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\begin{document}

\title{Fuzzy Control Based Mobility Framework for Evaluating Mobility Models in
MANET of Smart Devices.~~~~}



\author[1]{Tanweer Alam}%
\affil[1]{Islamic University of al-Madinah al-Munawarah}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\sloppy


\textbf{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

\emph{How to cite this article?}

{}\textbf{{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.}}

\textbf{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.\textbf{KEYWORDS}MANET Mobility Models, Ad Hoc Networks, Smart
Devices, Fuzzy Logic, Internet of Things.

\section*{1 INTRODUCTION}

{\label{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

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

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).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

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.

\subsection*{2 RELATED WORK}

{\label{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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

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{]}.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

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{]}.

\subsection*{3 APPLYING FUZZY CONTROL SYSTEM IN MOBILITY
MODEL}

{\label{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).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 8. Fuzzy control System\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

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\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Symbol} & \textbf{Description}\tabularnewline
\midrule
\endhead
E & Error\tabularnewline
CE & Changes in Error\tabularnewline
NegH & Negatives Highest\tabularnewline
NegM & Negatives Medium\tabularnewline
NegL & Negatives Lower\tabularnewline
N & NULL\tabularnewline
PosH & Positives Highest\tabularnewline
PosM & Positives Medium\tabularnewline
PosL & Positives Lower\tabularnewline
\bottomrule
\end{longtable}

The Table. 2 represents the linguistics variables with fuzzy rules.

Table 2. Rule Base Table with 49 Rules.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{E/CE} & \textbf{NegH} & \textbf{NegM} & \textbf{NegL} &
\textbf{N} & \textbf{PosH} & \textbf{PosM} &
\textbf{PosL}\tabularnewline
\midrule
\endhead
\textbf{NegH} & NegB & NegB & NegB & NegB & NegM & NegL &
N\tabularnewline
\textbf{NegM} & NegB & NegB & NegB & NegM & NegL & N &
PosH\tabularnewline
\textbf{NegL} & NegB & NegB & NegM & NegL & N & PosH &
PosM\tabularnewline
\textbf{N} & NegB & NegM & NegL & N & PosH & PosM & PosL\tabularnewline
\textbf{PosH} & NegM & NegL & N & PosH & PosM & PosL &
PosL\tabularnewline
\textbf{PosM} & NegL & N & PosH & PosM & PosL & PosL &
PosL\tabularnewline
\textbf{PosL} & N & PosH & PosM & PosL & PosL & PosL &
PosL\tabularnewline
\bottomrule
\end{longtable}

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).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

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\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Input} & \textbf{Error/ Change in Error}\tabularnewline
\midrule
\endhead
Smart Device Location & NegH, NegM, NegL,N, PosH, PosM,
PosL\tabularnewline
\bottomrule
\end{longtable}

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).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

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\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Output} & \textbf{Error/ Change in Error}\tabularnewline
\midrule
\endhead
Smart Device Location & NegH, NegM, NegL,N, PosH, PosM,
PosL\tabularnewline
\bottomrule
\end{longtable}

\textbf{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

\begin{enumerate}
\tightlist
\item
  Get the position (X\textsubscript{1}, Y\textsubscript{1}) of smart
  device in ad hoc network.
\item
  Get current Speed (s) of smart moving device in ad hoc network.
\end{enumerate}

The basic formula to get speed is as following.

Speed (s)= distance (d)/time (t).

If time=t and angle is o \selectlanguage{english}(positive) then we consider the new location of
smart device is as follows.

X\textsubscript{2} = X\textsubscript{1}+ s * t * cos (o\selectlanguage{english});

Y\textsubscript{2}= Y\textsubscript{1}+ s * t * sin (o\selectlanguage{english});

If o \selectlanguage{english}is negative then

X\textsubscript{2} = X\textsubscript{1}- s * t * cos (o\selectlanguage{english});

Y\textsubscript{2}= Y\textsubscript{1}- s * t * sin (o\selectlanguage{english});

The figure 12 shows that.

Find the real Location of smart device.

Location L= get\_New\_Location(new point(smart device);

Example: L=(x\textsubscript{1}, y\textsubscript{1})

Find theoretical location

Location ref= get\_Reference\_Location(new point(smart device);

Example: ref=(x\textsubscript{2}, y\textsubscript{2})

Find distance between L and ref.

Distance
(d)=sqrt((x\textsubscript{2}-x\textsubscript{1})\textsuperscript{2}--(y\textsubscript{2}-y\textsubscript{1})\textsuperscript{2})

Find random location (X, Y) of smart device at the diagonal of triangle.

X=Math.random(d.getX());

Y=Math.random(d.getY());

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+\selectlanguage{greek}ξ\selectlanguage{english}X;

Y=Y+\selectlanguage{greek}ξ\selectlanguage{english}Y;

Otherwise

X=X-\selectlanguage{greek}ξ\selectlanguage{english}X;

Y=Y-\selectlanguage{greek}ξ\selectlanguage{english}Y;

Return new Location(X, Y)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

Figure 13: speed of the smart moving device

Now we want to find angle o \selectlanguage{english}between base and diagonal. The figure 14
represent the fuzzy graph between maximum angle and minimum angle.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}

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\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Speed & Angle (o\selectlanguage{english}) & Angle (o\selectlanguage{english}) & Angle (o\selectlanguage{english}) & Angle (o\selectlanguage{english}) & Angle (o\selectlanguage{english}) &
Angle (o\selectlanguage{english}) & Angle (o\selectlanguage{english})\tabularnewline
\midrule
\endhead
& NegH & NegM & NegL & Zero & PosH & PosM & PosL\tabularnewline
NegH & N/A & N/A & N/A & NegH & N/A & N/A & N/A\tabularnewline
NegM & N/A & N/A & N/A & NegM & N/A & N/A & N/A\tabularnewline
NegL & N/A & N/A & N/A & NegL & N/A & N/A & N/A\tabularnewline
Zero & NegH & NegM & NegL & Zero & PosH & PosM & PosL\tabularnewline
PosH & N/A & N/A & Zero & PosL & N/A & N/A & N/A\tabularnewline
PosM & N/A & N/A & Zero & PosL & N/A & N/A & N/A\tabularnewline
PosL & N/A & N/A & Zero & N/A & N/A & N/A & N/A\tabularnewline
\bottomrule
\end{longtable}

Apply the fuzzy rule if o\selectlanguage{english}=0, velocity=0 then speed=0. But original
quality has standard that will be situated zero should a degree from
claiming o\selectlanguage{english}=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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}

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 o \selectlanguage{english}=0, v=NL then
s will be NL'' also ``assuming that o \selectlanguage{english}=PL, v=0 then s will be 0''.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image17/image17}

\end{center}
\end{figure}

Figure 17: Speed on Negative Low, Positive Low and Zero

Figure 18 shows that the effect of overlapping, it can be decreased.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image18/image18}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image19/image19}

\end{center}
\end{figure}

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\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Simulations Parameters} & \textbf{Values}\tabularnewline
\midrule
\endhead
Simulators & MATLAB\tabularnewline
Number of Mobile Nodes & 20\tabularnewline
Networks coverage & 1000x1000 sq m\tabularnewline
Transmissions & 250 meters\tabularnewline
Packets rates & 4 packets/sec\tabularnewline
Simulation Time & 500s\tabularnewline
Packets width & 64 Bytes\tabularnewline
\bottomrule
\end{longtable}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image20/image20}

\end{center}
\end{figure}

Figure 20: smart devices movement at 7.5 sec time\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image21/image21}

\end{center}
\end{figure}

Figure 21: smart devices movement at 157.9 sec time\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image22/image22}

\end{center}
\end{figure}

Figure 22: smart devices movement at 257.3 sec time\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image23/image23}

\end{center}
\end{figure}

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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image24/image24}

\end{center}
\end{figure}

Figure 24: routing load measurement

* FBMM- Fuzzy based mobility model

RWPM-Random waypoint mobility model

CMM- Column mobility model

GBMM- Graph based mobility model\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image25/image25}

\end{center}
\end{figure}

Figure 25: Delivered packets measurement\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image26/image26}

\end{center}
\end{figure}

Figure 26: Average delay measurement

\textbf{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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\selectlanguage{english}
\FloatBarrier
\end{document}

