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International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
9
Optimum Route Selection using Improved FF-
AOMDV to Increase Network Lifetime in MANET
Madhuram Jain
1
, Sanjay Mishra
2
, Dr. Amit Sinhal
3
1
M.Tech Scholar, Department of Information Technology, TIT Bhopal, madhuramjain0@gmail.com, India;
2, 3
Professor, Head of Department,Department of Information Technology, TIT Bhopal,
sanjaymishra2006@gmail.com,sinhal.amit@gmail.com, India;
Abstract– Mobile Ad Hoc Network (MANET) is a collection of
wireless mobile nodes that dynamically form a temporary
network without the reliance on any infrastructure or central
administration. Energy consumption is considered as one of the
major limitations in MANET, as the mobile nodes do not possess
permanent power supply and have to rely on batteries, thus
reducing network lifetime as batteries get exhausted very quickly
as nodes move and change their positions rapidly across
MANET. The researches performed till date highlights this very
specific disadvantage of energy consumption in MANETs and by
applying the protocol named Ad-hoc on Demand Multipath
Distance Vector with the Fitness perform (FF-AOMDV) and
dragonfly topology to reduced it. The f itness function is
employed to find the best path from the availability to the
destination to scale back the energy consumption in multipath
routing by using dragonfly topology. The performance of the
planned FF-AOMDV protocol with sewing needle topology was
evaluated using Network simulator Version two (NS-2), wherever
the performance was compared with AOMDV and Ad-hoc on
Demand Multipath Routing with Life Maximization (AOMR-
LM) protocols, the two preferred protocols of this area. In
proposed work Implemented FFAOMDV with Dragonfly
algorithm and gives improvement of Energy consumption,
Network lifetime, Packet Delivery Ratio, Throughput, End to
End delay and Routing overhead ratio, which gives percentage of
improvement as 10.7, 33.33, 5.9 13.47, 10.86, 8.47 percent
respectively with respect to time.
Keywords: Mobile Ad-hoc network, multipath routing, fitness
function, Dragonfly topology, AOMDV and FF-AOMDV.
I. INTRODUCTION
Today, wireless networks have been very popular in the
computing industry. Wireless networks can be categorized into
two classes.Mobile Ad-hoc Networks (MANETs) are
assortment of self routing enabled devices that communicate
among themselves with none specific network infrastructure.
Obviously, these networks are decentralized and believe
neighbors for communication [1]. The topology of the
networks isn't fixed and is subjected to alter over time because
of the mobile nature of the devices.
Fig.1.1 MANET architecture and components
The below figure 1.1 represents various mobile nodes general
structure for the MANET by considering the military
application. Headquarters mobile nodes access the
information from other mobile nodes that are located at
different positions. Using the routing protocol
communications between mobile nodes is done. While
working with the wireless networks, the network layer
receives most of the researcher’s attention. Due to this there
are many routing protocols proposed by various authors for
MANET with their different aims and objectives by targeting
the specific application needs. They communicate with each
other by using on peer-to-peer routing Mobile Ad hoc
Networks (MANETs). It can be defined as autonomous
system of mobile nodes connected with each other via
wireless. Every node in MANETs works as a router as well as
a host and forwards packets to each other to activate the
communication between nodes not directly connected by
wireless links. The main challenge on wireless MANETs is a
development of dynamic protocols that can efficiently find
routes between communication mobile nodes. This type of
routing protocol should be able to keep up with the high
degree of node mobility that is frequently changed into the
network topology. The combination for the quality of the links
differs with the use of broadcasting nature of the Wireless
channels [2].
The method of routing in energy dependant networks has to
meet stability and quality throughout the communication time.
Simply, the link stability and flawless communication depends
directly over the energy of the devices. Routing protocols are
responsible for ensuring energy efficient path discovery and
try to reduce energy consumption of the nodes within the
network. Major routing protocols minimize energy
consumption by choosing minimum hop distance nodes, so as
International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
10
to enhance transmission rates or to reduce delay in
transmissions [3]. Recent approaches in energy efficient
routing concentrate on choosing specific nodes according to
their offered residual energy, by which the protocol technique
insured to attain energy efficiency with different limited
network performance. Researchers have found several
improved solutions for achieving energy efficiency in these
decentralized networks. A number of them provide routing
with minimum energy utilization and aiding on lifetime
maximization. Routing Protocols should uplift and retain
network operations for longer time ensuring efficient ways
between communicating nodes. Prolonged communication
was achieved by minimizing node’s energy consumption
throughout its active and inactive states. Following are the
ways used to achieve energy efficiency in mobile ad-hoc
networks [4, 6, 7, 8].
II. METHOD
A. Dragonfly Topology
A novel intelligence optimization technique called
Dragonfly Algorithm is used. This topology is basically
design best network architecture.
In the traditional AOMDV, it builds multiple paths
using RREQs. It does not take into account the energy for
choosing the paths. Here the proposed protocol not only
considers residual energy but also transmission power of
nodes in paths selection to maximize the lifetime of networks.
The proposed system consists of three stages:
• Calculate residual energy in network
• design efficient network
• Calculate energy consumption in route discovery
• Find shortest route with higher residual energy
This algorithm is conscientious for deployment of nodes in
an exacting area.
In dragonfly topology no. of network terminals is known
as:
N=a*p(ah+1)
To steadiness channel load on load-balanced traffic, the
network should have a=2p=2h. Each of the router topology is
based on randomized placement of nodes using node
deployment algorithm.
The following symbols are used in our description of the
dragonfly topology.
N
= number of network terminal.
p
= Number of terminals connected to each router.
a
= Number of routers in each group.
h
= Number of channels within each router used to connect to
other groups.
Within the event on route selection once the chosen route
fails, the supply node can then select another route from its
routing table that represents the shortest route with higher
energy level and minimum energy consumption.
B. Fitness Function
The fitness function(FF) is an improved technique that
comes as a part of the many optimization algorithmic rules
like genetic algorithm, bee colony algorithmic rule, firefly
algorithmic rule and particle swarm optimization rule. The
fitness finds the most important factor of several factors
necessary in the optimization method that counts on the aim of
the analysis [9, 10, 11, 12]. In MANETs, the fitness factors
are energy, distance, delay, bandwidth etc. This matches the
reasons for designing any routing protocol, as they aim to
enhance the full utilization of network resources. In this
analysis, the fitness function used is Energy consumption in
association with a type of Swarm Intelligence(SI) called
Dragonfly Algorithm such as Particle Swarm optimization
(PSO) rule. It had been used with wireless sensor networks to
optimize the choice route in case the first route fails [13][14].
The factors that affect the selection of the optimum route are:
• The remaining energy functions for each node
• The distance functions of the links connecting the
neighboring nodes
• Energy consumption of the nodes
• Communication delay of the nodes.
C. FF-AOMDV
In antraditional AOMDV, once a RREQ is broadcasted by
a source node, more than one route to the destination are
found and the data packets are forwarded through these routes
without knowing the routes’ quality. By implementing the
above explained rule on an analogous scenario, the routes
selection is entirely different. Once a RREQ is broadcasted
and received, the provision node will have three (3) forms of
information to realize the selection of the shortest and
optimized route with reduced energy consumption
[15][16][17]. This information includes:
• Information about network’s each node’s energy level
• The distance of every route
• The energy consumed in the process of route discovery.
The route, that consumes less energy, may probably be (a) the
route that has the shortest distance; (b) the route with the very
best level of energy, or (c) both. The supply node can then
send the information packets via the route with highest energy
state, to minimize its energy consumption. Unlike of different
multipath routing protocols, this protocol also initiates new
route discovery method once all routes to the destination are
unsuccessful. Within the event once the chosen route fails, the
supply node can then select another route from its routing
table that represents the shortest route with higher energy
level and minimum energy consumption. The best route with
less distance to destination can consume less energy [18].
III. RELATED WORK
Many research papers have been studied based on
performance evaluation, optimization, sizing techniques,
efficiency improvement, and factors affecting system
performance, economical and environmental aspects of
Energy Efficient Multipath Routing Protocol for Mobile ad-
hoc Network using different topologies.In [1] analysis,
authors projected a new energy efficient multipath routing
International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
11
algorithmic rule referred to as FF-AOMDV (Fitness Function
Ad Hoc On Demand Multipath Distance Vector ) simulated
using NS-2 under 3 completely different situations, variable
node speed, packet size and simulation time.In [2] authors use
fuzzy logic and a fitness operate as a soft computing technique
for planning this projected protocol. The fuzzy logic is
essentially wont to calculate fitness price of every route. This
fitness price helps to outline the character of the route like that
route is efficient with reference to energy. In [3], algorithmic
rule supported reinforcement learning was projected that was
supported local data. The obtained results illustrated through
the figures indicated that reinforcement learning may be a
promising conception in MANETs.A wide vary of fields, like
business and military applications, use MANETs. Thus,
establishing a path from supply to focus on is important to
confirm that the information packet delivered meets the QoS
needs. However, this paper projected a QoS-routing
algorithmic rule applicable in MANETs that satisfies energy
and delay constraints.In [7], authors planned an ant colony-
based energy control routing protocol PSO-ACECR and
evaluated the affect of various quality models to the
performance of PSO-ACECR (ant colony-based energy
control routing) in MANETs. In PSO-ACECR, the routing
protocol can notice the higher route that has a lot of energy
than different routes through the analysis of average energy
and also the minimum energy of methods.
IV. PROPOSED METHODOLOGY
This paper proposed a new multipath routing protocol called
the FF-AOMDV routing protocol with Dragonfly topology,
which is a combination of Fitness Function and the AOMDV
protocol and dragonfly topology. In a normal scenario, when
a RREQ is broadcasted by a source node, more than one route
to the destination will be found and the data packets will be
forwarded through these routes without knowing the routes’
quality. By implementing the proposedalgorithm on the
same scenario, the route selection will be totally different.
When a RREQ is broadcasted and received, the source node
will have three (3) types of information in order to find the
shortest and optimized route with minimized energy
consumption.
Fig.3 Flow diagram of proposed work
In the traditional AOMDV, it builds multiple paths using
RREQs. It does not take into account the energy for choosing
the paths. Here the proposed protocol not only considers
residual energy but also transmission power of nodes in paths
selection to maximize the lifetime of networks.
V. RESULTS
By using NS2 simulator and utilized the Constant Bit Rate
(CBR) as a traffic source with 50 mobile nodes that are
distributed randomly in a 1500 m* 1500 m network area; the
network topology may therefore, undergo random change
since the nodes’ distribution and their movement are random.
The transmission range of the nodes was set to 200 m, while,
for each node, the initial energy level was set to 100 joules of
the network. Initial design MANET architecture using
dragonfly topology then random assign energy according to
International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
topology. After deployment of network architecture find
optimum path using FF-AOMDV According to different
parameter as below describe:-
A. Packet Delivery Ratio (PDR): Itis the ratio of the data
packets that were delivered to the destination node to the
data packets that were generated by the source. The
higher the ratio, the better the performance of the routing
protocol.
PDR=
Fig.4 Graph of packet delivery ratio with simulation time
Fig.4 shows graph of packet delivery ratio with simulation
time. The fig. shows the variation of packet delivery ratio on
varying simulation time for FF-AOMDV with Dragonfly
topology, AOMR-LM and AOMDV routing protocols.
Simulation time is varied as 4, 6, 8, to 14 seconds.
When the simulation time increases, the packet delivery
ratio also increases. The FF-AOMDV with Dragonfly
topology has better performance in terms of packet delivery
ratio than both AOMR-LM and AOMDV protocols. The FF-
AOMDV protocol with Dragonfly topology achieved 76% of
packet delivery ratio in 4 seconds of simulation time and 97%
in 14 second of simulation, the AOMR-LM protocol achieved
70.23% of packet delivery ratio in 50 seconds simulation time
and 76.2% in 14 seconds of simulation time and finally, the
AOMDV achieved 74.8% in 4 seconds simulation time and
78.7% of 14 seconds simulation time. The FF-AOMDV with
Dragonfly topology has higher PDR due to having multiple
paths always available in case of any chance or case of route
failure.
B.
Throughput: Throughput is known as the number of
bits that the destination has successfully received.
kbps 1000 * time)simulation / 8 * received bytes of(number TP =
Fig.5 Graph of throughput
Fig.5 shows the comparison of throughput behalf of
simulation time. In this figure x axis show the simulation time
and y axis show the throughput. In this fig. shows the effect
on the throughput on varying simulation time for FF-AOMDV
with Dragonfly topology, AOMR-LM and AOMDV routing
protocols. Simulation time is varied as 4-14 seconds. When
the simulation time increases, the throughput also increases.
The FF-AOMDV protocol with Dragonfly topology has better
performance in terms of throughput than both AOMR-LM and
AOMDV protocols. The FF-AOMDV with Dragonfly
topology has 171.6 kbps throughput in 4 second simulation
time and 1122 kbps in 14 second of
In FF-AOMDV with Dragonfly topology the packet-loss is
nearly zero because of its unique property of storing the
information about the various energy efficient paths available
for flawless communication.
C. End-to-end delay:End-to-End delay refers to theaverage
time taken by data packets in successfully transmitting
messages across the network from source to destination.
This includes all types of delays, such as packet queuing
at interface queue; propagation time and transfer time;
and buffering during the route discovery latency [21].
Fig.6 shows the comparison of E2E delay w.r.t. packet size.
The fig. shows the change of end-to-end delay for FF-
AOMDV with Dragonfly topology, AOMR-LM and
AOMDV. When the packet size increases as 64, 128, 256,
512, 1024 bytes, the end-to-end delay also increases. The
E2E delay in FF-AOMDV routing protocol with Dragonfly
topology increases from 14.8 ms to 25 ms, in the AOMR-
LM protocol it increases from 18.64 ms to 44 ms and
finally, in the AOMDV protocol it increases from 21.63 ms
to 42 ms. The FF-AOMDV routing protocol with Dragonfly
topology has better performance than both AOMR-LM and
AOMDV in terms of end-to-end delay.
100×
sentpacketsofnumber
receivedpacketsofnumber
International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
13
Fig.6 Graph of End to end delay
D. Energy Consumption:
Energy consumption refers to the
amount of energy that is spent by the network nodes
within the simulation time. This is obtained by calculating
each node’s energy level at the end of the simulation,
factoring in the initial energy of each one [22].
Fig.7 shows the comparison graph of energy consumption
behalf of node speed. The variation in energy consumption for
FF-AOMDVwith Dragonfly topology, AOMR-LM and
AOMDV are shown. When the node speed increases as
2,4,6,8,10 m/s, the energy consumption also increases. In the
FF-AOMDV with Dragonfly topology it increases from 60
joules to 98 joules as it is designed to select the path having
higher energy levels and shortest route from source to
destination, in AOMR-LM it increases from 61 joules to 112
joules and in AOMDV it increases from 72 joules to 158
joules.The FF-AOMDV with Dragonfly topology has least
energy consumption because it has the information of most
energy efficient paths stored.
Fig.7 Graph of energy consumption behalf of node speed
E. Network Lifetime:
The network lifetime refers to the
required time for exhausting the battery of n mobile nodes.
F.
Fig.8 Graph of network lifetime
Fig.8 shows the comparison of network lifetime behalf of
simulation time. In this figure x axis show the simulation time
and y axis shows the number of exhausted nodes for FF-
AOMDV with Dragonfly topology, AOMR-LM and AOMDV
when varying the simulation time. The FF-AOMDV with
Dragonfly topology exhausts 0 nodes in 50 seconds and 2
nodes in 250 seconds, the AOMR-LM exhausts 0 nodes in 50
seconds and 3 nodes in 250 seconds, while, the AOMDV
exhausts 2 nodes in 50 seconds but 6 nodes in 250 seconds.
The FF-AOMDV with Dragonfly topology enhances its
network lifetime as it routes the traffic to the nodes having
higher energy in the network. In the case, when the energy of
these nodes get exhausted the topology has the property of
storing information about various energy efficient routes and
hence it transfers the traffic to next energy efficient shortest
path, thus enhancing the network lifetime. case of any route
failure and hence reducing the overhead due to control packets
and queuing of data packets, thus improving Routing
overhead ratio. Now that we have seen all the improvements
in performance parameters in graphical form, we can easily
take the data values from the graphs and can compare them in
tabular form. The following are the tables in which we have
compared the performance metrics on varying simulation
scenarios. All the tables show the comparison of various
existing protocols (single run) with the proposed FF-AOMDV
with Dragonfly topology (three runs) over each value of
varying simulation values to show the justified values of the
proposed algorithm.
International Journal of Ethics in Engineering & Management Education
Website: www.ijeee.in (ISSN: 2348-4748, Volume 5, Issue 6, June 2018)
14
Table 1 Comparison of performance parameters and percentage improvement
w.r.t. Simulation Time
S. No. PARAMETERS
COMPARED
EXISTING
FFAOMDV
IMPLEMENTE
D FFAOMDV
WITH
DRAGONFLY
ALGORITHM
1. ENERGY
CONSUMPTI
ON(in joules)
68 61
2. NETWORK
LIFETIME
(nodes
exhausted)
3 2
3. PACKET
DELIVERY
RATIO(PDR)
76.46% 81%
4. THROUGH
PUT (in kbps)
400.78 454.68
5. END-TO-
END DELAY
(in mS)
26.74 24.12
6. ROUTING
OVERHEAD
RATIO
0.3468 0.3197
VI.
CONCLUSION
In this research paper, proposed a new energy efficient
multipath routing algorithm called FF-AOMDV with
Dragonfly topology simulated using NS-2 under three
different scenarios, varying node speed, packet size and
simulation time. These scenarios were tested by six
performance metrics Packet delivery ratio, Throughput, End-
to-end-delay, Routing overhead ratio, Energy consumption
and Network lifetime. Simulation results showed that the
proposed FF-AOMDV with Dragonfly topology has
performed better than the existing FF-AOMDV and the other
two protocols AOMR-LM and AOMDV in throughput, packet
delivery ratio, routing overhead ratio and end-to-end delay. It
also performed well against FF-AOMDV for conserving more
energy and enhancing the network lifetime. In proposed work
Implemented FFAOMDV with Dragonfly algorithm and gives
improvement of Energy consumption, Network lifetime,
Packet Delivery Ratio, Throughput, End to End delay and
Routing overhead ratio, which gives percentage of
improvement as 10.7, 33.33, 5.9 13.47, 10.86, 8.47 percent
respectively.
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