Fuzzy Control Based Mobility Framework for Evaluating Mobility Models in MANET of Smart Devices

Fuzzy Control Based Mobility Framework for Evaluating Mobility
Models in MANET of Smart Devices
Tanweer Alam
Department of Computer Science
Faculty of Computer and Information Systems
Islamic University in Medina, Saudi Arabia
Email: tanweer03@iu.edu.sa
How to cite this article?
Tanweer Alam. " Fuzzy Control Based Mobility Framework for Evaluating Mobility Models in
MANET of Smart Devices.", ARPN Journal of Engineering and Applied Sciences. Vol 12(15). 2017.
ABSTRACT
The MANET is one of the most useful networks that established dynamically among all connected devices without fixed
infrastructure in a decentralized approach. Smart devices such as Smart home automation entry point, smart air conditioners,
Smart hubs, Smart thermostat, Color changing smart LEDs, Smart Mobiles, Smart Watches and smart Tablets etc. are
ubiquitous in our daily life and becoming valuable device with the capabilities of wireless networking using different wireless
protocols that are typically used with an IEEE 802.11 access point. MANETs provide connectivity in heterogeneous network
with decentralized approach. MANET is formed by itself when two or more smart devices has active connection. The fuzzy
logic control system is a novel approach that is utilized in various area of research because of the performance ability to
control the system. The proposed research is focused mainly to design a fuzzy logic control mobility framework for
evaluating mobility models in MANET of smart devices in internet of things environment. To implement this research we
developed a new fuzzy control based mobility framework for communication in MANET of smart devices. Smart devices
are considered as mobility nodes in MANET network system. The related work shows various mobility models to
reproduction the movements of nodes but unfortunately most of them are not working in reality. The proposed mobility
framework is tested on simulation environment and results perform the better evaluation of mobility models in MANET.
This research may be useful in the development of internet of things framework, where smart devices are connected to each
other in real time.
KEYWORDS
MANET Mobility Models, Ad Hoc Networks, Smart Devices, Fuzzy Logic, Internet of Things.
1 INTRODUCTION
Wireless networking is bringing turned progressively
prominent in the computer organizations from last 50 years.
Wireless computing refers to computing systems that are
connected to their working environment via wireless links
[24]. Now a days most organizations use wireless network
that is based on cells, every cell must hold basic office that
is wired should an altered wired system. These basic wired
offices connect to the smart devices and provide them the
wireless facility to connect each other within the cell or
outside the cell network. The smart devices are becoming
more and more capable day by day [15]. In the last years,
smartphones, tablets and other mobile communication
devices have become popular [32]. When a smart device or
smart user moves from one location to a new location it has
to establish a new connection with the target access point or
a base station or neighborhood smart device [18]. Every
smart device user is free to connect any other smart device,
also they are free to move randomly [43]. Every pair of the
smart device has a way with various connections among
them in the area of similar communication. Because of the
higher use of mobility, the communication connections
among smart devices are transient and temporarily
connected [35]. It is expected that by 2020, the
development of internet of smart devices connected
together exponentially with 50 billion smart devices [44].
This development will not depend on mankind’s population
but the reality that units we utilize consistently. The reality
of interconnectedness things are cooperating man to
machines and machine to another machine. They will be
talking with each other. The definition of internet of things
can be described as “a pervasive and ubiquitous system
which empowers screening furthermore control of the
physical earth by collecting, processing, also analyzing
those information created eventually sensors”. In the article
[10], a unified architecture mobility model for context
information distributions in ubiquitous computing is
presented. In this model researcher presents the solution
and comparing a large number of another solutions and
based on the observations they find various research
challenges still unsolved. The Wireless sensors network is
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a part of MANET, in this network, all mobility nodes are
connected to neighborhood nodes, each node is operated by
the battery including low energy, computations also
wireless transceiver [40]. In an article [19], researcher
presents the electrical power grid that really contributed
great in our daily life as well as industries. Currently, the
minimization of whole energy consumption of the ad hoc
networks is equal to the minimization of average energy
consumption of all network devices [48]. Every node that is
equipped with antenna, can be controlled its transmission
energy [17]. The Fuzzy logic provides the logical reasoning
that are approximate rather than exact [49]. In this article
[14], researchers propose a new approach to handle the data
transfer management by applying the fuzzy logic concept
to a heterogeneous environment. This evolutionary
paradigm enables its users to deploy a connection to a
network of computing resources in an effortless fashion,
where users can rabidly scale up or down their demands
with trivial interaction from service provider. The ad hoc
network provide the facility to connect in heterogeneous
environment without centralized approach. It is created
automatically when two or more device has active
connection. An important issue for MANETs is mobility
[26]. MANET provides connection at any area and no time
bound among smart devices, if that area does not have the
facility of cellular network. Generally it is used in military
rescue operations or the disasters area. MANET provides
the best performance in that situations where we do not
want centralized storage of the information for example
meeting room. Some private talk need security or privacy,
at that situation MANET help us to create a virtual network
without centralized server, it is one of various scenarios
where MANETs perform excellent. The MANET has a
self-organizing decentralized approach that form the virtual
communication as shown in Figure 1.
Figure 1. Ad Hoc Network of Smart Devices
The probabilistic distributions of the life of separate
connections of smart devices in a vehicular ad hoc network
follow the combination of various assumptions in the
realistic transmissions of mobility devices [50]. Various
preexisting mobility models change generally over the
reality, they start with totally simulated and extremely
measurable practically. The review process of individual’s
mobility modeling help us to know the utilization of various
mobility models in MANET. The Ad Hoc means just for
this reason, it is a Latin expression for an autonomous
collection of mobile nodes, with networks built on the fly
for a specific purpose (i.e., emergency situations, rescue
operations, battlefield situations, etc.), that talk to each
other over bandwidth constrained wireless links [28]. The
proliferation of wireless portable devices as part of
everyday life, such as PDA, mobile phones, and laptops etc.
is progressing to add the facility of MANET inside the
device for sharing the data among smart devices. However,
communication over existing infrastructures may be
precluded due to deficient facilities, or impractical in terms
of time, expense and power. The mobile ad hoc network is
the network of autonomous smart devices where every
smart device acts as a source, a destination, as well as an
intermediate router [9]. MANET contains an extraordinary
sub form that claiming remote network without server for
transferring information from one smart device to another.
Actually wireless network requires the basic fixed offices
that are answerable to receive and sending the information
among smart mobility devices but ad hoc network do not
require this kind of fixed office (see figure 2).
Figure 2. Controlling the movement of Nodes using
wireless access point
Now-a-days, in whole world the smart devices are
increasing exponentially because operating systems used in
smart devices provide most user friendly platform [1]. The
smart devices mobility causes incessant and flighty
transforms the discretionary system topology for
transforming the information. The Messages requiring a
destination outside this local neighborhood zone must be
hopped or transformed through the nearest smart device that
is working like a router in the ad hoc network [4]. All
neighbors of one devices are ranked by their trust value
[47]. The growth of internet of things initially started from
2008 by connecting the physical objects to the internet. The
physical objects are connected with smart database that
have collection of smart data. The framework has image
recognition technology for identifying the physical object,
buildings, peoples, logo, location etc. for business and
customers. Now internet of things (IOT) is shifting from
information based technology to operational based
technology ie. IPV4 (man 2 machine) to IPV6 (machine 2
machine). It combines sensors, smart devices and interfaces
like Smart Grid. Figure 3 represents the number of
connected smart devices worldwide from 2012 and 2020.

Figure 3. Smart Devices yearly basis in millions [44]
In 2012, the number of connected devices worldwide
reached 8.7 billion [44]. In the article [42], the researcher
presents an architecture for removing the barriers among
network nodes to allow these devices seamlessly as well as
personally. The architecture has three kind of services- the
node discover service, the mobility sessions and profile
mobility. In the article [13], service discovery has been
done in a peer-to-peer mode rather than a centralized mode.
In the paper [3], the researcher present a very beautiful
technique to identify the traffic at routes and provide
feasible decisions to the traffic system, which makes the
decision support intelligent by using the wireless sensor
technologies. Ad hoc network of smart devices provide
facility to send messages from one smart device to another
without using intermediate devices inside the entire
network graph [2]. MANET does not have any controlling
office for communication. Every device itself in MANET
acts as a router for forwarding and receiving packets
to/from other devices [37]. In the article [33], authors are
proposed the use of fuzzy logic to perform role assignment
during route establishment and maintenance. For providing
the best performance of smart devices network and
avoiding the collisions problems using the proposed
research is the main goal of our study. One of the most
important routing technique proposed in the article [38] is
cluster based routing in Mobile Ad Hoc Networks. It is
considered one of the convenient method of routing to
discover the smart devices in the range of MANET.
According to recent research on the message passing in ad
hoc network, there are using basically two techniques- one
of them is deterministic and other is Probabilistic technique.
The first technique is used to build a network backbone for
covering all the devices in the entire network. Other
technique is used to rebuild the backbone for every devices
and select the device probably and then send the message.
The probability approach may very useful to find best route
[51] for sending data among smart devices by using fuzzy
control system [27]. Fuzzy controllers, designed using
fuzzy rules that are basic operations for fuzzy sets [25]. The
proposed research applied the fuzzy framework to find
optimal Mobility Models in MANET of Smart Devices. In
the article [52], researchers proposed a mobility mechanism
to maintain the connection among all smart devices using
fuzzy logic in order to help sensor mobile nodes for
controlling handoffs with the need of performance
evaluation guarantee. The study presented in the article [39]
is introduced a new procedure for fuzzy rule evolutions to
forms an expert system knowledge.
2 RELATED WORK
The movements, positions, and acceleration also velocity of
smart device users are changing time by time. The purpose
of MANET is to define the movements and analyze their
purpose over the time. So MANETs play the vital role to
perform simulations of movements in the area of wireless
network. It can be very helpful for researchers in the area of
mobility modeling. The vehicular ad hoc networks are
likely to be the first real large-scale deployment of a mobile
ad hoc network [8]. RWP mobility models are frequently
used mobility model that is widely spreading useful in the
area of Mobile Ad hoc Network. Presently researchers start
focusing on the alternatives of the mobility models with
advanced properties. Because the mobility models more
focuses on the neighborhoods smart devices for creating the
connections among smart devices so sometimes it takes
time to spread information among the smart devices within
the range of ad hoc network. Every mobility management’s
device in ad hoc network is embedded and every device
knows the position of all devices within the network and
can communicate to each other [22]. Routing becomes
more challenging when considering mobile relays [34]. In
the paper [20], researchers designed a fuzzy-based priority
scheduler to determine the priority of the packets with the
multicast routing protocols. Many researchers proposed
various types of mobility models for capturing different
kinds of properties in a real time. The evaluations of
mobility models are performed in mobile ad hoc networks
in [11], each experiment of simulation done followed by
various modeling conditions that effect realistic of the real
systems.
In Random walk mobility model, every smart device can
move within the range of ad hoc network from one position
to another position using the random directions as well as
random velocity and speed. But the movement has been
performed within the fixed timing and distance traveled.
After that the currently directions will be selected. In RWP
mobility model, assuming that total number of smart
devices within the area of rectangle that should be constant
area [12]. All smart devices can move randomly from one
position to another position using randomly chosen way
within the minimum also maximum velocity interval. When
the smart device will arrive to its destinations then this
device will choose the new speed and destination, after that
this process will continue until it reaches to the final
destination. In the research article [36], researchers solved
the distributed optimization problems in which every
mobility device minimizing the overheads incurred it selves
based on the mobility models as well as path finding
algorithm. They develop the simulation modeling results to
show the realistic behaviors of nodes [36]. In Random
Direction Mobility Model, all smart devices user can be
travelled in simulation range with fixed speed and
directions. After that the device stop then it discover the
latest directions and speed using the random selection. This
process will repeat randomly. Figure 4 represents the
random way point mobility model movements of the smart
devices.

Figure 4. The movement of one node with an RWP mobility
model
In Probabilistic Random Walk Mobility Model, the
movements of smart device users using the probabilistic
find directions from one location to another location. Figure
5 represents the random direction mobility movements.
Figure 5. In RDM mobility model movements of a node
The Random Gauss Markov Model is the improved model
that allow the smart devices to move randomly using
random walk mobility to the fluid flow. The Column
Mobility Model is used to move the devices using column
line. It is very useful for discovering the device within the
simulation area. Various researchers modify the column
mobility model that allowed smart devices to discover
neighborhood devices using a line. The smart devices are
shifted to the refer location inside the refer grid after shifted
the smart device then allow it for moving in a random refer
location. In the Generalized Trace Based Mobility Model,
the smart devices users are connected in MANET. It is an
emerging technology that allowed the access point for
connection creation process among the smart devices.
Figure 6 is representing the basic model of mobility
communication among smart devices.
Figure 6. Basic model for mobile communications
The smart device users mobility in MANET determine the
route of movement is the challenges and difficult for
predicting movement of smart devices. The smart devices
typically contain a large amount of sensitive personal and
corporate data and are often used in online payments and
other sensitive transactions [5]. In the article [6] authors
presents the simulation analysis that shows the significant
improvements in the performance evaluations of route
discovery [23]. The real tracing of smart users in mobility
environment is the real movement in geographic simulated
area. Sometimes these tracing perform accurate and result
produced like real tracing. Every device needs to update its
address in the ad hoc network database only when leaving
the area [46]. Various mobility models trace the movement
of devices and collect the result for evaluating the
performance. The smart devices on the boundary of the
routing area are called peripheral devices and play an
important role in the reactive zone-based routing discovery
[45]. The boundless simulation area mobility model
generate the torus shaped simulated area that allow smart
users to move from one location to another in a boundless
simulation area. This coverage area will folded in x-axis as
well as y-axis and generate a cylinder. The figure 7
represents the closed coverage rectangular area mapping
with torus [12].
Figure 7. Closed coverage rectangular area mapping with
torus
The graph based mobility model moves the smart device in
the edge of the graph in the network and visit all the vertices
among the graph. The smart device users choose the vertex
randomly in a graph and perform the evaluation in the
simulation area of the ad hoc network [12]. For the mobility

generation a wide variety of well understood random
mobility models is combined with a graph based zone
model, where each zone has its own mobility model [21].
The keys management scheme is one of the most important
technique for securing the data among the devices of ad hoc
network system, it is based on key pre distributions
technique for ad hoc network to solve the key management
problems in authorities-based MANET. The Keys
management’s scheme is designed for self-organizational
or authorities-based ad hoc networks [30].
3 APPLYING FUZZY CONTROL SYSTEM IN
MOBILITY MODEL
The fuzzy logic control system is a novel approach that is
utilized in various area of research because of the
performance ability to control the system. The approach
presented in [41] has been successfully applied to fuzzy
models of real world systems. The fuzzy control system is
the enhanced system from fuzzy logic theory (figure 8, 9).
Figure 8. Fuzzy control System
Figure 9. Fuzzy mobility framework
The Fuzzification technique is used to change input
variables like ERROR and Change in ERROR. This
technique is based on the linguistic rules rather than the
empirical model.
1) Fuzzy logic control system can work with negative
(High, Medium Low) inputs.
2) It can work with low speed processor.
3) It can work with less storage device.
The fuzzy rules engine is consisted of a set of linguistic
statements [16]. Fuzzy rules are in the form E(X), CE(Y) O
(Z). Suppose error is X, changes in error is Y then output
will be Z. so fuzzy rules are the combination of fuzzy
propositions. We consider fuzzy error symbols in Table 1
for consideration in Fuzzification.
Table 1. Fuzzy Symbols
Symbol Description
E Error
CE Changes in Error
NegH Negatives Highest
NegM Negatives Medium
NegL Negatives Lower
N NULL
PosH Positives Highest
PosM Positives Medium
PosL Positives Lower
The Table. 2 represents the linguistics variables with fuzzy
rules.
Table 2. Rule Base Table with 49 Rules.
E/CE NegH NegM NegL N PosH PosM PosL
NegH NegB NegB NegB NegB NegM NegL N
NegM NegB NegB NegB NegM NegL N PosH
NegL NegB NegB NegM NegL N PosH PosM
N NegB NegM NegL N PosH PosM PosL
PosH NegM NegL N PosH PosM PosL PosL
PosM NegL N PosH PosM PosL PosL PosL
PosL N PosH PosM PosL PosL PosL PosL
The Memberships service is one of the service that serves
as essentials buildings block in a varieties of other services
and applications in ad hoc networks of smart devices (figure
10).
Figure 10. Smart device location
This service is used to provide service to every device with
a view regarding another device inside the ad hoc network
[7]. The Memberships functions could be with different
shapes [31] (table 3).

Table 3. Smart Device Location Error/ Change in Error
When smart devices will connect in ad hoc network then
fuzzy rules will applied. If devices have connection error
(NegH) and changes in error is NegH then the output is
NegH so the connection is aborted in the comparison of
Connection establishment. When we find error or changes
in error then fuzzy control system will handle the input. The
fuzzy system will reduce the connection error to null (zero)
by using changing in altitude (figure 11).
Figure 11. Fuzzy system for smart moving device
Table 4 represents the output function that is composed of
seven fuzzy functions.
Table 4. Output functions
Output Error/ Change in Error
Smart Device
Location
NegH, NegM, NegL,N, PosH,
PosM, PosL
4. FUZZY CONTROL BASED MOBILITY
FRAMEWORK
The proposed framework is a collection of fuzzy random
waypoint mobility model, fuzzy estimation and matrix
generation and security. We can discover smart devices
within entire network and transmit secured data among
them.
The procedure to find new position of the smart device is as
follows
1. Get the position (X1, Y1) of smart device in ad hoc
network.
2. Get current Speed (s) of smart moving device in
ad hoc network.
The basic formula to get speed is as following.
Speed (s)= distance (d)/time (t).
3. If time=t and angle is ɵ (positive) then we consider
the new location of smart device is as follows.
X2 = X1+ s * t * cos (ɵ);
Y2= Y1+ s * t * sin (ɵ);
If ɵ is negative then
X2 = X1- s * t * cos (ɵ);
Y2= Y1- s * t * sin (ɵ);
The figure 12 shows that.
4. Find the real Location of smart device.
Location L= get_New_Location(new point(smart device);
Example: L=(x1, y1)
5. Find theoretical location
Location ref= get_Reference_Location(new point(smart
device);
Example: ref=(x2, y2)
6. Find distance between L and ref.
Distance (d)=sqrt((x2-x1)2
–(y2-y1)2
)
7. Find random location (X, Y) of smart device at the
diagonal of triangle.
X=Math.random(d.getX());
Y=Math.random(d.getY());
8. Find the actual location of smart device according
to the diagonal of triangle.
May be the device is up or down from diagonal.
If device is upper than the diagonal then increase the
value of X and Y as follows.
X=X+ξX;
Y=Y+ξY;
Otherwise
X=X-ξX;
Y=Y-ξY;
9. Return new Location(X, Y)
Figure 12. New Smart device location
By above observation, we can get the new position of the
smart device even it is on the diagonal or upper than the
diagonal or lower than the diagonal. Now we apply fuzzy
rules to find the actual location. Now we will make the level
of output like high, medium or low speed, we can say these
level as a membership function of the fuzzy logic. The
figure 13 represents the speed of the smart moving device.
Figure 13: speed of the smart moving device
Input Error/ Change in Error
Smart Device
Location
NegH, NegM, NegL,N, PosH,
PosM, PosL

Now we want to find angle ɵ between base and diagonal.
The figure 14 represent the fuzzy graph between maximum
angle and minimum angle.
Figure 14: fuzzy graph between maximum angle and
minimum angle
Now we will find velocity using speed and angle. The
figure 15 represents the fuzzy graph between max angular-
velocity and min angular-velocity.
Figure 15: fuzzy graph between max angular-velocity and
min angular-velocity
The following table 5 represents the velocity using speed
and angle.
Table 5. Fuzzy rules for speed and angle
Spee
d
Angle (ɵ)
Neg
H
Neg
M
Neg
L
Zero Pos
H
Pos
M
Pos
L
Neg
H
N/A N/A N/A Neg
H
N/A N/A N/
A
Neg
M
N/A N/A N/A Neg
M
N/A N/A N/
A
Neg
L
N/A N/A N/A Neg
L
N/A N/A N/
A
Zero Neg
H
Neg
M
Neg
L
Zero Pos
H
Pos
M
Pos
L
Pos
H
N/A N/A Zer
o
PosL N/A N/A N/
A
Pos
M
N/A N/A Zer
o
PosL N/A N/A N/
A
PosL N/A N/A Zer
o
N/A N/A N/A N/
A
Apply the fuzzy rule if ɵ=0, velocity=0 then speed=0. But
original quality has standard that will be situated zero
should a degree from claiming ɵ=0.8 and velocity= 0.4.
This will be AND rule. In this rule the least paradigm may
be utilized and the fuzzy set zero for speed may be reduced
at 0. 4 upon that range. Figure 16 is representing the angle,
velocity and speed.
Figure 16: speed using angle and velocity
Using fuzzy rules, the figure 17 represents the negative low,
positive low and zero rule. It show the outcomes “assuming
that ɵ =0, v=NL then s will be NL” also “assuming that ɵ
=PL, v=0 then s will be 0”.
Figure 17: Speed on Negative Low, Positive Low and Zero
Figure 18 shows that the effect of overlapping, it can be
decreased.
Figure 18: Effect of Overlapping
The figure 19 shows the final mobility speed using the
fuzzy control system. The result show the speed after
applying defuzzyfication center of gravity procedure.
Figure 19: Final Speed using center of gravity rule

We set the simulation parameters to find the performance
evaluation using MATLAB code. Table 6 represents the
simulation parameters within the range of 250 meters.
TABLE 6. Simulations Parameter values
Simulations Parameters Values
Simulators MATLAB
Number of Mobile Nodes 20
Networks coverage 1000x1000 sq m
Transmissions 250 meters
Packets rates 4 packets/sec
Simulation Time 500s
Packets width 64 Bytes
Figure 20, 21, 22 and 23 represents the movement of 20
smart devices in 3 dimensional area of 20X20 meters, we
find these results at 7.5, 157.9, 257.3 and 500 seconds.
Figure 20: smart devices movement at 7.5 sec time
Figure 23: smart devices movement at 500 sec time
For performance evaluation of mobility models using fuzzy
control based framework, we consider four mobility models
(1. Fuzzy based mobility, 2. Random way point, 3. Column
mobility, 4. Graph based mobility). We choose three areas-
small (500m*500m), medium (750m*750m) and large
(1000m*1000m) with varying the smart devices as 20, 50
and 100. The comparisons are measured by the delivered
packets measurement (DPM), average delay measurement
(ADM) and routing load measurement (RLM). The DPM is
calculated by dividing the delivered data packets (ddp) by
total number of packets (tnp). We can derive the formula to
calculate DPM as DPM=ddp/tnp. The ADM is defined as
the time taking by the packets to move from sender to the
receiver nodes. The RLM is defined as the load of
authenticated packets on the network divided by the total
number of packets. The comparison study in figure 23, 24
and 25 of three mobility models (RWPMM, CMM,
GBMM) with proposed fuzzy control based mobility model
(FCBMM) is illustrated based on DPM, ADM and RLM.
The results shows the better performance evaluation of
mobility models in MANET of smart devices.

Figure 24: routing load measurement
* FBMM- Fuzzy based mobility model
RWPM-Random waypoint mobility model
CMM- Column mobility model
GBMM- Graph based mobility model
Figure 25: Delivered packets measurement
Figure 26: Average delay measurement
5 CONCLUSION
The MANETs are self-organizing decentralized without
fixed infrastructure network of wireless smart gadgets that
provide facility to send and receive messages among
wireless smart devices within its transmission range. The
information passing requires a destination outside this local
neighborhood zone must be hopped or transferred through
the nearest device to the appropriate target address. As a
consequence of node mobility fixed source/destination
paths cannot be maintained for the lifetime of the network.
In this article we perform the evaluation of mobility
modeling using fuzzy logic control system. Based on the
smart device location, we analyze the fuzzy logic and check
the probability of connection, generated based on fuzzy
control system. The fuzzy control system framework
generates the realistic data for communication in mobile ad
hoc network of smart devices. The proposed system is
tested and results are generated using twenty smart devices.
We get high throughput with more efficient values using
proposed mobility model.
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