Performance and statistical analysis of ant colony route in mobile ad-hoc networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 3, June 2022, pp. 2818~2828
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i3.pp2818-2828  2818
Journal homepage: http://ijece.iaescore.com
Performance and statistical analysis of ant colony route in
mobile ad-hoc networks
Ibrahim Ahmed Alameri1,2
, Jitka Komarkova1
1
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice,
Czech Republic
2
Computer Center, Jabir Ibn Hayyan Medical University, Kufa, Iraq
Article Info ABSTRACT
Article history:
Received Apr 9, 2021
Revised Dec 31, 2021
Accepted Jan 19, 2022
Research on mobile ad-hoc networks (MANETs) is increasing in popularity
due to its rapid, budget-friendly, and easily altered implementation, and
relevance to emergencies such as forest firefighting and health care
provisioning. The main concerns that ad-hoc networks face is dynamic
topology, energy usage, packet drop rate, and throughput. Routing protocol
selection is a critical point to surmount alterations in topology and maintain
quality in MANET networks. The effectiveness of any network can be vastly
enhanced with a well-designed routing protocol. In recent decades, standard
MANET protocols have not been able to keep pace with growing demands
for MANET applications. The current study investigates and contrasts ant
colony optimization (ACO) with various routing protocols. This paper
compares ad-hoc on-demand multi-path distance vector (AOMDV),
dynamic source routing protocol (DSR), ad-hoc on-demand distance vector
routing (AODV), and AntHocNet protocols regarding the quality of service
(QoS) and statistical analysis. The current research aims to study the
behavior of the state-of-the-art MANET protocols with the ACO technique.
The ACO technique is a hybrid technique, integrating a reactive route
maintaining technique with a proactive method. The reason and motivation
for including the ACO algorithm in the current study is to improve by using
optimization algorithms proved in other domains. The ACO algorithm
appears to have substantial use in large-scale MANET simulation.
Keywords:
Mobile ad-hoc network
Nature inspired algorithms
metaheuristics
Routing protocols
Statistics
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ibrahim Ahmed Alameri
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of
Pardubice
Studentska 95, 532 10, Pardubice 9, Czech Republic
Email: st61833@upce.cz, ib.alameri@jmu.edu.iq
1. INTRODUCTION
Nowadays, the wireless communication technologies have changed to meet the modern-day
perspective of life [1]. We are surrounded by different types of wireless networks like wireless local area
networks (WLAN), also known as wireless fidelity or Wi-Fi, Bluetooth, infra-red (IR), mobile networks like
4G/LTE (5G networks is about to explode the market), radio/video broadcast technologies, satellite and other
and microwave communication systems. One varying possible explanation for wireless options’ availability
is the trade-off between overall system cost and solution services. According to the research and Market’
report on the global Wi-Fi market 2018-2022 trends, it is anticipated that the Wi-Fi network economy will
increase from $5,96 billion in 2017 to $15,60 billion by 2022 [2]. This report also examines the Wi-Fi
industry regarding components of products, densities, verticals, and regions.

Int J Elec & Comp Eng ISSN: 2088-8708 
Performance and statistical analysis of ant colony route in … (Ibrahim Ahmed Alameri)
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Wireless systems or networks are usually classified into two categories, infrastructure-based
networks and infrastructure-less networks [3], [4]. In infrastructure-based networks, the wireless network is
extended by fixed-wired backbone infrastructure. The backbone network performs all communication and
routing actions. This tactic is being used in standard mobile devices like the global system of mobile
communications (GSM) and WLAN. There is no such backbone infrastructure that exists in infrastructure
less wireless systems. Since there is no backbone network or infrastructure, nodes in the network
communicate directly via point-to-point connections/communication. This style of communication (point-to-
point) is feasible only if the nodes are placed next to each other [5].
Nodes at far remote locations cannot communicate or exchange information directly. They depend
on other nodes which can forward or relay their message towards intended recipients. These nodes are known
as relay points or relay nodes, and the network is known as a multi-hop network. Infrastructure-less wireless
networks created in this way are known as ad-hoc wireless multipoint networks (AHWMNs) because they
can be deployed or created on the fly without any proper prior planning and significant investment [6].
There are three types of AHWMNs: mobile ad-hoc networks (MANET), wireless mesh networks
(WMN), and wireless sensor networks (WSN) [7], [8]. Wireless ad-hoc network is also known as a mobile
ad-hoc network due to the absence of wired infrastructure [9]. MANET is a set of nodes connected without
any infrastructure or any centralized administration [10], [11]. The nodes keep moving adaptively to
communicate among themselves without depending on the central hub or infrastructure [12].
Nodes in a WMN are more heterogeneous, mesh client nodes and mesh routing nodes are included.
Mesh client nodes are identical to MANET nodes. Mesh routing nodes are typically less mobile and have
more resources such as computational and battery power. Routing nodes in WMN support a range of wireless
technologies and allow a more organized network topology to be created. Both fixed and mobile wireless
sensors make up the WSN. A sensing node consists of sensors, a remote control unit, and a radio
communications unit. The main challenge in WSN is the scarcity of sensor resources which limits the
lifetime of the network [13].
The current paper aims to investigate the influence of different design components at the ant colony
optimization (ACO) algorithm performance. The article seeks to address the routing challenges of MANET
by studying the pheromone’s usefulness on ant efforts concerning performance and the composite pheromone
metric on ant performance. Routing is a task that employs to select the best available connection path that
allowing efficient data exchange. Each node constructs a routing table which consists of known network
addresses and their next hops to perform routing. Each node constructs a routing table with the help and
assistance of routing protocol. The routing protocol is a set of messages which are exchanged by each node to
update topology information with each other. MANET routing protocols are classified into two groups,
reactive such as ad-hoc on-demand distance vector routing (AODV), and ad-hoc on-demand multi-path
distance vector (AOMDV), dynamic source routing protocol (DSR), and hybrid as AntHocNet ant colony
optimization (ACO). The presented work uses Perl to analyze various aspects of the studied protocols
supported by some statistical analysis
The remaining part of the paper proceeds as follows: section 2 includes taxonomy routing protocol
categories and an overview. Section 3 describes the BIO-Inspired ACO algorithms. Section 4 describes the
research method, including the simulation setup and the performance evaluation for the network parameters.
Results and discussion are in section 5. Finally, section 6 provides the conclusion and future work.
2. TAXONOMY ROUTING PROTOCOL CATEGORIES IN MANET
Routing is complicated in MANET because the topology is moving all the time. Each vertex node
needs to track each node’s validity to determine which nodes are linked at that vertex and available to
communicate. Routing protocol selection is an essential action to determining optimal routing paths and
transferring the packets through on inter-network [14]. There are three types of MANET routing protocols
[15]. They are reactive, proactive, and hybrid, each associated with different approaches to coping with the
traffic. The categories of routing protocols are shown in Figure 1.
2.1. Proactive routing protocols
Proactive routing protocols, also known as table-driven routing protocols, builds and maintains
routing information or database for the whole network in advance. The routing database (RDB) is usually
stored in the form of differently structured data tables. These tables are updated regularly or whenever there
is a change in the network topology, for instance, in case of link or node failure. It is a crucial task to keep
nodes up to date and synchronized all the time [16], [17].
One fundamental issue with these protocols is the amount of network traffic they generate. Proactive
routing protocols are very chatty in their operation. They generate a large volume of routing messages over
the network to keep nodes updated. Since nodes in MANET have limited resources (CPU, memory, and

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energy) due to high routing loads, these protocols consume a large amount of energy, CPU cycles, and
memory. To address these problems, researchers proposed reactive routing protocols, which are discussed in
section 2.2. Table 1 [18]–[20], presents a summary of different characteristics of proactive routing protocols.
Figure 1. Routing protocols categories
Table 1. Basic characteristics of proactive routing protocols
Protocol Routing Structure No. of Tables Frequency of Updates Hello Message Critical Nodes
AODV F 2 Periodic and as required Yes No
WRP F 4 Periodic Yes No
GSR F 3 Periodic NO No
FSR F Same as GSR Periodic No No
OLSR F 3 Periodic and as required Yes No
STAR H 1 Conditional Yes No
DREAM F 1 Mobility based NO No
HSR H 2 Periodic NO Yes, cluster-head
2.2. Reactive routing protocols
Reactive routing protocols, also known as on-demand routing protocols, are developed to address
the weaknesses of proactive routing protocols discussed in section 2.1. In reactive protocols, nodes discover
routes as needed instead of constructing a routing table for the entire network in advance. It means that routes
are populated and maintained for only those nodes, which require sending some data to a particular
destination. Reactive protocols require nodes to gather only the necessary information [21], [22].
Typically, obtaining routing information encompasses a route discovery or a route repair process.
Reactive protocols do not send periodic broadcast messages over the network. Thus, unnecessary network
load, congestion, energy, and computational load on the node are reduced. Active discovered routes are
stored for a particular time in the routing table and deleted after some inactivity. Routing tables size remains
of reasonable length, thus allowing to accommodate a much larger topology. A comparison of different
reactive routing protocols is presented in Table 2 [23]–[26].
Table 2. Basic characteristics of reactive routing protocols
Protocol RS Multiple routes Beacons Route metric method Route maintained in
AODV F No yes, hello message Freshest & SP RT
AOMDV F Yes yes, hello message Freshest & SP RT
DSR F Yes No SP, next available in RC RC
ROAM F Yes No SP RT
LMR F Yes No SP, next available in RT RT
TORA F Yes No SP, next available in RT RT
Note: RS=routing structure; F=flat; RT=route table; RC=route cache; SP=shortest path
2.3. Hybrid routing protocols
Hybridization is a technique in which features of two or more algorithms are superimposed together
to eliminate or reduce parent algorithms’ weaknesses. It is a fusion of the best properties of proactive and

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reactive routing protocols. Scalability is the main problem with reactive and proactive protocols. As
discussed in section 2.1, proactive routing protocols need to build and maintain a to the date routing table for
the whole network, and it becomes difficult as the network size grows. In addition, they also consume a lot of
network bandwidth, due to which the throughput of the network is decreased. In reactive protocols, as several
nodes or network size grows, reactive protocols introduce a delay factor because they discover route on the
fly. It becomes unacceptable for soft real-time applications like audio communication or video surveillance.
Hybrid protocols have the potential to address this problem. These protocols are designed to increase the
scalability by allowing nodes to distribute the network in zones (virtually) or backbone areas. Nodes near a
node come under the backbone area (zone), and remaining nodes in tertiary zones. For backbone, area routes
are discovered by exploiting proactive routing techniques, while the reactive approach is used to discover
routes for tertiary zones. Summary of different hybrid routing protocols is presented in Table 3 [27], [28].
Table 3. Basic characteristics of hybrid routing protocols
Protocol RS Multiple routes Bc Route metric method Route metric method & Route maintained in
ZRP F No Yes SP Intrazone & interzone tables
AntHocNet F No Yes SP Intrazone & interzone tables
RS=routing structure; H=hierarchical; F=flat; SP=shortest path; Bc=beacons
3. BIO-INSPIRED ANT COLONY OPTIMIZATION (ACO) ALGORITHM
Nature-inspired algorithms demonstrate promising results in high-performance computing [29].
These algorithms are inspired by the behavior of various animals, insects, and plants, [30]–[32]. The
simplicity of these algorithms represents their advantage. They are straightforward to analyze, evolve and
have shown outstanding scalability and flexibility to adapt to the problem’s changing nature. The article is
focused on ACO algorithm. ACO is a metaheuristics optimization algorithm that takes inspiration from
natural ants’ behavior in nature [11], [12]. ACO has been applied to various complex optimization problems
and often produces optimal results [33].
3.1. Nature of ants
The prime motivation of ACO and its applications in MANET is the foraging behavior of some ant
species [34], [35]. Ants have been noticed to find the shortest path between the feeding place and the hill.
Every personal ant has minimal computational and visual abilities (some ant species are completely blind).
Discovering the shortest route among several possible routes is undoubtedly a difficult task accomplished
through the colony’s members’ participation. Each ant deposits pheromone when it travels to find a food
source [36], [37]. Pheromone is an unstable organic and volatile compound naturally produced by ants in
attempting to manipulate other ants. Ants follow the path which is rich with pheromones and ignore paths
having low pheromone concentration. However, ants can take any random direction also. This is known as
local behavior or heuristic. It is essential for exploration purposes. Generally, the ants’ global behavior is the
result of colonial coordination, which is achieved with the help of pheromone [38], [39]. This indirect
communication mode is known as Stigmergy. Marco Dorigo got inspired by natural ants’ behavior and
proposed the ACO metaheuristic algorithm back in 1990 [40]. Initially, the ACO algorithm was applied to
the travelling salesman problem (TSP), but after that, it was widely accepted to solve many complex
optimization problems.
3.2. Double bridge experiment
The ant algorithm experimentation on the double bridge is essential in itself, which served as the
impetus for all of the recent studies that tend to interpret the ACO evolutionary algorithms [41]. In addition,
its equation is the starting point for all of ACO’s various studies. Figure 2 shows how a double bridge looks
like. Consider an ant move from the nest for food search and reaches the branch point. There will be two
pathways available for that ant. Since it is the first of its kind, there will be no previous deposits of
pheromone. It can randomly select any path and moves forward. While following any direction, it will be
depositing pheromone. The following ant comes has two paths again, however, since pheromone is already
deposited at one of the paths. The next coming ant will assume this is the shortest path.
3.3. Algorithm for classical ACO
This section presents an ACO-based algorithm technique [41], [42]. The ACO metaheuristic
consists of the following steps:
 In the initialization phase, different algorithmic parameters like initial pheromone value, are initialized.
 Regarding to that, a primary cycle is executed till the ending is achieved.

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 At the end of each cycle, ants are tasked with creating practical solutions.
 Applying a local search optimization could enhance the solution.
 At each iteration, the pheromone is updated, pheromone value increases or decreases depends on
pheromone evaporation.
 At each iteration, the next vertex to visit.
3.4. Pheromone update method
The amount of pheromone is updated by all the ants that have created solutions after each iteration
cycle. The pheromone amount related with each edge connecting two vertex (I) and (j) are up to date, as
shown in (1) [43]:
𝜏𝑖, 𝑗(𝑡 + 1) = (1 − 𝜌) ∗ 𝜏𝑖, 𝑗(𝑡) Σ∆𝜏𝑘(𝑡) (1)
where 1 ≤ 𝜌 ≤ 1 is the percentage of evaporation, the number of nodes is denoted by m, ∆τ the amount of
pheromone put at edge i, j is denoted by k.
Figure 2. Ant double bridge experiment shows that how ants learn shortest path
4. RESEARCH METHOD
This paper uses several methodologies and techniques described in this section for selecting the
proper routing protocol. Beginning with choosing the network simulator where there are several network
simulations tools, such as (NS-2), (NS-3) network simulator, and the network simulation software QualNet
(QualNet), all of which are not excluded. NS-2 has been chosen as the protocol simulator for this study
because of its abundance and support of several network protocols. In this section will focus on various
MANET routing protocols and their simulation. Four different routing protocols have been selected
belonging to different families, which are: DSR-reactive routing protocol, AODV, AOMDV, and AntHocNet
(Nature-inspired metaheuristic based) hybrid. We observed an AntHocNet execution in NS-2.35, but it needs
some tweaking to be usable, as it had no way to transmit the required prompt back ants. In addition, we wrote
a custom Perl script to calculate metrics such as packet drop rate (PDR), throughput, average end-to-end
delay, and energy consumption from the trace files. Finally, after these suggested modifications, the protocols
and the four MANET routing scenarios are installed and ready to be tested.
The mobility model refers to the movement pattern of the mobile nodes during the simulation study.
It plays a significant role in designing and implementing an excellent wireless infrastructure because a
routing protocol has performed well in one mobility model, even though it is not necessary to perform well in
other circumstances. Besides, the scripts presented in OTcl, an object-oriented language enhanced version of
Tcl modeling and analyzing UDP protocols, routers, and other network items, are used to execute the NS-2
software. Tcl scripts were used to create network scenario simulation, connection settings, nodes movement,
and position are implemented in the same fashion. Other modifications were implemented to adjust the
transmitting and receiving power at nodes to produce an effective influence per each packet.

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The simulation studies results are produced in a trace-file that included the stimulation details for
the network. Graphs are generated by the command-line tool Gnuplot using gathered statistics. In the current
study used random way point (RWP) mobility model in the network simulation parameter, and other
parameters have been shown in Table 4.
Table 4. Parameter values of the network simulator
Parameter Value
Routing Protocols AODV AOMDV AntHocNet DSR
Area 1500×1000 m
No. of Nodes 100
Simulation Time 180s
Mobility model Random way point (RWP)
Propagation model Shadowing
Initial Energy 1000 J
Packet Size 512 bytes Initial Nodes placement
No. of Nodes 100
4.1. Studied parameters
The results of this study show the result of the performance of the routing protocols. As mentioned
above, the current research uses the NS2 as a network simulator to investigate the routing protocols'
performance. However, several parameters for performance evaluation are used in this study, such as packet
drop rate, throughput, energy consumption, and average end-to-end delay. These parameters highly influence
selecting an efficient routing protocol in data transmission.
4.1.1. Packet drop rate (PDR)
PDR is described as the” quantity of dropped packets per second”. We extracted and calculated the
dropped packets from the simulation trace file. Every dropped packet increased the unit time counter. The
extracted data will then fed into the Gnuplot graphing software, which is used to plot the line graph for the
entire simulation time. The PDR can be calculated by (2) [44].
𝑃𝐷𝑅 =
Σ𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑎𝑡 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛
Σ𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑑 𝑏𝑦 𝑛𝑜𝑑𝑒
(2)
4.1.2. Throughput
A network throughput represents the total number of packets that have been delivered successfully
per period unit of time. The optimal protocol is the protocol that generate a higher throughput rate. In other
words, in evaluating the effectiveness and scalability of routing protocols, throughput is essential. As shown
in (3) [45] is used to calculate it.
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 =
Σ 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑃𝑎𝑐𝑘𝑒𝑡𝑠 𝐶𝑜𝑢𝑛𝑡 (𝑝𝑝𝑠)
𝑇𝑜𝑡𝑎𝑙 𝑇𝑖𝑚𝑒
(3)
4.1.3. Energy consumption
Generally, in MANET, nodes are connected with batteries having limited power supply. Energy
consumption is an important parameter to measure network lifetime. In our topology, all nodes are mobile;
the energy consumed (left) has measured at the end of the simulation. Nodes remaining energy can be
calculated by (4) [46].
𝑅𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔 𝐸𝑛𝑒𝑟𝑔𝑦 = 𝑁𝑜𝑑𝑒𝑠 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 − 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝐸𝑛𝑒𝑟𝑔𝑦 (4)
4.1.4. Average end to end delay (E2E)
Average E2E delay is the average time required for the packets to be delivered to their ultimate
destination. Also, it is defined as the difference between the transit time and the arrival time of the packet at
its destination. In (5) [47] is used to calculate E2E:
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑒𝑙𝑎𝑦 = 𝑃𝑎𝑐𝑘𝑒𝑡 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑇𝑖𝑚𝑒𝑎𝑣𝑔 − 𝑃𝑎𝑐𝑘𝑒𝑡 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒𝑎𝑣𝑔 (5)
4.2. Mobility model
The mobility model refers to the movement pattern of the mobile nodes during the simulation study.
It plays a significant role in designing and implementing an excellent wireless infrastructure because a

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routing protocol, which has performed well in one mobility model, will not necessarily perform well in other
arrangements the RWP mobility model used in proposed work. The following parameters are configured for
the RWP model: the minimum speed: 1 m/s, maximum speed: 10 m/s, and pause time: 1 s.
5. RESULTS AND DISCUSSION
The results are extracted with the help of PERL scripts explicitly developed for this study. We
extracted the trace file output with the help of custom PERL scripts. The extracted data is then passed to
Gnuplot and an excel sheet for further analysis.
Figure 3(a) presents the packet drop rate per second for AODV, AOMDV, DSR, and AntHocNet
routing protocols. The graph clearly shows that the packet drop rate for the AODV routing protocol is far less
than the other routing protocols. The experiment results showed extreme fluctuations for the AOMDV and
DSR routing protocols. This behavior is not suitable for real-time applications like audio or voice
communication. Figure 3(b), for the same parameter, also shows that AODV has a low average drop count,
and its standard deviation is also less than AntHocNet, AOMDV, and DSR routing protocols.
(a)
(b)
Figure 3. PDR calculation and discussion (a) PDR and (b) standard deviation of PDR
Figure 4(a) shows overall network throughput. It shows that the AODV routing protocol has an
overall high network throughput compared to the other routing protocols. Therefore, it helps bandwidth-
hungry applications such as virtual desktops, online gaming, and cloud storage. However, the simulation
results indicate fluctuating behavior. Table 5 presents that there is a massive gap between the average values.
Also, Figure 4(b) presents the standard deviation of AODV. It means that users will not have a steady
download speed and will experience high variations.
Figure 5(a) shows the average end-to-end delay for all four routing protocols. DSR has a higher
delay than reactive routing protocols, which is also proved from this graph. Figure 5(a) presents that DSR

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efficiency is better than AOMDV, AntHocNet, and AODV in terms of delay. AOMDV routing protocols are
inferior to AntHocNet and AODV. Figure 5(b) shows how much energy is consumed by each protocol. There
is no desired difference among them, with very little superiority being noted on the AntHocNet. AODV
consumed less energy as compared to DSR, AntHocNet, and AOMDV protocols. Less energy consumption
makes AODV suitable for regions where replacing batteries is practically not possible. It increases network
lifetime also.
The simulation results conclude that the AODV, on the whole, performs admirably in terms of
throughput, energy consumption and PDR compared with the AntHocNet, AOMDV, and DSR. At the same
time, the DSR was better in terms of delay. AODVs' feature of operating in connection state and as a routing
table causes an increase in throughput and other features. However, the simulation shows a higher delay of
AntHocNet (proactive and reactive protocol) than AODV, AOMDV, and DSR protocols, with a remarkable
advantage in PDR.
This has opened a new research area to explore and reduce its high delay factor. In addition, the
experimental result shows that the AODV is better in terms of throughput and energy consumption. On the
other hand, the AODV showed a slight delay in terms of delay. This will lead to be an efficient solution for
applications that require a long lifetime and higher performance.
(a)
(b)
Figure 4. Network throughput calculation and discussion (a) network throughput and (b) standard deviation
of AODV
Table 5. Standard deviation of throughput
AODV AOMDV AntHocNet DSR
Minimum 12.46875 4.15625 4.15625 4
Maximum 344.96875 295.09375 128.84375 96.09375
Average 198.58936 111.88814 41.681932 41.19436
Standard Deviation 82.753427 82.665883 22.319225 28.399975

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(a)
(b)
Figure 5. Simulation parameters (a) average end-to-end delay and (b) energy consumptions
6. CONCLUSION
In this article, the effectiveness of various routing protocols in MANET is evaluated using the NS-2
simulator. As part of the evaluation, mathematical statistics have been used to study and evaluate the
behavior of the transmitted data and measure the efficiency of the protocol based on the simulation results
and the standard deviation of the data. Despite its throughput, the results show that the ACO algorithm has
less potential to be used as a routing protocol due to the network parameters’ results compared to other
protocols. Furthermore, the results indicate an incentive to enhance AntHocNet. Its high delay in sending
data between source and destination and its energy consumption cause a shorter lifetime of the network. It
must shed light on the PDR due to its low packet drop rate compared to other protocols. In future work, we
propose improving the ACO algorithm based on the results and examining it with various scenarios such as
simulation time, network load, node speed, and ACO congestion, which can affect packet loss and lower
energy and throughput, leading to enhanced network lifetime and giving better performance. Also, this study
suggests more investigations of the AODV protocol, which can lead to a modification to the routing
mechanism of the protocol to handle the instability of the link quality and enhance the delay.
ACKNOWLEDGEMENT
This paper was supported by SGS University of Pardubice project No. SGS_2021_011.
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BIOGRAPHIES OF AUTHORS
Ibrahim Ahmed Alameri is an Iraqi national who was born in the country. He
graduated from the University of Al-Qadisiyah with a B.Sc. in computer science and an M.Sc.
in computer science from The South Ural State University (SUSU). He is pursuing a Ph.D. in
mobile Ad-Hoc networks at the University of Pardubice. Ad-hoc routing protocols, wireless
communication, and networking technologies are among his research interests. He could be
contacted by his email: st61833@upce.cz.
Jitka Komarkova works as an Associate Professor of Systems Engineering and
Informatics at the University of Pardubice, Faculty of Economics and Administration, Institute
of System Engineering and Informatics since 2009. Her researches are in the fields of systems
engineering and geoinformation technologies. She is major in the design of web-based GIS
with a focus on usability; and spatial data collection, including utilization of UAVs, and data
analyses. Recently, wireless networks as another source of spatial data and spatial issues have
been tackled. She has been a supervisor of both master and doctoral students at the faculty.
She teaches subjects focused on systems engineering and geoinformation technologies. She
has served as an invited reviewer of many scientific articles and papers. She could be
contacted at email: jitka.komarkova@upce.cz. Further info can be found on her homepage:
https://www.upce.cz/en/user/5057.

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