ. Ordinary nodes are not involved in

RREQ packets in inter-cluster communication.

5.4.3.3 Route maintenance

Similar to route maintenance, cluster maintenance starts when a

route fails within a cluster and is re-constructed locally using LRREQ and

RREQ with 2-hop topology information. When LRREQ fails, an AODV

procedure is used and the usual RERR is forwarded to source nodes for route

reconstruction. The source node follows the same process to repair failed

routes, first locally and then others.

The processes involving a new node which joins and an existing

node leaving are carried out based on hello messages from AODV. When

CHs exchange neighborhood information with cluster members, a new node

close by can register with a CH by using a RREQ message. A node acts as

gateway when it registers with two CHs. When a node goes away from the

present CH, it switches its role to that of an ordinary node, a gateway or will

be undecided. It will be erased from the old CH and old members’ routing

entries are updated accordingly.

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5.5 SELFISH NODES

In ad hoc networks, nodes are classified as failed, co operative,

selfish and malicious nodes. A node can refrain from being active in a

network when moving out of transmission range or when it is switched off by

malfunctions. Failed nodes are unused during route discovery. Co-operative

nodes are active in the network and participate in route discovery and packet

forwarding. Selfish nodes actively involved in route discovery and actively

send/receive own packets. But they are liable to drop other nodes data packets

either fully or selectively to lower CPU processing to save battery power.

Such nodes affect network performance greatly. But malicious nodes are also

active in route discovery and could involve data packets transfer similar to

selfish or co-operative nodes. However they also launch attacks on networks

either to disrupt services or steal network data..

As mobile nodes are autonomous they join or leave the network

freely and so nodes are unable to work out an effective system to prevent

prospective malicious behavior from nodes it communicate with. This is

because nodes behavioral diversity. Further, because of ad hoc network

mobility a compromised node can and will frequently change attack target

and indulge in malicious behavior with various network node, thereby making

it difficult to track malicious behavior of a compromised node in a large ad

hoc networks. Hence, threats from compromised nodes with networks are

more dangerous than attacks from outsiders. Also such attacks are harder to

detect as they are from compromised nodes, which behave correctly prior to

being compromised.

An example of such threats is from potential Byzantine failures

seen in the routing protocol for MANET (Mishra et al 2004). Byzantine

failure occurs when nodes are compromised so that incorrect/malicious

behavior cannot be easily detected due to cooperation among compromised

nodes when they indulge in malicious behavior. Such nodes may behave well,

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but they may use flaws and inconsistencies in routing protocols to destroy

routing network fabric without detection, generate and advertise new routing

information containing nonexistent link, provide fake link state information and

even flood other nodes with routing traffic. Such malicious behaviors are liable to

be ignored by other nodes as compromised nodes cannot be recognized easily.

One of the misbehavior which the malicious node exhibit is

selfishness, wanting to preserve own resources while using the services of the

other nodes in network and consuming their resources. Performance of ad hoc

network protocol depends upon the cooperation of all the nodes in the

network. Nodes transfer traffic by route detection and packet forwarding. As

transferring traffic consumes energy, CPU time, memory and bandwidth,

selfish nodes deny packet forwarding to other nodes but use the network

resources to deliver its own data. Detection and exclusion mechanism is used

to prevent selfishness in ad hoc networks.

The selfish node saves its own resources as follow (Bruce 2000):

1. Not participating in routing: Selfish node do not relay the

routing data such as route requests and route replies. Set hop

limit or TTL value at the lowest in route request / reply.

Selfish nodes modify routing data to modify route request,

insert additional hops, declare own ID as external in source

route.

2. Stop participation in current route: Do not participate in

route request, create arbitrary RERR messages, not sending

ACK messages.

3. Stop relaying data packets: Drop data packets.

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5.6 EXPERIMENTAL SETUP

The OPNET simulation tool is used to evaluate performance.

Simulation parameters are the same as used in the previous chapter. Three

scenarios are studied in this investigation,

Nodes moving in constant speed

To replicate real time scenario, experiments with 8 nodes

moving at an average speed of 100 Kmph in a predefined

circular route 4 nodes moving at an average speed of 65

Kmph in a predefined straight route. Balance nodes moving

randomly about the network.

To replicate real time scenario, selfish nodes are included in

the network.

Simulations are run using Fuzzy AODV, Proposed Fuzzy Clustered

AODV and Proposed Fuzzy Clustered AODV with selfish nodes. The

proposed AODV Clustering approach is evaluated for packet delivery ratio

and end to end delay.

5.7 RESULTS AND DISCUSSION

The experiments were conducted based on the above simulation

parameters. Each data point in the graph represents an average of ten

simulation runs. Figure 5.4 shows the packet delivery ratio for Nodes moving

in constant speed. Figure 5.5 show the end-to-end delay in MANET.

Table 5.1 tabulates the same.

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Table 5.1 Packet delivery ratio and end-to-end delay for nodes moving

in constant speed.

NodeMobility

speed

Packet Delivery Ratio End to End Delay

FuzzyAODV

ProposedFuzzy

ClusteredAODV

FuzzyAODV

ProposedFuzzy

ClusteredAODV

10.8 Kmph 0.9732 0.9817 0.000985 0.00102

18 Kmph 0.89264 0.92264 0.001225 0.001017

36 Kmph 0.8712 0.8912 0.01138 0.009857

54 Kmph 0.8688 0.8417 0.01434 0.01658

72 Kmph 0.7215 0.6817 0.0315 0.03652

90 Kmph 0.6823 0.6412 0.0336 0.03876

Figure 5.4 Packet delivery ratio for nodes moving in constant speed

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Figure 5.5 End-to-end delay nodes moving in constant speed

From Figures 5.4 and 5.5, it is seen that when the nodes move in

constant speed, the proposed Fuzzy clustered AODV performance is similar

to Fuzzy AODV. In high speed, Fuzzy AODV performs better than the

proposed clustered AODV. The increase in end to end delay is however

negligible.

Table 5.2 and Figure 5.6 shows the packet delivery ratio for Nodes

moving in varying speed for Fuzzy AODV, Proposed Fuzzy Clustered AODV

and Proposed Fuzzy Clustered AODV with selfish nodes. Table 5.3 and

Figure 5.7 show the end-to-end delay in MANET.

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Table 5.2 Packet delivery ratio for nodes moving in varying speed

Timein sec

FuzzyAODV

ProposedFuzzy

ClusteredAODV

Proposed FuzzyClustered AODVwith selfish nodes

10 0.8488 0.869171 0.809755

20 0.849 0.863433 0.708236

30 0.8538 0.872157 0.695505

40 0.8714 0.892314 0.681783

50 0.8718 0.886621 0.753845

60 0.8501 0.868377 0.692491

70 0.8676 0.888422 0.67881

80 0.8694 0.88418 0.75177

90 0.8772 0.89606 0.758515

100 0.8786 0.899686 0.715708

110 0.8314 0.87432 0.7245

120 0.8577 0.876141 0.741653

130 0.8805 0.901632 0.761368

140 0.8857 0.900757 0.721491

150 0.8872 0.906275 0.694145

160 0.8974 0.918938 0.775982

170 0.8501 0.8978 0.735081

180 0.8731 0.891872 0.711227

190 0.8776 0.898662 0.686634

200 0.8642 0.878891 0.747274

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Figure 5.6 Packet delivery ratio for nodes moving in varying speed

From figure 5.7 it can be seen that end to end delay is high for malicious

nodes as newer routes need to be discovered due to the packet drop caused by

selfish nodes.

Figure 5.7 End-to-end delay for nodes moving in varying speed

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Table 5.3 End-to-end delay for nodes moving in varying speed

Time insec

Fuzzy AODVProposed Fuzzy

Clustered AODV

Proposed Fuzzy

Clustered AODV

with selfish nodes

10 0.0161472 0.015098 0.016704

20 0.0163216 0.015375 0.018182

30 0.0163312 0.017474 0.018716

40 0.0148704 0.013978 0.017458

50 0.0151184 0.014136 0.01614

60 0.0146256 0.013777 0.017755

70 0.0146848 0.015713 0.016829

80 0.0148096 0.013921 0.018867

90 0.0148368 0.013872 0.01678

100 0.0148704 0.014008 0.016566

110 0.0151184 0.016177 0.017326

120 0.0151888 0.014277 0.020869

130 0.0152432 0.014176 0.015617

140 0.0153632 0.014357 0.017115

150 0.01572 0.014808 0.018785

160 0.013224 0.01415 0.015525

170 0.0137216 0.012898 0.014881

180 0.0138304 0.012862 0.015407

190 0.0140928 0.01317 0.01615

200 0.0141872 0.013364 0.016656

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From Figures 5.6 and 5.7, it is seen that the proposed Fuzzy Clustered AODV

performs better than Fuzzy AODV in terms of both packet delivery ratio and

end-to-end delay. It is also seen that the performance of the network

deteriorates considerably in the presence of selfish nodes.

5.8 CONCLUSIONS

This work presents an AODV-based clustering and routing scheme

for MANETs. The scheme is used for integrated routing and message delivery

in clustered networks. The proposed clustering architecture was evaluated

using simulation experiments. The simulation results show that the algorithm

builds stable clusters achieving better packet delivery ratio and end-to-end

delay. Further studies are required to address the deterioration in performance

due to the presence of selfish nodes.