Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy Logic

TELKOMNIKA, Vol.16, No.2, April 2018, pp. 728~738
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i2.6426  728
Received May 11, 2017; Revised September 7, 2017; Accepted February 2, 2018
Improved Routing Protocol in Mobile Ad Hoc Networks
Using Fuzzy Logic
Seyed Amin Hosseini Seno, Ali Abdi Seyedkolaei*
Ferdowsi University of Mashhad
Faculty of Engineering, Department of Computer Engineering
*Corresponding author, e-mail: aabdi64@gmail.com
Abstract
In mobile ad hoc networks, route selection is one of the most important issues that is studied in
these networks as a field of research. Many articles trying to provide solutions to choose the best path in
which the important parameters such as power consumption, bandwidth and mobility are used. In this
article, in order to improve the solutions presented in recent papers parameters such as power remaining,
mobility, degree node and available bandwidth are used by taking the factors for each parameter in
proportion to its influence in choosing the best path. Finally, we compare the proposed solution with the
three protocols IAOMDV-F, AODVFART and FLM-AODV with the help of OPNET simulation program
based on network throughput, routing discovery time, the average number of hops per route, network
delay.
Keywords: fuzzy logic, mobile ad-hoc network, routing, efficiency
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
In recent decades, following the advent of wireless communications technology, new
communication networks such as contingency networks have been introduced that have had
important applications in the military, industrial and social spheres [1]. Mobile ad hoc networks,
independent set of mobile hosts that wireless channels with limited bandwidth with each other.
The topology and arrangement of these types of networks are rapidly and unpredictably
changed due to the mobility of the nodes. Due to the easy expansion of the network, some
mobile ad hoc networking applications include use in emergency and rescue operations,
conferences, implementation of networks between cars and personal area networks. Given the
nature of these networks, each node in such networks is not only responsible for discovering a
new path for data transfer from the source node to the destination node, but also as a router for
transmitting messages [2].
There are no central controllers in these networks. A routing operation that involves
recognizing the network topology and delivering messages are distributed in a distributed and
independent way by nodes in the network, which can act as routers. In a mobile ad hoc network,
a message sent by a node to the entire neighboring node which is located in the radius of the
transfer. The problem of coating and binding has a great influence on the efficiency of the
mobile contingency network [3]. The mobility of nodes and the possibility of momentary
variations in network topology, variety of equipment, energy and bandwidth constraints, Search
and maintaining an optimal path in the network to other networks is a major challenge [4].
In mobile ad hoc networks, route selection is one of the most important issues that as a
field of research are studied in these networks. Mobility nodes in mobile ad hoc networks are
the biggest challenge. Because that makes links and routes destroyed and topology dynamically
change that this changes difficult network management. In general, high mobility changes the
topology frequently, which reduces the network performance. It is difficult to choose a
sustainable path to get better performance, because less time is spent on maintenance of paths
and more time is available to send useful information. This interaction between mobility,
topology and performance in the modeling of mobile ad hoc networks is fundamental and rarely
analyzed. Many articles trying to provide solutions to choose the best path in which the
important parameters such as power consumption, bandwidth and mobility is used. In this article

TELKOMNIKA ISSN: 1693-6930 
Improved Routing Protocol in Mobile Ad Hoc Networks Using… (Seyed Amin Hosseini Seno)
729
in order to improve the solutions presented in recent papers [5-7] parameters such as power
remaining, mobility, degree node and available bandwidth are used by taking the factors for
each parameter in proportion to its influence in choosing the best path. The values of these
parameters are defined as linguistic for input fuzzy system and we are considering fuzzy rules
stated obtains the output value of link stability. Fuzzy logic is a kind of computer science
approach that uses correct grade in Boolean logic rather than the common or false (zero or one)
commonly used modern computer system. Consider fuzzy logic 0 and 1 as reality states, but
there are several correct states between these two states. For example, the result of a
comparison between two things in height may be neither short nor long, but 0.38 higher than
one. Fuzzy Logic, the provider of the framework for controlling systems in which exact numerical
information from the system is not needed and the exact process identification mechanism is
not required. Fuzzy logic can analyze phenomena that are not repeatable, or they cannot be
regarded as an experience and under varying conditions under different circumstances [8].
Fuzzy logic is an important tool for designing controllers for nonlinear systems. The reason for
using fuzzy logic in ad hoc networks is the uncertainty in the values of the parameters that
influence the choice of optimal path, especially for mobile contingency networks. A fuzzy logic
system is a system that provides a natural way to accept multiple inputs of constraints that have
ambiguous values in nature.
There are two types of fuzzy logic inference system, one of which is Mamdani type, and
another is Sugeno type. Mamdani system is more popular and more used. The function of both
Mamdani and Sugeno systems is similar. But the difference is that in the Mamdani system, the
fuzzy output is neither linear nor fixed. But in the Sugeno system, the output is fuzzy or linear or
fixed. In this paper, the Mamdani system is used for the fuzzy logic system, since its fuzzy
output is linear and not constant.
Another point in this article is that the factor of motility parameter is considered
negative, since the mobility of nodes causes the changing network topology. This change leads
to destroy the best path and therefore do again which will be a large overhead for network route
discovery process that will be in terms of energy consumption.
Next sections will be discussed in the rest of the paper including: in section 2, we
describe related work. In Section 3, we present our proposed model. In Section 4, we present
simulation results of IAOMDV- F, AODVFART and FLM-AODV protocols and our proposed
model and we will do a comparison based on their results. Finally, in section 5, we will do
conclusion based on simulation results and comparison between these three protocols.
2. Related Work
DFES-AODV [9] Routing Protocol is one of the reactive protocol in which it is used
fuzzy logic and reinforcement learning. In this protocol, it is used on the dynamic membership
function to enhance the adaptive of legacy fuzzy logic systems. Using dynamic fuzzy logic for
energy aware routing in mobile ad-hoc networks is more appropriate than routing protocol
based on reinforcement learning. In the route discovery phase of the DFES–AODV protocol,
each node performs its route requests based on Mamdani fuzzy logic system that in fuzzy logic
system is used parameters power remaining and energy consumption by each node that the
amount of power remaining in this protocol is dynamically in contrast to previous protocols.
Abbaset et al. in [10] proposed reactive routing protocol based on fuzzy logic to
increase performance AODV routing protocol by selecting the most trusted nodes to create the
best route from the source node to the destination node. In this protocol, fuzzy logic system is
used as parameters such as power remaining, node mobility, and number of fop counts to
evaluate each node to select high-priority trusted nodes to create the best path. Simulation
results show that the proposed protocol performs better than AODV protocol and in terms of
average control overhead, packet delivery ratio, network throughput and average end-to-end
delay is more suitable than AODV protocol.
Prasanthi and et al. in [11] using fuzzy logic in AODV routing protocol, reduce delays
and congestion in AODV protocol and resulting in improved network performance than AODV
protocol. They consider the number of hop counts and network size as input parameters of
fuzzy logic system in the proposed method. The simulation results using software OPNET
showed that the average end to end delay and packet delivery ratio is improved compared to
AODV protocol.

 ISSN: 1693-6930
TELKOMNIKA Vol. 16, No. 2, April 2018 : 728 – 738
730
Baskaran et al [12] Improved Bee routing algorithms using fuzzy logic. Their goal
presenting this algorithm is to improve the reliability and limited bandwidth in routing algorithms.
In their research, they have used ideas of bee colony algorithm for finding the shortest path
between two source and destination nodes using the least number of steps. They used fuzzy
logic with power nodes, bandwidth and data traffic to select the best path. Their proposed
algorithm with AODV and Bee-MANET algorithms has been compared in terms of quality of
service, delay characteristics and stability path.
FBORP protocol [13] was recommended to improve the original AODV routing protocol
in mobile ad hoc networks to increase the stability using fuzzy logic. In this protocol have been
used three important parameters such as energy, bandwidth and node mobility. The simulation
results showed that this protocol compared with the original AODV protocol has better routing
discovery time, the average packet overhead, the average throughput and average final delay.
AODVFART Protocol [5] analyzes the performance of AODV protocol with optimized
active route timeout interval using fuzzy logic system. In this protocol, fuzzy logic system has
been used to decide on the amount of the active route timeout that its input parameter of fuzzy
logic system is transmission power and mobility of the nodes. The simulation results showed
that performance of AODVFART protocol is better than AODV protocol in terms of average
throughput, average end-to-end delay and average jitter.
IAOMDV-F Protocol [6] which is improved of AODV protocol has used fuzzy logic
system to select the best path. Fuzzy logic system used in this protocol chooses the best path
among multiple paths available to optimize energy consumption and the stability path. In this
protocol, fuzzy logic system uses three input parameters, average mobility, queue length and
the distance between nodes to select the best path. The simulation results showed that
performance of IAOMDV-F protocol compared to AODV, AOMDV and AOMDV-F protocols is
better in terms of end-to-end delay, throughput, and packet delivery ratio.
FLM-AODV Protocol [7] uses fuzzy logic to improve the AODV protocol. In the fuzzy
system, two parameters are considered the hop count and mobility. These two parameters
cause a better path than the AODV protocol. The simulation results show that the FLM-AODV
protocol improves average end-to-end delay and packet delivery ratio compared to the AODV
protocol.
3. Proposed Fuzzy-Based Model
In mobile ad hoc networks, route selection is one of the most important issues that is
studied as a field of research in these networks. Therefor their stability is dependent upon the
action of nodes in particular moving nodes. In mobile ad-hoc network, high mobility of nodes is
one of the main reasons for links failure; because it causes links and routes to be disappeared
then topology dynamically changes that this change makes it difficult to manage the network.
Generally, high mobility and topology frequently change that these changes will reduce the
performance of mobile ad hoc networks.
Many protocols has been introduced to choose the best path for sending data from the
source node to the destination node in which the parameters such as power consumption,
bandwidth and mobility are used to choose the best path. One of the most popular routing
protocols in mobile ad-hoc network is AODV protocol where paths are discovered only when
required and used during the time of their maintenance. Many methods to improve AODV
protocol have been presented in numerous articles. The most recent of these methods are
IAOMDV-F, AODVFART and FLM-AODV protocols. In AODVFART the protocol from
transmission power and nodes mobility parameters and in IAOMDV-F the protocol from queue
length and the distance between nodes parameters is used to choose the best path from the
source node to the destination node. In FLM-AODV the protocol from hop count and nodes
mobility parameters is used to choose the best path. In this paper, it has been tried that in
addition to parameters of previous papers from other parameters such as the degree node,
bandwidth and energy to be used. The degree node parameter causes to select the nodes close
together as the route node. This will lead to network outages occur later and we have energy
and less overhead to create the path. Also available bandwidth parameter causes that among
the routes available we select routes that have more stability than the other routes. And also
discuss the quality of service, reduce end-to-end delays and reduce packet delivery ratio which
is taken into account. Energy parameter also causes the nodes that are selected to create a

731
route including a high lifetime to cause disconnected communication route. In the following, in
different subsections, parameters used in fuzzy logic will be introduced along with the concept
"fuzzy rules and their membership functions".
3.1. The Effect of Energy Nodes on the Link Stability
Energy node is an evaluation criterion for link stability. The power remaining can be
used as a parameter to describe the energy situation i used as a custom node. We denote the
power remaining as. The value of is obtained with a very simple way with the help of Equation
(1):
remi av rtp p p  (1)
Where, is the available energy of node is the required transmit power of a data message of b-
bits between two nodes by a transmission range of d meters. The value of is obtained with the
help of Equation (2):
2
( , ) * ( * * )rt elec fsp b d p b e b d  (2)
Where, fse = 10PJ/bit/m2 is the energy consumed in free space at the output transmitter
antenna for transmitting one meter and elecp = 50nJ/bit is the energy expended in the radio
electronics [13-14].
The lifetime of the route has relationship with the minimum remip for all nodes in the
path. That is, if the energy of any node in the path is exhausted, then the link would crash. Thus,
remip represents the power remaining of the node i, therefore, the lifetime of the route is related
to min remip , that is:
min remipower p node i path    (3)
If the power is low, the probability of the link broken will be high. Thus, the rules for the
relationship between power and link stability should be as follows:
a. P1: If power is high then link stability must be high.
b. P2: If power is medium then link stability must be medium.
c. P3: If power is low then link stability must be low.
3.2. The Effect of Bandwidth on the Link Stability
We use the available bandwidth between two adjacent nodes, to describe the
bandwidth condition. A simple method gives the available bandwidth based on transmission
speed and can be used to measure the bandwidth for an (I, J) link. This link has an available
bandwidth that is obtained with the help of Equation (4):
_ ( , ) (1 ) * ( , )Available bandwith I J u Bandwith I J  (4)
Where u is the link utilization (u=A (t)/t; A (t) is the total amount of time in which the link is used
by nodes during an interval of time t). The link stability has relationship with the minimum
available bandwidth for all nodes in the path, when the route is congested, the shared
bandwidth of channels would be reduced, so that the route became unstable due to the
retransmission of the lost packets. That is, if the available bandwidth of any node in the path is
reduced, then the path would crash, therefore, the lifetime of the route is related to min available
bandwidth, for all nodes in the path. If the available bandwidth is low, the probability of the link
broken will be high. The rules for the relationship between available bandwidth and link stability
are
a. B1: If available bandwidth is high then link stability must be high.
b. B2: If available bandwidth is low then link stability must be low

 ISSN: 1693-6930
732
3.3. The effect of node mobility on the link stability
MANETs experience dynamic changes in network topology because of the unrestricted
mobility of the nodes in the network. If the end nodes experience much movement, then it is
highly probable that their path will break. The node movement can be measured by the number
of sent control packets between two sampling intervals. Sent control packet is any message of
the following type: RREQ, RREP, RERR and RREP_ACK.
If the RREP packet records the number of sending control packet of the intermediate
nodes is high, it represents the route is probably unstable. So, high number of these packets
means high probability to loose some of its current links or packets [15]. In general, a rule can
be defined: When sent control packet is high, the link stability must be low and vice versa.
Consequently the following fuzzy rules are proposed:
a. M1: If sent control packet is high then link stability must be low.
b. M2: If sent control packet is medium then link stability must be medium.
c. M3: If sent control packet is low then link stability must be high. In general, the link stability
is an inverse ratio sent control packet.
3.4. The effect of node degree on the link stability
Degree of a node (Di) is defined as the number of neighbouring nodes. To compute
node degree the concerned device broadcast a HELLO packet. The nearby nodes that can hear
any HELLO packet record the source node’s address as its neighbour node. Then the code can
compute total number of neighbours by counting the number of HELLO packets that it hears. A
table of nodes is maintained by each node to use it in future communication. Thus, if the degree
of any node in the path is increased, then the path would stable, therefore, the lifetime of the
route is related to max node degree, for all nodes in the path.
If the node degree is low, the probability of the link broken will be high. Thus, the rules
for the relationship between node degree and link stability should be as follows:
a. D1: If node degree is high then link stability must be high.
b. D2: If node degree is low then link stability must be low.
Now according to the defining the rules of four parameters effective in stability of link, it
can be specified as the table of fuzzy rules base to determine priority selection of the next node
in the routing as shown in Table 1.
Table 1. Fuzzy Logic Rules Base
No. i
Input Output
Available
Bandwidth
Power
Remaining
Node
Degree
Sent Control
Packet
Link Stability
1 Low Low Low Low Very Low
2 Low Low Low Medium Very Low
3 Low Low Low High Very Low
4 Low Low High Low Low
5 Low Low High Medium Rather Low
6 Low Low High High Rather Low
7 Low Medium Low Low Low
8 Low Medium Low Medium Rather Low
9 Low Medium Low High Rather Low
10 Low Medium High Low Medium
11 Low Medium High Medium Medium
12 Low Medium High High Medium
13 Low High Low Low Low
14 Low High Low Medium Rather Low
15 Low High Low High Rather Low
16 Low High High Low Medium
17 Low High High Medium High
18 Low High High High High
19 High Low Low Low Very Low
20 High Low Low Medium Low
21 High Low Low High Low
22 High Low High Low Rather Low
23 High Low High Medium Rather Low
24 High Low High High Rather Low
25 High Medium Low Low Medium

733
Table 1. Fuzzy Logic Rules Base
No. i
Input Output
Available
Bandwidth
Power
Remaining
Node
Degree
Sent Control
Packet
Link Stability
26 High Medium Low Medium Medium
27 High Medium Low High Medium
28 High Medium High Low High
29 High Medium High Medium High
30 High Medium High High Very High
31 High High Low Low Medium
32 High High Low Medium High
33 High High Low High High
34 High High High Low High
35 High High High Medium Very High
36 High High High High Very High
In the following, membership functions of four input parameters, sent control packet,
power remaining, available bandwidth and degree node and output parameter of link stability,
respectively will be shown in Figures 1 and 2.
Figure 1. Membership function for sent control packet, power remaining, available bandwidth
and node degree respectively
Figure 2. Membership function for link stability

 ISSN: 1693-6930
734
Overally, we can use the base rules expressed in the Table 1 to use in the fuzzy systems in the
following format:
If available bandwidth is Ai1 and power remaining is Ai2 and sent control packet
is Ai3 and node degree is Ai4 then Stable Path is Bi (5)
Where Ai2, Ai3 are the linguistic labels low, medium and high and Ai1, Ai4 are the linguistic
labels low and high and Bi are the linguistic labels very low, low, rather low, medium, high and
very high of the ith rule.
Based on the defined base rules according to with Equation (5), the link stability value
for different routes to the same destination can be calculated using Equation (6). In this equation
SP(i), B(i), M(i), D(i) and P(i) is the membership function related to the link stability, available
bandwidth, sent control packet, degree node and power remaining, respectively.
( ) * ( ) * ( ) * ( ) * ( )i i i i
SP i B i M i D i P i       (6)
In Equation (6) it is considered four-parameter available bandwidth, sent control packet,
degree nodes and power remaining with various factors that these factors can be in accordance
with system requirements and have a significant impact on the link stability. Considered factors
to any parameters take values between 0 and 1 according to the priority of each parameter that
the range of the values shown in Figure 3. The priority of parameters is from top to bottom in the
order which includes power remaining, degree node, available bandwidth and sent control
packet.
Figure 3. The range of values for parameter factors
The important point is the link stability which has an inverse ratio corresponding to sent
control packet and direct ratio to the available bandwidth, node degree and power remaining. As
a result, value of link stability i.e. SP(i) will be obtained through the combination of these
parameters.
4. Simulation Results
In this section, we compare the performance of the proposed model with the IAOMDV-
F, AODVFART and FLM-AODV protocols using OPNET simulation software. we compared the
20 nodes with the assumptions described in Table 2 and obtain index values include network
throughput, routing discovery time, the number of hops, network delay, package delivery
fraction and number of packets loss from the simulation results.
As shown in Figure 4, we simulated a model implemented of mobile ad hoc network
with regard to the assumptions described in Table 2. In this simulation, moving nodes is
according to moving model of random track point in which each node moves to a location
randomly selected with a specified speed. Then stops are performed for a pause time
configured before selecting another random location and repeating the same simulation
process. Moving patterns are for six different pause time: 0, 20, 40, 60, 80 and 100 seconds.
The results of this simulation shown in figures 5 to 8 by comparing the proposed model,
IAOMDV-F, AODVFART and FLM-AODV protocols.

735
Figure 4. A sample topology of MANET
Table 2. Simulation Parameters
Parameters Values
Number of mobile nodes 20
Simulation area 800 m * 800 m
Simulation time 100 s
Pause time for mobile nodes 20 s
Maximum speed for mobile nodes 0 – 12 m/s
Packet size 512 bytes
Traffic type CBR
Transmission range for mobile nodes 150 m
In this simulation, compare the three protocols been done based on the following indexes:
a. Network Throughput: it is the maximum rate that the network equipment can bear in the
case of no data loss.
b. Network Delay: it includes the routing discovery delay, waiting delay of packets in the
interface queues, transmission delay and retransmission delay of the MAC layer.
c. Routing Discovery Time: it is the time from launching the routing discovery of the source
node to receiving the RREP packet and establishing the route.
d. Average Number of Hops per Route: it is defined as the average time of the total number of
hops passed by the packet sent from the source node to the destination node, in the whole
simulation process.
Figure 5 shows the average performance in terms of throughput for different pause
times in a network of 20 nodes. The X-axis represents the pause times and the Y-axis
represents the throughput. The simulation in Figure 5 shows that throughput of the proposed
method with regard to the parameters considered in fuzzy logic system is better than
AODVFART, IAOMDV and FLM-AODV protocols. The reason is that in the proposed method it
is used of the two most important parameters of energy and the node degree. The parameter of
node degree is tries to select the nodes that are near each other which lead to low power
consumption to transmit and receive data from other nodes and thus will affect the network
throughput.

 ISSN: 1693-6930
736
Figure 5. Throughput at different pause times for 20 nodes
Figure 6 shows network delay of the proposed method, AODVFART, IAOMDV-F and
FLM-AODV protocols for different pause times. The X-axis represents the pause times and the
Y-axis represents the network delay. From Figure 6, it is observed that the average network
delay of the proposed method is lesser than other protocols. The reason is that one of the
factors implicated in network delay is route discovery delay. So the proposed method has the
better route discovery time because close nodes have faster sending time and less sending
delay. As a result, the amount of network delay will be more efficient than AODVFART,
IAOMDV-F and FLM-AODV protocols.
Figure 6. Delay at different pause time for 20 nodes
Figure 7 shows the route discovery time by all four protocols. The X-axis represents the
pause times and the Y-axis represents the route discovery time. Figure 7 shows that the
proposed model reduces route discovery time. The reason is that in the proposed method,
nodes that are close together are selected as nodes along the route and in case of failure of a
link, it spends less time to discover a new route. As a result, the proposed method will have less
discovery route time than AODVFART, IAOMDV-F and FLM-AODV protocols.
0
5000
10000
15000
20000
25000
30000
35000
0 20 40 60 80 100
Throughput(bits/sec)
Times
Throughput vs Times
Proposed
IAOMDV-F
AODVFART
FLM-AODV
0
0.1
0.2
0.3
0.4
0 20 40 60 80 100
NetworkDelay(sec)
Times
Network Delay vs Times
Proposed
IAOMDV-F
AODVFART
FLM-AODV

737
Figure 7. Route discovery time at different pause time for 20 nodes
Figure 8 shows the number of hops the proposed method compared to AODVFART,
IAOMDV-F and FLM-AODV protocols for different pause times. The X-axis represents the
pause time and the Y-axis represents the number of hops. Figure 10 shows that the number of
hops the proposed method is less than AODVFART, IAOMDV-F and FLM-AODV protocols. The
reason is to choose the shortest route in the proposed method. To do this, some nodes can be
chosen as the nodes of route which have a short distance towards each other.
Figure 8. Hop count at different pause time for 20 nodes
5. Conclusion
In this paper, we used fuzzy logic to improve routing protocol on demand. The reason
for using fuzzy logic is uncertainties in the values of the effective parameters in selecting the
optimal route, particularly for mobile ad hoc networks. In the proposed method, we used the four
effective parameters in selecting the route including battery energy, node degree, node mobility
and bandwidth as input parameters of fuzzy logic system. With the help of the rules base table,
we computed the amount of stability of links for each of the candidate routes. Finally, among the
candidate routes, we selected the path that has more stability links than any other path send
information from the source node to the destination node. In order to evaluate the proposed
method, we compared it with AODVFART, IAOMDV-F and FLM-AODV methods by taking the
same conditions simulated. Simulation results show that the proposed model outperforms
IAOMDV-F, AODVFART and FLM-AODV methods, especially in a high mobility environment.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 20 40 60 80 100
RouteDiscoverytime(sec)
Times
Route Discovery time vs Times
Proposed
IAOMDV-F
AODVFART
FLM-AODV
0
0.5
1
1.5
2
2.5
0 20 40 60 80 100
AverageNumberofHops
perRoute
Times
Number of Hops vs Times
Proposed
IAOMDV-F
AODVFART
FLM-AODV

 ISSN: 1693-6930
738
References
[1] Gopi AP, Babu ES, Raju CN, Kumar SA. Designing an adversarial model against reactive and
proactive routing protocols in Manets: A comparative performance study. International Journal of
Electrical and Computer Engineering (IJECE). 2015 1; 5(5):1111-1118.
[2] Vu TK, Kwon S. Mobility-assisted on-demand routing algorithm for MANETs in the presence of
location errors. The Scientific World Journal. 2014; 18; 1-11.
[3] Sivakumar B, Bhalaji N, Sivakumar D. A survey on investigating the need for intelligent power-aware
load balanced routing protocols for handling critical links in MANETs. The Scientific World Journal.
2014; 23; 1-11.
[4] Alaa A, Najlae I. Mobile Ad hoc Networks: Modelling, Application and Data Routing Evaluation.
International Journal of Informatics and Communication Technology (IJ-ICT). 2017; 22; 6(1):20-30.
[5] Jain B, Shrivastava L, Bhadauria SS. Performance Analysis of AODV with Fuzzy based Active Route
Timeout Interval in MANET. Journal of Basic and Applied Engineering Research. 2015; 2(16):1388-
1393.
[6] Agarwal MM, Jhankal AK, Govil MC, Sinha M. Fuzzy Logic Controlled Energy Efficient Routing in
Mobile Ad-Hoc Networks. International Journal of Computer Networks and Wireless
Communications. 2015; 5(4):556-561.
[7] Sharma V, Alam B, Doja M.N. Comparative Study of Fuzzy Logic Mobility Based FLM-AODV Routing
Protocol and AODV in MANETs. Indonesian Journal of Electrical Engineering and Computer Science
(IJEECS). 2017; 7(1):158-163.
[8] Mendel JM. Uncertain rule-based fuzzy logic systems: introduction and new directions. Upper Saddle
River: Prentice Hall PTR; Jan 2001.
[9] Chettibi S, Chikhi S. Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient
routing in mobile ad-hoc networks. Applied Soft Computing. 2016; 38:321-8.
[10] AAbbas NI, Ilkan M, Ozen E. Fuzzy approach to improving route stability of the AODV routing
protocol. EURASIP Journal on Wireless Communications and Networking. 2015; 1: 235.
[11] Prasanthi VV, Setty SP. Enhancing the Performance of AODV in MANETs using Fuzzy logic.
International Journal of Computer Science and Information Technologies. 2015; 6(5):4748-4750.
[12] BBaskaran A, Chaudhari N, Caytiles RD, Iyengar NC. Fuzzy Optimized and Bee inspired Routing
Protocol for Improved QoS in Mobile Ad Hoc Networks. International Journal of Control and
Automation. 2016; 9(8):391-402.
[13] TTabatabaei S, Teshnehlab M, Mirabedini SJ. Fuzzy-based routing protocol to increase throughput in
mobile ad hoc networks. Wireless Personal Communications. 2015; 84(4):2307-25.
[14] NNaruephiphat W, Usaha W. Balanced energy-efficient routing in MANETs using reinforcement
learning. InInformation Networking, 2008. ICOIN 2008. International Conference on 2008 Jan 23.
IEEE. 2008; 1-5.
[15] SSu BL, Wang MS, Huang YM. Fuzzy Logic Weighted Multi-Criteria of Dynamic Route Lifetime for
Reliable Multicast Routing in Ad Hoc Networks. Expert Systems with Applications. 2008; 35(1):476-
84.

Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy Logic

More Related Content

What's hot (16)

Similar to Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy Logic (20)

More from TELKOMNIKA JOURNAL (20)

Recently uploaded (20)

Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy Logic