Optimized Fuzzy Routing for MANET

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. V (Mar – Apr. 2015), PP 23-27
www.iosrjournals.org
DOI: 10.9790/0661-17252327 www.iosrjournals.org 23 | Page
Optimized Fuzzy Routing for MANET
Md. Rafeeq Asst. Prof
Dept of CSE BKIT, BHALKI
Abstract: An ad-hoc network is a collection of mobile hosts with wireless network interfaces that may form a
temporary network without the aid of any established infrastructure of centralized administration. In such
environment, the nodes operate both as hosts as well as routers. Due to mobility, the topology of the network
may change randomly, rapidly and unexpectedly.
The objective of the work is to present effective decision algorithm that improves network performance. In the
proposed technique, we defuzzyfy the values of delay, packet success rate, bandwidth and find out the threshold
for these parameters for current session. In the path selection each node checks for its suitability with respect to
the threshold corresponding to the parameters. If a node satisfies the condition then only it is included in the
path otherwise node is omitted from the path.
Keywords: Adhoc Network, Fuzzy Logic, AODV, Routing, MANET
I. Introduction:
The goal of the paper is to propose a FUZZY routing in MANET by considering Bandwidth, Packet
Success Rate and Energy. We propose optimized routing technique for MANET by taking routing decision
using fuzzy logic”. All possible paths from source to destination are obtained in multipath environment using
disjoint approach. In source nodes cache, the entire path will be present. RREQ is modified by appending extra
fields of bandwidth, Packet Success rate and energy. As the RREQ traverses from the intermediate nodes, they
append their values in the respective fields. Source node defuzzyfies them and select the best path based on the
above parameters.
i. Introduction to Mobile Ad-hoc Networking
The term MANET stands for Mobile Ad-hoc Network. This new networking concept defines simple
mechanisms, which enable mobile devices to form a temporary community without any planned installation, or
human intervention. The idea is to form a totally improvised network that does not require any pre-established
infrastructure. But, how can we make this possible? The answer is very simple. Each node acts as a host and a
router at the same time. This means that each node participating in a MANET commits itself to forward data
packets from a neighboring node to another until a final destination is reached.
ii. Introduction to Fuzzy Logic
Is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is
approximate rather than precise? Just as in fuzzy set theory the set membership values can range
(inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range between 0 and 1 and is
not constrained to the two truth values {true, false} as in classic predicate logic.
II. Existing System
[1] Propose a QoS-aware routing protocol, which is based on residual traffic condition estimation
during route set up. Propose QoS-aware routing protocol is built off AODV, in which the routing table is used to
forward packets, “Hello” messages are used to detect broken routes and “Error” messages are used to inform
upstream hosts about a broken route. We explore two ways to perform traffic condition estimation, and we
incorporate both an adaptive feedback-based scheme and an admission control scheme.
[2] Available bandwidth estimation is a vital component of admission control for quality-of-service
(QoS) in both wireline as well as wireless networks. In wireless networks, the available bandwidth undergoes
fast time-scale variations due to channel fading and error from physical obstacles. These effects are not present
in wireline networks, and make estimation of available bandwidth in wireless networks a challenging task.
Furthermore, the wireless channel is also a shared-access medium, and the available bandwidth also varies with
the number of hosts contending for the channel. Wireless last-hop networks employing the IEEE 802.11
protocol
[3] Reveals that most current multipath routing protocols do not concentrate on the uncertainty in the
MANET. They choose an “optimal” multipath set by considering only single route selection parameter. Genetic
fuzzy multipath routing protocol, which is a multipath routing protocol based on Fuzzy set theory and

evolutionary computing. It naturally deals with the uncertainty in MANET and adaptively constructs a set of
highly reliable paths by considering the interplays among multiple route selection parameters.
III. Methodology of the Proposed System
ID SRC DST HOP TLL BW DELAY PSR
Each node maintains a neighbor table comprising of neighbor information.
RREQ  Broadcast from source to its neighbors  Their Neighbors
3. As RREQ is received by any node, it must put its BW and Buffer in RREQ & Forward.
4. When it reaches to destination. It has multiple paths. It has to decide which path? Here destination will select
the path based on adaptive fuzzy routing.
If (BW_Min≥ BW_Required) && (Total_Hops ≤ Delay_Hops) && Buffer_Min ≥ Buffer_Required
{
Then select the path and store in cache
}
If (BW_Max < BW_Required)
{
Drop the Path
}
If (BW_of_Path > BW_of_all_Other_Path)
&& ((Buffer_of_Path) > Buffer_required))
{
Start transmitting through its path
}
Formation of the Routing decision
AODV Handle Message Function
If Receive packet is of type Hello packet
1. Check the node from which hello packet has arrived.
2. Check if already the node is available in the routing table or not.
If present update the information else add new node in the routing table.
If a node receive RREQ, so call handle RREQ function.
In handle RREQ
1. If this RREQ already processed discard the packet else process the packet.
2. If a node is blacklisted (low energy or bandwidth) then discard this RREQ.
So, Process RREQ like a Hello Packet.
So, If this node is destination generate the RREQ message.
If it is source node & has received RREQ, discard the packet.
RREP (Route Reply) so Handle RREP function will be called RREP will be treated as hello packet &
Statement 1 & 2 of the handle hello function will be performed.
If the node neither source nor destination then forward the packet. If it is the source node then copy the path.

IV. Implementation of the Proposed System
i. Steps to implement Optimized Fuzzy Routing for MANET
Step 1: First initialize number of nodes in omnetpp.ini file.
world.dim = 15
Step 2: Decide number of source node(omnetpp) world.mobileHost[1].app.active = 6 means first 6 nodes are
source
Step 3: Initialize load world.mobileHost[*].app.rate = 20 // means 20 packet/second
;pakets of 512 byte = 4096 bit
So Load= 4096*20*6=491 kb
For this load we have to develop fuzzy routing, Fuzzy routing has to minimize this
Step 4: Channel datarate(Bandwidth is world. mobileHost[*].physic.channelDatarate = 11.04858e+6 )means
11mbps) Effective bandwidth=datarate/total_neighbour;
Total neighbor variable is present in cArray nbList , of Physical layer. Bandwith is calculated in MAC
layer from formula (dataRate/length(nbList))
Step 5: Delay is find out once the packet is received at aodv::finish() method statistics.hopsSum /
statistics.deliveredDataMsg.
Step 6: Packet Success Rate is given by dataDelivery ratio at AODV::Finish() statistics. sentDataPkt / statistics
.deliveredDataMsg
Step 7: Steps to Calculate Energy (the property of Nodes which decreases as they transmit and receive).
It
is a physical Layer Property.
Step 8: E=E – Etx – Erx , if the energy is sufficient to transmit further m bits messages,
Check for the condition (value of the parameter should be between max and min)
If so forward RREQ else
Drop RREQ
ii. Fuzzy Implementation
First assume the parameters corresponding to the transmission. They are packet success rate, bandwidth, energy
& load. They are known as input variables. These parameters are combined to form a membership function.
Hence a membership function can be considered as a domain of value of the input parameters. But we don’t
know how these parameters are inter-related that is what is the significance of each parameter in the regard to
the whole domain. Hence this is to solve by defuzzification. “Defuzz” is a function which accept the
membership function & defuzzify them into range of values as high, low and medium. This range forms a
trapezoidal view with low, medium & high. Once the values are defuzzified the range is obtained. This range is
imported in Ad hoc network simulation environment and used as thresholds.
V. Simulation and Results
Result 1:Number of sessions v/s Packet Success Rate
The graph clearly depicts that packet delivery of ratio is better in case of proposed fuzzy based
technique then the conventional AODV. This is due to the fact fuzzy selects best possible path in terms of
available bandwidth rate decreases with the increase number of sessions as those is more congestion in the
network.

Result 2: Number of Node v/s Throughput ( Load: 3 sessions, 20packets/seconds)
As number of nodes increases the through put also increase. This is due to the fact that the number of
node increases there are more number of options are available for route selection.
Result 3: Number of Nodes v/s Average Available Bandwidth
Result four depicts average available bandwidth based on both the techniques bandwidth is calculated
as mean of residual bandwidth left in each node after every packet burst is transmitted. It shows that path
selected through fuzzy has more residual bandwidth left because slection includes nodes with maximum
bandwidth.
Result 4 Pause Time V/s Throughput
It shows that the proposed technique has better Throughput under low mobility. This is because of path
reconfiguration.
Result 5 Pause Time V/s Control Overhead
Control overhead of proposed technique is steady with respective to AODV. This is because using the
proposed technique always optimum path found.

VI. Conclusion
In this work we have adopted a multipath routing which gives the option for evaluating amongst the
best paths. Fuzzy technique is used to inter-relate different parameters like bandwidth, packet success rate and
energy. As the data domain of all the parameters is different, it is quite difficult to establish a relationship
amongst them. Using fuzzy rule set we can optimize these parameters and thus can adoptively get the thresholds
for the routes. Multipath routing also facilitates the possibility of backup routing whereby if a path fails, the
source can another path from the cache. Results show that the proposed technique performs better than
conventional routing like AODV when the load is very high. For mediocre load however the performance of
AODV is better.
References
[1]. Samarth H. Shah, Kai Chen, Klara Nahrstedt, Available Traffic condition Estimation in IEEE 802.11-based Wireless
Networks,,Department of Computer Science University of Illinois at Urbana-Champaign.
[2]. Lei Chen, and Wendi B. Heinzelman, QoS-Aware Routing Based on Traffic condition Estimation for Mobile Ad Hoc
Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 3, MARCH 2005
[3]. S. Chen and K. Nahrstedt, “Distributed quality-of-service in ad hoc networks,”IEEE J. Sel. Areas Commun., vol. 17, no. 8, Aug.
1999.
[4]. Fethi Filali,” QoS Issues and Solutions in Wireless Networks”, CNRS- GDR – ARP – QoS Day :Paris, June 4, 2004
[5]. R. S. Prasad M. Murray_ C. Dovrolis K. Claffy,”Traffic condition estimation: metrics, measurement techniques, and tools”
[6]. Yang Li, Hong Man, Three load matrices for routing in adhoc network
[7]. Chansu Yu, Ben Lee, Sridhar Kalubadi, Myungchul Kim, Mediam Access control Mechanisms in Mobile adhoc nework.
[8]. Charles E Parkins, Manet Internet Draft, March 2000.
[9]. Gasim – Alandjani Ford Eric E.Johnson, “Fuzzy Routing in Adhoc Networks 2002.
[10]. Emad Aboelela and Christos Douligerin,“Fuzzy Optimization Model for Qos Routing and band width Allocation, 1999.
[11]. Samarth H. Shah, Kai Chen, Klara Nahrstedt, Available Bandwidth Estimation in IEEE 802.11-based Wireless Networks
Department of Computer Science University of Illinois at Urbana-Champaign.

Optimized Fuzzy Routing for MANET

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