Enhancing Congestion Control and QoS Scheduling Using Novel Rate

Aware-Neuro-Fuzzy Algorithm in MANET

S. MOHAN1*, P. VIMALA2

1*Department of Electronics and Communication Engineering, FEAT, Annamalai University,

Annamalai Nagar, Chidambaram, Tamil Nadu, INDIA.

2Department of Electronics and Communication Engineering, Faculty of Engineering and

Technology, Annamalai University, Annamalainagar-608 002, INDIA.

Abstract: Mobile Ad Hoc Networks (MANET) provides a vibrant atmosphere wherein data may be

substituted deprived of the necessity of human authority or a centralized server, as long as nodes work

together for routing. As long as security throughout the multipath routing protocol and data transfer

over many routes in a MANET is a difficult problem, this work offers a message security technique.

This study presents the congestion control and QoS scheduling mechanism. The goal of this study is

to examine standardized MAC protocols on MANET, to measure performance under various node

densities and MAC protocols. Initially, this work presents the Centralized Congestion Detection

method to detect congestion with baseline parameters. Accordingly, the congestion is avoided using

Novel Rate Aware-Neuro-Fuzzy based Congestion Controlling strategy. This method effectively

controls the congestion in the Network. This mechanism has been proposed which defines three levels

of congestion based on which the data rate, throughput, overhead and delay. However, after controlling

the congestion, the optimal routes are given to the packets by proposing an Ambient Intelligence-based

Ant colony optimization quality-aware energy routing protocol (AIACOAR). This method finds the

most efficient route to a destination and decreases the time and energy required. Accordingly, for

securing the network against malicious attacks, an Elliptic Curve Cryptography (ECC) encryption

mechanism is presented. Consequently, the multihop scheduler performs QoS-based scheduling in

MANET. Schedulers in MANET take into account various QoS parameters such as end-to-end packet

delay, packet delivery ratio, flow priority, etc. The proposed method is implemented using Matlab

software, and the evaluation metrics are PDR, jitter, congestion detection time, delay, route selection

time, and throughput. The performance of the proposed method is compared to the existing AIFSORP

and LF-SSO techniques. While compared to these methods, the proposed method’s performance is

improved in terms of PDR, delay, throughput, etc. The PDR value of the proposed method reaches

approximately 99%, and it produces a very low delay. This produces reliable route discovery,

optimized congestion control, and better QoS scheduling, therefore, these improve the system

performance. In future, a recent bio-inspired technique is presented to even more minimize energy

consumption and further improve the system's performance.

Keywords: MANET, MAC Protocols, Congestion Control, Routing, QoS Scheduling, Elliptic Curve

Cryptography, Ambient Intelligence, and Ant Colony Optimization.

Received: May 25, 2022. Revised: May 11, 2023. Accepted: June 20, 2023. Published: July 20, 2023.

1. Introduction

Mobile Ad-hoc Networks (MANET) are very

enticing for cutting-edge applications. The

creation of a MANET network is fraught with

several issues and difficulties. End-systems

will acquire information about each used path,

including its capabilities, delay, and congestion

condition, at the transport layer. The traffic will

then be diverted away from engorged

techniques as a result of this information

responding to network congestion occurrences.

MANET are infuriatingly stunning components

for modern apps. Because of the active

topological structure and node modification on

location every second in MANET, congestion

is one of the ongoing difficulties. In MANET,

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S. Mohan, P. Vimala

E-ISSN: 2224-2864

Volume 22, 2023

a sender node must broadcast an initial

broadcast routing packet over the network and

locate the destination through the shortest or

minimal intermediary hop if it has to relay

information to the specified receiver [1] [2].

The dynamic implementation of efficient

collision avoidance and resolution techniques is

necessary for the creation of efficient

contention-based Medium Access Control

(MAC) protocols for mobile wireless ad hoc

networks [3].

To maximize channel use and maintain the

quality of service (QoS) for every node, a

multi-hop scheduler plans transmissions [4].

Due to the numerous characteristics of

MANET that result in distinctive queuing

dynamics, QoS-based scheduling in these

networks must be achieved under time-critical

circumstances. In MANET, schedulers take

into account a variety of QoS factors, including

flow priority, end-to-end packet delay, and

packet delivery ratio. Additionally, scheduling

in MANET can be fair, opportunistic,

distributed priority, etc. [5]. Due to real-time

content delivery in online games and video

conferences, multimedia traffic has sharply

increased during the past ten years. In some

situations, MANET is essential to everything

being hyperconnected in multimedia services.

A new scheduling method based on the

connectivity of context-aware mobile nodes is

used in [6].

The most significant and difficult concepts

in ad-hoc networks currently are routing,

reliability improvement, load balancing, and

congestion control. Every node will perform

routing to send both its messages and those of

the other nodes. All nodes function as routers.

It has long been recognized for these reasons

that mobile nodes use more energy than wired

nodes [7]. Due to the various utility of routing

in the MANET, there are some limitations on

bandwidth and communication range. These

limitations are making routing and data transfer

more difficult, in addition to the dynamic

network structure. Due to these issues,

traditional approaches are ineffective,

particularly when it comes to traffic regulation

and congestion [8]. Therefore, it is necessary to

suggest fresh ideas for these kinds of networks.

To reduce congestion and balance the

beneficial loads on the routes, multipath routing

based on the original protocol Ad-Hoc On-

Demand Multipath Distance Vector (AOMDV)

is introduced [9]. Nodes constantly alter the

network by using their mobility characteristic

to construct new paths between themselves. A

fuzzy Logic System is utilized in intermediate

and destination nodes as a dynamic tool to

control the congestion problem in MANET in a

cross-layer strategy that spans the transport,

network, and MAC layers. The DSR routing

algorithm is utilized at the network layer, and

messages sent back and forth between nodes

are contained in ACK packets. [10]

Due to its effectiveness in overcoming the

specific drawbacks of single-route routing,

multipath routing technology is widely used in

MANET. However, achieving energy-effective

secure MR is a huge issue in MANET due to

the lack of trusted centralized authority and also

insufficient resources. The secret key-centred

hybrid honey encryption technique is used to

protect the DPs from data transfer (DT) assaults

to overcome these difficulties. To find the best

way out of the multipath chosen, the levy flight-

centred shuffled shepherd optimization

algorithm is then used [11]. LEACH protocol,

in which the CHs and CMs are fixed for data

transport in the network, is recommended for

node clustering. To prevent battery depletion

and network failure, the energy is distributed

through the LEACH. Also, the DoS Pliancy

Algorithm is used for acknowledgement-based

flooding assaults [12]. Congestion State

prediction algorithm (CSPA) based on cross-

layered routing architecture is introduced in

[13]. CSPA aids in differentiating between

packet loss brought on by link failure and

random packet loss. Since the packet outlet

directive operates sequentially at the node,

current group, and all preceding group levels.

By performing the default network nodes in

Network Simulator NS2.35 with the AODV

and DSR routing techniques, congestion

control AODV was used to control congestion.

As a result, the performances are better in terms

of packet delivery ratio and throughput [14].

Equipping each device to keep the data

necessary for appropriate traffic routing is the

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Volume 22, 2023

main problem facing the MANET. MANET

can be studied in a variety of methods, for as by

using simulation tools like OpNet, NetSim, or

NS2 [15]. “A Novel Method for Avoiding

Congestion in a Mobile Ad Hoc Network for

Maintaining Service Quality in a Network” In

this title, under the mobile ad-hoc network

system, the main reason for causing congestion

is the limited availability of resources. The rest

of the work is organized as follows, section 2

portrays the literature survey of the work,

section 3 illustrates the problem definition and

motivation, and section 4 demonstrates the

proposed research methodology. Section 5

reveals the experimentation and result

discussion, and the conclusion of the research

is presented in section 6.

2. Literature Survey

The MANET network is an autonomous, self-

organizing system that doesn't rely on any pre-

existing infrastructure. It is a wireless network

with interconnected devices. Congestion is the

network's main issue because it is wireless. The

network's high volume of traffic (packets) is the

cause of this congestion. A routing technique

called Congestion Control AODV is introduced

in addition to Sumathi, et al [16] proposed

congestion control by simulating default

network nodes using Network Simulator

NS2.35 with the Routing Technique of AODV

and DSR. As a result, the final result shows the

viability and improved performance in packet

delivery ratio and throughput from this

proposed work. To determine the least

congested way, the cross-layer protocol and

evolutionary game theory technique were

developed by Thanappan, et al [17]. To prevent

congestion, cross-layering in MANET uses the

transport layer and MAC layer. One

evolutionary game theory method used to

identify the Least Congested Node is Linear

Rank Selection. According to simulations, the

proposed protocol performs better than the

GPSR protocol.

Researchers have focused on coming up

with superior methods for operating the

likelihood of MANET. It is now possible to use

machine learning techniques to prepare

artificial intelligence to increase the most

effective strategies for this function. It is

suggested in [18] to use Machine Learning-

based Efficient Clustering and Improve QoC in

MANET. In this study, the clustering method

enhances the QoS considerations for selecting

Cluster Heads. Even if the streaming protocols

were correctly created, media transfer or

streaming would be nearly impossible if QoS

criteria were not applied. Classes for QoS

Scheduling aid in managing packet priorities

and streamlining network traffic. The analysis

of QoS scheduling classes using video traffic in

a MANET is presented in [19]. It is advised to

utilize the ertPS scheduling class in MANET

where QoS consideration is of the utmost

importance, notably in multimedia streaming

applications. The DE route method is designed

for secure communication in the Internet of

Things architectures with mobile, ad-hoc

Internet of Things nodes. The Diffie-Hellman

key exchange technique generates secret keys

that are used to encrypt the node's addresses to

prevent the route list from being modified [20].

To identify secure nodes, the fuzzy system with

trust parameters, such as historical, indirect,

and direct trust factors, is taken into

consideration.

The test score examines both trusted and

untrusted nodes from the source initially to find

the best route for packet transmission in

MANET. An adaptive trust-based secure and

optimal route selection utilizing a hybrid fuzzy

optimization model was presented by Ravi et al

[21] based on the trusted nodes. An Advanced

Encryption Standard based on an Adaptive

Chaotic Grey Wolf Optimization algorithm

delivers secure communication after the Fuzzy

Butterfly Optimization Algorithm is used to

find the best routes. In MANET and cloud

systems, security is crucial for preventing

damaging assaults. Therefore, a MANET using

cloud-based 5G communications needs a

trusted environment. To defend the network

from attackers, Alghamdi [22] provided a

brand-new framework in this study termed the

trust-aware intrusion detection and prevention

system (TA-IDPS). A MANET, a cloudlet, and

a cloud service layer make up TA-IDPS.

Utilizing well-known measures, the

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Volume 22, 2023

effectiveness of the proposed TA-IDPS and

earlier techniques is examined.

Satyanarayana, et al [23] used a key-based

safety feature identification mechanism in

conjunction with trust ratings. To improve

communication security, this study also

identifies three categories of trust ratings,

including direct, indirect, and overall trust

scores. A cluster-based secured routing system

chooses the head of a cluster from among the

nodes based on QoS metrics and trust ratings.

The final path that must be chosen to carry out

the safe routing operation as effectively as

feasible depends on path trust, energy

consumption, and hop number. Vinayakan, et

al [24] proposes a message security technique

that combines multipath Ad hoc on Demand

Multipath Distance Vector (AOMDV) routing

established on trust with soft encryption in

MANET, resulting in the Trust based Ad hoc

on Demand Multipath Distance Vector (T-

AOMDV) protocol. Security throughout the

multipath routing protocol and data transfer

over many routes in a MANET remain

challenging problems. Simulation results

utilizing ns2 show that the system is much more

secure than conventional multipath routing

algorithms. In [25], the Hybrid Genetic Fuzzy

Neural Network (HGFNN) technology was

developed to create an energy-efficient routing

protocol and cross-layer congestion detection

system. This protocol detects the type of event

occurring when a networking event occurs to

handle it appropriately. Throughput is intended

to be increased by reducing energy

consumption, transmission latency, and packet

delivery ratio. The effectiveness of the

suggested solution is determined by evaluating

its performance in a network simulator in terms

of energy consumption, transmission delay, and

packet delivery ratio.

3. Research Problem Definition and

Motivation

Packet drop occurs due to congestion-related

issues like limited bandwidth, link failure and

interference also misbehaving node drops the

packet to harm the network. Differentiating

packet loss due to congestion or malicious node

is a tedious job. Securing mobile nodes from

attackers has become one of the crucial aspects

of providing QoS since nodes are weak to

different kinds of attacks and threats that

impact network connectivity and functionality.

Congestion occurs in MANET which has a

limited number of resources. Interference and

fading are experienced during packet

transmission in these networks because of the

shared wireless channel and dynamic topology.

Congestion results in packet victims and

bandwidth degradation; as a result, time and

energy are wasted while recovering from it.

When using a congestion-aware protocol, it is

possible to avoid congested areas by bypassing

the affected links. Congestion-related problems

such as severe throughput degradation and

massive fairness issues have been identified,

among other things. These issues arise at the

MAC, protocol routing, and transport layers,

among other levels of the protocol stack.

Congestion Control (CC), as the core

networking task to efficiently utilize network

capacity, received great attention and is widely

used in various Internet communication

applications such as 5G, Internet-of-Things,

UAN, and more. A range of measures has been

suggested to alleviate congestion in MANET.

MANET efficiency must be improved to ensure

data transmission to the respective destination,

especially safety messages. Managing data in

MANET has many problems and difficulties

wherein congestion control has evolved as a

prominent field of study. The benefits of using

data congestion management for MANET

include reduced end-to-end delay, enhanced

reliability, and packet delivery ratio. To reduce

energy consumption and end-to-end delay

while simultaneously improving packet

delivery ratio and throughput, the suggested

architecture makes use of the cross-layer idea.

Security and QoS targets may not necessarily

be similar but this framework seeks to bridge

the gap for the provision of an optimal

functioning MANET. The framework is

evaluated for throughput, jitter, and delay

against a sinkhole and malicious attack

presented in the network.

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4. Proposed Research Methodology

Congestion in the network is not only the cause

of unnecessary data but there may be other

factors such as contention, link failure, and

interference. Congestion in the network

increases the packet loss ratio, delays, and

degrades the overall network performance. One

of its greatest challenges is ensuring Quality of

Service (QoS) owing to channel sharing, high

traffic and topology changes in MANET.

Congestion control should be properly

considered to improve the network

performance and transmission of messages

over MANET. However, there seem significant

limitations to most of the other current

congestion control mechanisms. There are

many problems and challenges in coming up

with a MANET network. Congestion is one of

the live challenges in MANET because of the

active topology structure and node amendment

each second on its position.

Figure 1: Flow Diagram of the Proposed

Work

Figure 1 depicts the workflow diagram of

the research work. The goal of this study is to

examine standardized MAC protocols on

MANET, to measure performance under

various node densities and MAC protocols.

Because of the ubiquitous deployment of MAC

protocols for MANET, it is vital to evaluate

them from the perspective of the transport

layer, which benefits from the advantage of

decisive data transmission over the Internet.

The nodes have limited bandwidth and

processing capability. The routing protocols

cannot handle the congestion due to the heavy

load in mobile ad hoc networks. Several routes

are established in the network, and some

intermediate nodes are common. The routing

protocol establishes the connection between the

sender and the receiver. The efficient routing

approach uses the concept of load balancing to

reduce packet loss in a network. In this paper,

an enhanced congestion control model and QoS

scheduling scheme is proposed. QoS

scheduling makes decisions about the

assignment of resources and services to the

nodes at a current time.

MAC Layer: Medium Access Control

(MAC) Algorithms are used to let several users

share a single communication channel at the

same time to maximize channel utilization

while minimizing conflict and collisions. MAC

is comparable to highway traffic restrictions.

For example, on a highway, numerous vehicles

may cross the same road at the same time, but

there are laws in place to prevent collisions,

such as following traffic lights and constructing

flyovers. Layer 1 of the OSI reference model is

the Data Link Control layer, and layer 2 is the

MAC. The Media Access Control layer and the

Logical Link Control (LLC) layer make up

Layer 2. The DLC's job is to create a secure

point-to-point or point-to-multipoint

connection between two or more devices over

wired or wireless media.

4.1 Congestion Control

Cross-layer involves the transport layer and

MAC layer of MANET to avoid congestion. In

this research, Centralized Congestion Detection

and a Novel Rate Aware-Neuro-Fuzzy based

Congestion Controlling strategy in MANET is

utilized. In this work, a rate-aware and

centralized Congestion Detection is employed

to detect congestion with baseline parameters

like queue size, channel occupancy against the

capacity, and utilization rate. Besides the

congestion detection, the Novel Rate Aware-

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Neuro-Fuzzy based Congestion Controlling

strategy is employed to avoid negative impacts

to the maximum extent. The Rate Aware

defines three levels of congestion based on

which the data rate, throughput, overhead and

delay, which effectively control the congestion

in the Network. The MAC layer is responsible

for accessing the communication channel. At

MAC, a control channel is used in the selection

of collision-free paths for data transfer.

4.1.1 Centralized Congestion Control

Centralized Congestion Control approaches

assume a central controller such as RSU to

control the signal parameters and path

information to guide the vehicles. The RSUs

and OBUs direct all DSRC-connected vehicles

to provide on-demand information about the

ongoing network traffic such as speed, position,

acceleration, braking status, etc. of the

neighbouring vehicles. Centralized approaches

are easier to implement because they incur less

overhead in routing connectivity.

In this paper, a distributed congestion

avoidance scheme is selected, rather than

employing centralized servers and/or road-side

infrastructure. Also, consider a reactive

mechanism (i.e., request/reply) rather than a

system that relies on all-out periodic

broadcasts.

There are several advantages of centralized

congestion avoidance algorithms. First, a

centralized server can have more complete

knowledge of its realm, assuming it can receive

and hold information originating from

anywhere on the map. This may help deduce

more accurate navigation decisions, especially

over longer distances. Second, centralized

methods do not require vehicles to have

computation power and full knowledge of the

map. They can share very little information

with a vehicle (e.g., an optimal route to the

intended destination), as opposed to continuous

reports of congestion levels.

4.1.2 Novel Rate Aware-Neuro-Fuzzy based

Congestion Controlling Strategy

In this section, the Rate aware congestion

control mechanism (RACC) mechanism has

been proposed in the phases namely the

detection/management phase. The details of

this phase are explained below:

Detection and Management Phase

In this phase, the buffer management strategy is

incorporated to reduce traffic congestion.

Generally, the mobile nodes communicate with

their upstream and downstream nodes. The role

of mobile nodes is to get the traffic from the

upstream and forward the same to the

downstream node. To prevent the nodes from

congestion, the issue of congestion can be

easily eliminated. Congestion is predicted in

time ‘’ (when several mobile connections are

10) for buffer management strategy.

Even SN computes the number of packets

received from its upstream and the number of

packets forwarded to its downstream SNs. The

threshold value of buffer size 󰇛󰇜 for any 

node is half of its original size. Original buffer

size 󰇛󰇜. In the detection phase, if the value

of the congestion index 󰇛󰇜 at  node is

greater than  but less than , then it is

considered that the congestion is at the

intermediate level and therefore, the value of 

is lowered by reducing the date rate 󰇛󰇜 of

traffic by 2%. This state of congestion is called

an intermediate load state. On the other hand, if

the value of traffic is equal to , then means

that there is very less congestion. In this case,

the value of  is lowered by reducing the DR

by only 1%. This state is known as a low-

congestion state. Alternatively, if the value of

 is greater than BSo, then it is considered to

be an alarming state also known as buffer

overflow. Therefore, the  is reduced to 3%

of its current data rate. Thus, in this way

congestion amid nodes can be considerably

reduced or managed.

 is the time of prediction of congestion

when the number of mobile connections is 15,

and  is when the number of connections is

20 and  is when the number of mobile

connections is 25.  is an index value that is

computed based on the number of packets a

buffer can hold. The value of  is less than 

(number of mobile connections less than 10), it

means that there is no congestion and the traffic

flow is normal. Therefore, based on

observations, the RACC mechanism is

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S. Mohan, P. Vimala

E-ISSN: 2224-2864

Volume 22, 2023

triggered to mitigate the congestion

phenomenon.

Neuro-Fuzzy Based Congestion Control

Mechanism

A fuzzy system is made of a fuzzifier, a

defuzzifier, an inference engine, and a rule base

as shown in figure 2. The role of the fuzzifier is

to map the crisp input data values to fuzzy sets

defined by their membership functions

depending on the degree of “possibility” of the

input data. The goal of the defuzzifier is to map

the output fuzzy sets to a crisp output value. It

combines the different fuzzy sets with different

degrees of possibility to produce a single

numerical value. The fuzzy inference engine

defines how the system should infer through the

rules in the rule base to determine the output

fuzzy sets.

Figure 2: Flow Diagram for Fuzzy System

A binary threshold divides the buffer space

into two parts. Below or equal to the threshold

level, every arriving cell is given entry to the

network and above the threshold, every cell is

rejected. The change from entry in the network

to rejection is abrupt. A gradual change is more

intuitive here. This can be done with a fuzzy

threshold. A typical example is given in the

following section.

Neuro-Fuzzy Membership Functions

This technique enables the network for finding

the shortest path of the nodes to the cluster

heads and then to the sink node by applying

neuro-fuzzy rules. For this purpose, the NFIS

uses the triangular and trapezoidal membership

functions along with a convolutional neural

network to form decision rules. Moreover, the

neuro-fuzzy rule system uses the fuzzy

membership functions which are given in

equations (1) and (2).

󰇛󰇜











 



 



 

 

(1)

󰇛󰇜











 



 

 



 

 

(2)

Here, the variables z and x represent the

fuzzy and actual distances in which the fuzzy

distance value is measured using the

membership functions.

The last step in the fuzzy rule-based

inference process is the de-fuzzification step.

To obtain the crisp output value corresponding

to the fuzzy values, the de-fuzzification step

has been applied in this work. Among the de-

fuzzification methods which are available in

the literature, the Center of Area method is the

most widely used method and hence this work

adopted the COA method shown in (3).

󰇛󰇜

󰇛󰇜

(3)

However, utilizing this centralized

congestion detection, and rate-aware neuro-

fuzzy technique detects the congestion in the

network and it avoids or controls the congestion

in the network layer. Accordingly, after the

congestion control, a secure routing process is

required which is described in the following

subsection.

4.2 Routing Protocol

This paper proposes the Ambient Intelligence-

based Ant colony optimization quality-aware

energy routing protocol (AIACOAR) to find

the most efficient route to a destination and

decrease the time and energy required. This

quality-aware energy routing is developed

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S. Mohan, P. Vimala

E-ISSN: 2224-2864

Volume 22, 2023

based on a cross-layer. The multipath routes are

to be discovered primarily using the parameters

such as delay, channel occupation, link quality

and residual energy. In AIACOAR, nodes

quickly notify their neighbours when they

discover a possible route to their target. Only

when the route meets the threshold criterion is

it picked for data transmission and shared with

neighbouring nodes. This method identifies the

finest path among the selected relay nodes in

the direction of the destination. Optimization

plays a significant part in AIACOAR towards

determining the best route to the destination.

Subsequently, the cross-layer interaction

parameter-based link residual lifetime

calculation is used to assess the link's stability.

4.2.1 Ambient Intelligence Function

The ambient intelligence feature is included in

AIACOAR to enhance the ant's different

movement patterns (AIF). AIACOAR will

employ the AIF with all ants’ behaviour to

avoid making abrupt turns instead of smooth

turns. It is the primary goal of the AIACOAR

to determine the quality of the food (i.e., route)

in the immediate area and then work to raise the

overall consistency of the cluster.

To account for abrupt changes in ant

movement, AIACOAR uses its function (the

Ambient Intelligence Function (AIF)). The AIF

is utilized to map extensive routing information

into a region between zero and one. The curve

is created using this function, and it will be in

the shape of . As an alternative, the AIF may

be used when a mathematical model can’t be

found. Equation (4) represents the

mathematical representation of AIF.

󰇛󰇜



(4)

In equation (4), the natural logarithm is

denoted as , the maximum value of a curve is

indicated as , and  is indicated to represent an

integer that falls between  and .

4.2.2 Ant Colony Optimization

In MANET, ensuring the throughput rates is

important to meet the client demands with an

effective QoS. Due to different plan hardships

and imperative satisfaction, conventional

conventions fail to address user challenges.

Hence, upgrading throughput turns into a basic

issue to fulfil client needs and application

support. Therefore, throughput is the

significant factor for rendering the required

QoS for any kind of MANET application and

in this research, MACO streamlining technique

considers throughput as one of the factors in

selecting the optimal routing path for MANET

communication.

Solution Representation

In an optimization algorithm, the solution

representation signifies the solution declared by

the algorithm. In this research, the solution is

the routing path with the source node as the

initiating node or the data sender S and

destination node D as the terminating node or

the receiver, with the intermediating nodes

󰇛󰇜 such that 󰇛󰇜 are the

communicating nodes between the source and

destination nodes.

Where,  is the total nodes in the MANET

with  being the intermediate nodes in the

communicating nodes.

Fitness Measure

The optimal solution, which is the optimal

routing path, is decided by MACO using the

fitness measures, such as throughput, PDR,

routing overhead, and delay. The solution is

selected as optimal when the throughput and

PDR are high with minimal overhead and

transmission delay.

The ACO builds the connection's packet

transmission rate, bringing about a reasonable

course choice arrangement. Forward “ant” is

begun by the source hub at arbitrary to visit the

entirety of the open hubs in the course. During

their crossing, the ants leave a little amount of

pheromone on the visited joins. At the point

when the ants show up at their objective, the

ants update the pheromone of all hubs visited

all through the crossing. A hub’s throughput is

treated as a pheromone for this situation. The

throughput work is utilized to refresh a hub’s

pheromone.

Equation (5) is used to calculate 󰇛󰇜.

󰇛󰇜󰇛󰇜

󰇛󰇜





(5)

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E-ISSN: 2224-2864

Volume 22, 2023

Where,  denotes the packet transmission

limit, 󰇛󰇜 is the number of packets

successfully transferred, and 󰇛󰇜 denotes the

packet transmission time.

An ant 󰇛󰇜 is a collection of routes that link

all nodes. MACO's fitness function shown in

equation 6, also known as the objective

function, is shown as follows:

󰇛󰇜



 

 (6)

The pheromone is updated cyclically during

each traversal of a link . Equation 7 is used to

calculate the likelihood of an ant '' visiting

node '' from node .



󰇛󰇜󰇛󰇜󰇛󰇜󰇛󰇜

󰇛󰇜󰇛󰇜󰇛󰇜



(7)

The pheromone concentration in link  is

,  is the energy of the node, control

parameters are , , and , and the throughput

heuristic value  is 󰇛). Equation 8 is used to

calculate the pheromone concentration as it

decreases over time.

 󰇛󰇜 







(8)

Where, 

 is the change in pheromone

amount in the link , updated by the th ant,

and 󰇛󰇜 is a decreasing pheromone

constant. The following generation of ants

migrates to their goal via increasing pheromone

concentration nodes.

This cycle has proceeded until the state of

stagnation is satisfied. The street that arises

after a time of balance is viewed as the best way

for correspondence. This methodology is done

for every information transmission. This

progression flags the beginning of the

organization’s transmission interaction.

Alternate Path Building by ACO

If the network concentration is scarce all the

above schemes are avoided to save unwanted

energy consumption. The simple ACO is

considered and the shortest path is formed by

the high concentration pheromone value of

ANTS.

4.2.3 Quality Aware Energy Routing Protocol

A good link quality metric should (i) accurately

represent the quality of the physical channel;

(ii) be sensitive to gradual changes in the link

quality (such as mobility); (iii) not be sensitive

to the temporary changes in the link quality

(such as fading); and (iv) not be sensitive to the

link load. Denoting the SNR value of the  a

packet sent by node  by , the Smoothed

SNR (SSNR) value to node  can be

formulated as

 󰇛󰇜





(9)

Where,  is a parameter between 0 and 1.

The larger the  value, the more sensitive the

SSNR is to the current SNR. By using this

exponential averaging, each SNR sample

gradually loses its influence on the current

SSNR value as newer packets from the same

source arrive.

Route Selection Mechanism

During the route discovery process, the source

node broadcasts the Route Request packet

(RREQ) which also includes an additional 32-

bit Route Quality (RTQ) field in the packet

header. Upon receiving the RREQ, a node

updates the RTQ in the packet header with its

current SSNR if it is the first node receiving the

RREQ or if the RTQ in the packet header is

larger than its current SSNR. Note that the RTQ

in the packet header represents the signal

quality of the weakest link in the route. Each

routing table entry also contains a 32-bit RTQ

field, which will also be updated if either of the

following conditions is satisfied:

i. The sequence number is higher than the

sequence number stored in the routing table.

ii. The sequence numbers are equal, but

the hop count in the RREQ + 1 is smaller than

the hop count stored in the routing table.

iii. The sequence numbers are equal, the

RTQ in the routing table is smaller than the pre-

defined RTQ_THRESHOLD, which is used to

distinguish between good and bad routes, and

the RTQ in the packet header is larger than

RTQ_THRESHOLD.

iv. The routing table contains no entry with

the destination sequence specified in the packet

header in routing table.

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E-ISSN: 2224-2864

Volume 22, 2023

By doing this, the proposed protocol can

maintain the connectivity of the network and

keep the overhead low.

4.3 Data Encryption and QoS Scheduling

The final phase is securing the network against

malicious attacks. This is achieved by

designing a secure cryptography-based

mechanism for MANET that employs an

Elliptic Curve Cryptography (ECC) encryption

mechanism that provides security against

malicious attacks and gives high throughput.

Throughput in MANET is increased

considerably if protected against malicious

attacks. A multi-hop scheduler schedules

transmissions so that the channel utilization is

maximized while guaranteeing the quality of

service (QoS) for all nodes. QoS-based

scheduling in MANET must be obtained under

time-critical conditions as these networks have

several features that produce unique queuing

dynamics. Schedulers in MANET take into

account various QoS parameters such as end-

to-end packet delay, packet delivery ratio, flow

priority, etc. Also, scheduling in MANET takes

many forms such as distributed priority, fair,

opportunistic, etc.

4.3.1 Elliptic Curve Cryptography (ECC)

The ECC technique is implemented after

establishing the secured path between the

source and destination to encrypt the original

data into an unknown format. It is one of the

widely used cryptographic techniques in

network security, which offers fast

computation and reduced resource

consumption. Also, this technique establishes

equivalent security with minimized cost. The

strength of this algorithm is, it fully depends on

the key and alphabetical table. Also, it provides

a better solution for the data by enabling the

secure transmission of keys between the

communicating entities. Furthermore, different

characteristics are symbolized in this technique

as the coordinates of the curves. The group of

the structure of ECC is formed by the curve that

has a finite number of integer points with

determinate points. In this work, the main

reason for using ECC encryption is, it creates

complexity in the encrypted data, so the

unauthenticated user cannot easily access the

data.

Key Mechanism: The private key of the

node is only known to the sender (signer) and

used to create the signatures. Whereas, the

public key is distributed to all the partners in the

communication and can be verified by anyone

of the trusted party.

Key Generation: In ECC, the sender selects

the private key randomly and computes its

public key using a mathematical equation 10.



(10)

Where,  is the private key of the sender and

 is the coordinates of the elliptic curve.

Key Sharing Mechanism: The private key

is only known to the sender and the public key

is provided to the receiver via a secure channel

like Dife–Hellman key exchange or any other

key exchange mechanism.

Signing Mechanism: The first step of this

mechanism is the pre-computation of the hash

or the digest of the message to be signed using

the secure hash algorithm. The second step is to

compute the random number with the help of a

random number generator, this random number

provides the value for the elliptic curve

computations. After this, the message is signed

and the sender sends a random number along

with a signed message to the receiver.

Verifying Mechanism: The third

mechanism is known as verifying mechanism,

the signed message when received at the

receiver end and can be verified the authenticity

of the message using the public key of the

authenticator i.e. sender in this case. With the

help of the same hash algorithm which is used

for signing the message, again the hash is

computed on the receiver end along with the

public key and the parameters of digital

signatures. These hashes are then compared and

verified the signatures if match otherwise the

verification can be failed.

Communication Mechanism: There are

two types of nodes which are being categorized

for the implementation of AWSC. The first step

is where the nodes need to authenticate

themselves using their already stored

information.

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Signature Generation: After encrypting

the data, Schnorr’s signature generation

algorithm is utilized to generate the signature

for the encrypted data. It is a kind of key

generation mechanism that integrates both

digital signature schemes and public-key

encryption schemes. It analyses the discrete

logarithmic problem for generating the digital

signature, which increases the security of the

network. This signature generation has the

following steps:

 Setup

 Key generation at the sender side

 Key generation at the receiver side

 Signcryption

 Unsigncryption

In this technique, the source verifies the

public key of the packet by using the certificate.

Then, the integer is randomly selected, and

based on this the keys that are used for

generating the ciphertext are computed. Also,

the one-way keyed hash function is utilized to

generate the encrypted text, and it is forwarded

to the destination with the generated signature.

The working procedure of ECC-based

encryption and signature generation algorithms

is illustrated in table 1.

Table 1: Encryption and Signature Generation

Algorithm

Algorithm 1: Encryption and Signature

Generation

Source verifies the public key of  by using

its certificate;

Randomly select an integer , where 

Compute 󰇛󰇜

Compute 󰇛󰇜󰇛󰇜

The symmetric encryption algorithm is used

to generate cipher textt 󰇛󰇜

Use the one-way keyed hash function to

generate, 󰇟󰇜

Computes  



Compute 

Sends the signature added ciphertext

󰇛󰇜 to the receiver;

4.3.2 Quality of Service (QoS) Scheduling

It is a guarantee by the network to provide

certain performance for flow in terms of

quantities such as jitter, bandwidth, and packet

loss probability. The heterogeneity of

applications on the Internet has challenged the

network which can provide best-effort service

voice, live video and file transfer are some

examples. Thus, the delivery of the best quality

to the users gives rise to QoS.

Multi-Hop Scheduler

A scheduler (figure 3) includes the following

components: (1) an error-free service model

that describes how the algorithm provides

service to flows with error-free channels; (2) a

lead/lag counter for each flow that indicates

whether the flow is leading, in synch with, or

lagging its error-free service model and by how

much; (3) a compensation model used to

improve fairness among flows. A lagging flow

is compensated at the expense of leading flows

when its link becomes error-free again; (4)

separate slot and packet queues for each flow

can be used to support delay-sensitive and

error-sensitive flows. When a packet arrives, it

is time-stamped and placed in the packet queue;

(5) a means for monitoring and predicting the

channel state for every backlogged flow.

A wireless scheduler should possess the

following properties:

Efficient Link Utilization: The scheduler

should not assign a transmission slot to a flow

with a currently bad link;

Delay Bound: The algorithm should

provide delay bound guarantees for individual

flows;

Fairness: The algorithm should distribute

available resources fairly across flows;

Throughput: The scheduler should provide

guaranteed short-term throughput for error-free

flows and guaranteed long-term throughput for

all flows;

Low Complexity: A low-complexity

algorithm is preferred as scheduling decisions

in MANET have to be made very rapidly;

Graceful Service Degradation: A flow that

has received excess service should experience

a smooth service degradation when

relinquishing the excess service to lagging

flows whose links are now good;

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Isolation: The algorithm should isolate a

flow from the ill effects of misbehaving flows;

Energy Consumption: The algorithm

should take into account the need to prolong the

battery life of the mobile device. To conserve

energy, a node must transmit/receive in

contiguous time slots and then go into a sleep

(very low energy consumption) mode for an

extended period rather than rapidly switch

between transmit, receive, and sleep modes.

This preference has to be balanced against the

need to maintain QoS levels. For example, the

Sleep and awake scheduler enables the channel

during the data transmission only;

Delay/Bandwidth Decoupling: For

multimedia applications, the delay must be

tightly coupled with the reserved rate;

Scalability: As the number of flows

increases, the algorithm should operate

efficiently;

Topology-Transparency: A topology-

independent scheduler is preferred as it works

efficiently regardless of how frequently and

unpredictably the MANET topology changes;

Low Connectivity Information

Requirement: A scheduler should keep the

communication of network connectivity

information to a minimum, as this

communication consumes bandwidth.

Figure 3: Priority Scheduler for Data Packets

Figure 3 shows a scheduler that serves data

packets in a weighted round-robin fashion.

Each 󰇛󰇜represents a data flow that

can be identified by a source and destination

pair. Round-robin scheduling maintains per-

flow queues, while each flow queue is allowed

to send one packet at a time. Different weights

(priorities) can be assigned to data flows. A

weighted round-robin scheduler can be used as

a priority scheduler that guarantees that all

flows (service classes) are served according to

their priorities. Data packets in the queues are

transmitted based on priority levels. Priority

can be defined by using QoS parameters such

as bounded end-to-end packet delay, remaining

hops to traverse, remaining distance, etc.

5. Experimentation and Result

Discussion

The proposed method will be discussed in this

part utilizing Matlab software. Analysis of

different MANET routing protocols may be

done with this tool. In this section, the

performance of existing and proposed security

mechanisms is evaluated by using various

performance measures that include congestion

detection time, Packet Delivery Ratio (PDR),

delay, route selection time, and throughput. A

comparison of the proposed routing protocol to

current routing protocols was carried out using

the Matlab software. It's no secret that

MANET's protocol simulation and

implementation characteristics have long

baffled researchers, especially regarding the

network's overall performance. The simulation

configuration table for the proposed method is

presented in the following table 2.

Table 2: Simulation System Configuration

Simulation System Configuration

MATLAB

Version R2021a

Operation System

Windows 10 Home

Memory Capacity

6GB DDR3

Processor

Intel Core i5 @

3.5GHz

Simulation Time

10.190 seconds

The simulation system configuration of the

proposed work is portrayed in table 2.

Subsequently, the proposed technique is

evaluated and tested under the Matlab R2021a

software. The proposed work operates under

windows 10 home and its memory capacity is

6GB DDR3. Additionally, it utilizes an Intel

Core i5 @ 3.5GHz processor and the simulation

time of the work is 10.190 seconds.

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Figure 4: Jitter for Node Density

Figure 4 depicts the performance graph for

jitter for different node densities. This figure 4

demonstrated that the node density is taken

from 50 to 250, when the node density is 50, the

jitter value reaches 12, and when the node

density is 250, the jitter value reaches 52 If

Jitter is considered as a performance metric for

testing different modulation schemes, it

experiences least time delay in sending the data

packets over a network consisting of a varying

number of mobile connections.

Figure 5: Congestion Detection Time

Figure 5 demonstrates the congestion

detection time of the proposed method. The

time taken for congestion detection is evaluated

based on the node densities. However, the node

density is taken from 50 to 250. When the node

density is 50, the congestion detection time is

0.24, and when the node density is 250, the

congestion detection time is 0.375,

respectively.

Figure 6: Fitness Graph for the Proposed

Methodology

The fitness graph for the proposed work is

presented in figure 6. The number of iterations

is taken from 0 to 100. For the evolutionary

strategies, the fitness at each generation (epoch

with an increase in fitness) and the resulting

finesses for each test are plotted in figure 6. In

this figure, the fitness values reach

approximately 18.5%.

Delay: Delay is the time taken for a packet

to go from sender to receiver.

Figure 7: Comparison Graph for Delay with

Node Density

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Figure 7 portrays the comparison graph for

the delay, it is compared with existing Ambient

Intelligence-based Fish Swarm Optimization

Routing Protocol (AIFSORP) [16] and Levy

Flight centred Shuffled Shepherd Optimization

(LF-SSO) Algorithms [11]. The above figure

represented the delay faced by the packet in

every protocol with different node densities. In

this figure 7, the X-axis represents the node

density, whereas the Y-axis represents the

delay, measured in milliseconds. Figure 7

shows that the proposed routing protocol has a

minor delay from other present routing

protocols, which is easy to comprehend. It

employs a sharing strategy to take advantage of

the most efficient path. The uses of the current

position of nodes to find a better route rather

than utilizing the old position of nodes. The

considered current routing protocols use the

previous position of nodes rather than the

updated position of nodes, which ends them

facing more delay.

Packet Delivery Ratio

The PDR is estimated based on the fraction of

the number of packets that are transmitted by a

traffic source and the number of packets

received by a traffic sink. Also, it is used to

evaluate the efficiency and correctness of the

routing protocols by estimating the loss rate.

The PDR is calculated as follows,

 



(11)

Figure 8: PDR vs Node Density

Figure 8 shows the comparison graph for the

PDR of the proposed protocol with the existing

protocols for node density. The proposed

method is compared with the existing

AIFSORP and LF-SSO techniques. From the

evaluation, it is observed that if the node

density is the PDR of the proposed model can

be increased, but the other existing method can

be decreased. When compared to the other

techniques, the proposed method provides a

better PDR by increasing the node density.

Also, it uses multiple paths for forwarding the

packets, if there is any failure in the current, it

uses an alternate path for further

communication, which increased the PDR.

However, the PDR of the proposed method

reaches approximately 99% when the node

density is 250.

Figure 9: Route Selection Time

Figure 9 demonstrates the comparison graph

for the route selection time with the existing

methods. The comparison techniques are

AIFSORP and LF-SSO methods. Figure 9

demonstrates that the proposed scheme

produces the quickest route selection time. The

proposed systems require reliable routes in

message routing as there have been several

instances when the proposed scheme is unable

to rapidly find disjoint paths for routing, owing

to the presence of critical nodes in identified

chosen paths. While utilizing the existing

scheme, this is not essential for routing as the

latter can discover trustworthy node-disjoint

routes that cope through critical nodes'

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Volume 22, 2023

presence. When compared to these approaches,

the route selection process of the proposed

technique is much quicker.

Throughput

The throughput is defined as the average rate of

successful data delivery over the

communication channel. The throughput of the

network is calculated as follows:



󰇛󰇜

󰇛󰇜

(12)

Figure 10: Comparison Graph for Throughput

Figure 10 shows the analysis of throughput

for both existing and proposed methods for the

number of attackers. During this calculation,

the time window is estimated for measuring the

throughput based on the successfully delivered

packets per unit of time. In this evaluation, it is

proved that the proposed SSVC technique has

increased throughput when compared to the

other techniques.

6. Research Conclusion

Mobile Ad-hoc networks (MANET) are made

up of several mobile wireless nodes that may

move around and join or depart at any moment.

Most networks strive to provide good security

and an acceptable level of performance. Quality

of service (QoS) plays an important role in the

performance of a network. Mobile ad hoc

networks (MANET) are a decentralized and

self-configuring type of wireless network.

MANET is generally challenging and the

provision of security and QoS, and congestion

problems become a huge challenge. In this

research, a Centralized Congestion Detection

and Novel Rate Aware-Neuro-Fuzzy based

Congestion Controlling strategy is presented to

detect and avoid congestion in the network.

Accordingly, an Ambient Intelligence-based

Ant colony optimization quality-aware energy

routing protocol is presented for routing the

data packet. This method identified the most

efficient route to a destination and decreases the

time and energy required. The elliptic Curve

Cryptography (ECC) encryption method is

presented to secure the network against

malicious attacks. However, QoS-based

scheduling in MANET is performed by using

the multi-hop scheduler. The proposed method

is implemented using Matlab software. The

proposed congestion is based on which the

PDR, throughput, and delay. The performance

of the proposed method is compared with the

existing AIFSORP and LF-SSO methods.

While compared to these existing methods, the

proposed technique reduces transmission delay,

and route selection time, and enhances the

packet delivery ratio, hence increasing

throughput. Consequently, the presented

Routing Protocol discovers the most efficient

route and reduces delay faced and energy spent.

Subsequently, QoS Scheduling classes help in

network traffic optimization and the priority

management of packets. However, to minimize

energy consumption and further improve the

system performance, a recent bio-inspired

technique is presented in future studies.

References

[1] Kankane, S., Jhapate, A. and Shrivastava,

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WSEAS TRANSACTIONS on COMMUNICATIONS

DOI: 10.37394/23204.2023.22.6

S. Mohan, P. Vimala

E-ISSN: 2224-2864

Volume 22, 2023