Applying machine learning enabled myriad fragment empirical modes in 5G communications to detect profile injection attacks

Abstract

In order to facilitate communication, wireless networks are built from a collection of nodes that may be either static or dynamic. They are acquiring a lot of popularity in the area of research due to the fact that they are ad hoc in nature, and the number of users of mobile devices is rising day by day. Because of the ease with which these networks may be deployed in challenging and unsupervised rural places, the exchange of information has been a reality since the invention of these networks. Mobile ad hoc networks are simple to set up because of the properties that allow for self-organization and the fact that the medium is wireless. A lack of centralized fixed infrastructure, flexibility to frequent change in topologies, and other features like these are some of the other things that draw people's attention to wireless networks. Wireless networks are vulnerable to a wide range of assaults since their nodes are able to move around and their topologies are constantly changing. In addition, MANET operates in an environment that is both open and dynamic, which leaves it subject to a variety of threats from other types of network assaults. Routing protocols are almost always the target of one form or another of the same general category of attacks. Eavesdropping, causing damage, changing routing information, deleting routing information, manipulating information, advertising phoney routes, and misrouting information are all potential components of these assaults. The circumstances may make it difficult to maintain confidentiality in any communications. There are many different kinds of assaults, and each one may damage wireless networks on a different tier of the communication stack and bring the performance of the network down. Eavesdropping, jamming, traffic analysis and monitoring, denial of service attacks, grey hole attacks, black hole attacks, and wormhole assaults are a few examples of the many sorts of attacks that fall under this category. Ad-hoc networks are more susceptible to security breaches than traditional wired and wireless networks due to the usage of open wireless medium, dynamic topology, and dispersed and cooperative channel sharing. The wormhole attack on dispersed wireless networks is being described here by the person who conducted this study. Because this assault is so potent, it is very difficult to identify it before it has ever been launched. The invader may simply initiate it without having knowledge of the network or compromising any authorized nodes, which is a need for launching it. During a wormhole attack, a malicious node in one part of the network takes control of the packets and tunnels them to another hostile node in a different part of the network, which then repeats the packets locally. The thesis aims to do two things at the same time: (a) To simulate a variety of possible wormhole assaults on the MANET network (b) To investigate the functionality and efficiency of the proposed secure routing protocol within the context of these simulated attacks on the network.

Full text

Applying machine learning enabled myriad fragment empirical modes
in 5G communications to detect profile injection attacks
Mohammed S. Alzaidi
1
•Piyush Kumar Shukla
2
•V. Sangeetha
3
•Karuna Nidhi Pandagre
4
•
Vinodh Kumar Minchula
5
•Sachin Sharma
6
•Arfat Ahmad Khan
7
•V. Prashanth
8
Accepted: 15 February 2023
ÓThe Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
In order to facilitate communication, wireless networks are built from a collection of nodes that may be either static or dynamic.
They are acquiring a lot ofpopularity in the area ofresearch due to the fact that they are adhoc in nature, and the number of users of
mobile devices is rising day by day. Because of the ease with which these networks may be deployed in challenging and
unsupervised rural places, the exchange of information has been a reality since the invention of these networks. Mobile ad hoc
networks are simple to set up because ofthe properties that allow for self-organizationand the factthat the medium is wireless. A
lack of centralized fixed infrastructure, flexibility to frequent change in topologies, and other features like these are some of the
other thingsthat draw people’s attention to wireless networks. Wireless networks are vulnerable toa wide range of assaults since
their nodes are able to move around and their topologies are constantly changing. In addition, MANET operates in an envi-
ronment thatis both open and dynamic, which leavesit subject to a variety of threats fromother types of network assaults. Routing
protocols are almost always the target of one form or another of the same general category of attacks. Eavesdropping, causing
damage, changing routing information, deleting routing information, manipulating information, advertising phoney routes, and
misrouting information are all potential components of these assaults. The circumstances may make it difficult to maintain
confidentiality in any communications. There are many different kinds of assaults, and each one may damage wireless networks
on a different tier of the communication stack and bring the performance of the network down. Eavesdropping, jamming, traffic
analysis andmonitoring, denial of service attacks, greyhole attacks, black hole attacks, and wormholeassaults are a few examples
of the many sorts of attacks that fall under this category. Ad-hoc networks are more susceptible to security breaches than
traditional wired and wireless networks due to the usage of open wireless medium, dynamic topology, and dispersed and
cooperative channel sharing. The wormhole attack on dispersed wireless networks is being described here by the person who
conducted this study. Becausethis assault is so potent, it is very difficult to identify it beforeit has ever been launched. The invader
may simply initiate it without having knowledge of the network or compromising any authorized nodes, which is a need for
launching it. During a wormhole attack, a malicious node in one part of the network takes control of the packets and tunnels them
to another hostile node in a different part of the network, which then repeats thepackets locally. The thesis aims to do two things at
the same time: (a) To simulate a variety of possible wormhole assaults on the MANET network (b) To investigate the
functionality and efficiency of the proposed securerouting protocol withinthe context ofthese simulated attacks onthe network.
Keywords 5G communication Feature extraction Improved support vector classification Profile injection attack
detection
1 Introduction
The study of mobile ad hoc networks is becoming more
important in the realm of research, in addition to being a
subject that is gaining popularity in the industry. Mobile ad
hoc networks have become an appealing choice of tech-
nology as a result of the proliferation of applications that
may be used in a variety of contexts. Despite this, the
characteristics of ad hoc networks, such as self-adminis-
tration and changeable network topology, provide unique
security challenges [1]. Mobile ad hoc networks have
become an appealing choice of technology as a result of the
Extended author information available on the last page of the article
123
Wireless Networks
https://doi.org/10.1007/s11276-023-03301-z(0123456789().,-volV)(0123456789().,-volV)

proliferation of applications that may be used in a variety
of contexts. Researchers are now able to investigate the
idea of adapting these presently available solutions from
wired networks to mobile ad hoc networks. There is a large
selection of conventional security solutions that are already
available for use with wired networks. Since of the very
dynamic and self-configuring nature of MANETs, the
solutions that are available for wired networks are not
always effective and efficient for MANETs. This is the
case because wired networks are not subject to the same
characteristics. The characteristics of MANETs are to
blame for this particular circumstance.
Before developing solutions that are both effective and
efficient, it is vital to have a solid understanding of the
many types of attacks that may be carried out against
MANETs. The Mobile Ad-hoc Network (MANET) is a
combination of various electronic devices having net-
working and wireless communication capabilities. These
networks are absent of a physical backbone or permanent
infrastructure. The MANET is vulnerable to many security
attacks since the transmission channel is broadcast. Both
internal and external security attacks are possible with
MANET. Additionally, internal and external MANET
attacks are classified into passive and active attacks. The
bulk of the possible attacks that may be used against var-
ious levels of MANETs have been thoroughly discussed in
the literature that is relevant to the topic [2]. There are
numerous potential assaults that can be employed against
different types of MANETs. The myriad of threats and
attacks that may be launched against MANETs can be
categorised into subcategories according to the entities that
are the primary focus of each one.
The intensity of the attacks, such as when an adversary
publishes false information or just monitors how individ-
uals act in order to influence their decision-making pro-
cesses, is what differentiates the first group from the others.
The information itself is the target of an attacker’s efforts
in the second category, where they are looking to get
access to it. During this kind of attack, the attacker retains
the ability to update, amend, and replace the message even
while it is in the process of being sent. In the third category
[3], the purpose of the attacker is to make inappropriate use
of network resources in order to knock down the overall
performance of the network. This category includes attacks
that are classified as DoS (denial of service) attacks.
Attacks that are classified as the first kind are seen as being
passive, whilst attacks that are classified as the second or
third type are regarded as being aggressive.
In order to carry out this attack, it is necessary to
introduce spoofed routing packets into the network in order
to create a routing loop. Since of this, a significant portion
of the bandwidth available on the nodes will be used, and
because data packets will be transmitted, the nodes’ power
consumption will also grow. As a result of this, the legit-
imate items will not be delivered to the individual who was
meant to be the recipient of them [4].
As a consequence of this, we are going to label it as an
attack consisting of a denial of service.
The setup for the black hole attack is very similar to the
setup for the routing loop attack, in which the attacker
advertises the shortest path between the source and the
destination among the intermediate nodes in order to lure
all of the traffic via it. The setup for the black hole attack is
very similar to the setup for the routing loop attack. After
receiving packets from neighbouring nodes, the black hole
attacker will then throw away each and every one of the
packets that they have received. The above explanation has
made it clearly clear that the black hole attacker may
operate in the same manner as a black hole operates in the
universe [5].
This is a subtype of the black hole attack that was
mentioned earlier in the conversation. When carrying out
this kind of attack, the attacker does not throw away each
and every packet. On the other side, the attacker chooses a
specific strategy to use. When using a method that is
selective, the attacker will pick and choose the different
sorts of packets that they will throw away. As just one
illustration of their behaviour, the adversary is throwing
away routing packets but not data packets [6].
1.1 Partitioning
During this kind of attack, the attacker will restrict com-
munication between the majority of the network’s nodes
and a subset of the network’s nodes. This subset will be
determined by the attacker. Because of this, there will end
up being more than one group present inside a network.
Despite the fact that they will be in close proximity to one
another, these two groups will not be able to communicate
with one another in any way. An attacker may make
advantage of a single group in order to either inflict harm to
the network or cut down the amount of performance it is
capable of [7].
Network partitioning is the process by which an attacker
establishes groups inside a network. This technique may
also be thought of as network segmentation.
In the process of separating a network, two ways that
may be utilised are the introduction of fake routing packets
into the network and the employment of physical assaults
such as radio jamming. Both of these techniques are
examples of physical attacks.
Wireless Networks
123

2 Proposed multivarious empirical mode
fragmentation in 5G communication
to detect profile injection attacks
Because of AODV (Ad-hoc On-demand Distance Vector),
mobile nodes may immediately construct a path to a new
destination; however, the protocol does not let nodes to
store routes to destinations that are not presently being
utilized. A routing technique developed for wireless and
mobile ad hoc networks is called an Ad Hoc On-Demand
Distance Vector (AODV). This protocol provides on-de-
mand routes to destinations and supports both unicast and
multicast routing. The classful network threshold is where
the classful routing protocols (RIPv1 and IGRP) automat-
ically summarize routes; they do not support summarize on
any other bit boundaries. Any bit boundary may be sum-
marized using classless routing protocols. In accordance,
the AODV routing protocol is classified as a reactive
routing protocol; hence, routes may only be established
when it is essential to do so. Reactive routing protocols are
also known as on-demand routing protocols. The path is
only discovered in this method of routing when it is
appropriate. Route request packets are transmitted around
the mobile network to perform route discovery. The reac-
tive protocol is classified into two types such as Ad hoc
On-Demand Distance Vector (AODV) and Temporary
Ordering Routing Algorithms (TORA). A node will dis-
cover and monitor the paths that go to its neighbours by
first sending out ‘‘hello’’ messages to other nodes in the
network. Following this, the node will proceed to contin-
uously broadcast a greeting message to all of its sur-
rounding nodes at regular intervals. If a node does not
receive at least two different hello messages from a
neighbour, the connection between the node and the
neighbour is deemed to have been severed. A node’s
neighbors are the group of nodes that are connected to it up
to a given distance, or the number of steps between the
source node and its neighbors. In weighted graphs, the
neighbors can also be considered up to a given maximum
weight. After receiving the RREQ message, each node that
acts as an intermediate will set up a route that leads to the
source. If the destination node is successful in receiving the
RREQ message, then it will transmit the RREP message [8]
to the source in a unicast manner using a hop-by-hop
pattern. After being provided an RREP message to process,
every intermediate node in the path will produce a route
that leads to the final destination. As soon as an RREP
message is sent to the source node, it will instantly begin
the process of sending data while simultaneously continu-
ing to maintain the route to the destination. In the case that
the source gets more than one RREP message, the suc-
cessful route will be determined by which one has the
lowest total number of hops. This concept is used by the
wormhole attacker, who then sets up a connection to a
wormhole with the assistance of either two malicious nodes
or genuine nodes that are situated closer to the nodes that
are the source and the destination of the connection. In
VANETs and other ad-hoc networks, wormhole attacks are
a serious and common type of attack. Data packets from
one malicious node’s end are tunneled to some other
malicious node at a different point during this attack, which
includes two or more malicious nodes; then the data
packets are transmitted. A probing process overflows the
network with messages from a bootstrap node to enable all
network nodes to calculate the hop distance between
themselves and the bootstrap node in order to identify
wormhole attacks. The hop coordinates method serves as
the basis for the probe operation.
Ad hoc networks come with their own unique set of
security challenges due to the inherent limitations they
have in terms of communication and computing. Ad hoc
networks are more vulnerable to a variety of attacks due to
the manner in which they are set up [9], which makes them
a less desirable option. Ad-hoc networks have a low
maximum speed, a shorter range, and are more vulnerable
to interference. When connecting to an ad-hoc network,
users should be as near to the source as feasible, otherwise
the signal strength will be low and unreliable. Ad hoc
network security may also be dependent on protection at
the link or network layer. Some ad hoc solutions provide
robust security services for confidentiality and authenticity
protection at the link layer; in such cases, the network or
upper layers do not need to satisfy all security require-
ments. Because ad hoc networks are used in a wide variety
of applications, some of which require them to interact
physically with their surroundings, humans, and other
elements, these networks are more susceptible to being
compromised by malicious actors. Ad Hoc networks are
used in a variety of applications. Researchers believe that
ad hoc networks will be used for mission-critical applica-
tions such as battle zones, the protection of major terrain
features, buildings, and bridges; the measurement of traffic
movement; monitoring territory and husbandry; and the
protection of major terrain features, buildings, and bridges.
The restrictions that are built into Ad Hoc networks may be
divided into two categories: those that are specific to
individual nodes and those that are network-wide. Con-
cerns about privacy and security in ad hoc networks have
resulted in the emergence of several intriguing research
topics [10].
Ad hoc networks often have limited resources in terms
of their power supply, bandwidth, and memory capacities.
Additionally, power sources in ad hoc networks are often
restricted in their capacity. Establishing and maintaining
security in the region will be very challenging as a result of
Wireless Networks
123

this [11]. The ad hoc nodes become vulnerable to
prospective attacks when there is a large density of ad hoc
networks that have been installed in an environment that is
unattended. By seizing control of the Ad Hoc nodes,
attackers have the ability to enter the network and make it
simpler for malicious nodes to pass themselves off as real
ones [12].
When an attacker gets inside the architecture of the
network, they are able to carry out a wide variety of
attacks. Attack vectors can take many geometric patterns,
from malware and ransomware to man-in-the-middle
attacks, compromised credentials, and phishing. Attacks
that target users’ security and the network’s general
architecture as well as those that target network users’
human access points are examples of attack vectors.
Wireless networks are especially prone to security breaches
due to the broadcast nature of the transmission medium,
which makes wireless networks particularly vulnerable.
In addition, wireless Ad Hoc networks have an addi-
tional vulnerability since their nodes are often located in a
hostile or dangerous environment, and this environment
does not physically shelter the nodes from the dangers that
they face. This makes it possible for attackers to target the
nodes [13].
For this reason, the implementation of countermeasures
such as light weight encryption techniques, safe routing,
and secure key management is necessary for totally secure
Ad Hoc networks.
Figure 1is a shortened version of the AODV algorithm
is going to be explained in this part of the article. The ad
hoc on demand distance vector (AODV) routing algorithm
is an example of an on-demand algorithm. This method
was developed for usage in ad hoc mobile networks and is
an example of an on-demand algorithm. It is also possible
to utilise it for the routing of multicast traffic in addition to
unicast traffic. ‘‘On demand’’ refers to the fact that it helps
in the building of routes whenever a source node wishes to
connect with either a destination node or a target node [14].
This phrase is used in this context. These pathways will
remain operational for as long as the source has a need for
them to do so in order to ensure that the flow of information
is not disrupted. The sequence number is used by AODV in
order to guarantee that the route is as up to date as is
practically achievable. The Automatic Open Distributed
Routing algorithm (AODV) builds routes with the aid of a
cycle that consists of route requests and route answers.
When a source node wants to communicate with a desti-
nation node that does not already have a route established,
the source node will broadcast a route request packet to all
of its neighbours. Each route request packet includes the
destination sequence number and the source sequence
number, as well as the source and destination address. This
will let the source node to communicate with the destina-
tion node. The primary purpose of a router is to route traffic
to destination networks based on the destination address in
an IP packet. Routers also use the Address Resolution
Protocol to resolve MAC addresses (ARP). A router
examines its routing table after receiving a packet to see if
it contains the destination address specified in the packet’s
header. If the target address is removed from the table, the
router transmits the packet to another router that is listed in
the routing table. This method continues till route request
packet comes up to destination node. Following the
reception of the route request packet, each node will revise
the information it has about the source node and will add a
path linking back to the source node to the route tables it
maintains [15].
The broadcast ID, the presently utilised sequence num-
ber, and the most recent sequence number for the desti-
nation that the initiator is aware of are all included in the
RREQ packet. Additionally, the RREQ packet contains the
IP address of the source node. After receiving an RREQ
packet, a node is only permitted to send a route reply
(RREP) packet if it either has a route to the destination that
has a corresponding sequence number that is equal to or
greater than the one that was given in the RREQ packet, or
if it is the node that will ultimately be used as the desti-
nation of the packet. At this point, the RREP packet is
going to be unicast back to the source where it originated.
The RREQ packet will be redistributed at any point in time
when there is a transmission error of any type, regardless of
when it occurred [16]. In addition to the source IP address
of the RREQ, each node maintains a record of the broad-
cast ID that is associated with the RREQ. In the event that a
node gets an RREQ packet that it has already processed,
the node will not deliver the RREQ packet and will instead
reject it. After reaching this stage, the RREP will start to
propagate back to its initial source, and nodes will start
constructing forward pointers to the destination. After the
source node has obtained an RREP packet, the source node
Dataset
Aack Detecon
Feature extracon
classificaon
Fig. 1 Block diagram of profile injection attack detection
Wireless Networks
123

will start the transmission of data packets to the destination
after the destination node has received the RREP packet.
After this, if at any point in time the source node collects an
RREP containing the same sequence number with a smaller
hop count or a greater sequence number, it will update its
routing table or the destination, and it will begin using the
superior route. This will occur regardless of whether the
sequence number is greater or smaller. This will take place
irrespective of the order in which the smaller or the larger
sequence number is encountered. The route will continue to
be utilised as long as there are data packets that need to be
transferred from the source to the destination.
The route that has been constructed will ultimately time
out and be deleted from the routing tables of any inter-
mediate nodes if there are no data packets that need to be
transmitted in the case that there are no data packets that
need to be sent. In the case that a route break takes place in
the middle of the transmission of a data packet, the node
that is now receiving the packet will send a route error
(RERR) packet to the node that is currently sending the
packet in order to inform it of an unreachable destination.
If there is a packet that has to be transmitted, the source
node may restart the process of route discovery after it has
been issued the RERR. This is the circumstance that takes
place while using the unicast method [17].
Constructing multicast routes may also be done using a
technique that is somewhat similar to this one. Imple-
mentation of these requirements may also be possible for
the purpose of constructing multicast routes. When a node
wants to connect to a multicast group, it will send out an
RREQ with an IP address as the destination, modify the
appropriate field to Multicast, and set the flag to ’J’ to
indicate that it wants to join the group. This will tell the
group that the node wants to connect to it. Any node in the
network that is a part of the multicast group and has an
appropriate sequence number may send an RREP packet.
This is the only requirement for sending the packet. As the
RREP packet makes its way back to the node that it orig-
inated from, the intermediate nodes will change the
pointers in their multicast route tables to reflect the new
information. Once acquiring the RREPs, the source node
will keep a follow of the route that has the most current
sequence number and the fewest number of hops to reach
the following member of the multicast group. This will
occur after the RREPs have been successfully acquired. At
this point, the source node will transmit a MACT (Multi-
cast Activation) message to all of the following hops that
have been designated by unicast. This message is being
sent in order to enable travel along the path that links the
nodes together [18].
If the intermediary nodes, who are a part of the multicast
group, are unable to get the MACT message within the
specified length of time, then they remove the pointer from
their routing table. If nodes that are not part of the multicast
group get MACT message, then those nodes will also keep
track of the RREPs message and unicast MACT until it has
travelled all of its future hops. This is the case even if the
nodes are not receiving the message from the multicast
group. This process will be carried out again and again
until the message is delivered to a node that is a component
of the multicast tree. The AODV system will remember the
routes up to the point at which they are enabled. It is
necessary to keep the multicast tree operational for the
duration of the multicast group’s existence due to the fact
that the nodes in the network are movable and several link
breakages are possible throughout the course of the lifetime
of a route. Because of these two factors, it is imperative
that the multicast group continue to exist [19].
2.1 Feature extraction
Ad hoc On Demand Distance Vector routing is the full
version of the acronym AODV. It is a protocol that runs on
the network layer. The on-demand routing protocols are
where you’ll find this particular protocol falling under.
Even if there are no pathways accessible between the
sender and the recipient, it is still possible to utilize this
protocol. The operation of AODV may be summed up in a
few easy steps. Because of how easy it is to follow, this
procedure has garnered a lot of interest, and several studies
are now being conducted on it. There are several different
modified variants of AODV that may be found in the lit-
erature. AODV only maintains one path from the source
node to the destination node. When a communication
channel is interrupted, the source node must restart the
route discovery procedure, which lowers routing perfor-
mance by increasing power consumption, end-to-end time,
and packet delivery ratio inevitability. In addition, even if
there is no failure, the AODV routing protocol will elim-
inate the link when it times out. It is required to have a
fundamental understanding of AODV in order to compre-
hend these enhanced versions and their operation.
As a result, an illustration of the AODV algorithm’s
operation is provided below for your perusal. When a
source node A wishes to deliver data to a destination node
B using AODV [20], the node A must first determine
whether or not a route to the destination is available in its
cache table. In the event that there is no path between the
two nodes A and B, node A will send an RREQ message to
B that will be relayed across all of its neighboring and
intermediate nodes [21,22].
When an RREQ message is received by any node
between A and B, the intermediate nodes will retransmit
that message to the rest of the network. This procedure will
keep repeating itself until RREQ reaches its final destina-
tion at node B. During the process, each intermediate node
Wireless Networks
123

makes sure that it is not the node that will be used as the
destination, that there is a cached route to B, that it is the
first time that it is receiving RREQ, and that the value for
the TTL field is not equal to zero. Additionally, the onehop
neighbours of each node will have their individuality
recorded so that the RREQ may be sent farther. After the
RREQ message has been delivered to its final destination,
node B will send an RREP message in response. The
RREQ packet from Node A is sent in a broadcast fashion,
whereas the RREP packet is sent in a unicast manner. The
intermediate nodes that forward the RREQ packet also
construct a reverse route from the destination. The Route
Reply (RREP), which provides the number of hops needed
to reach the destination, is generated when the request
reaches a node which has a route to the destination node.
As a result, the RREP message is unicast to the neigh-
bours of node B that is just one hop away and that has
already transmitted the RREQ. In a way analogous to this,
the RREP will be unicast back to the source node A by
making use of the recorded one-hop neighbours that were
responsible for the origination of the RREQ. During the
process of AODV route discovery, the path nodes do not
have a complete understanding of the path from the source
to the destination, including the source node and the des-
tination node. As a result of the fact that the review module
requires familiarity with the personal security domain, the
route might become acquainted with once it has been
implemented as an auxiliary service,
CAV ¼c
p
CF 1
NUM
i¼1
slp
sip

Toff ð1Þ
valupper ¼1X
2
x¼1Y
2
i¼1
val lðÞ
th
"#
x
ð2Þ
CF ¼1
NX
N
n¼0
session value
countn
 ð3Þ
CAV ¼4
1
0:21 1
15
2
2
10 ð4Þ
2.2 Classification
It is common knowledge that clustering and routing are two
highly significant strategies that are now being used for the
purpose of extending the lifespan of wireless sensor net-
works. Adding spare nodes may significantly increase the
lifespan of a WSN. When a primary (original) WSN node
runs out of energy, the spares are ready to be activated. The
three most commonly used routing techniques of wireless
ad hoc and sensor network algorithms are source, shortest
path, and hierarchical-geographical. Following the con-
struction of clusters, each of these clusters will need to
choose a leader, who will be referred to as the Cluster head
(CH). Either the node that will meet the criteria intended
for the cluster head will be pre-assigned as the cluster head,
or the node might become the cluster head itself. Cluster
heads are responsible for sensing and collecting informa-
tion from the nodes that make up that cluster, as well as
processing that data. Following that, the information that
has been processed is sent to the sink.
The data is collected by only one node (the Cluster
head), and because its energy is greater than that of the
other nodes, the Cluster head (CH) can continue to act as
the cluster head until its energy falls below the threshold
level or until the specified duration has passed. This is one
of the advantages of the clustering process. The remaining
nodes have a lower burden, and as a result, both the energy
of those nodes and the energy of the network as a whole are
preserved. In wireless sensor networks, the lifespan of the
entire system is dependent on the lifetime of each sensor
node, energy efficiency is the primary criterion for routing
protocol design. A routing protocol may also be focused on
information. Attribute-based naming is necessary for a
data-centric routing protocol. Because all of the nodes
transmit the data only to the cluster heads, there will be a
decrease in the amount of identical messages exchanged,
which will result in the conservation of the available
communication bandwidth. Small routing tables are ade-
quate for the setting up of routes to the destination, which
in turn helps improve the scalability of the network. This
makes cluster administration easier, which in turn helps
improve the scalability of the network. The network’s
lifespan is increased because to the energy conservation
afforded by this technique, which also contributes to the
network’s overall reliability. Therefore, it is possible to
draw the conclusion that clustering not only simplifies the
architecture of the network, but it also saves energy by
allocating load to nodes that have a higher energy level. By
using clustering strategies, resource-constrained nodes can
avoid sending their information directly to gateways, which
can result in resource consumption, inefficient resource
utilization, and interference. The routing process aids in
picking routes that use the least amount of energy.
As was covered in the chapter before this one, the
research literature has a wide variety of different methods
for clustering data. In the current study, it is suggested that
the clusters be formed by choosing the centroids in a
haphazard fashion. A cluster’s centroid is the actual or
hypothetical point that serves as its geographic center.
Each point of data is assigned to a certain cluster by
minimizing the total of squares within each cluster. To
compute the centroid from a cluster table, simply add the
positions of any and all points in a single cluster, aggregate
them, and divide by the number of points. It has been
suggested that a group of three centroids be chosen, and
Wireless Networks
123

then clusters should be formed around these three
centroids.
(i) Decision matrix normalization
rv¼xv
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pn
i¼1x2
v
pð5Þ
(ii) Weighted normalized decision matrix
vg¼wjryð6Þ
(iii) Positive and negative ideal
Aþ¼fðmvjjj2J;mv1jj2J

gð7Þ
and
A0¼mv/jj2J

;mvijj2J

ð8Þ
where Jis the set of benefit puramesers, and J0is
the set of cost pathencters.iv. The Euclidean dis-
tance between the aliernatives atal the positive and
Hegative allernutives is calculatice,
dþ
j¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Xvþ
ivv
jj
sð9Þ
dþ
i¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Xv
iv4
jj
sð10Þ
c1¼d
d
iþd
i
ðÞ ð11Þ
In order to give a solution to the optimization difficul-
ties, several idealised rules that are structured analogously
to the behaviour of the elephant herd have been developed.
It is presumed that the base station may be found inside the
WSN region, closer to the network’s hub.
(i) There are a significant number of different herds
of elephants.
(ii) Each group must comprise a father elephant, a
mother elephant, and the offspring of the mother
elephant.
(iii) Initially, the father elephant will take charge of the
herd, but as it ages, it will choose to retire from the
herd and live alone; nonetheless, it will maintain
communication with the rest of the herd.
(iv) The mother is the one who assumes the role of
leader in the family.
This characteristic pattern of behaviour shown by the ele-
phant herd has been characterised by the use of two
operators: (a) the Clan updating operator, and (b) the
Separating operator.
3 Experimental settings
Spectrum choice in CRN may be described as the selection
of an appropriate frequency channel for the unlicensed
user, and this assessment is dependent on the predicted type
of the service as well as the quality of the channel that is
available. Finding spectrum parameters, determining free
channel, and reconfiguring CR are the three key duties
included in the spectrum decision process. Spectrum
decision refers to the capacity of SUs to determine the best
available spectrum band to satisfy the quality of service
(QoS) requirements of customers. Spectrum characteriza-
tion, spectrum selection, and CR reconfiguration are the
three basic processes involved in spectrum decision. After
locating the unoccupied portion of the frequency spectrum
through any one of the channel sensing approaches, the
distinction between the various accessible channels is made
based on the locally observed factual data of the licensed
customers. Following the channel characterization comes
the task of selecting an appropriate frequency band based
on the requirements of the licensed client. Channels can be
classified based on their width (w), depth (d), and velocity
(v). The width–depth ratio is probably the characteristic
that describes channel form the most. This decision is
based on the channel’s characteristics. A CR device needs
to have the flexibility to adjust its handset properties in
accordance with the characteristics of the channels in order
to achieve improved data transfer from the source to the
destination.
When using the MCDM approach, the goal of each CR
node is to choose the best available unused channel from a
list of accessible channels based on a set of predetermined
criteria as well as the needs of the user. Multiple criteria
decision-making (MCDM) is recognized as a multidimen-
sional decision-making (DM) method that considers both
quantitative and qualitative parameters. Application of the
multi-criteria decision-making (MCDM) theory refers to
the application of computational techniques that include a
number of criteria and order of preference in analyzing and
selecting the best choice among numerous possibilities
depending on the intended result. To provide the highest
possible Quality of Service (QoS), a CRN examines each
possible channel option using a set of criteria that includes
bandwidth, economic cost, information rate, and duty
cycle. A cognitive radio network (CRN) is divided into a
primary network and a secondary network. In consideration
of these characteristics, the channels are evaluated, and the
one with the highest overall score is chosen as the best
channel.
Elephants from various clans coexist in groups under the
leadership of a matriarch, and when male elephants mature,
they separate from their family groups. These two actions
Wireless Networks
123

can be represented by the two operator’s clan updating and
separating operator. Through the clan updating operator,
the matriarch and current location of the elephants are
updated, and after that, the separating operator is put into
operation. The matriarch of each clan rules over the entire
population of elephants; therefore, matriarch ci has an
impact on the subsequent position of each elephant in clan
ci.
4 Results and discussion
The behavior of elephants in their natural environment has
been idealized and used here as the basis for a generalized
global optimization strategy. The objective of global opti-
mization is to determine the globally optimum solution of
(potentially nonlinear) models in the presence of several
local optima (possible or known). Formally, global opti-
mization attempts the global solution(s) of a constrained
optimization model. In order to give a solution to the
optimization difficulties, several idealized rules that are
structured analogously to the behavior of the elephant herd
have been developed. It is presumed that the base station
may be found inside the WSN region, closer to the net-
work’s hub.
(i) There are a significant number of different herds
of elephants.
(ii) Each group must comprise a father elephant, a
mother elephant, and the offspring of the mother
elephant.
(iii) Initially, the father elephant will take charge of the
herd, but as it ages, it will choose to retire from the
herd and live alone; nonetheless, it will maintain
communication with the rest of the herd.
(iv) The mother is the one who assumes the role of
leader in the family.
This characteristic pattern of behavior shown by the ele-
phant herd has been characterized by the use of two
operators: (a) the Clan updating operator, and (b) the
Separating operator.
4.1 Performance metrics
The Table 1illustrates the experimental results of attack
detection rate with respect to the different number of users.
Graphical view of attack detection rate using proposed and
existing methods is provided in Fig. 2.
Figure 2demonstrates the result analysis of attack
detection rate based on the number of users.
4.2 Performance analysis of precision rate
The results of a comparison of the accuracy rate achieved
by using various suggested and current approaches are
shown in Table 2.
Table 1 Overall attack comparison
Number of users Existing Proposed
400 81 91
800 83 93
1200 82 94
1600 85 95
2000 86 93
2400 85 94
2800 87 96
3200 86 97
3600 85 93
4000 86 94
Fig. 2 Results of attack detection rate
Table 2 Comparison of precision rate
Number of users Existing Proposed
400 77.74 77.05
800 77.26 77.22
1200 77.64 77.25
1600 76.65 76.36
2000 77.67 72.47
2400 76.53 72.47
2800 77.62 74.44
3200 77.00 764
3600 77.37 77.70
4000 74.72 76.17
Wireless Networks
123

In Fig. 3, the results of the accuracy rate are shown
based on the varied numbers of users (from 600 to 6000)
that were used as input.
4.3 Performance analysis of recall rate
The term ‘‘sensitivity’’ refers to the recall rate, or RR. The
RR is calculated by comparing the number of people who
were correctly identified to the sum of the number of
individuals whose results were either actually positive or
false-negative. Recall is defined in this context as the sum
of true positives and false negatives, or elements that
should have been classified as positives but weren’t
because of incorrect labeling. Also, recall is the ratio of
true positives to all components that truly fall into the
positive class. The recall rate may be calculated as follows
using some basic arithmetic.
RR ¼Tp
Tp þFn

100 ð12Þ
According to the previous Eq. (12), the term ‘‘Tp’’
refers to the true positive (i.e., the number of attackers who
were accurately identified as attackers), whereas the term
‘‘Fn’’ refers to the false negative (attacker profile is
incorrectly detected as genuine profile). It is measured in
percentages to assess the recall rate (percent).
Table 3describes the performance analysis of recall rate
with respect to the different input users.
Figure 4illustrates the experimental results of accuracy
rate with respect to the diverse number of users. This
results in the formation of 16–20 clusters. 20 nodes are
maintained unconnected. When the energy of the cluster
head exceeds its limit, the node with the second highest
energy becomes the new Cluster head. This happens
automatically. This is analogous to the mother elephant or
matriarch assuming leadership of the group or clan, while
the father elephant is relegated to a subordinate role within
the group. Elephant mothers and their children are all
collected into herds. The herd is commanded by the eldest
mother, known as the MATRIARCH. Male elephants leave
their herds between the ages of 7 and 12 whereas female
elephants remain in them for life. Everything the herd
needs to know to live is known by the matriarch. After that,
the nodes that were previously unattached are connected to
the cluster, with one or two nodes connecting to each
cluster. This is analogous to the younger elephants, often
known as youngsters’ elephants, becoming more powerful
and eventually joining the group or clusters. This results in
the formation of clusters.
After the clustering step has been completed, we go on
to the following procedure, which is the routing step. For
the purpose of data transmission, this method makes use of
the cluster heads that are in the best physical condition. For
purposes of routing, the Cluster head of the first cluster
communicates with the Cluster heads of all of the clusters
that are next to it.
The CH that has the most energy is the one that is taken
up. This CH is used as a conduit for the data traffic. This
Fig. 3 Results of attack
Table 3 Comparison of recall rate
Number of users Existing Proposed
400 76.73 76.05
800 77.44 777
1200 77.76 77.03
1600 76.4 77.17
2000 77.14 73.47
2400 777 71.74
2800 77.07 73.77
3200 77.17 743
3600 77.03 76.57
4000 77.63 76.03
Fig. 4 Results of accuracy rate
Wireless Networks
123

process of selecting the cluster head that is the healthiest
and most productive in the area is continued until the
objective is achieved.
4.4 Performance analysis of execution time
Calculating the means for both AODV and SAODV allows
one to investigate the importance of the disparity that exists
between the means of ‘‘With Attack & Using AODV’’
values and the means of ‘‘With Attack & Using AODV’’
values. The P-value comes in at less than 0.0001, as
expected. This result demonstrates that the values of
AODV and SAODV are significantly connected with one
another and have a very substantial impact statistically. It
has been determined that both AODV and SAODV have a
degree of freedom equal to 42. The AODV protocol’s
SAODV (Secure AODV) security improvement is based on
public key cryptography. The integrity and validity of
SAODV routing messages (RREQs, RREPs, and RERRs)
are maintained by digital signatures. The AODV protocol
is reactive, indicating that nodes in the network only
communicate routing information when a communication
is necessary and only maintain its current for the duration
of the conversation. The applicability of the t-test may be
determined based on the results of this test. Acceptance
testing is a type of testing used to evaluate whether or not
the software system satisfies the requirements. This test’s
primary objective is to assess the system’s integrity to the
business requirements and determine whether it has satis-
fied the structure which includes for delivery to end users.
The result obtained from the t-test turns out to be 12.5783.
As a result of the fact that the value is greater than the t
critical value of 2.021 for a two-tailed test at the 0.05 level
with 42 degrees of freedom, the null hypothesis H0 may be
decisively dismissed in favour of the alternative hypothesis
H1. The effect values that are obtained from SAODV are
able to considerably represent the threat aspect when used
in conjunction with the current technique in the packet
delivery ratio when a wormhole attack is being carried out
out of band.
ET ¼TUT identify user as genuine or attackerðÞð13Þ
It is essential that the acceptability of the proposed
framework be subjected to a validity check. In order to
determine whether or not the framework is significant, a
paired ttest has been used. In the case of an out-of-band
wormhole assault, the two data sets are acquired by
employing AODV and SAODV respectively. In this
experiment, a hypothesis is being tested using a paired
t-test, and a confidence interval is being determined by
observing the difference between the two-standard means.
In light of this, the alternative hypothesis and the null
hypothesis are detailed in the following, respectively.
The effect values that are produced from SAODV are
unable to meaningfully represent the danger aspect when
using the current technique in End-to-End Delay while
facing an out-of-band wormhole assault, as stated by the
null hypothesis H0.
The effect values produced from SAODV may consid-
erably represent the danger element with the current
technique in end-to-end delay when subjected to an out-of-
band wormhole assault, according to an alternative
hypothesis (H1) stated below.
Table 4reports the performance evaluation of execution
time calculating the means for both AODV and SAODV
allows one to investigate the importance of the disparity
that exists between the means of ‘‘With Attack & Using
AODV’’ values and the means of ‘‘With Attack & Using
AODV’’ values. The P-value comes in at less than 0.0001,
as expected. This result demonstrates that the values of
AODV and SAODV are significantly connected with one
another and have a very substantial impact statistically.
Both AODV and SAODV have a degree of freedom equal
to 22, however. The applicability of the t-test may be
determined based on the results of this test. The number
obtained from the t-test amounts to 6.1625. As a result of
the fact that the value is greater than the t critical value of
2.074 for a two-tailed test at the 0.05 level with 22 degrees
of freedom, the null hypothesis H0 has been conclusively
disproven, and the alternative hypothesis H1 has been
accepted. The effect values that are obtained using SAODV
are able to considerably represent the threat aspect when
used in conjunction with the current technique in end-to-
end delay when wormhole attack is being used.
Figure 5illustrates the result analysis of execution time
based on the different number of users considered in the
range of 600–6000 as input for experimentation. Calcu-
lating the means for both AODV and SAODV allows one
to investigate the importance of the disparity that exists
between the means of ‘‘With Attack & Using AODV’’
Table 4 Comparison of execution time
Number of users Existing Proposed technique
400 32 28
800 34 30
1200 36 32
1600 38 34
2000 40 36
2400 42 38
2800 44 40
3200 46 42
3600 48 44
4000 50 46
Wireless Networks
123

values and the means of ‘‘With Attack & Using AODV’’
values. The P-value comes in at less than 0.0001, as
expected. This result demonstrates that the values of
AODV and SAODV are significantly connected with one
another and have a very substantial impact statistically. It
has been determined that both AODV and SAODV have a
degree of freedom equal to 42. The applicability of the
t-test may be determined based on the results of this test.
The result obtained from the t-test turns out to be 10.3865.
As a result of the fact that the value is greater than the t
critical value of 2.021 for a two-tailed test at the 0.05 level
with 42 degrees of freedom, the null hypothesis H0 may be
decisively dismissed in favour of the alternative hypothesis
H1. Under the conditions of an out-of-band wormhole
assault, the effect values that are produced from SAODV
are able to strongly represent the threat aspect with the
current technique in end-to-end delay.
5 Conclusion
When it comes to mobile Ad hoc networks, there is a wide
variety of various routing protocols that may be imple-
mented. These protocols can be used to route data. A
number of different characteristics, such as the load on the
network, the size of the network, the routing overhead, the
mobility requirement, and the throughput, decide whether
or not a certain routing protocol will be used in a mobile ad
hoc network. Other routing systems have been given less
attention in mobile Ad hoc networks in favour of on-de-
mand routing protocols. This is owing to the fact that on-
demand routing protocols provide more flexibility in
deployment and are more capable in terms of throughput.
This is because, compared to other routing systems, on-
demand routing protocols are able to attain better
throughputs. When compared to table-driven protocols,
they are capable of organising themselves in a dynamic
way, while at the same time needing less memory overhead
and having needs for lower bandwidth. This is due to the
fact that they have a lower demand for bandwidth. There
are already a number of distinct on-demand routing pro-
tocols that may be used for mobile ad hoc networks
(MANETS). When it comes to the passing of data packets,
the overwhelming majority of protocols do not have any
form of security awareness built into them. As a conse-
quence of this, there is a need for a protocol of this kind
that is able to guarantee the integrity of the transfer of data
packets.
During the course of this investigation, the SAODV
algorithm is implemented, and then, afterward, a compre-
hensive analysis is performed with the assistance of the
MATLAB simulator. The results of the simulation suggest
that selecting SAODV is the best course of action to take
in situations in which there is a high degree of mobility, a
dense network of nodes, and a significant amount of traffic.
Because of this, there will be reduced congestion in the
traffic, a greater packet delivery ratio, and more resilience
to route failures. One of the most significant things that can
be learned from this research is that the SAODV routing
protocol is the most suitable choice for moving toward a
network that has a greater Quality of Service (QoS).
Acknowledgements The researchers would like to acknowledge the
Deanship of Scientific Research, Taif University, for funding this
work.
Author contributions All author is contributed to the design and
methodology of this study, the assessment of the outcomes and the
writing of the manuscript.
Data availability No datasets were generated or analyzed during the
current study.
Declarations
Conflict of interest Authors do not have any conflicts.
References
1. Zhou, Q., Wu, J., & Duan, L. (2020). Recommendation attack
detection based on deep learning. Journal of Information Security
and Applications, 52, 1–13.
2. Cai, Y., & Zhu, D. (2019). Trustworthy and profit: A new value-
based neighbor selection method in recommender systems under
shilling attacks. Decision Support Systems, 124, 1–15.
3. Cai, H., & Zhang, F. (2021). BS-SC: An unsupervised approach
for detecting shilling profiles in collaborative recommender sys-
tems. IEEE Transactions On Knowledge And Data Engineering,
33(4), 1375–1388.
4. Pateriya, P., Singhai, R., Shukla, P., & Singhai, J. (2022). Real-
ization of new radio 5G-IoT connectivity using mmWave-mas-
sive MIMO technology. In 5G and beyond (1st edn.). Imprint
Chapman and Hall/CRC, ISBN: 9781003045809.
Fig. 5 Results of execution time
Wireless Networks
123

5. Cai, H., & Zhang, F. (2019). Detecting shilling attacks in rec-
ommender systems based on analysis of user rating behavior.
Knowledge-Based Systems, 177, 22–43.
6. Yang, L., & Niu, X. (2021). A genre trust model for defending
shilling attacks in recommender systems. Complex & Intelligent
Systems, 66, 1–14.
7. Zhang, F., Qu, Y., Xu, Y., & Wang, S. (2020). Graph embedding-
based approach for detecting group shilling attacks in collabo-
rative recommender systems. Knowledge-Based Systems, 199,
1–17.
8. Chen, J., Wang, B., Ouyang, Z., & Wang, Z. (2020). Dynamic
clustering collaborative filtering recommendation algorithm
based on double-layer network. International Journal of Machine
Learning and Cybernetics, 12, 1097–1113.
9. Gupta, R. K., Almuzaini, K. K., Pateriya, R. K., Shah, K., Shukla,
P. K., & Akwafo, R. (2022). An improved secure key generation
using enhanced identity-based encryption for cloud computing in
large-scale 5G. Wireless Communications and Mobile Comput-
ing, 2022, Article ID 7291250.
10. Paleti, L., Krishna, P. R., & Murthy, J. V. R. (2020). Approaching
the cold-start problem using community detection based alter-
nating least square factorization in recommendation systems.
Evolutionary Intelligence, 14, 835–849.
11. Alonso, S., Bobadilla, J., Ortega, F., & Moya, R. (2019). Robust
model-based reliability approach to tackle shilling attacks in
collaborative filtering recommender systems. Advanced Software
and Data Engineering for Secure Societies, 7, 41782–41798.
12. Xu, Y., & Zhang, F. (2019). Detecting shilling attacks in social
recommender systems based on time series analysis and trust
features. Knowledge-Based Systems, 178, 25–47.
13. Batmaz, Z., Yilmazel, B., & Kaleli, C. (2022). Shilling attack
detection in binary data: A classification approach. Journal of
Ambient Intelligence and Humanized Computing, 11, 2601–2611.
14. Rani, S., Kaur, M., Kumar, M., Ravi, V., Ghosh, U., & Mohanty,
J. R. (2021). Detection of shilling attack in recommender system
for YouTube video statistics using machine learning techniques.
Soft Computing, 66, 1–13.
15. Alzaidi, M. S., Subbalakshmi, C., Roshini, T. V., Shukla, P. K.,
Shukla, S. K., Dutta, P., & Alhassan, M. (2022). 5G-telecom-
munication allocation network using IoT enabled improved
machine learning technique. Wireless Communications and
Mobile Computing, 2022, Article ID 6229356.
16. Sharma, K., Saini, S., Sharma, S., Kang, H. S., & Bouye, M.
(2022). D Krah (2022) Big data analytics model for distributed
document using hybrid optimization with-means clustering.
Wireless Communications and Mobile Computing, 66,6.
17. Singh, R., Mehbodniya, A., Webber, J. L., Dadheech, P., Pavi-
thra, G., Alzaidi, M. S., & Akwafo, R. (2022). Analysis of net-
work slicing for management of 5G networks using machine
learning techniques. Wireless Communications and Mobile
Computing, 6, 66.
18. Sagduyu, Y. E., Erpek, T., & Shi, Y. (2021). Adversarial machine
learning for 5G communications security. Game Theory and
Machine Learning for Cyber Security, 66, 270–288.
19. Lei, L., Kou, L., Zhan, X., Zhang, J., & Ren, Y. (2022). An
anomaly detection algorithm based on ensemble learning for 5G
environment. Sensors, 22(19), 7436.
20. https://backend.orbit.dtu.dk/ws/portalfiles/portal/281615167/
PhD_Thesis.pdf
21. Shanthini, J., Punitha, P., & Karthik, S. (2023). Improvisation of
node mobility using cluster routing-based group adaptive in
manet. Computer Systems Science and Engineering, 44(3),
2619–2636.
22. Ramesh, S., Yaashuwanth, C., & Muthukrishnan, B. A. (2020).
Machine learning approach for secure communication in wireless
video sensor networks against denial-of-service attacks. Inter-
national Journal of Communication Systems, 33(12), e4073.
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with the
author(s) or other rightsholder(s); author self-archiving of the
accepted manuscript version of this article is solely governed by the
terms of such publishing agreement and applicable law.
Mohammed S. Alzaidi received
the B.Sc. degree in Electrical
Engineering from Umm Al-
Qura University, Makkah, Saudi
Arabia, in 2007, the M.Eng.
degree and Ph.D. in Electrical
Engineering from Stevens
Institute of Technology, Hobo-
ken, NJ, USA in 2014, 2019,
respectively. He is currently an
Assistant Professor with the
Department of Electrical Engi-
neering, Faculty of Engineering,
Taif University, Saudi Arabia.
He is also the Vice Dean of the
Deanship of Scientific Research at Taif University. His research
interests include nano molecular communications, wireless commu-
nications, signal processing, digital techniques, machine learning, and
deep learning.
Dr. Piyush Kumar Shukla (PDF,
Ph.D., SMIEE, LMISTE) is
Associate Professor in Com-
puter Science & Engineering
Department, University Institute
of Technology, Rajiv Gandhi
Proudyogiki Vishwavidyalaya
(Technological University of
Madhya Pradesh). He has com-
pleted Post Doctorate Fellow-
ship (PDF) under ‘‘Information
Security Education and Aware-
ness Project Phase II’’ funded
by the Ministry of Electronics
and IT (MeitY). He is the editor
and reviewer of various prestigious SCI, SCIE, and WOS-indexed
journals. He has over 300 publications in highly indexed journals and
prestigious conferences, including many books.
Wireless Networks
123

Dr. V. Sangeetha working as
Associate Professor in Depart-
ment of Computer Science and
Engineering at Ramaiah Insti-
tute of Technology, Bangalore.
Completed Ph.D. in 2019 from
VTU. Having 17 years of
teaching and research experi-
ence. Research specialization
includes Networks and Security,
Blockchain, Artificial Intelli-
gence and Machine Learning.
Published over 32 research
papers in reputed National and
International Conferences and
Journals. Also, technical reviewer for indexed international journal
and conferences. Member of various professional bodies like IEEE,
IEI, ISTE (Life Member), IAENG (Member). Received various
awards such as ‘‘STAR of the Year Award’’ in recognition of con-
tinuing Excellence in Teaching by HKBK Group of Institutions,
‘‘Research Excellence Award’’ for presenting the research paper titled
ZIDS: Zonal-based Intrusion Detection System for Studying the
Malicious Node Behaviour in MANET in conference from IEAE,
‘‘Young Woman Educator & Researcher Award’’ in recognition of
contribution towards Teaching & Research in the field of Computer
Science and Engineering by NFED, Coimbatore.
Dr. Karuna Nidhi Pandagre is
working as an Associate Pro-
fessor in the Department of
MCA at Bansal Institute of
Science and Technology, Bho-
pal. She graduated in Computer
Application at Barkatullah
University, Bhopal, Madhya
Pradesh, India. She secured a
Master of Computer Applica-
tion at IGNOU University, New
Delhi, India. She completed her
Ph. D in Computer Application
in Data Mining Area at Rabin-
dranath Tagore University,
Raisen, India. She is in the teaching profession for more than 13
years. She has presented a number of papers in National and Inter-
national Journals, conferences, and Symposiums. Her achievements
are invited as a resource person in more than seven various training &
FDP programs. Her main area of interest includes Data Science,
Computer Network, and the Internet of Things. Published two books
and awarded NPTEL SPOC for Bansal Institute of Science and
Technology, Bhopal. She invited more than seven expert talks in
various sectors. As a professor in an Institute deals with a number of
students’ success, and many more things related to learning and
personal aspects.
Dr. Vinodh Kumar Minchula has
completed his doctoral research
degree in year 2019 in the area
of Wireless Communications
from Andhra University under
the UGC national research fel-
lowship Govt. of India. In 2021
he received the ‘‘International
Scholarship Award’’ Japan-East
Asia Network of Exchange for
Students and Youths (JENE-
SYS)—2021 program from the
Ministry of Foreign Affairs
Japan, an initiative from the
Government of Japan under the
70th Anniversary of JAPAN INDIA friendship ties program 2022. In
2017 he received an international fellowship, ‘‘Japan-Asia youth
exchange program in science’’ under the Sakura Science program
sponsored by Japan Science and Technology, Japan. He got a total
experience of 14 years, in 2009 started his career as an Assistant
Professor at MVGR College of Engineering(A). From 2021, he
started working as an Associate Professor at Chaitanya Bharati
Institute of Technology(A), Hyderabad. He is a Senior Member of
IEEE, Life Fellow member of IETE, and Life member of Sakura
Science club of Japan Sci. & Tech. He is Vice president of the AU
Toyama Sakura science club of Andhra University. He has published
27 research publications in reputed international SCI/Scopus/UGC
referred journals and conferences. He is currently the Reviewer &
Technical Program Committee (Associate Editor) of the International
Journal of Electrical and Computer Engineering (Scopus). He is also a
reviewer of SCIE journals such as Elsevier ICT express, PIER
Journal, and IEI: Series B journal. He can be contacted at email:
vinodh.edu@gmail.com.
Dr. Sachin Sharma is currently
working as an Associate Pro-
fessor in Computer Applications
in Manav Rachna International
Institute of Research and Stud-
ies. He is a doctorate in Com-
puter Applications in the field of
Data Mining and having more
than 24 years academic experi-
ence. He contributed as keynote
speaker in national/international
conference, session chair of
various international confer-
ences; in association with IEEE
and ACM. He has published 4
books and on a verse of three upcoming publications. He is involved
as a Journal Reviewer and Editor in reputed organizations like IEEE
Journal of Biomedical and Health Informatics, Open Computer Sci-
ence, Elsevier—International Journal of Intelligent Networks,
InderScience Publishers and Wiley. He is member of various pro-
fessional bodies such as ACM, Computer Society of India, Computer
Science Teacher Association, and of International Journal of
Emerging Technologies and more. He has authored more than 35
research articles, conference papers, and book chapters. He has
published five international patents to his credit. He is currently
supervising one PhD scholar and his research interests are related to
Data Mining, Big Data, Machine learning and Data Structures. He is
an avid reader and believes in the power of positivity to embrace
success in life.
Wireless Networks
123

Dr. Arfat Ahmad Khan received
the B.Eng. degree in electrical
engineering from the University
of Lahore, Pakistan, in 2013, the
M.Eng. degree in electrical
engineering from the Govern-
ment College University
Lahore, Pakistan, in 2015, and
the Ph.D. degree in telecom-
munication and computer engi-
neering from the Suranaree
University of Technology,
Thailand, in 2018. From 2014 to
2016, he was an RF Engineer
with Etisalat, UAE. From 2018
to 2022, he worked as a lecturer and senior researcher with the
Suranaree University of Technology. Currently, he is working as a
senior lecturer and researcher at Khon Kaen University, Thailand. His
research interests include optimization and stochastic processes,
channel and the mathematical modeling, wireless sensor networks,
ZigBee, green communications, massive MIMO, OFDM, wireless
technologies, signal processing, and the advance wireless
communications.
Dr. V. Prashanth received the
M.E., in Power and Energy
Systems; UVCE, Bangalore
University, in 2003 and the
Ph.D. from dept. of Electricals,
Visveswaraya Technological
University, Karnataka, India in
2020, with more than 18 years
of academic teaching experi-
ence including Five years of
research experience has been
working as an Associate Pro-
fessor and Head in the Depart-
ment of Electrical and
Electronics Engineering. His
research interests mainly include Math Models in Control systems,
Renewable energy Sources and Signal Processing. He has published
10 Papers in International Journals, 10 plus Papers in National and
International Conferences.
Authors and Affiliations
Mohammed S. Alzaidi
1
•Piyush Kumar Shukla
2
•V. Sangeetha
3
•
Karuna Nidhi Pandagre
4
•Vinodh Kumar Minchula
5
•Sachin Sharma
6
•Arfat Ahmad Khan
7
•V. Prashanth
8
&Arfat Ahmad Khan
arfat_ahmadk78@outlook.com
1
Department of Electrical Engineering, College of
Engineering, Taif University, Taif 21944, Saudi Arabia
2
Department of Computer Science and Engineering,
University Institute of Technology, Rajiv Gandhi
Proudyogiki Vishwavidyalaya (Technological University of
Madhya Pradesh), Bhopal, Madhya Pradesh 462033, India
3
Department of Computer Science and Engineering, Ramaiah
Institute of Technology, Bangalore, Karnataka, India
4
Department of MCA, Bansal Institute of Science and
Technology, Bhopal, Madhya Pradesh, India
5
Department of ECE, Chaitanya Bharathi Institute of
Technology (A), Hyderabad, Telangana, India
6
Manav Rachna International Institute of Research and
Studies, Faridabad, India
7
Department of Computer Science, College of Computing,
Khon Kaen University, Khon Kaen 40002, Thailand
8
Department of Electrical and Electronics, NITTE Meenakshi
Institute of Technology, Bangalore, Karnataka, India
Wireless Networks
123