1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME
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A REVIEW ON FUZZY LOGIC BASED ROUTING IN
AD HOC NETWORKS
Pimal Khanpara
CSE Department, Institute of Technology, Nirma University, Ahmedabad, India – 382481
ABSTRACT
Ad hoc networks are increasing in popularity due to the spread of mobile devices. These
networks are widely used in emergency situations like natural disasters and military conflicts. The
dynamic and infra structureless nature of ad hoc networks makes them suitable for such applications.
Self organization and self administration are the key features of any ad hoc network. Due to the
dynamic and rapid movement of nodes, routing becomes very difficult in the ad hoc networks.
Various routing schemes based on different approaches like Swarm based routing, Fuzzy logic based
routing, Trust based routing, Energy level based routing, Secure routing etc. are proposed by the
researchers. In the comparison of all the existing approaches, Fuzzy Logic Theory is proved to be a
good approach for routing in the ad hoc networks. The main reason behind this is the capability of
Fuzzy theory to handle uncertainties and randomness. This paper aims at reviewing the existing
fuzzy logic theory based routing protocols that consider QoS parameters as routing metrics.
Keywords: Ad hoc networks, Fuzzy Logic, Quality of Service parameters, Routing.
I. INTRODUCTION
An ad hoc network [1] is a group of nodes which are connected dynamically and moving
rapidly. Such a network is decentralized in manner and wireless in nature. The topology of the
network is not fixed due to the movement of the participating nodes. In an ad hoc network, the nodes
can enter or leave the network at any time. In this dynamic environment, the implementation of
routing functionality is challenging because a good route will probably be unavailable after a short
while. The routing information is required to be updated with every change in the network topology.
This results in consuming the precious network resources like bandwidth. Therefore, discovering and
maintaining a route in the ad hoc environment is difficult.
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II. ROUTING PROTOCOLS
Routing protocols proposed for ad hoc networks are categorized as:
II.i. REACTIVE
In the reactive routing protocol, the required routes are established only on demand. This may
result into long waiting time for a node that wants to transmit because before the route discovery,
transmission process can not be started. The advantage of reactive protocols is that routing
information is not required be maintained and broadcasted with the changing network topology as a
fresh route is always used for each transmission request. Examples of such protocols are AODV
(Ad hoc On Demand distance Vector) [2] and DSR (Dynamic Source Routing) [3].
II.ii. PROACTIVE
Proactive or table driven routing protocols maintain up-to-date and consistent routing
information on all the nodes in the network. Whenever the topology changes, the routing information
stored in routing tables is required to be updated. The clear advantage of the proactive protocols are
that the transmitting node can immediately take the routing decision as the required path information
is always available in the routing table. Some popular proactive protocols for ad hoc networks are
TBRPF (Topology Broadcast based on Reverse Path Forwarding) [4] and OLSR (Optimized Link
State Routing) [5].
II.iii. HYBRID
Hybrid routing protocols incorporate the good features of both reactive and proactive routing
protocols. These protocols are highly scalable and do not require to flood the network randomly.
Some widely used hybrid routing protocols are HopNet [6] and ZRP (Zone Routing Protocol) [7].
III. FUZZY LOGIC THEORY
Fuzzy Logic (FL) [8] is an approach for computing the results based on “degrees of truth”. It
differs from the conventional binary logic. In the binary or boolean logic, we can use only two
values, “true or false” or “0 or 1”. This logic is widely used in designing the modern computers. But
for some problems, the results can not be represented into the absolute terms of 0 and 1. For such
problems, Fuzzy Logic can be used to give the reasoning that is approximate rather than fixed or
exact.
In Fuzzy Logic Theory, the truth values range between completely true and completely false.
This theory uses non-numeric variables called the linguistic variables to express the rules and facts
for the given system. In Fuzzy Logic, the rules are defined in form of IF-THEN. We can also use
some boolean operators like AND, OR and NOT in Fuzzy Logic. In some literature, other operators
are also used which are linguistic in nature.
Fuzzy Logic Theory has been used in numerous applications like artificial intelligence,
pattern recognition, controlling unmanned military vehicles, knowledge based systems, weather
forecasting systems, stock trading, subway control system and medical diagnosis. In fact, almost any
control system can be replaced with a fuzzy logic based control system.
A Fuzzy Logic System is mainly comprised of four components: Fuzzifier, Defuzzifier,
Fuzzy Rule Base and Fuzzy Inference Engine. These components are arranged as follows in any
Fuzzy Logic System.

3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME
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Figure 1: Fuzzy Logic System
Fuzzification is the first process that takes place in the FLS. A numeric or crisp input value is
given to the Fuzzifier. The crisp input value is required to be converted to the corresponding fuzzy
value as the rules for determining the result, are defined for fuzzy inputs. This task is performed by
the Fuzzifier and then the fuzzy input values are supplied to the Fuzzy Inference Engine, which is
responsible for computing the set of outputs based on the IF-THEN rules defined in the Fuzzy Rule
Base. Usually, when more than one inputs are required, AND operator is used to combine them. The
last process in the Fuzzy Logic System is defuzzification. It coverts the fuzzy output values into their
corresponding crisp values.
There are different methods for fuzzification and defuzzification. Some widely used
fuzzifiers are Singleton fuzzifier, Gaussian fuzzifier and Trapezoidal or Triangle fuzzifier. Singleton
fuzzifier is the simplest fuzzifier which basically assigns a precise value to the given input and hence
no fuzziness is introduced by fuzzification in this case. Gaussian and Triangular fuzzifiers are used to
suppress the noise in the given inputs. Examples of defuzzifiers are Maximum defuzzifier, Mean of
maxima defuzzifier, Centroid defuzzifier, Height defuzzifier, Modified height defuzzifier, center of
sets and center of sums.
IV. WHY FUZZY ROUTING? [9]
The advantages of Fuzzy Logic are its simplicity, flexibility of combining conventional
control techniques, ability to model nonlinear functions and imprecise information, use of empirical
knowledge and dependency on heuristics.
Due to the basic characteristics of ad hoc networks like uncertainty due to dynamic topology
and mobility of nodes, limited resources and unstable links; a precise and accurate model is not
possible to implement. In such an environment, Fuzzy Logic Theory has been proved a good
approach for routing compared to other routing methods. Fuzzy Logic ccan be used to solve the
problem of routing in ad hoc networks where the final outcome is based on the factors with
uncertainty.

4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME
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V. QUALITY OF SERVICE
Quality of service (QoS) is a set of service requirements that needs to be met by the network
while transmitting a stream of packets from a source to its destination. QoS is considered as an
important aspect to utilize the network resources in a better and efficient manner. In ad hoc networks,
we have to consider bandwidth, jitter, node energy level, queue length, delay, cost and reliability
constraints as the main QoS parameters [10]. These QoS parameters can be given as inputs to the
fuzzy controller for determining the most preferred path.
VI. FUZZY LOGIC BASED ROUTING PROTOCOLS
There are many algorithms proposed for routing in the ad hoc networks. Most of them are
either purely reactive or proactive and thus fail to have the features of the hybrid routing protocols.
Another important limitation of almost all the existing algorithms is that they do not take all the
Quality of Service parameters into consideration while determining the “best” path. Most of the
proposed routing algorithms consider path cost and delay as the routing metrics. But in an ad hoc
environment, it is actually important to focus on all or maximum QoS parameters at the time of
finding the optimal route. This section gives the overview of the existing Fuzzy Logic based routing
algorithms for the ad hoc networks.
VI.i. RRAF (Reliable Routing Algorithm based on Fuzzy logic) [11]
In this algorithm, two parameters, trust value and energy value are defined for each node.
Based on the values of these parameters, lifetime of the routes are determined. This scheme basically
uses AODV for routing. At the time of route discovery, each node inserts its trust value and energy
capacity in the Route Request (RREQ) packet. Fuzzy Logic is used at the destination. A parameter
called “Reliability value” is generated by the destination using the input trust and energy values. This
reliability value is then used for routing. A path which is having greater reliability value is preferred
over the others. So, this algorithm improves the performance of AODV but fails to consider the
important QoS parameters except reliability and cost.
VI.ii. FSRS ( Fuzzy based Stable Routing Scheme) [12]
The objective of this algorithm is to find the most stable route for routing. For this, it takes
the number of intermediate nodes, packet queue occupancy and the distance between the nodes as the
input parameters. A fuzzy controller is used by the algorithm to calculate the lifetime of the route.
Route cost is the input given to the fuzzy controller. The proposed scheme considers the average end
to end delay, packet delivery ratio and routing load as the metrics.
VI.iii. FQRA ( Fuzzy QoS Routing Algorithm) [13]
This algorithm is an extension of the shortest routing path algorithm and uses the bandwidth
and delay as the routing metrics. Improved path success ratio, improved throughput and reduced end
to end delay are good features of this scheme.
VI.iv. FLWMR ( Fuzzy Logic Wireless Multi path Routing) [14]
Fuzzy Logic is used by this algorithm for allocating the resources in the network considering
the traffic in the network. Zero or more maximally disjoint routes are used to transport the messages.
Some packets are transmitted redundantly over more than one disjoint paths to increase the
reliability, while some packets are suppressed at the source. This decision is taken based on the
importance of the packets. When a request for data transmission comes, the route request messages
are broadcasted by the source node to every other node in the network. When the request packet
reaches to the destination, the path traveled by that request packet is recorded and then that path is

5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME
79
used for transmitting the data.
VI.v. FLWLAMR ( Fuzzy Logic Wireless Load Aware Multi path Routing) [14]
FLWLAMR is an extension of FLWMR algorithm. It considers the network status while
selecting the route. To determine the network status, Fuzzy Logic is used. Fuzzy rules are defined to
get the value for the network state, ranging from excellent to poor. Packets are forwarded
immediately if the network is in excellent state.
VI.vi. Multiclass FQRA [15]
This algorithm considers the traffic state and bandwidth as the input parameters to take the
routing decision based on the Fuzzy Logic. It used weighted round robin scheduler concept for
forwarding the packets. The weights are assigned to the queues containing the data and based on
these weight values, fuzzy controller determines the routing process.
VI.vii. SSR (Source Select Route) [16]
This routing algorithm aims at finding the optimized path between the source and the
destination. Maximum distance between the nodes, maximum relative speed between the neighbour
nodes and the total number of data transmission links in the intermediate nodes are the important
factors for selecting the route. The authors claimed that the proposed algorithm performs better over
the conventional routing algorithms for the ad hoc networks.
VI.viii.Fuzzy Scheduler [17]
The routing decision made by this scheme is dependent on the priority values of the packets.
To calculate the priority of a packet, the algorithm considers various input parameters. The algorithm
uses the fuzzy scheduler to determine the priority index values of the packets. Packet lifetime and the
transmission rate are the important factors to be given to the fuzzy scheduler as the inputs.
VI.ix. FAQM (Fuzzy Algorithm for QoS Multicast routing) [18]
This algorithm used the concept of Swarm Intelligence with the Fuzzy Logic Theory. The
authors of this algorithm have considered three Quality of Service parameters,: bandwidth, jitter and
delay. This multicast routing protocol outperforms the conventional routing protocols for ad hoc
networks.
VI.x. ImRMR (Improved Rank based Multicast Routing) [19]
In this multipath routing protocol, the routes are selected based upon the values of five
parameters: bandwidth, number of hops, efficiency of the selected path, power consumption and
traffic state. The algorithm tries to evaluate the rank for each existing path using the five resource
constraints. According to the authors, this protocol performs better than the existing protocol
ODMRP (On Demand Multicast Routing Protocol).
VI.xi. FCMQR (Fuzzy Cost based Multi constrained QoS Routing) [20]
This algorithm is based on multi criterion objective fuzzy measure. To select an optimal path,
this protocol takes different parameters like bandwidth, number of intermediate hops and end to end
delay into account. All the available resources for a path are used to compute the fuzzy cost for that
path. The path which is having minimum fuzzy cost and maximum lifetime is chosen as the optimal
route for transmission.
Table 1 shows the comparison of the above existing Fuzzy Logic based routing algorithms
with respect to a variety of QoS parameters. None of these protocols consider all the required QoS
parameters.

6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 75-81 © IAEME
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Table 1
Comparison of fuzzy logic based routing protocols
Routing Protocol QoS Parameters Remarks
RRAF (Reliable Routing Algorithm
based on Fuzzy logic) [14]
Trust value, energy level Fails to consider time, frequency, space and
cost metrics
FSRS (Fuzzy based Stable Routing
Scheme) [20]
Hop count, queue status,
distance between nodes
Uses two fuzzy controllers into each node that
take three QoS parameters as inputs
FQRA (Fuzzy QoS Routing
Algorithm) [17]
Delay, bandwidth Based on Dijkstra’s shortest path algorithm;
bandwidth and delay are taken into
consideration
FLWMR (Fuzzy Logic Wireless
Multipath Routing) [2]
Number of intermediate nodes Considers only hop count to find the optimal
route
FLWLAMR (Fuzzy Logic Wireless
Load Aware Multipath Routing) [2]
Network status Extension of FLWMR; deals with load
balancing by considering network traffic
Multiclass FQRA [18] Scheduler; bandwidth, delay Based on the packet weight, the scheduler
takes the routing decision considering time and
frequency constraints
SSR (Source Select Route) [23] Maximum distance between
intermediate nodes, maximum
relative speed, total number of
links
Does not consider all the QoS parameters
Fuzzy scheduler [16] Priority value Routing takes place based on the priority index
of the packet
FAQM (Fuzzy Algorithm for QoS
Multicast Routing) [19]
Bandwidth, delay, jitter Multicast routing algorithm based on Artificial
Fish Swarm algorithm. Bandwidth, delay and
jitter are taken as routing metrics
ImRMR (Improved Rank based
Multicast Routing) [21]
Bandwidth, computing power,
hop count, network load,
energy consumption
Does not consider all the QoS parameters
FCMQR (Fuzzy Cost based Multi
constrained QoS Routing) [22]
Delay, link expiration time,
bandwidth
Fails to consider space, cost, energy level and
reliability constraints
VII. CONCLUSION
This paper discusses how the Fuzzy Logic Theory can be used for implementing routing in
the ad hoc networks. To select an optimal route in the ad hoc networks, it is necessary to consider all
or maximum Quality of Service parameters. But to the best of my knowledge, none of the existing
algorithms has this feature. Most of the authors have used two or three parameters as routing metrics.
So, the objective of this survey is to find out the possibility of research in this area. The future scope
of this work lies into the development of a new, Fuzzy Logic based routing protocol for ad hoc
networks that take all the important QoS parameters into consideration.
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