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Citation: Ramalingam, R.; Muniyan,
R.; Dumka, A.; Singh, D.P.; Mohamed,
H.G.; Singh, R.; Anand, D.; Noya, I.D.
Routing Protocol for MANET Based
on QoS-Aware Service Composition
with Dynamic Secured Broker
Selection. Electronics 2022,11, 2637.
https://doi.org/10.3390/
electronics11172637
Academic Editor: Christos J. Bouras
Received: 6 July 2022
Accepted: 18 August 2022
Published: 23 August 2022
Publisher’s Note: MDPI stays neutral
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iations.
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Licensee MDPI, Basel, Switzerland.
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distributed under the terms and
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Attribution (CC BY) license (https://
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4.0/).
electronics
Article
Routing Protocol for MANET Based on QoS-Aware Service
Composition with Dynamic Secured Broker Selection
Rajakumar Ramalingam 1, Rajeswari Muniyan 2, Ankur Dumka 3, Devesh Pratap Singh 4,
Heba G. Mohamed 5, * , Rajesh Singh 6, 7, * , Divya Anand 8,9 and Irene Delgado Noya 7,9
1Department of CST, Madanapalle Institute of Technology & Science,
Madanapalle 512325, Andhra Pradesh, India
2Department of Computer Science, Sri Manakula Vinayagar Engineerring College,
Puducherry 605107, Puducherry, India
3Department of Computer Science and Engineering, Women Insitute of Technology,
Dehradun 248007, Uttarakhand, India
4Department of Computer Science and Engineering, Graphic Era Deemed to be University,
Dehradun 248007, Uttarakhand, India
5
Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University,
P.O. Box 84428, Riyadh 11671, Saudi Arabia
6Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248001, Uttarakhand, India
7Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
8
School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
9Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
*Correspondence: hegmohamed@pnu.edu.sa (H.G.M.); rajeshsingh@uttaranchaluniversity.ac.in (R.S.)
Abstract:
MANET is a mobile ad hoc network with many mobile nodes communicating without a
centralized module. Infrastructure-less networks make it desirable for many researchers to publish
and bind multimedia services. Each node in this infrastructure-less network acts as self-organizing
and re-configurable. It allows services to deploy and attain from another node over the ad hoc
network. The service composition aims to provide a user’s requirement by combining different
atomic services based on non-functional QoS parameters such as reliability, availability, scalability,
etc. To provide service composition in MANET is challenging because of the node mobility, link
failure, and topology changes, so a traditional protocol will be sufficient to obtain real-time services
from mobile nodes. In this paper, the ad hoc on-demand distance vector protocol (AODV) is used
and analyzed based on MANET’s QoS (Quality of Service) metrics. The QoS metrics for MANET
depends on delay, bandwidth, memory capacity, network load, and packet drop. The requester
node and provider node broker acts as a composer for this MANET network. The authors propose
a QoS-based Dynamic Secured Broker Selection architecture (QoSDSBS) for service composition in
MANET, which uses a dynamic broker and provides a secure path selection based on QoS metrics.
The proposed algorithm is simulated using Network Simulator (NS2) with 53 intermediate nodes
and 35 mobile nodes of area 1000 m
×
1000 m. The comparative results show that the proposed
architecture outperforms, with standards, the AODV protocol and affords higher scalability and a
reduced network load.
Keywords: MANET; QoS metrics; routing protocol; cluster formation; link failure
1. Introduction
MANET consists of autonomous mobile nodes, which are capable of self-organizing
and frequently reconfiguring due to the mobility in their nature. Therefore, the nodes are
incapable of sharing services to other nodes available in the surrounding area [
1
,
2
]. Gener-
ally, web-service composition enables application-to-application interactions over networks
by comprising functional and non-functional characteristics. Functional requirements are
related to the conformance of web-service composition to the conditions of its functionality.
Electronics 2022,11, 2637. https://doi.org/10.3390/electronics11172637 https://www.mdpi.com/journal/electronics
Electronics 2022,11, 2637 2 of 17
In contrast, nonfunctional requirements are associated with the QoS, e.g., response time,
availability, and cost [
3
]. It provides interoperability because a single service cannot satisfy
user requirements. The system requires composite services to achieve interoperability,
consisting of many existing atomic services. These services may be hardware, software, raw
data, etc. For example, you can consider that a laptop, which has software to be exchanged,
can act as a service. Service composition is the integration of any existing e-services com-
municated over the Internet through wired media [
4
]. Recently, the research [
5
–
11
] on
service composition over infrastructure-less networks has evolved enormously. Most of
the works in the literature deliberate that the services available on the wired network are
easily accessible; whereas, the services offered by the wireless network can be accessed by
matching service composition protocols with infrastructure-less protocols.
In addition, these services need brokers to exchange services among the requester
and provider nodes in MANET. So, the research follows on providing service composition
based on non-functional QOS parameters such as throughput, end-to-end delay, and
packet-delivery ratio in MANET. A dynamic service composer is assigned based on QoS
metrics among nodes, to compose various services for a wireless network based on user
requirements. The various challenges in developing a dynamic service broker are due to
node mobility, link failure, and topology changes in MANET, as discussed in [
12
]. The
mobile nodes are grouped to form clusters, and each cluster is capable of assisting the
scalability of the mobile nodes. A mobile node is selected to act as a cluster head based
on the Multiple Cluster Head Gateway (MCHG) [
13
]. QoS is the term used to measure
the objectives of both service provider and service requester; the QoS composition process
will select the task based on metrics and aggregate the services that have obtained the
maximum value by satisfying the user’s requirement. Selection of the protocol for routing
based on the QoS metrics in MANET is required.
The research problem of embedding service composition in pervasive environments
is divided into two approaches. The first approach is based on developing a language
and workflow approaches such as BPEL4WS, DARPA, and Web Services Flow Language
discussed in [
14
]. The second approach is based on constructing architecture for discovering
and integrating composite services in an infrastructure-less environment. This paper deals
with the second approach, since it is indeed important to consider QoS parameters for the
pervasive environment.
The main contributions of this paper are presented below:
1.
Initially, an efficient cluster formation mechanism, namely the MCHG algorithm, is
introduced to balance the load in the network.
2.
A dynamic broker selection based on the QoS metrics is formulated to select an active
broker and routing path.
3.
A secured communication link within the intra-cluster is designed based on symmetric
encryption and key-exchange protocol to prevent intrusion in the network.
The rest of the paper are organized as follows. Section 2discusses the various literature
works. Section 3illustrates the problem definition, and Section 4deals with the overview of
the QoSDSBS model. Further, a detailed discussion of the QoSDSBS model is presented in
Section 5. Experimentation and result analysis are carried out in Section 6. Finally, Section 7
concludes the work with its outcomes.
2. Related Work
Service composition is an active research topic in a wired network environment; most
research is on wired composition in a centralized architecture. First, a wired environment-
based background work is discussed. At first, Zeng et al. proposed a service class that
consists of many distinct services and used service class to differentiate from other services,
which have the same functionality but differ in terms of their QoS metrics [
14
]. The
process consists of selecting multiple QoS metrics for wired networks such as cost, time,
security, reliability, and availability. The local selection is based on QoS metrics for various
composite services and uses global selection to select the best provider. Many researchers
Electronics 2022,11, 2637 3 of 17
have proposed frameworks for service composition based on ontology and QoS metrics.
Aggarwal et al. proposed a framework called METEOR-S, that selects the services based on
semantic values and groups those values into functional, non-functional, and execution
data [
15
]. With this framework, they choose the best and unique service to fulfil the
user requirements based on their SLA (Service Level Agreement). Many research works
are done toward considering multiple QoSs for the composition to find the outstanding
service. Xingzhi Feng et al. [
16
] have proposed an architecture for considering multiple QoS
constraints for service composition and used both QoS-based functional and non-functional
parameters. Their composition is highly critical because they need to select the best service
from many specific services and provide QoS metrics, to satisfy the user’s requirements
with maximum utility function, as constraints.
L. Zeng et al. [
17
] developed a standard QoS middleware that provides interoperability
for service composition by integrating with integer programming search. The authors
obtained better QoS performance with less complexity and combined global planning
with local optimization [
18
]. Multiple QOS constraints are proposed into MANET to assist
the network in its energy-saving method and effective data transfer process [
19
]. Peng
Cheng Xiong et al. [
20
] developed a Petri net framework based on graph structure and
algebraic property for considering multiple QoS attributes. This workflow will support
functional requirements and also QoS parameters. It deals with a business process for
sequencing and runtime execution. Message-forwarding applications such as traffic block
notice have been addressed in an effective QOS-based metric analysis system. It also deals
with the intelligent traffic-management system to determine the features and service area
of coverage to maintain the data-transmission rate [
21
]. The multi-objective evolutionary
algorithm, namely MOEQA, has been addressed to solve the multicast-routing problem
in MANET. It also includes the greedy and family competition approach to maintain the
convergence and diversity among the density coverage area [
22
]. However, this mechanism
requires lot of information to process an efficient selection.
The second research work is based on wireless network composition. The research
work by [
23
] developed a hierarchical task graph-based workflow for service composition in
the infrastructure-less environment. The authors represented a graph with each composite
service in a task graph with leaf nodes containing atomic services. The subtrees for the
task graph can be computed in distributed networks. The services are combined based on
demand or are dynamic. This architecture provides better resource utilization. The authors
in [
24
] have considered both the requester node and composer node in a single module;
however, this approach will decrease the system efficiency by increasing network load.
Therefore, an efficient cluster-head-selection mechanism can handle the network load
as well as boost network efficiency. The research work in [
6
] proposed a Linked Cluster
Algorithm that assigns an id to each cluster and selects the cluster head with highest id
value. A node with higher mobility does not get a chance to act as the head. The authors
in [
25
] proposed a mechanism for cluster formation based on QoS metrics for a wireless
network. However, this approach is inefficient and stagnates in local issues, while there
is an increase in scalability of the network. The authors in [
26
,
27
] proposed an extended
ZRP (Zone Routing Protocol) for the clustering-based network that selects a single cluster
head with overlapping zones. Various security issues in web services including DDOS [
28
]
are solved. A detailed survey regarding various QoS metrics in web services is discussed
in [
29
,
30
]. These algorithms selected a single header, resulting in a centralized cluster that
might lead to single point of failure.
From the existing research, we observed that an efficient cluster-head election should
possess the following eminences in MANET:
•Maximum utilization of a resource such as battery power;
•Header should be capable of withstanding any packet traffic;
•Load balancing;
•Lesser node mobility.
Electronics 2022,11, 2637 4 of 17
Therefore, our proposed work considers the above-mentioned eminences along with
QoS metrics for dynamic secured broker selection mechanism. In addition, a secured
communication path is established between the broker and CHs to eradicate the intrusion.
3. Problem Definition
Services provided in MANET are afforded by the broker available in the middle. Based
on the cluster head algorithm, a single broker can be selected to transfer the benefits to
the service requester and provider in a centralized system. As a result, it is more prone
to a single point of failure. However, a multiple broker-selection mechanism converts the
centralized system into a decentralized system that might eradicate the single point of
failure. The service traffic will increase with respect to the link failure between the requester
or provider and the broker. By using ZRP [
27
], a region can be formed and transfer services
to different zones in a network. The zones in the architecture become overlapped, while
the clusters will not become overlapped in the network. The ZRP requires more control
messages to be transmitted for updating the routing table. So, we need cluster-based
architecture and a dynamic broker based on QoS metrics to avoid and overcome link failure
as well as balance the network load.
The cluster-based dynamic broker architecture is required to recover from link failure
and provide reliability to the network. More than one dynamic broker is selected, which
provides the decentralized model. When the link fails between brokers, it automatically
redirects to another broker. The dynamic broker in our proposed architecture is selected
based on the QoS metrics. We need a protocol based on the QoS metrics to choose the broker
in a cluster architecture. The dynamic broker is determined based on the QoS metric’s
value calculated by each mobile node, providing a secure transfer of services from one
cluster to another cluster by the AODV protocol. The proposed system architecture using
QoSDSBS consists of four modules:
1. Cluster formation of mobile nodes;
2. Routing protocol;
3. Dynamic broker selection;
4. Secure broker selection.
The working process of the proposed methodology with four modules is discussed in
detail in Section 5.
4. QoSDSBS System Model
Our system model, QoSDSBS, consists of an environment where mobile nodes use
services to publish and bind with another node. Each mobile node provides services and
acts as a service provider, and the service requester communicates using MANET protocols.
Each node will have specific services, power, and battery life. Service composition is a
process to integrating all the services, according to the user requirement from multiple
mobile nodes. In our proposed system architecture, the QoSDSBS broker acts as a service,
which can incorporate these composite services from numerous mobile nodes, as depicted
in Figure 1.
Each N mobile node from N1 to N7 will have specific or the same services, with
different QoS metrics for each service. Consider the nodes N2 and N6 in Figure 1, which
provide the same service S2 as the other QoS metrics. In Figure 1, seven nodes offer six
different and standard services. In Figure 2, the broker will group the mobile nodes based
on their services. The services offered by each node are depicted, and the broker that acts
as a composer will combine and integrate these services based on the user’s requirements.
Different nodes offer the same services. In this process, the broker utilizes QoS metrics
such as such as delay, bandwidth, throughput, etc., to select the best service from the
distinct nodes. The service registration takes place at the broker, whenever the mobile
nodes within the cluster initiate a service. So, a broker’s responsibility is to find adequate
services by applying the QoS metrics. The broker will match the service and lists the node
Electronics 2022,11, 2637 5 of 17
offering atomic services inside the cluster. According to Figures 1and 2, we list the nodes
services as follows.
N1 = [Service 1, Service 2]
N2 = [Service 2, Service 3]
N3 = [Service 1, Service 4]
N4 = [Service 4, Service 5]
N5 = [Service 2, Service 3]
N6 = [Service 2, Service 4, Service 6]
N7 = [Service 5, Service 6]
Electronics 2022, 11, x FOR PEER REVIEW 5 of 17
Figure 1. Composite services of mobile nodes.
Each N mobile node from N1 to N7 will have specific or the same services, with
different QoS metrics for each service. Consider the nodes N2 and N6 in Figure 1, which
provide the same service S2 as the other QoS metrics. In Figure 1, seven nodes offer six
different and standard services. In Figure 2, the broker will group the mobile nodes based
on their services. The services offered by each node are depicted, and the broker that acts
as a composer will combine and integrate these services based on the user’s require-
ments.
Figure 2. Selection of broker based on its services.
Different nodes offer the same services. In this process, the broker utilizes QoS met-
rics such as such as delay, bandwidth, throughput, etc., to select the best service from the
distinct nodes. The service registration takes place at the broker, whenever the mobile
nodes within the cluster initiate a service. So, a broker’s responsibility is to find adequate
services by applying the QoS metrics. The broker will match the service and lists the node
offering atomic services inside the cluster. According to Figures 1 and 2, we list the nodes
services as follows.
N1 = [Service 1, Service 2]
N2 = [Service 2, Service 3]
Figure 1. Composite services of mobile nodes.
Electronics 2022, 11, x FOR PEER REVIEW 5 of 17
Figure 1. Composite services of mobile nodes.
Each N mobile node from N1 to N7 will have specific or the same services, with
different QoS metrics for each service. Consider the nodes N2 and N6 in Figure 1, which
provide the same service S2 as the other QoS metrics. In Figure 1, seven nodes offer six
different and standard services. In Figure 2, the broker will group the mobile nodes based
on their services. The services offered by each node are depicted, and the broker that acts
as a composer will combine and integrate these services based on the user’s require-
ments.
Figure 2. Selection of broker based on its services.
Different nodes offer the same services. In this process, the broker utilizes QoS met-
rics such as such as delay, bandwidth, throughput, etc., to select the best service from the
distinct nodes. The service registration takes place at the broker, whenever the mobile
nodes within the cluster initiate a service. So, a broker’s responsibility is to find adequate
services by applying the QoS metrics. The broker will match the service and lists the node
offering atomic services inside the cluster. According to Figures 1 and 2, we list the nodes
services as follows.
N1 = [Service 1, Service 2]
N2 = [Service 2, Service 3]
Figure 2. Selection of broker based on its services.
Figure 3describes the overall architecture of QoSDSBS for service composition in
MANET, with nodes functions in different layers of service. The service-composition layer
can cope with other protocols; the communication layer provides wireless connectivity with
nodes in their region. The connectivity for such a network includes ad hoc 802.11 standards,
Bluetooth, etc. The communication with other networks is done with the help of IEEE
802.11. In the network layer, it provides general routing between the mobile nodes. For
MANET, many traditional routing protocols are available such as the AODV [
30
], DSDV
Electronics 2022,11, 2637 6 of 17
(Destination-Sequenced Distance Vector) [
31
], etc. For the proposed QoSDSBS architecture,
the AODV acts as a routing protocol because it outperforms with the QoS metrics for mobile
nodes. The service-discovery layer provides the protocol to discover the services available
in the mobile nodes.
Electronics 2022, 11, x FOR PEER REVIEW 6 of 17
N3 = [Service 1, Service 4]
N4 = [Service 4, Service 5]
N5 = [Service 2, Service 3]
N6 = [Service 2, Service 4, Service 6]
N7 = [Service 5, Service 6]
Figure 3 describes the overall architecture of QoSDSBS for service composition in
MANET, with nodes functions in different layers of service. The service-composition
layer can cope with other protocols; the communication layer provides wireless connec-
tivity with nodes in their region. The connectivity for such a network includes ad hoc
802.11 standards, Bluetooth, etc. The communication with other networks is done with
the help of IEEE 802.11. In the network layer, it provides general routing between the
mobile nodes. For MANET, many traditional routing protocols are available such as the
AODV [30], DSDV (Destination-Sequenced Distance Vector) [31], etc. For the proposed
QoSDSBS architecture, the AODV acts as a routing protocol because it outperforms with
the QoS metrics for mobile nodes. The service-discovery layer provides the protocol to
discover the services available in the mobile nodes.
Figure 3. Layered architecture of QoSDSBS.
The protocol used for service discovery is GSD (Group-based Service Discovery)
and any cluster-based broker selection for service discovery [32,33]. In the service com-
position layer, broker-based integration of services based on the QoS metrics from the
QoS management is used. This protocol is used to discover the service with different QoS
metrics and integrate those services that provide a composite service to the requester
node, with the help of a broker node. In the application layer, it gives a different platform
for service composition. The services offered include either real-time service or
non-real-time service.
5. Proposed System
Our proposed QoSDSBS architecture model consists of MANET service composi-
tion, which uses a broker to integrate services. The broker will use the QoS as a metric to
acquire distinct services from different nodes. For the routing services in the proposed
architecture, the authors have considered a dynamic broker selection based on the QoS
metrics. The QoS metrics include bandwidth, delay, throughput, load balancing, and
Figure 3. Layered architecture of QoSDSBS.
The protocol used for service discovery is GSD (Group-based Service Discovery) and
any cluster-based broker selection for service discovery [
32
,
33
]. In the service composition
layer, broker-based integration of services based on the QoS metrics from the QoS man-
agement is used. This protocol is used to discover the service with different QoS metrics
and integrate those services that provide a composite service to the requester node, with
the help of a broker node. In the application layer, it gives a different platform for service
composition. The services offered include either real-time service or non-real-time service.
5. Proposed System
Our proposed QoSDSBS architecture model consists of MANET service composition,
which uses a broker to integrate services. The broker will use the QoS as a metric to acquire
distinct services from different nodes. For the routing services in the proposed architecture,
the authors have considered a dynamic broker selection based on the QoS metrics. The
QoS metrics include bandwidth, delay, throughput, load balancing, and node mobility. The
entire node architecture is grouped into clusters based on its coverage area. Each cluster
consists of one or two brokers that are dynamically selected. In the literature, the authors
have considered one broker to avoid link failure. However, the centralized broker might be
prone to aa single point of failure. In this work, we introduced the multiple broker concept
using a dynamic broker-selection approach, which might eradicate the above-mentioned
issue. This dynamic approach selects a broker based on the QoS metrics that provides
reliability and scalability even if there is an increase in the number of nodes in the future.
As depicted in Figure 4, the architecture consists of many mobile nodes and the cluster
with a broker encircled is formed based on the coverage area.
Electronics 2022,11, 2637 7 of 17
Electronics 2022, 11, x FOR PEER REVIEW 7 of 17
node mobility. The entire node architecture is grouped into clusters based on its coverage
area. Each cluster consists of one or two brokers that are dynamically selected. In the lit-
erature, the authors have considered one broker to avoid link failure. However, the cen-
tralized broker might be prone to aa single point of failure. In this work, we introduced
the multiple broker concept using a dynamic broker-selection approach, which might
eradicate the above-mentioned issue. This dynamic approach selects a broker based on
the QoS metrics that provides reliability and scalability even if there is an increase in the
number of nodes in the future. As depicted in Figure 4, the architecture consists of many
mobile node,s and the cluster with a broker encircled is formed based on the coverage
area.
Figure 4. Cluster-based dynamic broker-selection process.
The communication between the inter-cluster and intra-cluster takes place with the
help of traditional routing protocol, namely the AODV. If a requester node in Cluster 1
requires a service from Cluster 4, then it broadcasts a request to the rest of the other
nodes present in cluster1 to act as a broker. Then, the requester node will select the
nearest broker and further process the reply message to the responding node as the bro-
ker. Next, the dynamic broker will provide the details of the available service providers
in Cluster 4. Each broker will have the address of the service provider through which a
requester node can get the service. The brokers are responsible to find the shortest path
between cluster1 and Cluster 4 through the intermediate Cluster 3, using the AODV
protocol. The dynamic broker is automatically selected in each cluster based on the QoS
metrics.
Our proposed architecture consists of four different modules. We will discuss each
module precisely.
5.1. Cluster Formation of Mobile Nodes
Clusters are formed based on the node’s range and bandwidth. It provides a hier-
archical formation that divides mobile nodes into groups to avoid transfer rates and
provide scalability. Clusters can be formed by the MCHG algorithm; here, each cluster
will have more than one cluster head to act as a broker for our architecture. Multiple
brokers based on their neighbor cluster are inherited to avoid a single point of failure. In
Figure 4, there are five clusters and the brokers are encircled; in cluster1, two brokers are
selected and used for interconnection between Cluster 2 and Cluster 3. In Cluster 4, a
single broker helps to serve the services. According to number of services, selection of
brokers will increase and that will withstand for more scalability and ease of data trans-
Figure 4. Cluster-based dynamic broker-selection process.
The communication between the inter-cluster and intra-cluster takes place with the
help of traditional routing protocol, namely the AODV. If a requester node in Cluster 1
requires a service from Cluster 4, then it broadcasts a request to the rest of the other nodes
present in cluster1 to act as a broker. Then, the requester node will select the nearest broker
and further process the reply message to the responding node as the broker. Next, the
dynamic broker will provide the details of the available service providers in Cluster 4. Each
broker will have the address of the service provider through which a requester node can
get the service. The brokers are responsible to find the shortest path between cluster1 and
Cluster 4 through the intermediate Cluster 3, using the AODV protocol. The dynamic
broker is automatically selected in each cluster based on the QoS metrics.
Our proposed architecture consists of four different modules. We will discuss each
module precisely.
5.1. Cluster Formation of Mobile Nodes
Clusters are formed based on the node’s range and bandwidth. It provides a hierarchi-
cal formation that divides mobile nodes into groups to avoid transfer rates and provide
scalability. Clusters can be formed by the MCHG algorithm; here, each cluster will have
more than one cluster head to act as a broker for our architecture. Multiple brokers based
on their neighbor cluster are inherited to avoid a single point of failure. In Figure 4, there
are five clusters and the brokers are encircled; in cluster1, two brokers are selected and used
for interconnection between Cluster 2 and Cluster 3. In Cluster 4, a single broker helps
to serve the services. According to number of services, selection of brokers will increase
and that will withstand for more scalability and ease of data transfer. If cluster1 wants to
transfer its services to Cluster 5 and Cluster 3, it can use the nearest brokers to process the
request; thus, our proposed architecture provides load balancing and an increased data
delivery rate.
5.2. Routing Protocol
All the required QoS metrics are exchanged among the nodes using hello messages.
Each node should maintain a table that consists of node identification, service provider
name, services available, and the QoS metrics for each service. Table 1shows the structure
of the fields required to acquire the service description. The traditional protocol AODV
is used to find the shortest path between the service provider and the dynamic broker;
the broker will find the best service by acquiring hello messages from different nodes and
computing the QoS metrics based on Table 1. Table 1contains the list of service providers
available for a particular service, with their specifications. Service provider SP1 will provide
Electronics 2022,11, 2637 8 of 17
services (S1, S4, and S6). They will calculate the QoS metrics for their services and exchange
the table with other mobile nodes using the routing protocol AODV.
Table 1. Service description in hello packet.
P. Name Node ID
Services QoS Metrics
S1 S2 S3 S4 S5 S6 Cost A T(s)
SP1 10.10.1.1 √ √ √ 245 0.90 0.5
SP2 10.10.12.2 √ √ 754 0.87 0.9
SP3 10.10.32.5 √ √ √ √ 438 0.70 1.4
SP4 10.10.3.28 √ √ √ √ 783 0.96 0.67
P. Name represents the provider name; A means availability of the node; and T(s) represents response time
in seconds.
5.3. Dynamic Broker Selection
The QoS requirement for selecting the dynamic broker is based on the QoS metrics.
Here, we will discuss what needs to be considered when selecting a dynamic broker and
routing path. Algorithm 1 will describe the selection of the broker based on the QoS metrics.
Some of the QoS metrics are described as follows.
Algorithm 1 Broker Formation
1. Input: Consider nnodes,
Calculate the QoS metrics and compute Wt
2. For every node nin N
3. If Wt>Wiwhere ieï(n)// ï(n)is the neighbor set of
nodes n
4. Then
5. Broker = n
6. For every weight factor WeNj//Njis the set of uncovered
nodes
7. If distance (Broker, z) <= Broker transmission-range
8. Then
9. Broker Z= Broker
10. End for
11. End for
•Availability
Availability is the occurrence of services to a node; it is the absence of service downtime
and is associated with time to repair, which is the time it takes to repair the failed services. It
is the probability of assessing the services for a particular node. The availability of services
can be represented as
Availability (A) = No.o f s ervices res ponde d
Total no.o f s ervices (1)
•Data Packet Delivery Rate
The data packet delivery rate is calculated by dividing the total number of services
received by the service provider by the total number of benefits that originated in the
network. The data packet delivery rate can be expressed as
Data Delivery (DD)=No.o f ser vices re ceived
Total no.o f s ervices ori ginated (2)
•Data Packet Loss Rate
Electronics 2022,11, 2637 9 of 17
The data packet loss rate is calculated by dividing the total number of services trans-
mitted by the service requester by the total number of services received by the service
provider in a network. The data packet loss ratio can be calculated as
Data Loss (DL)=services transmitted by requester
services received by provider (3)
•Comparative Mobility
Comparative mobility can be calculated by the node that exists in the network for a
longer time and remains static with its own neighbor node. The node with lesser node
mobility is considered to act as a broker. The mobility of the particular node
n
at time tis
given by
Mt
n=Nt
Nt−1
(4)
where Nis the neighbor set of nodes for ‘n’, N
t
is the neighbor set of node nat time t
−
1, and N
t−1
is the neighbor set of node nat time tbut not in time t
−
1. The comparative
mobility for ‘s’ time is given as
Ct
n=1
s
t
∑
k=t−n
Mt
n(5)
•Energy
Energy can be calculated by the physical battery life of a node to hold the services.
•Network Load Balancing
The network load balancing can be calculated by dividing the total number of service
messages transmitted for routing by the total number of services received by the node.
Network load balancing is expressed as
Load Balancing (LB)=total number o f service messages transmitted
tot al num ber o f service messag es re ceived (6)
•Delay
Delay is when the service travels from the service-requester node to the service-
provider node across the prescribed channel. Delay can be calculated by
delay(d) = dpro pa gation +dtransmission +dpro cessing . (7)
•Throughput
Troughput is defined as the number of service requests served successfully for a given
amount of time period over the prescribed channel. Throughput can be calculated by
Throughput (T) = No.o f service req uest
Communication Channel (8)
•Node Memory Capacity
Node memory capacity indicates the amount of data storage available for a node to
store service descriptions.
The overall total weight of a node can be calculated by
Wt = (W1∗A+W2∗D D +W3∗DL +W4∗M+W5∗LB +W6∗d
+W7∗T+W8∗Energy +W9∗Power +W10 ∗Memory)(9)
where W1,2 . . . 10 is the weight factor for each node, with their metrics.
Electronics 2022,11, 2637 10 of 17
5.4. Secure Broker Selection
Our cluster broker architecture uses a secured link between the broker and the nodes
in a cluster. Inside a cluster, the broker acts as a Certification Authority (CA), which
provides a secured communication link within the intra-cluster by using symmetric en-
cryption and a key-exchange protocol to prevent intrusion, based on Algorithm 2. The
stenography method offers a secure connection from one cluster to another (intra-cluster)
for communication. Each broker sends its address and security methods over a hidden
channel, by maintaining a neighbor node and routing table based on Algorithm 3. The
security methods travel through a hello message to another cluster; the broker will provide
secure communication for the cluster-to-cluster architecture.
Algorithm 2 Intra-Cluster Security
1. Input: Select Prime Number “p”, an element g which is the prime root of “p”, and
consider two nodes A and B
2. For any “n” node in the cluster
3. “A” select random integer X.A. < p
4. Compute
5. YA= gXA(mod p)
6. Set
7. Private KeyA= XA
8. “B” select random integer X.B. < p
9. Compute
10. YB= gXB(mod p)
11. Set
12. Private KeyB= XB
13. Broadcast “Y”
14. “A” compute
15. K = (YB)XA(mod p)
16. “B” compute
17. K = (YA)XB(mod p)
18. End for
Algorithm 3 Inter-Cluster Security
1. For any “n” node
2. If (broker-node + Wn) exceeds then
//Wnis the Net Weight of a particular node
3. Listen (Address, Channel)
4. If (Listen + Wn) exceeds then
5. Update (Routing Table)
6. End if
7. End if
8. If (Listen) then
9. Stegnomsg (Listen)
//Compute steganography for listen hello message
10. Find (Channel, Address)
11. End if
12. If (Update)
13. Update Routing table
14. End if
15. End for
6. Simulation and Results Discussion
6.1. Simulation Setup
To simulate our routing protocol for MANET with secured dynamic broker selection,
to provide composite services for a particular user according to their requirement, we used
Electronics 2022,11, 2637 11 of 17
NS2 [
34
]. The required simulation parameters and their values are provided in Table 2.
The proposed system is implemented in Network Simulator version 2.35 (NS2) under the
Ubuntu 11.10 Linux operating system with 4 GB of RAM. We simulated 53 intermediate
nodes and 35 mobile nodes moving into an area of 1000 m
×
1000 m according to the
random-mobility model [
35
]. We have considered 12 brokers at the data rate of 1 Mbps
with a transmission range of 250 m and bandwidth of 1000 kHz, and nodes are scattered in
the simulation area randomly throughout 450 s at each simulation. The traffic follows the
Constant Bit Rate (CBR) model, and the authors have used 25,600 bits for the buffer size.
Our model with dynamic broker and cluster formation is described in Figure 5.
Table 2. Simulation parameters.
S. No. Parameter Value
1. Number of nodes 53
2. Number of mobile nodes 35
3. Number of brokers 12
4. Number of packets 8
5. Area size 1000 ×1000 (m2)
6. Mobility 0–15 m/s
7. Data rate 1 Mbps
8. Transmission range 250 m
9. Routing protocol AODV
10. Speed 10 m/s
11. Buffer size 25,600 Bits
12. Bandwidth 1000 KHz
13. Simulation time 450 s
Electronics 2022, 11, x FOR PEER REVIEW 11 of 17
4. If (Listen + Wn) exceeds then
5. Update (Routing Table)
6. End if
7. End if
8. If (Listen) then
9. Stegnomsg(Listen)
//Compute steganography for listen hello message
10. Find (Channel, Address)
11. End if
12. If (Update)
13. Update Routing table
14. End if
15. End for
6. Simulation and Results Discussion
6.1. Simulation Setup
To simulate our routing protocol for MANET with secured dynamic broker selec-
tion, to provide composite services for a particular user according to their requirement,
we used NS2 [34]. The required simulation parameters and their values are provided in
Table 2. The proposed system is implemented in Network Simulator version 2.35 (NS2)
under the Ubuntu 11.10 Linux operating system with 4 GB of RAM. We simulated 53
intermediate nodes and 35 mobile nodes moving into an area of 1000 m × 1000 m ac-
cording to the random-mobility model [35]. We have considered 12 brokers at the data
rate of 1 Mbps with a transmission range of 250 m and bandwidth of 1000 kHz, and
nodes are scattered in the simulation area randomly throughout 450 s at each simulation.
The traffic follows the Constant Bit Rate (CBR) model, and the authors have used 25,600
bits for the buffer size. Our model with dynamic broker and cluster formation is de-
scribed in Figure 5.
Figure 5. Dynamic broker and cluster formation.
Table 2. Simulation parameters.
S.No. Parameter Value
1. Number of nodes 53
2. Number of mobile nodes 35
3. Number of brokers 12
Figure 5. Dynamic broker and cluster formation.
The performance evaluation metrics such as throughput, end-to-end delay, delivery
ratio, average path lifetime, and routing control overhead are used to measure the perfor-
mance of the algorithms. The results of the proposed QoSDSBS algorithm are compared
with existing algorithms such as the energy efficient routing based on the hierarchical rout-
Electronics 2022,11, 2637 12 of 17
ing algorithm (EE-OHRA) [
36
], energy efficient demand routing protocol (EE-DRP) [
37
],
and novel energy efficient trust aware routing (NETAR) [38].
6.2. Results and Discussion
6.2.1. Bandwidth vs. End-to-End Delay
It refers to the data bandwidth and end-to-end delay; it measures the amount of data
transferred in the link by the service provider, which is not yet received by the service
requester. Our model QoSDSBS shows that the overall delay is reduced to 25 Mbps/s.
Figure 6
shows the bandwidth and end-to-end delay for both the securities-enabled QoS-
DSBS and other existing algorithms. In this case, our proposed work reduces the number of
data lost to the desired level, as depicted in Table 3. The outcome of the proposed algorithm
deliberates better performance in providing minimal end-to-end delay compared to other
existing algorithms.
Electronics 2022, 11, x FOR PEER REVIEW 12 of 17
4. Number of packets 8
5. Area size 1000 × 1000 (m2)
6. Mobility 0–15 m/s
7. Data rate 1 Mbps
8. Transmission range 250 m
9. Routing protocol AODV
10. Speed 10 m/s
11. Buffer size 25
,
600 Bits
12. Bandwidth 1000 KHz
13. Simulation time 450 s
The performance evaluation metrics such as throughput, end-to-end delay, delivery
ratio, average path lifetime, and routing control overhead are used to measure the per-
formance of the algorithms. The results of the proposed QoSDSBS algorithm are com-
pared with existing algorithms such as the energy efficient routing based on the hierar-
chical routing algorithm (EE-OHRA) [36], energy efficient demand routing protocol
(EE-DRP) [37], and novel energy efficient trust aware routing (NETAR) [38].
6.2. Results and Discussion
6.2.1. Bandwidth vs. End-to-End Delay
It refers to the data bandwidth and end-to-end delay; it measures the amount of data
transferred in the link by the service provider, which is not yet received by the service
requester. Our model QoSDSBS shows that the overall delay is reduced to 25 Mbps/s.
Figure 6 shows the bandwidth and end-to-end delay for both the securities-enabled
QoSDSBS and other existing algorithms. In this case, our proposed work reduces the
number of data lost to the desired level, as depicted in Table 3. The outcome of the pro-
posed algorithm deliberates better performance in providing minimal end-to-end delay
compared to other existing algorithms.
Figure 6. Bandwidth vs. end-to-end delay.
Table 3. Results of bandwidth vs. end-to-end delay.
Bandwidth (Mbps) End-to-End Delay
EE-OHRA EA-DRP NETAR QoSDSBS
10 49.1023 47.75 45.15 43.141
0
10
20
30
40
50
60
10 15 20 25
Delay (ms)
Bandwidth (Mbps)
EE-OHRA
EA-DRP
NETAR
QoSDSBS
Figure 6. Bandwidth vs. end-to-end delay.
Table 3. Results of bandwidth vs. end-to-end delay.
Bandwidth
(Mbps)
End-to-End Delay
EE-OHRA EA-DRP NETAR QoSDSBS
10 49.1023 47.75 45.15 43.141
15 47.2451 44.73 42.57 39.5123
20 34.691 31.69 27.91 24.4581
25 24.742 22.16 19.13 15.762
6.2.2. Throughput vs. Delivery Ratio
It measures the average rate of successful data delivery over the communication
channel. The throughput value rises gradually with the number of nodes, and the packet-
delivery ratio rises along with the throughput. Figure 7depicts the throughput and delivery
ratio for both the securities-enabled dynamic secure broker (DSB) system and other existing
Electronics 2022,11, 2637 13 of 17
algorithms. Our model, QoSDSBS, outperforms with an increase in the delivery ratio for
increased throughput with DSB, as shown in Table 4. The proposed algorithm attains better
results than the compared algorithms, EE-OHRA, EA-DRP, and NETAR. However, the
NETAR algorithm competes with the proposed algorithm, but it fails to attain the maximal
delivery ratio.
Electronics 2022, 11, x FOR PEER REVIEW 13 of 17
15 47.2451 44.73 42.57 39.5123
20 34.691 31.69 27.91 24.4581
25 24.742 22.16 19.13 15.762
6.2.2. Throughput vs. Delivery Ratio
It measures the average rate of successful data delivery over the communication
channel. The throughput value rises gradually with the number of nodes, and the pack-
et-delivery ratio rises along with the throughput. Figure 7 depicts the throughput and
delivery ratio for both the securities-enabled dynamic secure broker (DSB) system and
other existing algorithms. Our model, QoSDSBS, outperforms with an increase in the de-
livery ratio for increased throughput with DSB, as shown in Table 4. The proposed algo-
rithm attains better results than the compared algorithms, EE-OHRA, EA-DRP, and
NETAR. However, the NETAR algorithm competes with the proposed algorithm, but it
fails to attain the maximal delivery ratio.
Figure 7. Throughput vs. delivery ratio.
Table 4. Results of throughput vs. delivery ratio.
Throughput (Mbps) Delivery Ratio (%)
EE-OHRA EA-DRP NETAR QoSDSBS
5 42.75 47.13 53.45 69.51
25 52.87 59.94 65.84 78.63
45 65.78 71.48 79.51 85.91
65 74.84 79.76 84.17 89.42
85 83.75 88.45 93.75 96.74
6.2.3. Lifetime with Routing Control Overhead
The average path lifetime is the minimum time in which the maximum number of
mobile nodes is desired to shut down. The routing control overhead is the number of
control packets required to send each data packet in a network. We have used the chan-
nel-access AODV protocol. Figure 8 shows the lifetime and routing control overhead
with the secured DSB and other existing algorithms. Our proposed model, QoSDSBS,
with the desired channel-access AODV protocol is depicted in Table 5. Table 5 deliberates
that the proposed QoSDSBS algorithm attains minimal routing overhead compared to the
other existing algorithms. However, the proposed algorithm stagnates in a lifetime of 4
10
25
40
55
70
85
100
5 25456585
Delivery Ratio (%)
Throughput (Mpbs)
EE-OHRA
EA-DRP
NETAR
QoSDSBS
Figure 7. Throughput vs. delivery ratio.
Table 4. Results of throughput vs. delivery ratio.
Throughput
(Mbps)
Delivery Ratio (%)
EE-OHRA EA-DRP NETAR QoSDSBS
5 42.75 47.13 53.45 69.51
25 52.87 59.94 65.84 78.63
45 65.78 71.48 79.51 85.91
65 74.84 79.76 84.17 89.42
85 83.75 88.45 93.75 96.74
6.2.3. Lifetime with Routing Control Overhead
The average path lifetime is the minimum time in which the maximum number of
mobile nodes is desired to shut down. The routing control overhead is the number of
control packets required to send each data packet in a network. We have used the channel-
access AODV protocol. Figure 8shows the lifetime and routing control overhead with
the secured DSB and other existing algorithms. Our proposed model, QoSDSBS, with the
desired channel-access AODV protocol is depicted in Table 5. Table 5deliberates that the
proposed QoSDSBS algorithm attains minimal routing overhead compared to the other
existing algorithms. However, the proposed algorithm stagnates in a lifetime of 4 ms.
Although, in a later case, it improves nearly 20% in a lifetime of 12 ms, due to the efficient
broker-selection process.
Electronics 2022,11, 2637 14 of 17
Table 5. Results of average path lifetime vs. routing control overload.
Lifetime (ms) Routing Overhead (104Packets)
EE-OHRA EA-DRP NETAR QoSDSBS
2 3.9178 3.5879 2.8451 2.4456
3 3.1128 2.8421 2.507 2.207
4 2.7423 2.6124 2.387 2.014
5 2.4712 2.3156 2.145 1.8098
6 1.9023 1.8612 1.7451 1.5256
8 1.437 1.372 1.278 1.0162
12 1.254 1.197 1.1345 0.9489
Electronics 2022, 11, x FOR PEER REVIEW 14 of 17
ms. Although, in a later case, it improves nearly 20% in a lifetime of 12 ms, due to the ef-
ficient broker-selection process.
Figure 8. Average path lifetime vs. routing control overload.
Table 5. Results of average path lifetime vs. routing control overload.
Lifetime (ms) Routing Overhead (104 Packets)
EE-OHRA EA-DRP NETAR QoSDSBS
2 3.9178 3.5879 2.8451 2.4456
3 3.1128 2.8421 2.507 2.207
4 2.7423 2.6124 2.387 2.014
5 2.4712 2.3156 2.145 1.8098
6 1.9023 1.8612 1.7451 1.5256
8 1.437 1.372 1.278 1.0162
12 1.254 1.197 1.1345 0.9489
6.2.4. Throughput vs. Delay
It measures the success rate of packet delivery during the communication with less
data loss due to the reduced delay. The throughput will increase with the number of
mobile nodes at the range of bits per second, and the delay is measured in milliseconds.
Our proposed model, QoSDSBS, provides a decline in packet loss, a reduced delay, and
an increase in throughput, as shown in Table 6. Figure 9 shows the throughput and delay
with the securities-enabled DSB and other existing techniques. The above experimental
results show that the proposed QoSDSBS model provides the desired level with in-
creased QoS parameters compared to EE-OHRA, EA-DRP, and NETAR.
0.5
1.2
1.9
2.6
3.3
4
4.7
23456812
Routing Overhead (10^4 Packets)
Average Path Lifetime (ms)
EE-OHRA
EA-DRP
NETAR
QoSDSBS
Figure 8. Average path lifetime vs. routing control overload.
6.2.4. Throughput vs. Delay
It measures the success rate of packet delivery during the communication with less
data loss due to the reduced delay. The throughput will increase with the number of mobile
nodes at the range of bits per second, and the delay is measured in milliseconds. Our
proposed model, QoSDSBS, provides a decline in packet loss, a reduced delay, and an
increase in throughput, as shown in Table 6. Figure 9shows the throughput and delay with
the securities-enabled DSB and other existing techniques. The above experimental results
show that the proposed QoSDSBS model provides the desired level with increased QoS
parameters compared to EE-OHRA, EA-DRP, and NETAR.
Table 6. Results of throughput vs. delay.
Throughput
(Mbps)
Delay (ms)
EE-OHRA EA-DRP NETAR QoSDSBS
5 17.0178 15.789 12.842 8.518
25 19.8112 17.459 13.741 11.314
45 26.4712 23.541 20.845 15.8256
65 29.4712 27.8423 25.842 23.0362
85 38.0023 35.7121 31.8745 29.9489
Electronics 2022,11, 2637 15 of 17
Electronics 2022, 11, x FOR PEER REVIEW 15 of 17
Figure 9. Throughput vs. delay.
Table 6. Results of throughput vs. delay.
Throughput
(Mbps)
Delay (ms)
EE-OHRA EA-DRP NETAR QoSDSBS
5 17.0178 15.789 12.842 8.518
25 19.8112 17.459 13.741 11.314
45 26.4712 23.541 20.845 15.8256
65 29.4712 27.8423 25.842 23.0362
85 38.0023 35.7121 31.8745 29.9489
7. Conclusions
This paper presents the cluster formation and dynamic broker selection, which pro-
vides security. The services are transferred among nodes in a secure environment, with
the aid of a broker that is selected based on the QoS metrics among the mobile nodes. We
have analyzed the performance of the AODV routing protocol in MANET with secured
and unsecured conditions, with the effects of the QoS metrics. Our proposed model pro-
vides a better QoS and scalability with the help of the cluster formation and a number of
brokers to avoid congestion. By the results shown, it is proven that bandwidth,
end-to-end delay, throughput, delivery ratio, routing-control overhead, and network
lifetime outperform better with our proposed model. Finally, our simulation results show
better performance with the AODV routing protocol in the dynamic secured environ-
ment. The delay and packet-drop ratio scales down in the proposed secured model. Thus,
the proposed architecture reduces the network load for the single mobile node and
achieves more scalability. In the future, the optimization algorithm can be incorporated
into our current proposed architecture to select the optimal broker node to improve the
efficacy in a large mobile area network. In addition, the same can be evaluated with dif-
ferent QoS metrics for a large mobile area network.
Author Contributions: Conceptualization, R.R., R.M., and A.D.; methodology, D.P.S., H.G.M., and
R.S.; validation, R.S.; formal analysis, D.A. and I.D.N.; investigation, R.S. and R.R.; writ-
ing—original draft preparation, R.R., R.M., and A.D.; writing—review and editing, D.P.S., H.G.M.,
and R.S.; supervision, D.A. and I.D.N.; project administration, D.A. All authors have read and
agreed to the published version of the manuscript.
Funding: Princess Nourah bint Abdulrahman University Researchers Supporting Project number
(PNURSP2022TR140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
5
10
15
20
25
30
35
40
5 25456585
Delay (ms)
Throughput (Mbps)
EE-OHRA
EA-DRP
NETAR
QoSDSBS
Figure 9. Throughput vs. delay.
7. Conclusions
This paper presents the cluster formation and dynamic broker selection, which pro-
vides security. The services are transferred among nodes in a secure environment, with the
aid of a broker that is selected based on the QoS metrics among the mobile nodes. We have
analyzed the performance of the AODV routing protocol in MANET with secured and
unsecured conditions, with the effects of the QoS metrics. Our proposed model provides a
better QoS and scalability with the help of the cluster formation and a number of brokers
to avoid congestion. By the results shown, it is proven that bandwidth, end-to-end delay,
throughput, delivery ratio, routing-control overhead, and network lifetime outperform
better with our proposed model. Finally, our simulation results show better performance
with the AODV routing protocol in the dynamic secured environment. The delay and
packet-drop ratio scales down in the proposed secured model. Thus, the proposed archi-
tecture reduces the network load for the single mobile node and achieves more scalability.
In the future, the optimization algorithm can be incorporated into our current proposed
architecture to select the optimal broker node to improve the efficacy in a large mobile area
network. In addition, the same can be evaluated with different QoS metrics for a large
mobile area network.
Author Contributions:
Conceptualization, R.R., R.M. and A.D.; methodology, D.P.S., H.G.M. andR.S.;
validation, R.S.; formal analysis, D.A. and I.D.N.; investigation, R.S. and R.R.; writing—original draft
preparation, R.R., R.M. and A.D.; writing—review and editing, D.P.S., H.G.M. and R.S.; supervision,
D.A. and I.D.N.; project administration, D.A. All authors have read and agreed to the published
version of the manuscript.
Funding:
Princess Nourah bint Abdulrahman University Researchers Supporting Project number
(PNURSP2022TR140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Acknowledgments:
Princess Nourah bint Abdulrahman University Researchers Supporting Project
number (PNURSP2022TR140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Conflicts of Interest: The authors declare no conflict of interest.
Electronics 2022,11, 2637 16 of 17
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